基于垂直观测同化的风场及降水预报改进研究进展

优化和改进变分资料同化系统中的背景误差协方差模型，使之能够正确反映随流型演变的不确定性信息，是提高中期数值预报精度的重要技术手段。集合四维变分资料同化的一大关键是如何根据自身的背景误差协方差模型提取和应用流依赖背景信息。为了提高变分资料同化解算的效率，通过将某些变量划分为平衡部分和非平衡部分，平衡部分依据平衡约束关系与一个特定的变量相联系，剩余的非平衡部分在各变量间互不相关。随着数值预报模式的不断优化，非平衡方差在总方差中的影响越来越重要。本文介绍了YH4DVAR的背景误差协方差模型和集合四维变分资料同化系统架构，重点分析了平衡算子。通过集合方法估计得到了散度、温度、地面气压的非平衡项流依赖方差。最后，为了减少有限样本噪声对方差估计的影响，对非平衡项方差进行了校正和滤波。

雷达水平风廓线资料可以很直观地显示随时间变化风场的垂直结构。为了利用风廓线雷达进行降水研究，分析了2010年7月南京风廓线雷达探测降水的个例。通过风廓线雷达提供的大气折射率结构常数、水平速度、垂直速度等多种资料，可从多种角度了解降水过程，清楚地反映降水的开始、结束以及降水的强度，得出了强降水预报的着眼点和定性指标。

雷达预估信息(0~2 h)、数值天气预报产品(0~72 h)及基于高空和地面大气探测资料的综合预报信息(0~24 h)在不同预见期的降雨预报各有差异，预报效果上各有优劣，而针对山洪的预报存在准确率低、预警有效时间短等问题，研究面向山洪灾害防治区的数值预报模式的选取、模式最优物理参数化组合方案、同化方案、降水偏差订正技术以及基于大数据的自分型雷达估测降水最优化算法。以湖南临湘市为试验区进行验证，结果表明：WRF中尺度数值模式适用于山洪灾害降雨预报，其WSM6云微物理过程、Grell-Devenyi ensemble对流参数化方案和YSU边界层参数方案对致洪山洪暴雨过程模拟较好，基于频率(或面积)匹配的降水偏差订正方法能显著改善模式降水预报中雨量和雨区范围的系统性偏差，大数据分析方法应用于雷达估测降雨能显著提高准确度。在此研究基础上，提出了基于高空和地面大气探测、数值预报模式的0~72 h短期定量降雨预报和基于雷达的0~2 h临近定量降雨预报的多元信息耦合预报方法，将对致灾山洪的预报时间由2 h延长至72 h，并可显著提高了山洪预报的精度。

利用ERA5再分析资料、模式探空资料对重庆江北机场2024年6月21日发生的一次大暴雨过程进行了诊断分析。结果表明：本次强降水过程环流背景复杂，500 hPa副高稳定与高原槽东移、700 hPa西南涡与850 hPa低涡叠加、地面锋面东移南下的多系统协同作用下，为水汽输送、动力抬升及不稳定能量积累提供较好条件。垂直速度场显示850~500 hPa整层强上升运动配合“高空辐散–低空辐合”机制，叠加低空高湿环境与CAPE激增、K指数升高，驱动此次强降水持续发展，该形势背景可作为典型的夏季西南涡暴雨理论模型。数值模式性能显示，SWC1KM、SWC3KM在落区位置和起始时间上表现一般，CMA-MESO综合表现较好。而睿图系统对降水开始时间及落区分布预报较准确，但降水量及落区精准度差异显著，其中SWC3KM最优、CMA-MESO次之、EC较差。

本文利用贵阳机场CFL-03型风廓线雷达结合多源数据对2021年7月17日贵阳机场一次强对流天气进行了综合分析，研究表明：风廓线雷达资料能很好地反映贵阳机场上空水平风随高度的分布情况，从而为强对流天气的短临监测和预警提供十分有用的帮助信息，诸如判断低层大气冷暖平流，低空急流强度，冷空气入侵信号等；通过风廓线雷达的实时监测和产品应用，可以有效地捕捉到强对流天气发生前本地边界层内大气环境发生变化的微弱讯号，风廓线雷达的垂直速度、信噪比和谱宽都与短时强降水有着很好的关联，且信噪比和谱宽在强降水开始前30分钟左右出现了显著的变化特征，这些信号再结合多普勒气象雷达的实时探测，可以为高时空分辨率的预警服务提供有力的支撑；贵阳机场风廓线雷达安装于跑道南端入口处，其不仅可以实时测风，还可以计算出水平风的垂直切变指数，从而为进近和起飞的飞机提供垂直方向上的风切变预警。

研究成都地区风廓线的演变特征具有重要的科学意义和实际应用价值。本文利用成都市温江气象站2020~2024年观测数据通过风速廓线、风向玫瑰图，总结归纳风廓线的演变规律，采用WRF数值模拟评估了常用的三种边界层参数化方案(YSU、MYJ、ACM2)对成都地区风场的模拟性能。结果表明：(1) 成都地区08时与20时风场垂直结构特征相似，多项式拟合可较好地表征风廓线变化特征；平均风速随高度呈现先增后减趋势，低层冬季平均风速最小夏季最大，中高层相反，春季、秋季特征相似；其中850 hPa主导风向为东北风，到700 hPa偏南风增加，500 hPa以西风为主导，季节主导风向在700 hPa及以上层次存在差异，而且夏季的风向转换较多。(2) 三种方案均能有效模拟出成都地区风场特征，通过模拟结果和观测对比显示，采用YSU边界层方案为最优参数化方案。

利用WRF中尺度数值模式对2012年5月16日江苏北部一次飑线过程进行了数值模拟，采用三重嵌套，模式水平分辨率最高为5 km。利用模式输出的高分辨率资料，分析了地面和中层的飑线结构及飑线发展机制，结果表明，模拟的地面散度场存在一个“散度环”、三条辐合线，雷暴高压带形成一条辐散线，其前部和后部各存在一条辐合线，中层500 hPa上升运动中心南侧和北侧分别存在正涡度和负涡度中心。飑线的气流与经典模式的顺切变和逆切变气流不同，上升气流和下沉气流基本处在同一垂直气柱中，并在中层分离。中高空产生的降水物质掉入下半部分的下沉气流中，不会对上升气流形成拖曳作用，下降的降水物质在下沉气流中蒸发降温，在地面形成雷暴高压和冷池。利用涡度方程，分析了中层涡度偶形成的机制，垂直风切变和强垂直运动梯度相互作用在上升运动中心北侧形成负涡度，南侧形成正涡度。雷暴高压中的冷池和垂直风切变相互作用的结果使得雷暴在冷池前缘新生，而在冷池后部消亡，飑线自激发展。

Abstract Recently, a humidity estimation technique was developed by using the turbulence echo characteristics detected with a wind-profiling radar. This study is concerned with improvement of the retrieval algorithm for delineating a humidity profile from the refractive index gradient (M) inferred from the echo power. To achieve a more precise estimate of humidity, a one-dimensional variational method is adopted. Because the radar data provide only the absolute value of M, its sign must be determined in the retrieval. A statistical probability for the sign of M [Pr(z)] is introduced to the cost function of the variational method to determine the optimum result with reduced calculation cost. GPS-derived integrated water vapor (IWV) was assimilated together with the radar-derived |M| for constraining the signs of |M| to agree with the radar-derived IWV and the GPS-derived IWV. Humidity profiles were retrieved from the Middle and Upper Atmosphere (MU) radar–Radio Acoustic Sounding System (RASS) data for July–August 1999 using the first guess calculated from the time interpolation of radiosonde results. The |M| profiles from the MU radar–RASS were assimilated at 21 height layers between 1.5 and 7.5 km. A genetic algorithm is employed to find the global optimum. The humidity profiles are retrieved with the same vertical resolution as that of the observation values. The precision of the retrieval result using the new method is superior to that of the conventional method. The difference between the analysis and simultaneous radiosonde results was related to a large error in the first guess. The sensitivity of the analysis result to the shape of the Pr(z) profile was investigated, and the result appears to be insensitive to the profile of Pr(z). The improvement over the conventional method is especially evident for the case of a large error in the first guess.

