激光测风雷达的质量控制和同化应用

风电可以弥补我国东南沿海经济重心能源供应不足，但随着风机高度不断突破，传统的测风手段已无法满足监测需求。为此，采用布设在福建三沙沿海和内陆两个观测点的多普勒测风激光雷达，针对2020年8~11月100~200米典型风机高度上湍流强度、风功率密度分布情况进行精细化对比分析。结果表明：无论是沿海还是内陆监测点，随着垂直高度的上升，湍流强度逐渐减小而风功率密度逐步增大；从日变化来看，在103.9 m、155.9 m、207.8 m垂直高度上，日出前湍流强度最大，风功率密度最小。在这三个高度上，沿海区域的湍流强度最大值分别为0.178、0.154、0.133，风功率密度最小值分别为138.463 W/m2、175.860 W/m2、186.455 W/m2，而内陆区域的湍流强度最大值分别为0.180、0.144、0.121，风功率密度最小值分别为145.835 W/m2、184.868 W/m2、196.712 W/m2。日落后的湍流强度最弱，风功率密度达到最大，沿海区域的湍流强度的最小值分别为0.106、0.088、0.075，风功率密度最大值分别为259.219 W/m2、299.590 W/m2、322.200 W/m2。内陆区域的湍流强度最小值分别为0.116、0.086、0.074，风功率密度最大值分别为254.318 W/m2、303.084 W/m2、328.150 W/m2。从风资源的月际变化看，8、9月份风功率密度为100 W/m2左右；10、11月份风能资源丰富，11月份的平均风功率密度可达到800 W/m2左右。

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Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (CNR) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R2) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R2 increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R2 increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms.

The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

Abstract. After the successful launch of Aeolus, which is the first spaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. To ensure the quality of the measurement data from CDLs and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s−1, 2.64 m s−1 and −0.25 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 0.93, 0.92 and −0.33 m s−1. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m s−1, 5.77 m s−1 and −1.15 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 1.00, 0.96 and −1.2 m s−1. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Abstract. A 2-year measurement campaign of the ZephIR 300 vertical profiling continuous-wave (CW) focusing wind lidar has been carried out by the Royal Netherlands Meteorological Institute (KNMI) at the Cabauw site. We focus on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213 m tall meteorological mast. We find an overall availability of quality-controlled wind lidar data of 97 % to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm h−1 or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m s−1 with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. The mean bias in the wind direction is within 2∘, which is of the same order as the combined uncertainty in the alignment of the wind lidars and the mast wind vanes. The well-known 180∘ error in the wind direction output for this type of instrument occurs about 9 % of the time. A correction scheme based on data of an auxiliary wind vane at a height of 10 m is applied, leading to a reduction of the 180∘ error below 2 %. This scheme can be applied in real-time applications in the situation that a nearby freely exposed mast with wind direction measurements at a single height is available.

This study evaluated the impact of a future space-borne Doppler wind lidar (DWL) on a super-low-altitude orbit by using an observing system simulation experiment (OSSE) based on a sensitivity observing system experiment (SOSE) approach. Realistic atmospheric data, including wind and temperature, was provided as “pseudo-truth” (PT) to simulate DWL observations. Hourly aerosols and clouds that are consistent with PT winds were also created for the simulation. A full-scale lidar simulator, which is described in detail in the companion paper, simulated realistic line-of-sight wind measurements and observation quality information, such as signal-to-noise ratio (SNR)and measurement error. Quality control (QC) procedures in the data assimilation system were developed to select high-quality DWL observations on the basis of the averaged SNR from strong backscattering in the presence of aerosols or clouds. Furthermore, DWL observation errors used in the assimilation were calculated using the measurement error estimated by the lidar simulator.

Evaluation of model skill in predicting winds over the ocean was performed by comparing retrospective runs of numerical weather prediction (NWP) forecast models to shipborne Doppler lidar measurements in the Gulf of Maine, a potential region for U.S. coastal wind farm development. Deployed on board the NOAA R/V Ronald H. Brown during a 2004 field campaign, the high-resolution Doppler lidar (HRDL) provided accurate motion-compensated wind measurements from the water surface up through several hundred meters of the marine atmospheric boundary layer (MABL). The quality and resolution of the HRDL data allow detailed analysis of wind flow at heights within the rotor layer of modern wind turbines and data on other critical variables to be obtained, such as wind speed and direction shear, turbulence, low-level jet properties, ramp events, and many other wind-energy-relevant aspects of the flow. This study will focus on the quantitative validation of NWP models’ wind forecasts within the lower MABL by comparison with HRDL measurements. Validation of two modeling systems rerun in special configurations for these 2004 cases—the hourly updated Rapid Refresh (RAP) system and a special hourly updated version of the North American Mesoscale Forecast System [NAM Rapid Refresh (NAMRR)]—are presented. These models were run at both normal-resolution (RAP, 13 km; NAMRR, 12 km) and high-resolution versions: the NAMRR-CONUS-nest (4 km) and the High-Resolution Rapid Refresh (HRRR, 3 km). Each model was run twice: with (experimental runs) and without (control runs) assimilation of data from 11 wind profiling radars located along the U.S. East Coast. The impact of the additional assimilation of the 11 profilers was estimated by comparing HRDL data to modeled winds from both runs. The results obtained demonstrate the importance of high-resolution lidar measurements to validate NWP models and to better understand what atmospheric conditions may impact the accuracy of wind forecasts in the marine atmospheric boundary layer. Results of this research will also provide a first guess as to the uncertainties of wind resource assessment using NWP models in one of the U.S. offshore areas projected for wind plant development.

Abstract. The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the Atmospheric LAser Doppler INstrument (ALADIN) Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of ALADIN, the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer's high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented, which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent DWL that was deployed in parallel on board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 to about 1.5 m s−1, demonstrating the capability of such a direct detection DWL. Furthermore, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument's near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m s−1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) winds for ascending and descending orbits, as well as random errors of about 3 m s−1 (HLOS) for the Mie and close to 6 m s−1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality.

This study presents wind observations from an airborne Doppler Wind Lidar (ADWL) in 2016 tropical cyclones (TC). A description of ADWL measurement collection and quality control methods is introduced for the use in a TC environment. Validation against different instrumentation on-board the National Oceanographic and Atmospheric Administration's WP-3D aircraft shows good agreement of the retrieved ADWL measured wind speed and direction. Measurements taken from instruments such as the global positioning system dropsonde, flight-level wind probe, tail Doppler radar, and Stepped Frequency Microwave Radiometer are compared to ADWL observations by creating paired datasets. These paired observations represent independent measurements of the same observation space through a variety of mapping techniques that account for differences in measurement procedure. Despite high correlation values, outliers are identified and discussed in detail. The errors between paired observations appear to be caused by differences in the ability to capture various length scales, which directly relate to certain regions in a TC regime. In validating these datasets and providing evidence that shows the mitigation of gaps in 3-dimensional wind representation, the unique wind observations collected via ADWL have significant potential to impact numerical weather prediction of TCs.

Accurate power spectrum analysis of weak backscattered signals are the primary constraint in long-distance coherent Doppler wind lidar (CDWL) applications. To study the atmospheric boundary layer, an all-fiber CDWL with 300µJ pulse energy is developed. In principle, the coherent detection method can approach the quantum limit sensitivity if the noise in the photodetector output is dominated by the shot noise of the local oscillator. In practice, however, abnormal power spectra occur randomly, resulting in error estimation and low inversion probability. This phenomenon is theoretically analyzed and shown to be due to the leakage of a time-varying DC noise of the balanced detector. Thus, a correction algorithm with accurate noise modeling is proposed and demonstrated. The accuracy of radial velocity, carrier-to-noise ratio (CNR), and spectral width are improved. In wind profiling process, a robust sine-wave fitting algorithm with data quality control is adopted in the velocity-azimuth display (VAD) scanning detection. Finally, in 5-day continuous wind detection, the inversion probability is tremendously enhanced. As an example, it is increased from 8.6% to 52.1% at the height of 4 km.