A microwave radiometer is described that provides continuous thermodynamic (temperature, water vapor, and moisture) soundings during clear and cloudy conditions. The radiometric profiler observes radiation intensity at 12 microwave frequencies, along with zenith infrared and surface meteorological measurements. Historical radiosonde and neural network or regression methods are used for profile retrieval. We compare radiometric, radiosonde, and forecast soundings and evaluate the accuracy of radiometric temperature and water vapor soundings on the basis of statistical comparison with radiosonde soundings. We find that radiometric soundings are equivalent in accuracy to radiosonde soundings when used in numerical weather forecasting. A case study is described that demonstrates improved fog forecasting on the basis of variational assimilation of radiometric soundings. The accuracy of radiometric cloud liquid soundings is evaluated by comparison with cloud liquid sensors carried by radiosondes. Accurate high‐resolution three‐dimensional water vapor and wind analysis is described on the basis of assimilation of simulated thermodynamic and wind soundings along with GPS slant delays. Examples of mobile thermodynamic and wind profilers are shown. Thermodynamic profiling, particularly when combined with wind profiling and slant GPS, provides continuous atmospheric soundings for improved weather and dispersion forecasting.

Abstract On 15 March 2005, the Meteorological Service of Canada (MSC) proceeded to the implementation of a four-dimensional variational data assimilation (4DVAR) system, which led to significant improvements in the quality of global forecasts. This paper describes the different elements of MSC’s 4DVAR assimilation system, discusses some issues encountered during the development, and reports on the overall results from the 4DVAR implementation tests. The 4DVAR system adopted an incremental approach with two outer iterations. The simplified model used in the minimization has a horizontal resolution of 170 km and its simplified physics includes vertical diffusion, surface drag, orographic blocking, stratiform condensation, and convection. One important element of the design is its modularity, which has permitted continued progress on the three-dimensional variational data assimilation (3DVAR) component (e.g., addition of new observation types) and the model (e.g., computational and numerical changes). This paper discusses some numerical problems that occur in the vicinity of the Poles where the semi-Lagrangian scheme becomes unstable when there is a simultaneous occurrence of converging meridians and strong wind gradients. These could be removed by filtering the winds in the zonal direction before they are used to estimate the upstream position in the semi-Lagrangian scheme. The results show improvements in all aspects of the forecasts over all regions. The impact is particularly significant in the Southern Hemisphere where 4DVAR is able to extract more information from satellite data. In the Northern Hemisphere, 4DVAR accepts more asynoptic data, in particular coming from profilers and aircrafts. The impact noted is also positive and the short-term forecasts are particularly improved over the west coast of North America. Finally, the dynamical consistency of the 4DVAR global analyses leads to a significant impact on regional forecasts. Experimentation has shown that regional forecasts initiated directly from a 4DVAR global analysis are improved with respect to the regional forecasts resulting from the regional 3DVAR analysis.

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 Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS) is a three-dimensional variational data assimilation suite for generating atmospheric state estimates to satisfy a variety of navy needs. These needs range from global initial conditions for navy global prediction models to environmental input into forward-deployed shipboard tactical decision aids. In common with many other U.S. Navy applications, the NAVDAS system has been designed to be robust, flexible, and portable. In particular, it can perform central site global assimilation on massively parallel machines as well as local data assimilation on workstations with the same code. NAVDAS is an observation space algorithm. The preconditioned conjugate gradient method is used as the descent algorithm to minimize the three-dimensional cost function. The number of iterations required to reach convergence is minimized through the use of dual block diagonal preconditioners with Choleski decomposition. Vertical eigenvector decomposition of the background error covariance matrix leads to great generality in formulating nonseparable error covariances as well as enormous efficiencies in handling vertical profile and sounding observations. Forward operators are formulated and used for the direct assimilation of Television Infrared Observation Satellite Operational Vertical Sounder radiances and Special Sensor Microwave Imager wind speeds and total precipitable water. NAVDAS also contains a complete diagnostic suite, which includes complete observation trackability, Web-based observation monitoring, χ2 monitoring of innovations, the adjoint of the assimilation system, and analysis error estimation.

Abstract This paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

On 19 September 1996, a squall line stretching from Nebraska to Texas with intense embedded convection moved eastward across the Kansas-Oklahoma area, where special observations were taken as part of a Water Vapor Intensive Observing Period sponsored by the Atmospheric Radiation Measurement program. This provided a unique opportunity to test mesoscale data assimilation strategies for a strong convective event. In this study, a series of real-data assimilation experiments is performed using the MM5 four-dimensional variational data assimilation (4DVAR) system with a full physics adjoint. With a grid size of 20 km and 15 vertical layers, the MM5-4DVAR system successfully assimilated wind profiler, hourly rainfall, surface dewpoint, and ground-based GPS precipitable water vapor data. The MM5-4DVAR system was able to reproduce the observed rainfall in terms of precipitation pattern and amount, and substantially reduced the model errors when verified against independent observations. Additional data assimilation experiments were conducted to assess the relative importance of different types of mesoscale observations on the results of assimilation. In terms of the assimilation model's ability to recover the vertical structure of moisture and in reproducing the rainfall pattern and amount, the wind profiler data have the maximum impact. The ground-based GPS data have a significant impact on the rainfall prediction, but have relatively small influence on the recovery of moisture structure. On the contrary, the surface dewpoint data are very useful for the recovery of the moisture structure, but have relatively small impact on rainfall prediction. The assimilation of rainfall data is very important in preserving the precipitation structure of the squall line. All the data are found to be useful in this mesoscale data assimilation experiment.

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 The feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation has been demonstrated in the authors’ recent studies via observing system simulation experiments (OSSEs) both under a perfect-model assumption and in the presence of significant model error. The current study extends the EnKF to assimilate real-data observations for a warm-season mesoscale convective vortex (MCV) event on 10–12 June 2003. Direct comparison between the EnKF and a three-dimensional variational data assimilation (3DVAR) system, both implemented in the Weather Research and Forecasting model (WRF), is carried out. It is found that the EnKF consistently performs better than the 3DVAR method by assimilating either individual or multiple data sources (i.e., sounding, surface, and wind profiler) for this MCV event. Background error covariance plays an important role in the performance of both the EnKF and the 3DVAR system. Proper covariance inflation and the use of different combinations of physical parameterization schemes in different ensemble members (the so-called multischeme ensemble) can significantly improve the EnKF performance. The 3DVAR system can benefit substantially from using short-term ensembles to improve the prior estimate (with the ensemble mean). Noticeable improvement is also achieved by including some flow dependence in the background error covariance of 3DVAR.

A Doppler radar wind data assimilation system has been developed for the three-dimensional variational data assimilation (3DVAR) scheme of the High Resolution Limited Area Model (HIRLAM). Radar wind observations can be input for the multivariate HIRLAM 3DVAR either as radial wind superobservations (SOs) or as vertical profiles of horizontal wind obtained with the velocity–azimuth display (VAD) technique. The radar wind data handling system, including data processing, quality control, and observation operators for the 3DVAR, are described and evaluated. Background error standard deviation (σb) in observation space for wind and radial wind have been estimated by the so-called randomization method. The derived values of σb are used in the quality control of observations and also in the assignment of radar wind observation error standard deviations (σo). Parallel data assimilation and forecast experiments confirm reasonably tuned error statistics and indicate a small positive impact of radar wind data on the verification scores, for both inputs.

Abstract The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution numerical simulations of a mesoscale convective system and associated cyclonic line-end vortex (LEV) that spawned several tornadoes in central Oklahoma on 8–9 May 2007. The simulation uses a 1000 km × 1000 km domain with 2-km horizontal grid spacing. The ARPS three-dimensional variational data assimilation (3DVAR) is used to assimilate a variety of data types. All experiments assimilate routine surface and upper-air observations as well as wind profiler and Oklahoma Mesonet data over a 1-h assimilation window. A subset of experiments assimilates radar data. Cloud and hydrometeor fields as well as in-cloud temperature are adjusted based on radar reflectivity data through the ARPS complex cloud analysis procedure. Radar data are assimilated from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network as well as from the Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) network of four X-band Doppler radars. Three-hour forecasts are launched at the end of the assimilation window. The structure and evolution of the forecast MCS and LEV are markedly better throughout the forecast period in experiments in which radar data are assimilated. The assimilation of CASA radar data in addition to WSR-88D data increases the structural detail of the modeled squall line and MCS at the end of the assimilation window, which appears to yield a slightly better forecast track of the LEV.

Abstract Four‐dimensional (4D) wind fields were derived from radiance measurements of the Geosynchronous Interferometric Infrared Sounder (GIIRS) onboard the FengYun‐4A satellite with 15‐min temporal resolution during Typhoon Maria (2018). Results are evaluated with independent ERA5 reanalysis, Global Data Assimilation System (GDAS) analysis and dropsonde wind profiles, and show a statistical root mean squared error less than 2 m/s for U and V components in troposphere against ERA5 and GDAS. The temporal variation of the wind fields from GIIRS at 15‐min intervals is consistent with that of the hourly ERA5. The added value of wind profiles over the numerical weather predictions (NWP) background field is also revealed. Further experiments confirm that higher temporal resolution from geostationary infrared (IR) sounder measurements could provide better dynamic information. 4D dynamic information can be extracted from high temporal resolution geostationary hyperspectral IR radiances in a consistent and continuous manner that can be used together with the thermodynamic information for various quantitative applications such as NWP data assimilation, near real‐time weather monitoring, situational awareness and nowcasting.