Abstract Sandia National Laboratories and the National Renewable Energy Laboratory conducted a wake-steering field experiment at the Scaled Wind Farm Technology facility. The campaign included the use of two highly instrumented V27 wind turbines, an upstream meteorological tower, and high-resolution wake measurements of the upstream wind turbine using a customized scanning SpinnerLidar from the Technical University of Denmark. The present work details how the SpinnerLidar data uploaded to the Department of Energy Atmosphere to Electrons Data Archive and Portal was processed, quality controlled and assured to guarantee high data availability with the removal of invalid measurements. A multidimensional approach to processing the SpinnerLidar Doppler spectra was developed based on matching erroneous measurements within the two-dimensional lidar scan with patterns inside the multidimensional lidar Doppler spectra. This method allows image processing techniques to be used to remove regions of the Doppler spectra that are contaminated by hard targets and isolate the velocity field of interest, allowing more accurate line-of-sight velocity measurements and enabling the estimation of the turbulence of the line-of-sight velocities within the probe volume.

Abstract. In the present study a non-motion-stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December 2015–January 2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented. The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-to-noise ratio (SNR) threshold is presented, which can be calculated for individual measurement set-ups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship. The performance of the lidar measurements in comparison with radio soundings generally shows small root mean square deviation (bias) for wind speed of around 1 m s−1 (0.1 m s−1) and for wind direction of around 10∘ (1∘). The post-processing of the non-motion-stabilized data shows a comparably high quality to studies with motion-stabilized systems. Two case studies show that a flexible change in SNR threshold can be beneficial for special situations. Further the studies reveal that short-lived low-level jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motion-stabilized Doppler lidar can be operated successfully on an icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.

Abstract This study introduces a validation technique for quantitative comparison of algorithms that retrieve winds from passive detection of cloud- and water vapor–drift motions, also known as atmospheric motion vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh-rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds, which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages mesoscale, multilayer, multiband wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or “optical flow,” retrieval algorithms used within AMVs, 1) patch matching (PM; used within operational AMVs) and 2) an advanced variational optical flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multilayer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g., gravity waves), where the image temporal noise exceeds the natural variability, and when the height assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh-rate imagery, such as 1- versus 10-min data.

Abstract. The fusion of drone and wind lidar technology introduces the exciting possibility of performing high-quality wind measurements virtually anywhere for substantially lower costs than established in-situ and remote sensing techniques. In this paper we will present a proof of concept (POC) drone-lidar system and report results from several test campaigns that demonstrate its ability to measure accurate wind speeds. The POC system is based on a dual-telescope Continuous Wave (CW) lidar, with drone-borne telescopes and ground-based opto-electronics. Commercially available drone and gimbal units are employed. The demonstration campaigns started with a series of comparisons of the wind speed measurements acquired by the POC system to simultaneous measurements performed by nearby mast based sensors. Generally very good agreement was found. Subsequently the extent of the flow disturbance caused by the drone downwash was investigated. These tests vindicated the somewhat conservative choice of lidar measurement range made for the initial wind speed comparisons. Overall, the excellent results obtained without any drone motion correction and with fairly primitive drone position control indicate the potential of drone-lidar systems in terms of accuracy and applications. The next steps in the development are outlined in the paper and several potential applications are discussed.

Abstract The European Space Agency Aeolus mission was launched in August 2018. This satellite carries the first Doppler lidar able to provide global measurements of wind profiles. Aeolus Level‐2B products have been generated and monitored by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in near real‐time since a few weeks after the launch. These products include the horizontal line‐of‐sight (HLOS) winds that are suitable for data assimilation in numerical weather prediction systems. This article presents a series of observing system experiments conducted over summer 2019 to assess the value of the Level‐2B HLOS winds and their impact on the Environment and Climate Change Canada global forecasts. The impact of atmospheric motion vectors (AMVs) on forecasts is also examined and compared with the impact of HLOS winds. Two datasets are used: the HLOS winds produced in near real‐time at ECMWF and those reprocessed later in fall 2020. It is found that the near real‐time data are significantly biased and should be corrected. A look‐up table bias correction based on observation minus background departures is applied to this dataset as initially proposed by ECMWF. The reprocessed data are of better quality and bias corrected using the telescope's primary mirror temperature variations as predictor. The impacts of the near real‐time and reprocessed HLOS winds on forecasts are generally positive for both temperature and wind. The impacts are largest in the troposphere over the Tropics and polar regions. The positive impacts on forecasts are larger with the reprocessed data, particularly in the stratosphere, where a significant degradation over the Southern Hemisphere is found from assimilating the near real‐time data. The normalized forecast error reductions at days 1 and 2 for the wind are ∼1.25% over the Tropics and Southern Hemisphere. The positive impact of the HLOS winds on forecasts is enhanced by ∼40% when the AMVs are not assimilated in the control experiment. The forecast error reduction from assimilating AMVs is, however, two times larger than from assimilating HLOS winds in the extratropics. Conversely, the impact of HLOS winds on forecasts is generally larger in the Tropics.

Abstract. Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2 µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z score, thereby ensuring an error distribution with a high degree of normality while retaining a large portion of wind results from the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s−1 with respect to both the ECMWF model background and the 2 µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is 5.3 m s−1, Mie vs. DWL is 4.1 m s−1, Rayleigh vs. model is 7.8 m s−1, and Rayleigh vs. DWL is 8.2 m s−1) are elaborated in the text.

The Doppler lidar system can accurately obtain wind profiles with high spatiotemporal resolution, which plays an increasingly important role in the research of atmospheric boundary layers and sea–land breeze. In September 2019, Doppler lidars were used to carry out observation experiments of the atmospheric wind field and pollutants in Shenzhen. Weather Research and Forecasting showed that the topography of Hongkong affected the sea breeze to produce the circumfluence flow at low altitudes. Two sea breezes from the Pearl River Estuary and the northeast of Hong Kong arrived at the observation site in succession, changing the wind direction from northeast to southeast. Based on the wind profiles, the structural and turbulent characteristics of the sea breeze were analyzed. The sea breeze front was accurately captured by the algorithm based on fuzzy logic, and its arrival time was 17:30 on 25 September. The boundary between the sea breeze and the return flow was separated by the edge enhancement algorithm. From this, the height of the sea breeze head (about 1100 m) and the thickness of the sea breeze layer (about 700 m) can be obtained. The fluctuated height of the boundary and the spiral airflow nearby revealed the Kelvin–Helmholtz instability. The influence of the Kelvin–Helmholtz instability could be delivered to the near-surface, which was verified by the spatiotemporal change of the horizontal wind speed and momentum flux. The intensity of the turbulence under the control of the sea breeze was significantly lower than that under the land breeze. The turbulent intensity was almost 0.1, and the dissipation rate was between 10−4 and 10−2 m2·s−3 under the land breeze. The turbulent intensity was below 0.05, and the dissipation rate was between 10−5 and 10−3 m2·s−3 under the sea breeze. The turbulent parameters showed peaks and large gradients at the boundary and the sea breeze front. The peak value of the turbulent intensity was around 0.3, and the dissipation rate was around 0.1 m2·s−3. The round-trip effect of sea–land breeze caused circulate pollutants. The recirculation factor was maintained at 0.5–0.6 at heights where the sea and land breeze alternately controlled (below 600 m), as well as increasing with a decreasing duration of the sea breeze. The factor exceeded 0.9 under the control of the high-altitude breeze (above 750 m). The convergence and rise of the airflow at the front led to collect pollutants, causing a sharp decrease in air quality when the sea breeze front passed.