Abstract In this study, it is demonstrated how temperature, humidity, and wind profile data from the lower troposphere obtained with a lightweight unmanned aerial system (UAS) can be used to improve high-resolution numerical weather simulations by four-dimensional data assimilation (FDDA). The combined UAS and FDDA system is applied to two case studies of northeasterly flow situations in southwest Iceland from the international Moso field campaign on 19 and 20 July 2009. Both situations were characterized by high diurnal boundary layer temperature variation leading to thermally driven flow, predominantly in the form of sea-breeze circulation along the coast. The data assimilation leads to an improvement in the simulation of the horizontal and vertical extension of the sea breeze as well as of the local background flow. Erroneously simulated fog over the Reykjanes peninsula on 19 July, which leads to a local temperature underestimation of 8 K, is also corrected by the data assimilation. Sensitivity experiments show that both the assimilation of wind data and temperature and humidity data are important for the assimilation results. UAS represents a novel instrument platform with a large potential within the atmospheric sciences. The presented method of using UAS data for assimilation into high-resolution numerical weather simulations is likely to have a wide range of future applications such as wind energy and improvements of targeted weather forecasts for search and rescue missions.

Abstract A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity. The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity. An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals. The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

Results from a case study of the four-dimensional variational assimilation of total zenith delay (TZD) observations from a dense global positioning system (GPS) network into the Pennsylvania State University–National Center for Atmospheric Research Fifth-Generation Mesoscale Model are reported. TZD is made up of the rescaled pressure and precipitable water at the site of the GPS receiver. Profiler-wind and radio acoustic sounding system (RASS) virtual temperature observations are also included in the assimilation experiments. Four experiments are performed. The study targets the 12-h period from 0000 to 1200 UTC 6 December 1997, characterized by the passage of a frontal system that produced intense rainfall over southern California. Forecasts prior to data assimilation underestimate the observed 6- and 12-h accumulated rainfall for most of the domain. The (sole) assimilation of TZD observations is found to have a small but beneficial impact on the short-range precipitation forecast. Measured against the control forecast, area-mean improvements of up to 33.15% and 25.08% are found in the 6- and 12-h accumulated rainfall in Los Angeles County. The inclusion of profiler-wind observations is found to have a significant impact on the model precipitation, with improvements in the 6- and 12-h accumulated precipitation as high as 88.26% and 32.53%, respectively. However, these increments are noticeably reduced when the TZD data are excluded from the assimilation experiments. Further improvements are achieved when the TZD and profiler-wind data are assimilated along with the RASS virtual temperature data. Increases of up to 93.21% and 50.58% are found in the 6- and 12-h accumulated precipitation, respectively. Because the virtual temperature also contains information on the three-dimensional moisture field, these findings point to the potential benefit that may result from the future assimilation of GPS slant-path delay data.

Abstract This paper presents the results of a preoperational assimilation of radial velocities from Doppler radars of the French Application Radar la Météorologie InfraSynoptique (ARAMIS) network in the nonhydrostatic model, the Application of Research to Operations at Mesoscale (AROME). For this purpose, an observation operator, which allows the simulation of radial winds from the model variables, is included in the three-dimensional variational data assimilation (3DVAR) system. Several data preprocessing procedures are applied to avoid as much as possible erroneous measurements (e.g., due to dealiasing failures) from entering the minimization process. Quality checks and other screening procedures are discussed. Daily monitoring diagnostics are developed to check the status and the quality of the observations against their simulated counterparts. Innovation biases in amplitude and in direction are studied by comparing observed and simulated velocity–azimuth display (VAD) profiles. Experiments over 1 month are performed. Positive impacts on the analyses and on precipitation forecasts are found. Scores against conventional data show mostly neutral results because of the much-localized impact of radial velocities in space and in time. Significant improvements of low-level divergence analysis and on the resulting forecast are found when specific sampling conditions are met: the closeness of convective systems to radars and the orientation of the low-level horizontal wind gradient with respect to the radar beam. Focus on a frontal rainband case study is performed to illustrate this point.

Abstract This study examines the performance of a hybrid ensemble-variational data assimilation system (E3DVar) that couples an ensemble Kalman filter (EnKF) with the three-dimensional variational data assimilation (3DVar) system for the Weather Research and Forecasting (WRF) Model. The performance of E3DVar and the component EnKF and 3DVar systems are compared over the eastern United States for June 2003. Conventional sounding and surface observations as well as data from wind profilers, aircraft and ships, and cloud-tracked winds from satellites, are assimilated every 6 h during the experiments, and forecasts are verified using standard sounding observations. Forecasts with 12- to 72-h lead times are found to have noticeably smaller root-mean-square errors when initialized with the E3DVar system, as opposed to the EnKF, especially for the 12-h wind and moisture fields. The E3DVar system demonstrates similar performance as an EnKF, while using less than half the number of ensemble members, and is less sensitive to the use of a multiphysics ensemble to account for model errors. The E3DVar system is also compared with a similar hybrid method that replaces the 3DVar component with the WRF four-dimensional variational data assimilation (4DVar) method (denoted E4DVar). The E4DVar method demonstrated considerable improvements over E3DVar for nearly all model levels and variables at the shorter forecast lead times (12–48 h), but the forecast accuracies of all three ensemble-based methods (EnKF, E3DVar, and E4DVar) converge to similar results at longer lead times (60–72 h). Nevertheless, all methods that used ensemble information produced considerably better forecasts than the two methods that relied solely on static background error covariance (i.e., 3DVar and 4DVar).

Abstract The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere’s (CASA) IP-1 radar network. The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution predictions of an MCS and the associated cyclonic line-end vortex that spawned several tornadoes in central Oklahoma on 8–9 May 2007, while the ARPS three-dimensional variational data assimilation (3DVAR) system in combination with a complex cloud analysis package is used for the data analysis. A set of data assimilation and prediction experiments are performed on a 400-m resolution grid nested inside a 2-km grid, to examine the impact of radar data on the prediction of meso-γ-scale vortices (mesovortices). An 80-min assimilation window is used in radar data assimilation experiments. An additional set of experiments examines the impact of assimilating 5-min data from the Oklahoma Mesonet in addition to the radar data. Qualitative comparison with observations shows highly accurate forecasts of mesovortices up to 80 min in advance of their genesis are obtained when the low-level shear in advance of the gust front is effectively analyzed. Accurate analysis of the low-level shear profile relies on assimilating high-resolution low-level wind information. The most accurate analysis (and resulting prediction) is obtained in experiments that assimilate low-level radial velocity data from the CASA radars. Assimilation of 5-min observations from the Oklahoma Mesonet has a substantial positive impact on the analysis and forecast when high-resolution low-level wind observations from CASA are absent; when the low-level CASA wind data are assimilated, the impact of Mesonet data is smaller. Experiments that do not assimilate low-level wind data from CASA radars are unable to accurately resolve the low-level shear profile and gust front structure, precluding accurate prediction of mesovortex development.

Abstract This study compares the performance of an ensemble Kalman filter (EnKF) with both the three-dimensional and four-dimensional variational data assimilation (3DVar and 4DVar) methods of the Weather Research and Forecasting (WRF) model over the contiguous United States in a warm-season month (June) of 2003. The data assimilated every 6 h include conventional sounding and surface observations as well as data from wind profilers, ships and aircraft, and the cloud-tracked winds from satellites. The performances of these methods are evaluated through verifying the 12- to 72-h forecasts initialized twice daily from the analysis of each method against the standard sounding observations. It is found that 4DVar has consistently smaller error than that of 3DVar for winds and temperature at all forecast lead times except at 60 and 72 h when their forecast errors become comparable in amplitude, while the two schemes have similar performance in moisture at all lead times. The forecast error of the EnKF is comparable to that of the 4DVar at 12–36-h lead times, both of which are substantially smaller than that of the 3DVar, despite the fact that 3DVar fits the sounding observations much more closely at the analysis time. The advantage of the EnKF becomes even more evident at 48–72-h lead times; the 72-h forecast error of the EnKF is comparable in magnitude to the 48-h error of 3DVar/4DVar.