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Abstract. An improved representation of 3-D air motion and precipitation structure through forecast models and assimilation of observations is vital for improvements in weather forecasting capabilities. However, there are few independent data to properly validate a model forecast of precipitation structure when the underlying dynamics are evolving on short convective timescales. Using data from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2 µm Doppler Aerosol Wind (DAWN) lidar collected during the 2017 Convective Processes Experiment (CPEX), the NASA Unified Weather Research and Forecasting (WRF) Ensemble Data Assimilation System (EDAS) modeling system was used to quantify the impact of high-resolution sparsely sampled DAWN measurements on the analyzed variables and on the forecast when the DAWN winds were assimilated. Overall, the assimilation of the DAWN wind profiles had a discernible impact on the wind field as well as the evolution and timing of the 3-D precipitation structure. Analysis of individual variables revealed that the assimilation of the DAWN winds resulted in important and coherent modifications of the environment. It led to an increase in the near-surface convergence, temperature, and water vapor, creating more favorable conditions for the development of convection exactly where it was observed (but not present in the control run). Comparison to APR-2 and observations by the Global Precipitation Measurement (GPM) satellite shows a much-improved forecast after the assimilation of the DAWN winds – development of precipitation where there was none, more organized precipitation where there was some, and a much more intense and organized cold pool, similar to the analysis of the dropsonde data. The onset of the vertical evolution of the precipitation showed similar radar-derived cloud-top heights, but delayed in time. While this investigation was limited to a single CPEX flight date, the investigation design is appropriate for further investigation of the impact of airborne Doppler wind lidar observations upon short-term convective precipitation forecasts.

On 4 June 2021, short-duration extreme precipitation occurred in Taipei. Within 2 h, over 200 mm of rainfall accumulated in the Xinyi district. In this study, advanced data assimilation technology (e.g., hybrid data and 3D variations) was incorporated to develop a high-resolution, small-scale (e.g., northern Taiwan) data assimilation forecast system, namely the weather research and forecast-grid statistical interpolation (WRF-GSI) model. The 3D wind field data recorded by the Doppler wind lidar system of Taipei Songshan Airport were assimilated for effective simulation of the extreme precipitation. The results revealed that the extreme rainfall was caused by the interaction between the northeast wind incurred by a front to the north of Taiwan, a humid southerly wind generated by Typhoon Choi-wan, and the regional sea–land breeze circulation. For the Xinyi district, the WRF-GSI_lidar model reported accumulated rainfall 30 mm higher than that in the non-assimilated experiment (WRF-GSI_noDA), indicating that the WRF-GSI model with lidar observation was improved 15% more than the nonassimilated run.

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.

Abstract Aeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line‐of‐sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level‐2B (L2B) wind statistics have been assessed using the European Centre for Medium‐range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0–7.0 m·s −1 for the Rayleigh‐clear and 2.8–3.6 m·s −1 for the Mie‐cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms. Complex systematic HLOS wind error variations on time‐scales less than one orbit were identified, most strongly affecting the Rayleigh‐clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020. In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short‐range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root‐mean‐square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere. Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh‐clear and Mie‐cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh‐clear and Mie‐cloudy winds providing similar overall short‐range impact in 2020.

The European Space Agency (ESA) wind mission, Aeolus, hosts the first space-based Doppler Wind Lidar (DWL) world-wide. The primary mission objective is to demonstrate the DWL technique for measuring wind profiles from space, intended for assimilation in Numerical Weather Prediction (NWP) models. The wind observations will also be used to advance atmospheric dynamics research and for evaluation of climate models. Mission spin-off products are profiles of cloud and aerosol optical properties. Aeolus was launched on 22 August 2018, and the Atmospheric LAser Doppler INstrument (Aladin) instrument switch-on was completed with first high energy output in wind mode on 4 September 2018 [1], [2]. The on-ground data processing facility worked excellent, allowing L2 product output in near-real-time from the start of the mission. First results from the wind profile product (L2B) assessment show that the winds are of very high quality, with random errors in the free Troposphere within (cloud/aerosol backscatter winds: 2.1 m/s) and larger (molecular backscatter winds: 4.3 m/s) than the requirements (2.5 m/s), but still allowing significant positive impact in first preliminary NWP impact experiments. The higher than expected random errors at the time of writing are amongst others due to a lower instrument out-and input photon budget than designed. The instrument calibration is working well, and some of the data processing steps are currently being refined to allow to fully correct instrument alignment related drifts and elevated detector dark currents causing biases in the first data product version. The optical properties spin-off product (L2A) is being compared e.g. to NWP model clouds, air quality model forecasts, and collocated ground-based observations. Features including optically thick and thin particle and hydrometeor layers are clearly identified and are being validated.

Abstract. In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the Northern Hemisphere (23.5–65∘ N) show strong variations of the Aeolus wind bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land–sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

Abstract Observations across the North Atlantic jet stream with high vertical resolution are used to explore the structure of the jet stream, including the sharpness of vertical wind shear changes across the tropopause and the wind speed. Data were obtained during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) by an airborne Doppler wind lidar, dropsondes, and a ground-based stratosphere–troposphere radar. During the campaign, small wind speed biases throughout the troposphere and lower stratosphere of only −0.41 and −0.15 m s−1 are found, respectively, in the ECMWF and Met Office analyses and short-term forecasts. However, this study finds large and spatially coherent wind errors up to ±10 m s−1 for individual cases, with the strongest errors occurring above the tropopause in upper-level ridges. ECMWF and Met Office analyses indicate similar spatial structures in wind errors, even though their forecast models and data assimilation schemes differ greatly. The assimilation of operational observational data brings the analyses closer to the independent verifying observations, but it cannot fully compensate for the forecast error. Models tend to underestimate the peak jet stream wind, the vertical wind shear (by a factor of 2–5), and the abruptness of the change in wind shear across the tropopause, which is a major contribution to the meridional potential vorticity gradient. The differences are large enough to influence forecasts of Rossby wave disturbances to the jet stream with an anticipated effect on weather forecast skill even on large scales.

Abstract The European Space Agency Aeolus mission launched the first‐of‐its‐kind space‐borne Doppler wind lidar in August 2018. The Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) wind observations are integrated into the NOAA Finite‐Volume Cubed‐Sphere Global Forecast System (FV3GFS). Components of the data assimilation system are optimized to increase the forecast impact from these Aeolus observations. Three observing‐system experiments (OSEs) are performed using the Aeolus L2B HLOS winds for the period of August 2–September 16, 2019: a baseline experiment assimilating all observations that are operationally assimilated in NOAA's FV3GFS but without Aeolus; an experiment adding the Aeolus L2B HLOS winds on top of the baseline configuration; and an experiment adding the Aeolus L2B HLOS winds on top of the baseline but also including a total least‐squares (TLS) regression bias correction applied to the HLOS winds. The variances of the Aeolus HLOS wind random errors (i.e., observation errors) are estimated using the Hollingsworth–Lonnberg (HL) method. Results from both OSEs demonstrate positive impact of Aeolus L2B HLOS winds on the NOAA global forecast. The largest impact is seen in the tropical upper troposphere and lower stratosphere where the Day 1–3 wind vector forecast root‐mean‐square error (RMSE) is reduced by up to 4%. Additionally, the assimilation of Aeolus impacts the steering currents ambient to tropical cyclones, resulting in a 15% reduction in track forecast error in the Eastern Pacific basin Day 2–5 forecasts, and a 5% and 20% reduction in track forecast error in the Atlantic basin at Day 2 and Day 5, respectively. In most cases, the additional TLS bias correction increases the positive impact of Aeolus data assimilation in the NOAA global numerical weather prediction (NWP) system when compared to the assimilation of Aeolus without bias correction.

Abstract Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.

We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.

Abstract. Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22-hourly forecasts from the NASA Unified – Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection at Night (PECAN) campaign on 11 July 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with six independent radiosonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had a small impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the Doppler lidar PBLH observations was found to improve the temperature and V-velocity profiles relative to independent radiosonde profiles. Water vapor was overcorrected, leading to increased differences with independent data. Errors in the U velocity were made slightly larger. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.

Doppler wind lidar has played an important role in alerting low-level wind shear (LLW). However, these high-resolution observations are underused in the model-based analysis and forecasting of LLW. In this regard, we employed the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system to investigate the impact of lidar data assimilation (DA) on LLW simulations. Eight experiments (including six assimilation experiments) were designed for an LLW process as reported by pilots, in which different assimilation intervals, assimilation timespans, and model vertical resolutions were examined. Verified against observations from Doppler wind lidar and an automated weather observing system (AWOS), the introduction of lidar data is helpful for describing the LLW event, which can represent the temporal and spatial features of LLW, whereas experiments without lidar DA have no ability to capture LLW. While lidar DA has an obviously positive role in simulating LLW in the 10–20 min after the assimilation time, this advantage cannot be maintained over a longer time. Therefore, a smaller assimilation interval is favorable for improving the simulated effect of LLW. In addition, increasing the vertical resolution does not evidently improve the experimental results, either with or without assimilation.

Abstract Due to lack of high spatial and temporal resolution boundary layer (BL) observations, the rapid changes in the near-storm environment are not well represented in current convective-scale numerical models. Better representation of the near-storm environment in model initial conditions will likely further improve the forecasts of severe convective weather. This study investigates the impact of assimilating high temporal resolution BL retrievals from two ground-based remote sensing instruments for short-term forecasts of a tornadic supercell event on 13 July 2015 during the Plains Elevated Convection At Night field campaign. The instruments are the Atmospheric Emitted Radiance Interferometer (AERI) that retrieves thermodynamic profiles and the Doppler lidar (DL) that measures horizontal wind profiles. Six sets of convective-scale ensemble data assimilation (DA) experiments are performed: two control experiments that assimilate conventional and WSR-88D radar observations using either relaxation-to-prior-spread (RTPS) or the adaptive inflation (AI) technique and four experiments similar to the control but that assimilate either DL or AERI or both observations in addition to all other observations that are in the control experiments. Results indicate a positive impact of AERI and DL observations in forecasting convective initiation (CI) and early evolution of the supercell storm. The experiment that employs the AI technique to assimilate BL observations in DA enhances the humidity in the near-storm environment and low-level convergence, which in turn helps forecasting CI. The forecast improvement is most pronounced during the first ~3 h. Results also indicate that the AERI observations have a larger impact compared to DL in predicting CI.

Abstract The European Space Agency's Aeolus satellite was launched in August 2018. Measurements of wind profiles are provided for the first time from space using an onboard Doppler wind lidar. The quality of Aeolus Level‐2B (L2B) wind products has been found suitable for data assimilation in the Météo‐France global model ARPEGE since April 2020, in particular, when applying a suitable bias correction method. This article describes a series of Observing System Experiments (OSEs) conducted in April–May 2020 to assess the impact of Aeolus horizontal line‐of‐sight winds ( HLOSW ) on Météo‐France's global numerical weather prediction analyses and forecasts. Innovation statistics and a posteriori diagnostics from a period of July–August 2019 were used to scale the random observation errors provided by the L2B processor (mostly for Rayleigh‐clear winds). Although the HLOSW data represent only 0.42% of the total amount of all observations assimilated in ARPEGE, their contribution to the reduction of the global analysis variance is up to 2.3% (measured by the Degree of Freedom for Signal). The assimilation of HLOSW showed improvement in 6 hr short‐range forecasts which is demonstrated by an overall reduction of innovations statistics for various operational observing systems. From a Forecast Sensitivity to Observations impact ( FSOi ) study Aeolus is found to be the third most effective observing system (per individual observation) at reducing global 24‐hour forecast errors. For longer forecast ranges, the largest positive impacts are noticed over the tropics, particularly in the lower stratosphere up to 102 hr ahead (with up to 2% root‐mean‐square error reduction for wind and temperature), but also in the troposphere up to 72 hr ahead. To a lesser extent, a similar improvement is observed over the Southern Hemisphere. This positive impact of Aeolus HLOSW in OSEs has led to their operational assimilation at Météo‐France starting in June 2020.

Abstract This work focuses on the potential of a network of Doppler lidars for the improvement of short‐term forecasts of low‐level wind. For the impact assessment, we developed a new methodology that is based on ensemble sensitivity analysis (ESA). In contrast to preceding network design studies using ESA, we calculate the explicit sensitivity including the inverse of the background covariance matrix to account directly for the localization scale of the assimilation system. The new method is applied to a pre‐existing convective‐scale 1,000‐member ensemble simulation to mitigate effects of spurious correlations. We evaluate relative changes in the variance of a forecast metric, that is, the low‐level wind components averaged over the Rhein–Ruhr metropolitan area in Germany. This setup allows us to compare the relative variance change associated with the assimilation of hypothetical observations from a Doppler wind lidar with respect to the assimilation of surface‐wind observations only. Furthermore, we assess sensitivities of derived variance changes to a number of settings, namely observation errors, localization length scale, regularization factor, number of instruments in the network, and their location, as well as data availability of the lidar measurements. Our results demonstrate that a network of 20–30 Doppler lidars leads to a considerable variance reduction of the forecast metric chosen. On average, an additional network of 25 Doppler lidars can reduce the 1–3 hr forecast error by a factor of 1.6–3.3 with respect to 10‐m wind observations only. The results provide the basis for designing an operational network of Doppler lidars for the improvement of short‐term low‐level wind forecasts that could be especially valuable for the renewable energy sector.

Abstract High-spatiotemporal-resolution airborne Doppler Aerosol Wind (DAWN) lidar profiles over the Caribbean Sea and Gulf of Mexico region were collected during the NASA Convective Processes Experiment (CPEX) field campaign from 27 May to 24 June 2017. This study examines the impact of assimilating these wind profiles on the numerical simulation of moist convective systems using an Advanced Research version of the Weather Research and Forecasting (WRF) Model (WRF-ARW). A mesoscale convective system and a tropical storm (Cindy) that occurred on 16 June 2017 in a strong shear environment and on 21 June 2017 in a weak shear environment, respectively, are selected as case studies. The DAWN wind profiles are assimilated with the NCEP Gridpoint Statistical Interpolation analysis system using a three-dimensional variational (3DVar) and a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation systems to provide the initial conditions for a short-range forecast. Results show that the assimilation of DAWN wind profiles has significant positive impacts on convective simulations with the two assimilation approaches. The assimilation of DAWN wind profiles creates notable adjustments in the analysis of the divergence field for WRF simulations with a good agreement of wind forecasts with radiosonde observations. The quantitative precipitation forecasting is also improved. In general, the 3DEnVar data assimilation method is deemed more promising for DAWN data assimilation. There are cases with Tropical Storm Cindy in which DAWN data have slight to neutral impact on rainfall forecasts with 3DVAR, implying complicated interactions between errors of retrieved wind data and background error covariance in the lower and upper troposphere.