Abstract. By applying four-dimensional variational data-assimilation (4-D-Var) to a combined ozone and dynamics Numerical Weather Prediction model (NWP), ozone observations generate wind increments through the ozone-dynamics coupling. The dynamical impact of Aura/MLS satellite ozone profiles is investigated using Météo-France operational ARPEGE NWP 4-D-Var assimilation system for a period of 3 months. A data-assimilation procedure has been designed and run on 6-h windows. The procedure includes: (1) 4-D-Var assimilating both ozone and operational NWP standard observations, (2) ARPEGE transporting ozone as a passive-tracer, (3) MOCAGE, the Météo–France chemistry and transport model re-initializing the ARPEGE ozone background at the beginning time of the assimilation window. Using observation minus forecast statistics, it is found that the ozone assimilation reduces the wind bias in the lower stratosphere. Moreover, the Degrees of Freedom for Signal diagnostics show that the MLS data covering the 68.1–31.6 hPa vertical pressure range are the most informative and their information content is nearly of the same order as tropospheric humidity-sensitive radiances. Furthermore, with the help of error variance reduction diagnostics, the ozone contribution to the reduction of the horizontal divergence background-error variance is shown to be better than tropospheric humidity-sensitive radiances.

Abstract The Variational Doppler Radar Analysis System (VDRAS) was implemented in Beijing, China, and contributed to the Beijing 2008 Forecast Demonstration Project (B08FDP) in support of the Beijing Summer Olympics. VDRAS is a four-dimensional variational data assimilation system that produces frequently updated analyses using Doppler radar radial velocities and reflectivities, surface observations, and mesoscale model data. The system was tested in real time during the B08FDP pretrials in the summers of 2006 and 2007 and run during the Olympics to assist the 0–6-h convective weather nowcasting. This paper provides a description of the upgraded system and its Beijing implementation, an evaluation of the system performance using data collected during the pretrials, and its utility on convective weather nowcasting through two case studies. Verification of VDRAS wind against a wind profiler shows that the analyzed wind is reasonably accurate with a smaller RMS difference for 2006 than for 2007 due to better radar data coverage in 2006. The analyzed cold pools in three convective episodes are compared with surface observations at selected stations. The result shows good agreement between the analysis and the observations. The two case studies demonstrate the role that VDRAS could play in nowcasting convective initiation.

This work is a first assessment of utilizing Doppler Weather Radar (DWR) radial velocity and reflectivity in a mesoscale model for prediction of Bay of Bengal monsoon depressions (MDs). The Weather Research Forecasting (WRF) modelling system—Advanced Research version (ARW) is customized and evaluated for the Indian monsoon region by generating domain-specific Background Error (BE) statistics and experiments involving two assimilation strategies (cold start and cycling). The monthly averaged 24 h forecast errors for wind, temperature and moisture profiles were analysed. From the statistical skill scores, it is concluded that the cycling mode assimilation enhanced the performance of the WRF three-dimensional variational data assimilation (3DVAR) system over the Indian region using conventional and non-conventional observations. DWR data from a coastal site were assimilated for simulation of two different summer MDs over India using the WRF-3DVAR analysis system. Three numerical experiments (control without any Global Telecommunication System (GTS) data, with GTS, and GTS as well as DWR) were performed for simulating these extreme weather events to study the impact of DWR data. The results show that even though MDs are large synoptic systems, assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. All aspects of the MD simulations such as mean-sea-level pressure, winds, vertical structure and the track are significantly improved due to the DWR assimilation. Study results provide a positive proof of concept that the assimilation of the Indian DWR data within WRF can help improve the simulation of intense convective systems influencing the large-scale monsoonal flow. Copyright © 2010 Royal Meteorological Society

Abstract This paper presents a case study on the assimilation of observations from multiple Doppler radars of the Next Generation Weather Radar (NEXRAD) network. A squall-line case documented during the International H2O Project (IHOP_2002) is used for the study. Radar radial velocity and reflectivity observations from four NEXRADs are assimilated into a convection-permitting model using a four-dimensional variational data assimilation (4DVAR) scheme. A mesoscale analysis using a supplementary sounding, velocity–azimuth display (VAD) profiles, and surface observations from Meteorological Aerodrome Reports (METAR) are produced and used to provide a background and boundary conditions for the 4DVAR radar data assimilation. Impact of the radar data assimilation is assessed by verifying the skill of the subsequent very short-term (5 h) forecasts. Assimilation and forecasting experiments are conducted to examine the impact of radar data assimilation on the subsequent precipitation forecasts. It is found that the 4DVAR radar data assimilation significantly reduces the model spinup required in the experiments without radar data assimilation, resulting in significantly improved 5-h forecasts. Additional experiments are conducted to study the sensitivity of the precipitation forecasts with respect to 4DVAR cycling configurations. Results from these experiments suggest that the forecasts with three 4DVAR cycles are improved over those with cold start, but the cycling impact seems to diminish with more cycles. The impact of observations from each of the individual radars is also examined by conducting a set of experiments in which data from each radar are alternately excluded. It is found that the accurate analysis of the environmental wind surrounding the convective cells is important in successfully predicting the squall line.

Abstract This study investigates the impact of temperature and moisture profiles from Atmospheric Infrared Sounder (AIRS) on the prediction of the Indian summer monsoon, using the variational data assimilation system annexed to the Weather Research and Forecasting model. In this study, three numerical experiments are carried out. The first is the control and includes no assimilation; in the second, named Conv, assimilation of conventional Global Telecommunication System data is performed. The third one, named ConvAIRS, is identical to the Conv except that it also includes assimilation of AIRS profiles. The initial fields of tropospheric temperature and water vapor mixing ratio showed significant improvement over the model domain. Assimilation of AIRS profiles has significant impact on predicting the seasonal mean monsoon characteristics such as tropospheric temperature, low‐level moisture distribution, easterly wind shear, and precipitation. The vertical structure of the root‐mean‐square error is substantially affected by the assimilation of AIRS profiles, with smaller errors in temperature, humidity, and wind magnitude. The consequent improved representation of moisture convergence in the boundary layer (deep convection as well) causes an increase in precipitation forecast skill. The fact that the monsoonal circulation is better captured, thanks to an improved representation of thermal gradients, which in turn leads to more realistic moisture transport, is particularly noteworthy. Several previous data impact studies with AIRS and other sensors have focused on the short or medium range of the forecast. The demonstrated improvement in all the predicted fields associated with the Indian summer monsoon, consequent to the month long assimilation of AIRS profiles, is an innovative finding with large implications to the operational seasonal forecasting capabilities over the Indian subcontinent.

The importance of wind observations has been recognized for many years. However, wind observations—especially three‐dimensional global wind measurements—are very limited. A satellite‐based Doppler Wind Lidar (DWL) is proposed to measure three‐dimensional wind profiles using remote sensing techniques. Assimilating these observations into a mesoscale model is expected to improve the performance of the numerical weather prediction (NWP) models. In order to examine the potential impact of the DWL three‐dimensional wind profile observations on the numerical simulation and prediction of tropical cyclones, a set of observing simulation system experiments (OSSEs) is performed using the advanced research version of the Weather Research and Forecasting (WRF) model and its three‐dimensional variational (3DVAR) data assimilation system. Results indicate that assimilating the DWL wind observations into the mesoscale numerical model has significant potential for improving tropical cyclone track and intensity forecasts.

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In this paper, a simple coupled land surface–boundary layer model and its adjoint are presented. The primary goal is to demonstrate the capabilities of the adjoint model as a general tool for sensitivity analysis and data assimilation. The adjoint method was chosen primarily for two reasons: 1) the adjoint model can be used not only to obtain parameter sensitivities with greater efficiency but, more important, to provide added insight into the sensitivities as compared with that obtained with traditional simulation techniques (e.g., pathways, time variations in sensitivity) and 2) the adjoint model can be used in a variational data assimilation framework to combine measurements and the model of the physical system optimally in order to estimate state variables and fluxes. Two simple examples are presented to illustrate how the framework can be used for performing both diagnostic sensitivity experiments and hydrologic data assimilation. In the sensitivity experiment, temporal patterns and total influence of model states and parameters on average daily ground temperature are shown. In the synthetic data assimilation example, the adjoint model is used as an estimation tool to initialize the coupled model through assimilation of ground temperature observations. As a result, great improvement was gained in simulation of model states and surface fluxes based only on a minimal set of basic land temperature measurements and the auxiliary parameters: incident solar radiation, large-scale wind speed, and free atmosphere profiles of temperature and humidity. Forthcoming studies will use the framework developed here to examine thoroughly the consequences of using uncoupled versus coupled models of the land and the atmospheric boundary layer (ABL). In assimilation mode, the coupled surface–ABL model and its adjoint will be used to estimate surface fluxes and micrometeorological conditions based on remote sensing measurements of land temperature and minimal auxiliary data.

Abstract This paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System (VDRAS). Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational (4DVAR) data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments—assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function—are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line—including the surface warm inflow, cold pool, gust front, and low-level wind—are much closer to the observations after assimilating the surface data in VDRAS.

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China's FengYun-4A geostationary satellite provides an unprecedented opportunity to observe the three-dimensional thermodynamic structure of typhoons in the western North Pacific Ocean with high spatiotemporal resolutions. Field campaigns of targeting observation based on the conditional nonlinear optimal perturbation (CNOP) sensitivity were carried out utilizing the GIIRS on FY-4A for five typhoons: Chan-hom, Maysak, and Higos in 2020 and typhoons Chanthu and Conson in 2021 and collected temporally continuous observations for the thermodynamic structure of typhoons and their environments. This study investigated the impact of atmospheric temperature profile retrieved from the collected GIIRS radiance on typhoon analysis and prediction in a regional Hurricane WRF (HWRF) model using the ensemble-variational data assimilation scheme. Despite the case dependence, the assimilation of the additional satellite retrieval generally improves the typhoon track forecasts relative to those with simply the operational observations assimilated. The improvement mainly occurs beyond two days and the average reduction of track errors reaches up to 100 km at about day 3. Diagnostics show that the improvement of initial temperature condition influences the geopotential height and wind fields roughly through the hydrostatic relationship. The assimilation of temperature retrieval also shows some potential in improving wind forecasts in the near-coast areas at upper and lower levels and the precipitation forecasts on land.

Abstract The major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, and vertical velocity and their error statistics. An ensemble forecast with 80 members is used to produce background error covariance. The preliminary testing presented in this paper includes single-observation experiments as well as real data assimilation experiments on a squall line with assimilation windows of 5, 15, and 30 min. The results indicate that the system is able to obtain anisotropic multivariate analyses at the convective scale and improve precipitation forecasts. The results also suggest that the incremental approach with successive basic-state updates works well at the convection-permitting scale for radar data assimilation with the selected assimilation windows.

Abstract A major revision is described to the analysis component of the Meteorological Office data assimilation scheme for both the global and the regional numerical weather prediction models. Initialization by repeated insertion of data and divergence damping is retained but a more flexible modified successive‐correction scheme replaces optimum interpolation for the analysis step. The scheme is derived as an approximate iterative solution to a general equation for the optimal analysis. Within iterations, the analyses of each model variable are performed sequentially and multivariate increment fields are derived for dynamical balance at intermediate stages. The horizontal influence area of data is substantially increased, giving a beneficial impact in data‐sparse regions. All data contribute to the analysis at each gridpoint within their radius of influence. The observation weights are recalculated every model time‐step, allowing asynoptic data to be used at their true valid time, though this feature is found to have only slight impact on forecasts. Each analysis is factorized into a vertical step, then into a horizontal step. The structure of each is described in detail, with system parameters listed in appendices. Results of two kinds are presented. Sensitivity experiments highlight the effects of some individual constituents of the scheme; operational trials against the old scheme reveal the impact of the whole system. The new analyses are better balanced and lead to a better short‐period forecast, especially for the wind field. Medium‐range impact is clearly beneficial in the southern hemisphere but ambiguous in the northern hemisphere. Precipitation forecasts in the regional model are improved.

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA), in cooperation with the Japan Aerospace Exploration Agency (JAXA), to measure global profiles of aerosols, clouds and precipitation properties together with radiative fluxes and derived heating rates. The simultaneous measurements of the vertical structure and horizontal distribution of cloud and aerosol fields, together with outgoing radiation, will be used in particular to evaluate their representation in weather forecasting and climate models and to improve our understanding of cloud and aerosol radiative impact and feedback mechanisms. To achieve the objective, the goal is that a retrieved scene with footprint size of 10 km × 10 km is measured with sufficiently high resolution that the atmospheric vertical profile of short-wave (solar) and long-wave (thermal) flux can be reconstructed with an accuracy of 10 W m−2 at the top of the atmosphere. To optimise the performance of the two active instruments, the platform will fly at a relatively low altitude of 393 km, with an equatorial revisit time of 25 d. The scientific payload consists of four instruments: an atmospheric lidar, a cloud-profiling radar with Doppler capability, a multi-spectral imager and a broadband radiometer. Co-located measurements from these instruments are processed in the ground segment, which produces and distributes a wide range of science data products. As well as the Level 1 (L1) product of each instrument, a large number of multiple-instrument L2 products have been developed, in both Europe and Japan, benefiting from the data synergy. An end-to-end simulator and several test scenes have been developed that simulate EarthCARE observations and provide a development and test environment for L1 and L2 processors. Within this paper the EarthCARE observational requirements are addressed. An overview is given of the space segment with a detailed description of the four science instruments, demonstrating how the observational requirements will be met. Furthermore, the elements of the space segment and ground segment that are relevant for science data users are described and the data products are introduced.

Abstract The status of current efforts to assimilate cloud‐ and precipitation‐affected satellite data is summarised with special focus on infrared and microwave radiance data obtained from operational Earth observation satellites. All global centres pursue efforts to enhance infrared radiance data usage due to the limited availability of temperature observations in cloudy regions where forecast skill is estimated to strongly depend on the initial conditions. Most systems focus on the sharpening of weighting functions at cloud top providing high vertical resolution temperature increments to the analysis, mainly in areas of persistent high and low cloud cover. Microwave radiance assimilation produces impact on the deeper atmospheric moisture structures as well as cloud microphysics and, through control variable and background‐error formulation, also on temperature but to lesser extent than infrared data. Examples of how the impacts of these two observation types are combined are shown for subtropical low‐level cloud regimes. The overall impact of assimilating such data on forecast skill is measurably positive despite the fact that the employed assimilation systems have been constructed and optimized for clear‐sky data. This leads to the conclusion that a better understanding and modelling of model processes in cloud‐affected areas and data assimilation system enhancements through inclusion of moist processes and their error characterization will contribute substantially to future forecast improvement. Copyright © 2011 Royal Meteorological Society, Crown in the right of Canada, and British Crown copyright, the Met Office

Abstract We carried out the first Mobile Field Observation Campaign of Atmospheric Profiles (MFOCAP) in the southeast Tibet and the Three‐River Source Region (TRSR) of the Tibetan Plateau (TP) by adopting two vehicle‐mounted integrated mobile observations (MO) system from July 18 to 30, 2021. Reliable MO data sets of air temperature (Ta), water vapor density (WVD) and relative humidity (RH) with high spatio‐temporal resolution over the TP were obtained and assimilated to improve precipitation forecast using the four‐dimensional variational (4DVAR) data assimilation (DA) method. The results show that Ta, WVD and RH profile data retrieved with the mobile microwave radiometer (MR) are credible over the TP. The atmospheric vertical structure measured by the mobile MR can reproduce the spatio‐temporal evolution characteristics of water vapor transport, temperature stratification and cloud structure. The distribution pattern of 24‐hr accumulated rainfall prediction with Ta profile DA was closer to measurements, and 6–12 hr forecasts for low to moderate rainfall in the central and western regions of Qinghai province were improved significantly. Data assimilation with air temperature retrievals from mobile MR observations were found beneficial for accurate simulation of water vapor transport, convergence and divergence of wind field, and upward motion associated with precipitation events. The finding of this study highlights the value of MR remote sensing observations in improving the rainfall monitoring and forecasts over the TP and downstream regions.

Abstract Satellite images in the visible spectral range contain high‐resolution cloud information, but have not been assimilated directly before. This paper presents a case‐study on the assimilation of visible Meteosat SEVIRI images in a convective‐scale data assimilation system based on a local ensemble transform Kalman filter (LETKF) in a near‐operational set‐up. For this purpose, a fast look‐up table‐based forward operator is used to generated synthetic satellite images from the model state. Single‐observation experiments show that the assimilation of visible reflectances improves cloud cover under most conditions and often reduces temperature and humidity errors. In cycled experiments for two summer days with convective precipitation, the assimilation strongly reduces the errors of cloud cover and improves the precipitation forecast. While these results are promising, several issues are identified that limit the efficacy of the assimilation process. First, the linearity assumption of the LETKF can lead to errors as reflectance is a nonlinear function of the model state. Second, errors can arise from the fact that visible reflectances alone are ambiguous and only weakly sensitive to the water phase and cloud‐top height. And lastly, it is not obvious how to localise vertical covariances as visible reflectances are sensitive to clouds at all heights. For the latter reason, no vertical localisation was used in this study. To investigate the robustness of the results, the horizontal localisation scale, the assigned observation error and the spatial density of observations were varied in sensitivity experiments. The best results were obtained for an observation error close to the Desroziers estimate. High observation density combined with small localisation radii resulted in the smallest 1 hr forecast error. These settings were also beneficial for 3 hr forecasts, but forecasts at that lead time were less sensitive to the observation density and the localisation scale.