Abstract The Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (TOMACS) began as a Japanese domestic research project in 2010 and aimed to elucidate the mechanisms behind local high-impact weather (LHIW) in urban areas, to improve forecasting techniques for LHIW, and to provide high-resolution weather information to end-users (local governments, private companies, and the general public) through social experiments. Since 2013, the project has been expanded as an international Research and Development Project (RDP) of the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO). Through this project, the following results were obtained: 1) observation data for LHIW around Tokyo were recorded using a dense network of X-band radars, a C-band polarimetric radar, a Ku-band fast-scanning radar, coherent Doppler lidars, and the Global Navigation Satellite System; 2) quantitative precipitation estimation algorithms for X-band polarimetric radars have been developed as part of an international collaboration; 3) convection initiation by the interaction of sea breezes and urban impacts on the occurrence of heavy precipitation around Tokyo were elucidated by a dense observation network, high-resolution numerical simulations, and different urban surface models; 4) an “imminent” nowcast system based on the vertically integrated liquid water derived from the X-band polarimetric radar network has been developed; 5) assimilation methods for data from advanced observation instruments such as coherent Doppler lidars and polarimetric radars were developed; and 6) public use of high-resolution radar data were promoted through the social experiments.

Abstract. The European Space Agency Aeolus mission launched the first of its kind spaceborne Doppler wind lidar in August 2018. To optimize assimilation of the Aeolus Level-2B (L2B) Horizontal Line-of-Sight (HLOS) winds, systematic differences (referred as biases hereafter) between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent biases between Aeolus HLOS winds (L2B10) and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6-h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large well-defined, speed-dependent biases are found particularly in the lower stratosphere and troposphere of the tropics and Southern Hemisphere. These large biases should be corrected to increase the forecast impact of Aeolus data assimilated into global NWP systems.

Abstract. The WISSDOM (Wind Synthesis System using Doppler Measurements) synthesis scheme was developed to derive high-resolution 3-dimensional (3D) winds under clear-air conditions. From this variational-based scheme, detailed wind information was obtained from scanning Doppler lidars, automatic weather stations (AWSs), sounding observations, and local reanalysis datasets (LDAPS, Local Data Assimilation and Prediction System), which were utilized as constraints to minimize the cost function. The objective of this study is to evaluate the performance and accuracy of derived 3D winds from this modified scheme. A strong wind event was selected to demonstrate its performance over complex terrain in Pyeongchang, South Korea. The size of the test domain is 12×12 km2 extended up to 3 km a.m.s.l. (above mean sea level) height with a remarkably high horizontal and vertical resolution of 50 m. The derived winds reveal that reasonable patterns were explored from a control run, as they have significant similarity with the sounding observations. The results of intercomparisons show that the correlation coefficients between derived horizontal winds and sounding observations are 0.97 and 0.87 for u- and v-component winds, respectively, and the averaged bias (root mean square deviation, RMSD) of horizontal winds is between −0.78 and 0.09 (1.77 and 1.65) m s−1. The correlation coefficients between WISSDOM-derived winds and lidar QVP (quasi-vertical profile) are 0.84 and 0.35 for u- and v-component winds, respectively, and the averaged bias (RMSD) of horizontal winds is between 2.83 and 2.26 (3.69 and 2.92) m s−1. The statistical errors also reveal a satisfying performance of the retrieved 3D winds; the median values of wind directions are −5 to 5 (0 to 2.5)∘, the wind speed is approximately −1 to 3 m s−1 (−1 to 0.5 m s−1), and the vertical velocity is −0.2 to 0.6 m s−1 compared with the lidar QVP (sounding observations). A series of sensitivity tests with different weighting coefficients, radius of influence (RI) in interpolation, and various combination of different datasets were also performed. The results indicate that the present setting of the control run is the optimal reference to WISSDOM synthesis in this event and will help verify the impacts against various scenarios and observational references in this area.

Abstract The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.

<p>Over the last years, climate monitoring and operational weather forecasts have become an important topic for the renewable energy sector. An effective operation of national, and in the case of EU international, power generation aims to find the right balance between the minimization of CO2 emission and reduction of energy costs. In Germany, a considerable part of the electricity generation comes from wind. Therefore, an accurate forecast of low-level wind is essential to predict the generation of electrical power produced by wind parks. This enables timely adjustments of the conventional power plants. Currently, short-term low-level wind forecasts have considerable uncertainties. One of the cost-effective solutions to improve low-level wind forecasts is the assimilation of new observations into numerical weather prediction models. Even though in the last decade, the number of remote-sensing sites has been continuously growing, the coverage is far from being optimal to achieve significant improvement in the short-term wind forecast. However, before building new large networks of ground-based instruments it is important to estimate in advance which instruments to install, what effect to expect, and what spatial density of the distributed instruments should be.</p><p>Ground-based instruments that can provide valuable information for low-level wind forecasts are Doppler lidars. In this study, we focus on the estimation of the potential impact of Doppler lidars for short-term low-level wind forecasts essential for sustainable energy applications. The potential impact is analyzed using the ensemble sensitivity analysis (ESA) [1]. ESA is based on the Ensemble Transform Kalman Filter and allows us to investigate how the assimilation of hypothetical Doppler lidars can reduce the wind forecast variance. The impact of a Doppler lidar network was analyzed with respect to surface measurements operationally assimilated by national weather services. We investigated the sensitivity of the obtained results to the number of Doppler lidars in the network, the number of altitude layers observed by Doppler lidars, and forecast lead time. Our analysis is based on a 1000-member ensemble simulation for the urban and highly populated Rhein-Ruhr area and surrounding regions [2]. The simulation uses a full-physics non-hydrostatic regional model (SCALE-RM) and covers a two-week time period in May/June 2016.</p><p>This work has been conducted in the framework of the Hans-Ertel-Centre for Weather Research funded by the German Federal Ministry for Transportation and Digital Infrastructure (grant number BMVI/DWD 4818DWDP5A). This online publication is based upon work within the COST Action CA18235 supported by COST (European Cooperation in Science and Technology). We acknowledge RIKEN for providing the SCALE-RM model data.</p><p>References <br>[1] Ancell, B., and G. J. Hakim, 2007: Comparing adjoint-and ensemble-sensitivity analysis with applications to observation targeting, MWR., doi.org/10.1175/2007MWR1904.1. </p><p>[2] Necker, T., et al, 2020: A convective-scale 1000-member ensemble simulation and potential applications. QJRMS, doi.org/10.1002/qj.3744.</p>

Abstract. The European Space Agency Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B (B10) horizontal line-of-sight (HLOS) winds, significant systematic differences between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent systematic differences between the Aeolus HLOS winds and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6 h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large, well-defined, speed-dependent systematic differences are found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Correction of these systematic differences improves the forecast impact of Aeolus data assimilated into the NOAA global NWP system.

This study focused on improving the forecasting of the afternoon thunderstorm (AT) event on 5 August 2018 near Pingtung Airport in southern Taiwan through a three-dimensional variational data assimilation system using Doppler lidar-based wind profiler data from the Weather and Research Forecast model. The assimilation of lidar wind profiler data had a positive impact on predicting the occurrence and development of ATs and wind fields associated with the local circulations of the sea–land breeze and the mountains. Evaluation of the model quantitative precipitation forecast by using root-mean-square error analysis, Pearson product–moment correlation coefficient analysis, Spearman rank correlation coefficient analysis, and threat and bias scores revealed that experiments using data assimilation performed much better than those not using data assimilation. Among the experiments using data assimilation, when the implementation time of assimilation of the wind profiler data in the model was closer to the occurrence time of the observed ATs, the forecast performance greatly improved. Overall, our assimilation strategy has crucial implications for the prediction of short-duration intense rainfall caused by ATs with small temporal and spatial scales of few hours and a few tens of kilometers. Our strategy can help guarantee the flight safety of aircraft.