Abstract A comprehensive set of physical parametrizations has been linearized for use in the European Centre for Medium‐Range Weather Forecasts (ECMWF's) incremental four‐dimensional variational (4D‐Var) system described in Part I. The following processes are represented: vertical diffusion, subgrid‐scale orographic effects, large‐scale precipitation, deep moist convection and long‐wave radiation. The tangent‐linear approximation is examined for finite‐size perturbations. Significant improvements are illustrated for surface wind and specific humidity with respect to a simplified vertical diffusion scheme. Singular vectors computed over 6 hours (compatible with the 4D‐Var assimilation window) have lower amplification rates when the improved physical package is included, due to a more realistic description of dissipative processes, even though latent‐heat release contributes to amplify the potential energy of perturbations in rainy areas. A direct consequence is a larger value of the observation term of the cost‐function at the end of the minimization process when improved physics is included in 4D‐Var. However, the larger departure of the analysis state from observations in the lower‐resolution inner‐loop is in better agreement with the behaviour of the full nonlinear model at high resolution. More precisely, the improved physics produces smaller discontinuities in the value of the cost‐function when going from low to high resolution. In order to reduce the computational cost of the linear physics, a new configuration of the incremental 4D‐Var system using two outer‐loops is defined. In a first outer‐loop, a minimization is performed at low resolution with simplified physics (50 iterations), while in the second loop a second minimization is performed with improved physics (20 iterations) after an update of the model trajectory at high resolution. In this configuration the extra cost of the physics is only 25%, and results from a 2‐week assimilation period show positive impacts in terms of quality of the forecasts in the Tropics (reduced spin‐down of precipitation, lower root‐mean‐square errors in wind scores). This 4D‐Var configuration with improved physics and two outer‐loops was implemented operationally at ECMWF in November 1997.

As a follow-on to the Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration in the United States, the National Space Development Agency of Japan, and the European Space Agency are considering a satellite mission to measure the global rainfall. The plan envisions an improved TRMM-like satellite and a constellation of eight satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-h intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and two Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. It is shown that assimilating the 6-h-averaged TMI and SSM/I surface rain rate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper-tropospheric moisture in the analysis produced by the Goddard Earth Observing System Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System instrument and brightness temperature observations by the Television Infrared Observational Satellite Operational Vertical Sounder instruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the Tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the Tropics. These results were obtained using a variational procedure based on a 6-h time integration of a column model of moist physics with prescribed dynamical and other physical tendencies. The procedure estimates moisture tendency corrections at observation locations by minimizing the least square differences between the observed TPW and rain rates and those generated by the column model over a 6-h analysis window. These tendency corrections are then applied during the assimilation cycle to compensate for errors arising from both initial conditions and deficiencies in model physics. Our results point to the importance of addressing deficiencies in model physics in assimilating data types such as precipitation, for which the forward model based on convective parameterizations may have significant systematic errors. This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of four-dimensional global datasets for climate analysis and weather forecasting applications.

Advances in remote sensing from earth- and spaceborne systems, expanded in situ observation networks, and increased low-cost computer capability will allow an unprecedented view of mesoscale weather systems from the local weather office. However, the volume of data from these new instruments, the nonconventional quantities measured, and the need for a frequent operational cycle require development of systems to translate this information into products aimed specifically at aiding the forecaster in 0- to 6-h prediction. In northeast Colorado an observing network now exists that is similar to those that a local weather office may see within 5–7 years. With GOES and TIROS satellites, Doppler radar, wind profilers, and surface mesonet stations, a unique opportunity exists to explore the use of such data in nowcasting weather phenomena. The scheme, called LAPS (the Local Analysis and Prediction System), objectively analyzes data on a high-resolution, three-dimensional grid. The analysed fields are used to generate mesoscale forecast products aimed at specific local forecast problems. An experiment conducted in the summer of 1989 sought to test the use of a preconvective index on the difficult problem of convective rain forecasting. The index was configured from surface-based lifted index and kinematically diagnosed vertical motion. The index involved a number of LAPS-derived meteorological fields and the results of the test measured in some sense the quality of those fields. Using radar reflectivity to verify the occurrence or nonoccurrence of convective precipitation, forecasts were issued for three time periods on each of 62 exercise days. The results indicated that the index was significantly better than persistence over a range of echo intensities. Skill scores computed from contingency tables indicated that the index had substantial skill in forecasting light convective precipitation with 1- to 3-h lead time. Less skill was shown for heavier convective showers. The skill of the index did not depend strongly on the density of surface data, but was negatively influenced by mountainous terrain.

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Abstract An important issue in developing a forecast system is its sensitivity to additional observations for improving initial conditions, to the data assimilation (DA) method used, and to improvements in the forecast model. These sensitivities are investigated here for the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). Four parallel sets of 7-day ensemble forecasts were generated for 100 forecast cases in mid-January to mid-March 2016. The sets differed in their 1) inclusion or exclusion of additional observations collected over the eastern Pacific during the El Niño Rapid Response (ENRR) field campaign, 2) use of a hybrid 4D–EnVar versus a pure EnKF DA method to prepare the initial conditions, and 3) inclusion or exclusion of stochastic parameterizations in the forecast model. The Control forecast set used the ENRR observations, hybrid DA, and stochastic parameterizations. Errors of the ensemble-mean forecasts in this Control set were compared with those in the other sets, with emphasis on the upper-tropospheric geopotential heights and vorticity, midtropospheric vertical velocity, column-integrated precipitable water, near-surface air temperature, and surface precipitation. In general, the forecast errors were found to be only slightly sensitive to the additional ENRR observations, more sensitive to the DA methods, and most sensitive to the inclusion of stochastic parameterizations in the model, which reduced errors globally in all the variables considered except geopotential heights in the tropical upper troposphere. The reduction in precipitation errors, determined with respect to two independent observational datasets, was particularly striking.

Abstract An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of global deterministic NWP. In an EnKF experiment having the same spatial resolution as the inner loop in the four-dimensional variational data assimilation system (4D-Var), the mean of each analysis ensemble is used to initialize the higher-resolution deterministic forecasts. Five different variational data assimilation experiments are also conducted. These include both 4D-Var and 3D-Var (with first guess at appropriate time) experiments using either (i) prescribed background-error covariances similar to those used operationally, which are static in time and include horizontally homogeneous and isotropic correlations; or (ii) flow-dependent covariances computed from the EnKF background ensembles with spatial covariance localization applied. The fifth variational data assimilation experiment is a new approach called the Ensemble-4D-Var (En-4D-Var). This approach uses 4D flow-dependent background-error covariances estimated from EnKF ensembles to produce a 4D analysis without the need for tangent-linear or adjoint versions of the forecast model. In this first part of a two-part paper, results from a series of idealized assimilation experiments are presented. In these experiments, only a single observation or vertical profile of observations is assimilated to explore the impact of various fundamental differences among the EnKF and the various variational data assimilation approaches considered. In particular, differences in the application of covariance localization in the EnKF and variational approaches are shown to have a significant impact on the assimilation of satellite radiance observations. The results also demonstrate that 4D-Var and the EnKF can both produce similar 4D background-error covariances within a 6-h assimilation window. In the second part, results from medium-range deterministic forecasts for the study period of February 2007 are presented for the EnKF and the five variational data assimilation approaches considered.