Abstract. To detect global wind profiles and improve numerical weather prediction (NWP), the European Space Agency (ESA) launched the Aeolus satellite carrying a spaceborne Doppler wind lidar in 2018. After the successful launch, the European Centre for Medium-Range Weather Forecasts (ECMWF) performed the observing system experiments (OSEs) to evaluate the contribution of Aeolus data to NWP. This study aims to assess the impact of Aeolus wind assimilation in the ECMWF model on near-surface (10 m height) wind forecasts over tropical ocean regions by taking buoy measurements for reference and over high-latitude regions by taking weather station data for reference for the year 2020. The assessments were conducted mainly through inter-comparison analysis. The results show that Aeolus data assimilation has a limited impact on sea surface wind forecasts for tropical regions when compared with buoy measurements. For the high-latitude regions in the Northern Hemisphere, Aeolus is able to improve near-surface wind forecasts. This positive impact is more evident as the forecast time step is extended, during the first half year of 2020 and during the winter months. In addition, the v component tends to benefit more from the Aeolus observations than the u component. For the Southern Hemisphere, a few error reductions are observed but exist randomly. Overall, this in situ data-based assessment expands our understanding of the role of Aeolus data assimilation with the global NWP model in predicting near-surface wind for tropical oceans and high-latitude regions.

Abstract The novel Aeolus satellite, which carries the first Doppler wind lidar providing profiles of horizontal line‐of‐sight (HLOS) winds, addresses a significant gap in direct wind observations in the global observing system. The gap is particularly critical in the tropical upper troposphere and lower stratosphere (UTLS). This article validates the Aeolus Rayleigh–clear wind product and short‐range forecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF) with highly accurate winds from the Loon super pressure balloon network at altitudes between 16 and 20 km. Data from 229 individual balloon flights are analysed, applying a collocation criterion of 2 hr and 200 km. The comparison of Aeolus and Loon data shows systematic and random errors of 0.31 and 6.37 ms, respectively, for the Aeolus Rayleigh–clear winds. The horizontal representativeness error of Aeolus HLOS winds (nearly the zonal wind component) in the UTLS ranges from 0.6–1.1 ms depending on the altitude. The comparison of Aeolus and Loon datasets against ECMWF model forecasts suggests that the model systematically underestimates the HLOS winds in the tropical UTLS by about 1 ms. While Aeolus winds are currently considered as point winds by the ECMWF data assimilation system, the results of the present study demonstrate the need for a more realistic HLOS wind observation operator for assimilating Aeolus winds.

Abstract The present study describes methods to reduce the uncertainty of velocity–azimuth display (VAD) wind and deformation retrievals from downward-pointing, conically scanning, airborne Doppler radars. These retrievals have important applications in data assimilation and real-time data processing. Several error sources for VAD retrievals are considered here, including violations to the underlying wind field assumptions, Doppler velocity noise, data gaps, temporal variability, and the spatial weighting function of the VAD retrieval. Specific to airborne VAD retrievals, we also consider errors produced due to the radar scans occurring while the instrument platform is in motion. While VAD retrievals are typically performed using data from a single antenna revolution, other strategies for selecting data can be used to reduce retrieval errors. Four such data selection strategies for airborne VAD retrievals are evaluated here with respect to their effects on the errors. These methods are evaluated using the second hurricane nature run numerical simulation, analytic wind fields, and observed Doppler radar radial velocities. The proposed methods are shown to reduce the median absolute error of the VAD wind retrievals, especially in the vicinity of deep convection embedded in stratiform precipitation. The median absolute error due to wind field assumption violations for the along-track and for the across-track wind is reduced from 0.36 to 0.08 m s −1 and from 0.35 to 0.24 m s −1 , respectively. Although the study focuses on Doppler radars, the results are equally applicable to conically scanning Doppler lidars as well.

This study reports preliminary results from the three-dimensional variational method (3DVAR) with incremental analysis updates (IAU) of the surface wind field, which is suitable for real-time processing. In this study, 3DVAR with IAU was calculated for the case of a tornadic storm using 500-m horizontal grid spacing with updates every 10 min, for 6 h. Radial velocity observations by eight X-band multi-parameter Doppler radars and three Doppler lidars around the Tokyo Metropolitan area, Japan, were used for the analysis. In this study, three types of analyses were performed between 1800 to 2400 LST (local standard time: UTC + 9 h) 6 September 2015. The first used only 3DVAR (3DVAR), the second used 3DVAR with IAU (3DVAR+IAU), and the third analysis did not use data assimilation (CNTL). 3DVAR+IAU showed the best accuracy of the three analyses, and 3DVAR alone showed the worst accuracy, even though the background was updated every 10 min. Sharp spike signals were observed in the time series of wind speed at 10 m AGL, analyzed by 3DVAR, strongly suggesting that a “shock” was caused by dynamic imbalance due to the instantaneous addition of analysis increments to the background wind components. The spike signal was not shown in 3DVAR+IAU analysis, therefore, we suggest that the IAU method reduces the shock caused by the addition of analysis increments. This study provides useful information on the most suitable DA method for the real-time analysis of surface wind fields.

<p>Low-level wind shear could occur not only in rainy weather conditions but also in non-rainy weather conditions, which is dangerous to aircraft safety for its rapid changes in wind direction or velocity. Recently, dry wind shear occurred in non-rainy condition has drawn more and more attention. Rain-detecting Doppler radar has no capabilities in detecting dry wind shear occurred in non-rainy condition, while Doppler Lidar observations with higher spatial and temporal resolution provide valuable information for dry wind shear. For this, considering dry wind shear cases reported by pilots at Lanzhou Zhongchuan International Airport as study object, lidar observations (radial velocities) were assimilated along with surface data to improve the prediction skill of dry wind shear events.</p><p>All experiments were conducted with Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system. Three-nested domains were employed with 1-km horizontal resolution in the innermost domain. The model was derived by the NCEP FNL data. Lidar data was processed and only assimilated in the innermost domain. Experimental results show that the low-level wind shear can not be found in the experimental results without lidar data assimilation, while lidar data assimilation experiment successfully represented wind shear small-scale characteristics and simulated radial wind pattern was close to lidar observation. In addition, assimilation cycles with short time intervals effectively improved simulation accuracy of wind shear events.</p>

Abstract. Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

Abstract. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

Abstract The ability of Atmospheric Emitted Radiance Interferometer (AERI) and Doppler lidar (DL) wind profile observations to impact short-term forecasts of convection is explored by assimilating retrievals into a partially cycled convection-allowing ensemble analysis and forecast system. AERI and DL retrievals were obtained over 12 days using a mobile platform that was deployed in the preconvective and near-storm environments of thunderstorms during the afternoon in the U.S. Great Plains. The observation locations were guided by real-time ensemble sensitivity analysis (ESA) fields. AERI retrievals of temperature and dewpoint and DL retrievals of the horizontal wind components were assimilated into a control experiment that only assimilated conventional observations. Using the fractions skill score within 25-km neighborhoods, it is found that the assimilation of the AERI and DL retrievals results in far more times when the forecasts are improved than degraded in the 6-h forecast period. However, statistical confidence in the improvements often is not high and little to no relationships between the ESA fields and the actual changes in spread and skill is found. But, the focus on convective initiation and early convective evolution—a challenging forecast problem—and the fact that frequent improvements were seen despite observations from only one system over a limited period, provides encouragement to continue exploring the benefits of ground-based profilers to supplement the current upper-air observing system for severe weather forecasting applications.

Abstract This article presents the prospects of measurement systems for wind hazards and turbulence at airports, which have been explored in the Ultrafast Wind Sensors (UFO) project. At France’s Toulouse–Blagnac Airport, in situ, profiling, and scanning sensors have been used to collect measurements, from which wind vectors and turbulence intensities are estimated. A scanning 1.5- µ m coherent Doppler lidar and a solid state X-band Doppler radar have been developed with improved update rates, spatial resolution, and coverage. In addition, Mode-S data downlinks have been collected for data analysis. Wind vector and turbulence intensity retrieval techniques are applied to demonstrate the capabilities of these measurement systems. An optimal combination of remote measurement systems is defined for all weather monitoring at airports. In this combination, lidar and radar systems are complementary for clear-air and rainy conditions, which are formulated in terms of visibility and rain rate. The added value of the measurement systems for high-resolution numerical weather prediction models is estimated by an observing system experiment, and a positive impact on the local wind forecast is demonstrated.