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Abstract Rain‐ and cloud‐affected Special Sensor Microwave/Imager (SSM/I) observations are assimilated operationally at the European Centre for Medium‐Range Weather Forecasts (ECMWF). The four‐dimensional variational analysis (4D‐Var) assimilates total column water vapour (TCWV) derived from one‐dimensional variational retrievals (1D‐Var). From the SSM/I radiances, 1D‐Var retrieves surface wind and the vertical profiles of temperature, humidity, cloud and precipitation. The main shortcoming of the ‘1D + 4D‐Var’ technique is that, of all this information, only TCWV gets into the 4D‐Var analysis. More information could be used: the rainwater path agrees well, in an instantaneous comparison, with observations from the precipitation radar on the Tropical Rainfall Measuring Mission. There are other issues, however: the simplified moist physics operators used in 1D‐Var produce roughly twice the observed amount of rain, but the problem is masked by a sampling bias, which comes from applying 1D + 4D‐Var when the observations are cloudy or rainy, but not when the first guess is rainy or cloudy and the observations are clear. The shortcomings of 1D + 4D‐Var will be addressed by moving to a direct 4D‐Var assimilation which includes all SSM/I observations, whether clear, cloudy or rainy, in the same stream. Copyright © 2008 Royal Meteorological Society

Space‐borne active instruments, providing a vertically resolved characterization of clouds, promise a new dimension of information to be used in numerical weather prediction systems. Research activities are ongoing at the European Centre for Medium‐Range Weather Forecasts to exploit these data for monitoring and assimilation purposes. Using currently available observations from CloudSat and CALIPSO, a technique combining one‐dimensional variational (1D‐Var) assimilation with four‐dimensional variational (4D‐Var) data assimilation has been used to study the impact of cloud‐related observations on analyses and subsequent forecasts. Temperature and specific humidity vertical profiles retrieved from 1D‐Var using observations of cloud radar reflectivity and lidar backscatter, either separately or in combination, were used as pseudo‐observations in the 4D‐Var system. Results indicate that 1D‐Var analyses get closer to assimilated and also independent observations when appropriate quality control, bias correction and error estimate are applied. The performed 1D+4D‐Var assimilation experiments also suggest a slight positive impact of the new observations on the subsequent forecast. Generally, the impact of lidar backscatter from clouds is smaller than that of cloud radar reflectivity.

Abstract This paper focuses on Intensive Observation Period 17 of the Fronts and Atlantic Storm‐Track EXperiment (FASTEX): the FASTEX special soundings obtained during that period are added to the conventional dataset and assimilated with a 4‐dimensional variational (4D‐Var) scheme under development at Météo‐France. Results show a consistent use of the high density FASTEX ship soundings by the 4D‐Var formulation and an improvement brought by this new scheme over 3D‐Var when using only the vertical profiles synchronous with synoptic time. the application of the 4D‐Var analysis to dropsondes launched by a system‐relative flight designed by the Joint Centre for Mesoscale Meteorology also produces a fine‐scale description of sub‐structures of the mature system.

Abstract This paper presents a new application of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations for indirect assimilation into the European Centre for Medium‐Range Weather Forecasts (ECMWF) model. The PR reflectivities are first processed using a one‐dimensional variational (1D‐Var) method to adjust model temperature and specific humidity. The retrieved Total Column Water Vapour (TCWV) is then assimilated into the operational four‐dimensional variational (4D‐Var) system. The applicability of the 1D+4D−Var approach to the radar observations is discussed in detail. Several case studies were run to assess the feasibility and the effectiveness of assimilating PR reflectivities with a 1D‐Var approach. Results show good behaviour of the 1D‐Var system in terms of convergence and stability. Its performance in terms of retrieved TCWV is comparable to that of other 1D‐Vars which make use of TRMM Microwave Imager (TMI) observations. When the 1D‐Var TCWV pseudo‐observations are input into the 4D‐Var system, a positive impact is shown in the analysis and the subsequent forecasts, both on moisture‐related fields and also on winds and surface pressure. The quality of the forecast is verified using track observations for the tropical cyclones. The track forecasts from the experiments which include 1D‐Var TCWV are generally closer to the observed track than a control run. Despite their much smaller spatial coverage than TMI observations, it is found that the PR data have a comparable impact, provided the satellite samples a meaningful portion of the storm, possibly its centre. This is possibly due to the fact that TCWV increments from PR and from TMI brightness temperature have similar magnitudes. These results show that active sensor data can provide indirect yet useful information on the moisture field and that this information can effectively be assimilated to improve the analysis and the forecast of tropical disturbances. Although this is a sub‐optimal use of PR observations, due to the underexploitation of the vertical information contained in the reflectivity profiles, it is still a step forward towards using active sensor data in preparation for future satellite missions which will deliver this type of data on a global scale and at higher temporal resolution. Copyright © 2005 Royal Meteorological Society

Some problems posed by the coupling of moist-convective and stratiform precipitation processes for variational assimilation of precipitation-rate data are examined in a 1D-Var framework. Background-error statistics and vertical resolution are chosen to be representative of current operational practice. Three advanced parameterization schemes for moist-convection are studied: the relaxed Arakawa–Schubert (RAS) scheme, Tiedtke’s mass-flux scheme (operational at the European Centre for Medium-Range Weather Forecasts), and the Betts–Miller scheme. Both fractional-stepping and process-splitting approaches for combining physical processes are examined. The behavior of the variational adjustment for background profiles of temperature and specific humidity in the neighborhood of saturation is of particular interest. In the 1D-Var context examined here, it is demonstrated that the introduction of the stratiform precipitation process can have a negative impact on the minimization in the sense that, even when only slight supersaturation occurs, the minimization is controlled by the stratiform precipitation process at the expense of convective precipitation. This is generally true in process-splitting mode and conditionally true in fractional-stepping mode. The net result in such cases is an adjustment to the wrong type of precipitation over convective regions. In some of the cases examined (1D-Var with the RAS scheme, for instance), it is preferable to deactivate the stratiform precipitation process and to explicitly control the degree of supersaturation during the adjustment of convection. Evaporation of precipitation in subsaturated layers also appears as an important factor influencing the partition of precipitation. The method of fractional stepping appears less problematical compared to the process-splitting approach. These results also indicate the need for a detailed examination of the partition of precipitation between convective and stratiform type in more sophisticated 3D/4D-Var data assimilation systems, and for a better combined parameterization of the two physical processes.

Abstract Data assimilation was recently suggested to smooth out the sharp gradients that characterize the tropopause inversion layer (TIL) in systems that did not assimilate TIL‐resolving observations. We investigate whether this effect is present in the ERA‐Interim reanalysis and the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast system (which assimilate high‐resolution observations) by analyzing the 4D‐Var increments and how the TIL is represented in their data assimilation systems. For comparison, we also diagnose the TIL from high‐resolution GPS radio occultation temperature profiles from the COSMIC satellite mission, degraded to the same vertical resolution as ERA‐Interim and ECMWF operational analyses. Our results show that more recent reanalysis and forecast systems improve the representation of the TIL, updating the earlier hypothesis. However, the TIL in ERA‐Interim and ECMWF operational analyses is still weaker and farther away from the tropopause than GPS radio occultation observations of the same vertical resolution.

The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated observations, experiments are performed in a perfect-model context. By using forward interpolation operators from the model state to the observations, the ensemble Kalman filter is able to utilize nonconventional observations.

To accurately calculate the impact of renewables on power production in complex electric power grids, high-resolution and ideally seamless data within the planetary boundary layer are required. Therefore, the quality of different regional reanalyses and hindcasts is evaluated with respect to the representation of the planetery boundary layer and related sub-daily processes. On the one hand, high resolution regional reanalysis from the UERRA (UE-SMHI, UE-UKMO) and a similar project (COSMO-REA6) are considered. On the other hand, two hindcasts based on the COSMO-REA6 configuration are included in this study, i.e. a simulation with perfect boundaries and a simulation additionally utilizing spectral nudging. The focus of the evaluation is on measurements at four flux towers that are not part of any assimilation procedure. In this paper, we will show that the model’s quality depends on both the complete model system – assimilation method, resolution and physical parameterization – as well as on the performance measure. The daily cycle is best depicted by the hindcasts and even COSMO-REA6 hardly introduces spurious variability. UE-SMHI (3D-Var) suffers from spin-up in particular visible at the elevated levels, whereas the spin-up is damped in UE-UKMO (4D-Var). Investigation of atmospheric stability reveals that diurnal variation of stratification is for the most part well reproduced, but strong deficits were found for all COSMO simulations in reproducing strong stratification and corresponding wind speed gradients. Moreover, an overestimation of superadiabatic lapse rates and corresponding overly weak turbulent mixing is found for UE-UKMO. Furthermore, a combination of ramp statistics and contingency tables is utilized to detect a clear advantage of sophisticated assimilation systems over hindcasts. The evaluation framework presented underpins the importance of ramp statistics and vertical measurement profiles, especially with respect to assessing long-term simulations.

Abstract After six years of scientific, technical developments and meteorological validation, the Application of Research to Operations at Mesoscale (AROME-France) convective-scale model became operational at Météo-France at the end of 2008. This paper presents the main characteristics of this new numerical weather prediction system: the nonhydrostatic dynamical model core, detailed moist physics, and the associated three-dimensional variational data assimilation (3D-Var) scheme. Dynamics options settings and variables are explained. The physical parameterizations are depicted as well as their mutual interactions. The scale-specific features of the 3D-Var scheme are shown. The performance of the forecast model is evaluated using objective scores and case studies that highlight its benefits and weaknesses.