Abstract Annually and seasonally averaged wind profiles from three Doppler lidars were obtained from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR–NCEP model’s ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s −1 within the wind-turbine rotor layer, and 2 m s −1 for 200–500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west—closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s −1 ) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s −1 . The RMSE between measured and modeled winds at all sites was about 3 m s −1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Moreover, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

Doppler light detection and ranging (lidar) wind profilers have proven their capability to measure vertical wind profiles with an accuracy comparable to anemometers and radiosondes. However, most of these comparisons were performed over short time periods or at mid-latitudes. This study presents a multi-year assessment of the accuracy of Doppler lidar wind-profile measurements in the Arctic by comparing them with coincident radiosonde observations, and excellent agreement was observed. The suitability of the Doppler lidar for verification case studies of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction is also demonstrated, by using Environment and Climate Change Canada’s (ECCC) global environmental multiscale model (GEM-2.5 km and GEM-10 km). Since 2016, identical scanning Doppler lidars were deployed at two supersites commissioned by ECCC as part of the Canadian Arctic Weather Science project. The supersites are located in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) with a third Halo Doppler lidar located in Squamish (50°N, 123°W). Two lidar wind-profile measurement methodologies were investigated; the velocity-azimuth display method exhibited a smaller average bias (−0.27 ± 0.02 m/s) than the Doppler beam-swinging method (–0.46 ± 0.02 m/s) compared to the sonde. Comparisons to ECCC’s NWP models indicate good agreement, more so during the summer months, with an average bias < 0.71 m/s for the higher-resolution (GEM-2.5 km) ECCC models at Iqaluit. Larger biases were found in the mountainous terrain of Whitehorse and Squamish, likely due to difficulties in the model’s ability to resolve the topography. This provides evidence in favor of using high temporal resolution lidar wind-profile measurements to complement radiosonde observations and for NWP model verification and process studies.

Soon after its successful launch in August 2018, the spaceborne wind lidar ALADIN (Atmospheric LAser Doppler INstrument) on-board ESA’s Earth Explorer satellite Aeolus has demonstrated to provide atmospheric wind profiles on a global scale. Being the first ever Doppler Wind Lidar (DWL) instrument in space, ALADIN contributes to the improvement in numerical weather prediction (NWP) by measuring one component of the horizontal wind vector. The performance of the ALADIN instrument was assessed by a team from ESA, DLR, industry, and NWP centers during the first months of operation. The current knowledge about the main contributors to the random and systematic errors from the instrument will be discussed. First validation results from an airborne campaign with two wind lidars on-board the DLR Falcon aircraft will be shown.

Abstract The current atmospheric observing systems fail to provide a satisfactory amount of spatially and temporally resolved observations of temperature and humidity in the planetary boundary layer (PBL) despite their potential positive impact on numerical weather prediction (NWP). This is particularly critical for humidity, which exhibits a very high variability in space and time or for the vertical distribution of temperature, determining the atmosphere’s stability. Novel ground-based lidar remote sensing technologies and in situ measurements from unmanned aerial vehicles can fill this observational gap, but operational maturity was so far lacking. Only recently, commercial lidar systems for temperature and humidity profiling in the lower troposphere and automated observations on board of drones have become available. Raman lidar can provide profiles of temperature and humidity with high temporal and vertical resolution in the troposphere. Drones can provide high-quality in situ observations of various meteorological variables with high temporal and vertical resolution, but flights are complicated in high-wind situations, icing conditions, and can be restricted by aviation activity. Both observation systems have shown to considerably improve analyses and forecasts of high-impact weather, such as thunderstorms and fog in an operational, convective-scale NWP framework. The results of this study demonstrate the necessity for and the value of additional, high-frequency PBL observations for NWP and how lidar and drone observations can fill the gap in the current operational observing system.

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (∼-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (∼-0.5ms-1) offshore and a slight positive bias (∼+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.

ESA’s Doppler Wind lidar mission, the Atmospheric Dynamics Mission (ADM-Aeolus, hereafter abbreviated to Aeolus), was chosen as an Earth Explorer Core mission within the Living Planet Programme in 1999. It shall demonstrate the potential of space-based Doppler Wind lidars for operational measurements of wind profiles and their use in Numerical Weather Prediction (NWP) and climate research. Spin-off products are profiles of cloud and aerosol optical properties. Aeolus carries the novel Doppler Wind lidar instrument ALADIN. The mission prime is Airbus Defence & Space UK (ADS-UK), and the instrument prime is Airbus Defence & Space France (ADS-F).

Abstract To advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.

Abstract Aeolus is the first Doppler wind lidar (DWL) to measure wind profiles from space. Aeolus is an ESA (European Space Agency) explorer mission with the objective to retrieve winds from the collected atmospheric return signal which is the result of Mie and Rayleigh scattering of laser‐emitted light by atmospheric molecules and particulates. The focus of this paper is on winds retrieved from instrument Mie channel collected data, that is, originating from Mie scattering by atmospheric aerosols and clouds. The use of simulated data from numerical weather prediction (NWP) models is a widely accepted and proven concept for the monitoring of the performance of many meteorological instruments, including Aeolus. Continuous monitoring of Aeolus Mie channel winds against model winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) has revealed systematic errors in retrieved Mie winds. Following a reverse engineering approach, the systematic errors could be traced back to imperfections of the data in the calibration tables which serve as input for the on‐ground wind processing algorithms. A new algorithm, denoted NWP calibration, makes use of NWP model winds to generate an updated calibration table. It is shown that Mie winds retrieved by making use of the NWP‐based calibration tables show reduced systematic errors, not only when compared to NWP model winds but also when compared to an independent dataset of very‐high‐resolution aircraft wind data. The latter gives high confidence that the NWP‐based calibration algorithm does not introduce model‐related errors into retrieved Aeolus Mie winds. Based on the presented results in this paper, the NWP‐based calibration table, as part of the level‐2B wind processing, has become part of the operational processing chain since 01 July 2021.

A working group is studying the feasibility of a future Japanese space-borne coherent Doppler wind lidar (CDWL) for global wind profile observation. This study is composed of two companion papers: an instrumental overview of the space-borne CDWL for global wind profile observation (Part 1), and the wind measurement performance (error and bias) investigated using a full-fledged space-borne CDWL simulator (Part 2). This paper aims to describe the future space-borne CDWL in terms of technical points and observation user requirements. The future mission concept is designed to have two looks for vector wind measurement with vertical resolutions of 0.5 (lower troposphere: 0-3 km), 1 (middle troposphere: 3-8 km), and 2 km (upper troposphere: 8-20 km) and horizontal resolution of < 100 km along a satellite. The altitude and orbit of the satellite are discussed from a scientific viewpoint. The candidate altitude and orbit of the satellite are 220 km and an inclination angle of 96.4° (polar orbit) or 35.1° (low-inclination-angle orbit). The technical requirements of the space-borne CDWL are a single-frequency 2-μm pulse laser with an average laser power of 3.75 W, two effective 40-cm-diameter afocal telescopes, a wide-bandwidth (> 3.4 GHz) detector, a high-speed analog-to-digital converter, and a systematic lidar efficiency of 0.08. The space-borne CDWL looks at two locations at a nadir angle of 35° at two azimuth angles of 45° and 135° (225° and 315°) along the satellite track. The future space-borne CDWL wind profile observation will fill the gap of the current global wind observing systems and contribute to the improvement of the initial conditions for numerical weather prediction (NWP), the prediction of typhoons and heavy rain, and various meteorological studies.

The understanding of the atmospheric processes in coastal areas requires the availability of quality datasets describing the vertical and horizontal spatial structure of the Atmospheric Boundary Layer (ABL) on either side of the coastline. High-resolution Numerical Weather Prediction (NWP) models can provide this information and the main ingredients for good simulations are: an accurate description of the coastline and a correct subgrid process parametrization permitting coastline discontinuities to be caught. To provide an as comprehensive as possible dataset on Mediterranean coastal area, an intensive experimental campaign was realized at a near-shore Italian site, using optical and acoustic ground-based remote sensing and surface instruments, under different weather characteristic and stability conditions; the campaign is also fully simulated by a NWP model. Integrating information from instruments responding to different atmospheric properties allowed for an explanation of the development of various patterns in the vertical structure of the atmosphere. Wind LiDAR measurements provided information of the internal boundary layer from the value of maximum height reached by the wind profile; a height between 80 and 130 m is often detected as an interface between two different layers. The NWP model was able to simulate the vertical wind profiles and the eight of the ABL.

Abstract Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone‐related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large‐eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long‐term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially‐averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to street level, given pre‐computed scenarios of atmospheric conditions. For Helsinki, the observed winds are stronger during winter than summer, and, on average, higher wind speeds were observed at the urban site than the sub‐urban site.

Launched on August 22th of 2018, the Atmospheric Dynamics Mission - Aeolus of the European Space Agency (ESA) carries a direct detection Doppler Wind Lidar (DWL) to measure wind profiles in the atmosphere from space. The primary product of this Earth observation satellite is the measurement of profiles of the horizontally projected line-of-sight (HLOS) wind component, resulting into a single wind component measurement rather than the complete wind vector. The main motivation of the Aeolus mission is to reduce the deficiency in the current global coverage of wind observations, as part of the current Global Observing System (GOS) of the World Meteorological Organization (WMO). Aeolus is a next step in the aim for a homogeneous spatial and temporal global network of wind observations. \n\nIn this study, HLOS wind observations of Aeolus have been validated with independent and high-resolution aircraft-derived Mode-S EHS wind observations, together with the Numerical Weather Prediction (NWP) model of the European Centre for Medium-Range Weather Forecasts (ECMWF). Validation is a challenge for a unique instrument which is still in its commissioning phase, meaning that observed winds have not yet been well-calibrated, hence not yet suitable for operational use in NWP. Nevertheless, the main validation results are very promising ; demonstrating a high agreement between Aeolus, Mode-S EHS and ECMWF with correlation values exceeding 0.9. The known systematic, and slowly drifting over time, bias in the order of 2 m/s is observed and confirmed from standard statistics as well as from the more advanced triple collocation technique.

Global wind profile observation is important to improve initial conditions for numerical weather prediction (NWP), general circulation model, and various other meteorological studies. A space-borne Doppler wind lidar (DWL) is one of promising remote sensing techniques for global wind measurement.We describe a study based on simulated satellite measurements for assessing the measurement performances of a Japanese coherent DWL. Global simulations are performed using pseudo-truth atmospheric model of an observing system simulation experiment (OSSE) conducted using the global NWP system of the Japan Meteorological Agency. Wind profile retrieval simulations have been performed for 1 month (August, 2010) and the results show that the percentage of good quality estimates is 40% below 8 km, and it decreases to 10% at 8-20 km in the southern hemisphere and is 20-50% in the northern hemisphere. Expected line-of-sight wind speed errors for good quality estimates are 0.5 m s−1 below 8 km and 1.1 m s−1 at 8-20 km.In the future, the simulated observations will be used in the OSSE to quantitatively infer the potential impacts on NWP accuracy. To illustrate such analysis, results are shown from an initial validation test using a simple wind measurement model instead of the realistic DWL simulations.

&amp;lt;p&amp;gt;The European Space Agency (ESA)&amp;amp;#8217;s Earth Explorer Aeolus was launched in August 2018 carrying the world&amp;amp;#8217;s first spaceborne wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN uses a high spectral resolution Doppler wind lidar operating at 355nm to measure profiles of line-of-sight wind components in near-real-time (NRT). ALADIN samples the atmosphere from 30km altitude down to the Earth&amp;amp;#8217;s surface or to the level where the lidar signal is attenuated by optically thick clouds.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The global wind profiles provided by ALADIN help to improve weather forecasting and the understanding of atmospheric dynamics as they fill observational gaps in vertically resolved wind profiles mainly in the tropics, &amp;amp;#160;southern hemisphere, and over the northern hemisphere oceans. In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) became the first numerical weather prediction (NWP) centre to assimilate Aeolus observations for operational forecasting.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;A main prerequisite for beneficial impact is data of sufficient quality. Such high data quality has been achieved through close collaboration of all involved parties within the Aeolus Data Innovation and Science Cluster (DISC), which was established after launch to study and improve the data quality of Aeolus products. The tasks of the Aeolus DISC include the instrument and platform monitoring, calibration, characterization, retrieval algorithm refinement, processor evolution, quality monitoring, product validation, and impact assessment for NWP.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The achievements of the Aeolus DISC for the NRT data quality and the current status of Aeolus wind measurements will be described and summarized. Further, an outlook on future improvements and the availability of reprocessed datasets with enhanced data quality will be provided.&amp;lt;/p&amp;gt;

Abstract. In August 2018, the European Space Agency (ESA) launched the first Doppler wind lidar into space, which has since then been providing continuous profiles of the horizontal line-of-sight wind component at a global scale. Aeolus data have been successfully assimilated into several numerical weather prediction (NWP) models and demonstrated a positive impact on the quality of the weather forecasts. To provide valuable input data for NWP models, a detailed characterization of the Aeolus instrumental performance as well as the realization and minimization of systematic error sources is crucial. In this paper, Aeolus interferometer spectral drifts and their potential as systematic error sources for the aerosol and wind products are investigated by means of instrument spectral registration (ISR) measurements that are performed on a weekly basis. During these measurements, the laser frequency is scanned over a range of 11 GHz in steps of 25 MHz and thus spectrally resolves the transmission curves of the Fizeau interferometer and the Fabry–Pérot interferometers (FPIs) used in Aeolus. Mathematical model functions are derived to analyze the measured transmission curves by means of non-linear fit procedures. The obtained fit parameters are used to draw conclusions about the Aeolus instrumental alignment and potentially ongoing drifts. The introduced instrumental functions and analysis tools may also be applied for upcoming missions using similar spectrometers as for instance EarthCARE (ESA), which is based on the Aeolus FPI design.

Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s −1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Within ESA’s Living Planet Programme, the Atmospheric Dynamics Mission (ADM-Aeolus) was chosen as the second Earth Explorer Core mission in 1999. It shall demonstrate the potential of high spectral resolution Doppler Wind lidars for operational measurements of wind profiles and their use in Numerical Weather Prediction (NWP). Spin-off products are profiles of cloud and aerosol optical properties. ADM-Aeolus carries the novel Doppler Wind lidar instrument ALADIN.

Wind is fundamental in many atmospheric phenomena. Global wind profile observation is important to improve numerical weather prediction (NWP) and various meteorological studies. Wind profile observations are measured mainly by radiosonde networks. Most of the weather stations are on land, while weather stations on oceans and remote land areas are sparsely distributed. The current global wind observing systems do not satisfy the globally homogeneous wind profile observation. A space-based Doppler wind lidar is one of the candidates for future global wind profile observations. In the paper, we present results from feasibility study of space-based Doppler Wind Lidar.