Observations related to cloud, such as radiances from microwave imagers, have been at the forefront of recent developments in data assimilation for numerical weather prediction (NWP). While they offer unrivalled spatial coverage, they contain limited information on the vertical structure of clouds. In contrast, active observations from profiling instruments such as cloud radar and lidar contain a wealth of information on the structure of clouds and precipitation, providing the much‐needed vertical context of clouds, but have never been assimilated directly in global NWP models. To explore the potential benefits of these profiling observations, the European Centre for Medium‐Range Weather Forecasts (ECMWF) Four‐Dimensional Variational (4D‐Var) data assimilation system has been recently adapted to allow direct assimilation of cloud profile observations from space‐borne radar and lidar instruments. In this paper, in conjunction with its companion paper, the first‐time direct assimilation of cloud radar and lidar observations into a global NWP model is demonstrated. Using CloudSat radar reflectivity and CALIPSO attenuated backscatter shows that the assimilation brings the analysis closer to these observations and has a mainly neutral affect on other assimilated observations. Some improvements in the forecast skill are also observed when verified against the experiment's own analysis, with the largest positive impact noticed for temperature at the lowest model levels and for vector wind above 500 hPa, but longer experiments are required to reach 95% statistical significance of the results. The potential improvements in the model radiation budget is explored by verifying with Clouds and the Earth's Radiation Energy System (CERES) observations. Sensitivity of the results to observation error and to the observation reduction by increased averaging is also discussed. The demonstration of statistically significant improvements to forecast skill in some metrics without any significant degredation in others shows great promise for the future use of cloud radar and lidar observations in NWP.

Abstract Spatial aspects of the physics–dynamics coupling in the Global and Regional Assimilation and Prediction System (GRAPES) for global medium‐range numerical weather prediction (GRAPES_GFS) are studied. As a Charney–Philips (CP) grid is used for the dynamics but all physical processes are calculated on a Lorenz grid in GRAPES_GFS V2.2 and its previous versions, interpolation has to be used for potential temperature and moisture between full and half levels in the physics–dynamics coupling. Besides interpolation error, a computational mode appears in the solutions of the vertical heat diffusion equations. An idealised test using a simple one‐dimensional heat conduction equation shows definitively that inconsistency of vertical grid in the dynamics–physics coupling and an improper boundary condition can together produce the computational mode. Based on 1st‐order K‐closure, a modified parametrization scheme for implementation of the planetary boundary layer (PBL) scheme on the CP grid (PBL_CP) has been developed, together with a cloud scheme implementation on the CP grid (CLOUD_CP), such that there is no need for interpolation for both the PBL and cloud scheme coupling to the dynamics. By using the PBL_CP scheme in a case‐study, the computational mode in potential temperature and moisture predictions is shown to have been successfully eliminated and the corresponding vertical profiles appear to be reasonably smooth. Because of the improvements in potential and absolute temperature and moisture predictions, the prediction of low‐level cloud water has also been improved greatly. Meanwhile, the prediction of water vapour at high levels is more reasonable with CLOUD_CP. Consistent with these improvements in a case‐study, an overall and significant enhancement is found in 8‐day forecasts of absolute temperature, moisture, vector wind and stratocumulus with the revised model in a 4D‐Var (four‐dimensional variational) cycle experiment over a period of 3 months. Key points A revised PBL scheme called PBL_CP was developed on a Charney–Phillips grid in the GRAPES_GFS model to avoid interpolation in the coupling of vertical heat diffusion to the dynamical equations. Implementation of the PBL_CP scheme in GRAPES_GFS eliminates the computational mode in potential temperature and moisture prediction in the PBL. The revised model containing PBL_CP and cloud implementation on the CP grid has improved the GRAPES_GFS forecasts of absolute temperature, moisture, vector winds and stratocumuli significantly.

Traditionally, the nowcasting of precipitation was conducted to a large extent by means of extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of traditional extrapolation-based techniques with high-resolution numerical weather prediction (NWP) is gaining popularity in the nowcasting community. The increased need of NWP products in nowcasting applications poses great challenges to the NWP community because the nowcasting application of high-resolution NWP has higher requirements on the quality and content of the initial conditions compared to longer-range NWP. Considerable progress has been made in the use of NWP for nowcasting thanks to the increase in computational resources, advancement of high-resolution data assimilation techniques, and improvement of convective-permitting numerical modeling. This paper summarizes the recent progress and discusses some of the challenges for future advancement.

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The assimilation of cloudy and rainy microwave observations is under investigation at Météo-France with a method called “1D-Bay+3D/4D-Var”. This method comprises two steps: (i) a Bayesian inversion of microwave observations and (ii) the assimilation of the retrieved relative humidity profiles in a 3D/4D-Var framework. In this paper, two estimators for the Bayesian inversion are used: either a weighted average (WA) or the maximum likelihood (ML) of a kernel density function. Sensitivity studies over the first step of the method are conducted for different degrees of freedom: the observation error, the channel selection and the scattering properties of frozen hydrometeors in the observation operator. Observations over a 2 month period of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the Global Precipitation Measurement Core Observatory satellite and forecasts of the convective scale model Application of Research to Operations at Mesoscale (AROME) have been chosen to conduct these studies. Two different meteorological situations are analyzed: those predicted cloudy in AROME but clear in the observations and, those predicted clear in AROME but cloudy in the observations. The main conclusions are as follows: First, low observational errors tend to be associated with the profiles with the highest consistency with the observations. Second, the validity of the retrieved profiles varies vertically with the set of channels used. Third, the radiative properties used in the radiative transfer simulations have a strong influence on the retrieved atmospheric profiles. Finally, the ML estimator has the advantage of being independent of the observation error but is less constrained than the WA estimator when few frequencies are considered. Although the presented sensitivities have been conducted to incorporate the scheme in a data assimilation system, the results may be generalized for geophysical retrieval purposes.

Abstract An ensemble Kalman filter (EnKF) has been implemented for atmospheric data assimilation. It assimilates observations from a fairly complete observational network with a forecast model that includes a standard operational set of physical parameterizations. To obtain reasonable results with a limited number of ensemble members, severe horizontal and vertical covariance localizations have been used. It is observed that the error growth in the data assimilation cycle is mainly due to model error. An isotropic parameterization, similar to the forecast-error parameterization in variational algorithms, is used to represent model error. After some adjustment, it is possible to obtain innovation statistics that agree with the ensemble-based estimate of the innovation amplitudes for winds and temperature. Currently, no model error is added for the humidity variable, and, consequently, the ensemble spread for humidity is too small. After about 5 days of cycling, fairly stable global filter statistics are obtained with no sign of filter divergence. The quality of the ensemble mean background field, as verified using radiosonde observations, is similar to that obtained using a 3D variational procedure. In part, this is likely due to the form chosen for the parameterized model error. Nevertheless, the degree of similarity is surprising given that the background-error statistics used by the two procedures are rather different, with generally larger background errors being used by the variational scheme. A set of 5-day integrations has been started from the ensemble of initial conditions provided by the EnKF. For the middle and lower troposphere, the growth rates of the perturbations are somewhat smaller than the growth rate of the actual ensemble mean error. For the upper levels, the perturbation patterns decay for about 3 days as a consequence of diffusive model dynamics. These decaying perturbations tend to severely underestimate the actual error that grows rapidly near the model top.

Abstract. Vertical profiles of atmospheric pollutants, acquired by uncrewed aerial vehicles (UAVs, known as drones), represent a new type of observation that can help to fill the existing observation gap in the planetary boundary layer (PBL). This article presents the first study of assimilating air pollutant observations from drones to evaluate the impact on local air quality analysis. The study uses the high-resolution air quality model EURAD-IM (EURopean Air pollution Dispersion – Inverse Model), including the four-dimensional variational data assimilation system (4D-Var), to perform the assimilation of ozone (O3) and nitrogen oxide (NO) vertical profiles. 4D-Var is an inverse modelling technique that allows for simultaneous adjustments of initial values and emissions rates. The drone data were collected during the MesSBAR (automated airborne measurement of air pollution levels in the near-earth atmosphere in urban areas) field campaign, which was conducted in Wesseling, Germany, on 22–23 September 2021. The results show that the 4D-Var assimilation of high-resolution drone measurements has a beneficial impact on the representation of regional air pollutants within the model. On both days, a significant improvement in the vertical distribution of O3 and NO is noticed in the analysis compared to the reference simulation without data assimilation. Moreover, the validation of the analysis against independent observations shows an overall improvement in the bias, root mean square error, and correlation for O3, NO, and NO2 (nitrogen dioxide) ground concentrations at the measurement site as well as in the surrounding region. Furthermore, the assimilation allows for the deduction of emission correction factors in the area near the measurement site, which significantly contributes to the improvement in the analysis.