基于风廓线雷达同化的风场及降水预报改进研究进展

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。

本文基于常规气象观测资料，卫星数据资料，闪电定位资料，结合中尺度数值 WRF模式模拟研究了2014年4月16日至17日江淮流域一次雷雨闪电天气过程。分析结果表明，本次过程中触发雷雨闪电的机制是江淮切变线和低层不稳定层结，促进了雷电活动的发生发展。雷暴云团在发展前酝酿阶段闪电频次达到峰值，发展至成熟阶段时闪电次数反而减少。雷暴云团上风方以含负电荷的云水粒子为主，下风方积聚大量带正电荷的冰相粒子。WRF模式能够较好地模拟出降水的强度和落区，地形的影响使降水回波在背风面呈现强弱相间的波状分布，具有典型的地形降水分布特征。

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。

本文针对预报漏报的深圳机场2024年8月12日早间周边海域水龙卷过程，从雷达特征及触发条件开展分析发现，该水龙卷水平尺度较小，为百米级别，强度偏弱，需综合使用相控阵雷达对特定的钩状回波进行识别。此外，水龙卷由于垂直发展高度及云底高度较低，在雷达规划时需考虑土建海拔高度，并合理使用不同仰角扫描产品，避免龙卷特征出现在雷达高度以下。本次过程由弱暖区西南气流增长提供动力抬升，配合强烈能量释放形成强对流，同时叠加超过28 m/s的强低层风切变和底层涡旋触发出水龙卷。在短期预报中，低层垂直风切变、相对螺旋度等指标较难指示出水龙卷发生的可能性，但在临近预报中可重点关注上游风廓线雷达，可提供近1小时提前量，有利于及早开展短临预警。

复杂下垫面下地面弱压场尤其是风向的准确预报对于飞机跑道的选取，避免飞机在进近过程中由于顺风过大复飞有积极意义。本文利用气象局自动站5 mins风场资料、激光雷达、风廓线雷达、以及多普勒天气雷达资料，选取2023年8月9日一次典型弱压场天气背景的风场进行分析讨论，得出：气象局自动站、激光雷达、风廓线、多普勒天气雷达资料在风场预报中各有千秋；对于地面弱压场下风向的预报，多普勒雷达0.5˚仰角速度资料优势明显；在实际业务中需要综合利用以上资料来获得更准确的预报。

利用航空器语音报告分析贵阳机场低空风切变天气统计学特征，发现贵阳机场低空风切变呈现逐年增多趋势，并且多发于冬春两季。然后利用常规观测资料及风廓线探测资料将影响贵阳机场的低空风切变进行分型，按照风要素特征分型为：风的水平切变、风的垂直切变及垂直气流的切变；按照影响天气系统分为：冷锋型、低空急流型、静止锋型和局地热对流型，其中冷锋型低空风切变发生最多，持续时间最长，影响最大，最后通过实际案例对贵阳机场一次冷锋型过境导致的低空风切变进行分析。

Abstract During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown ( RHB ), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.

Abstract The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts. Significance Statement Due to the high spatial–temporal wind information provided by profiler and radar radial velocity measurements, their combined assimilation would be expected to improve wind analysis. To fully utilize dense profiler data and radar radial wind in future operational applications, this study proposes a suitable assimilation strategy. If the profilers are defined as synoptic-scale observations, the profiler and Doppler radar data must be assimilated in different passes to adopt different length and variance scales. Whereas it is more reasonable to use a small background correlation length consistent with the radial velocity and, therefore, assimilate in the same pass if the profiler data are considered to better sample storm-scale features. Single- and dual-observation experiments indicate that profiler data provide important complements, while the assimilation of single-radar radial wind may yield analyzed wind results that do not depict the ground truth. A low-level jet case and a 1-month impact study further show that the combined assimilation strategy of assimilating both profiler and Doppler radar using smaller background correlation lengths enhances the analysis and forecasting of wind, resulting in more accurate accumulated precipitation forecasts.

Abstract. The article reports on the impact of the assimilation of wind vertical profile data in a kilometre-scale NWP system on predicting heavy precipitation events in the north-western Mediterranean area. The data collected in diverse conditions by the airborne W-band radar RASTA (Radar Airborne System Tool for Atmosphere) during a 45-day period are assimilated in the 3 h 3DVAR assimilation system of AROME. The impact of the length of the assimilation window is investigated. The data assimilation experiments are performed for a heavy rainfall event, which occurred over south-eastern France on 26 September 2012 (IOP7a) and over a 45-day cycled period. Results indicate that the quality of the rainfall accumulation forecasts increases with the length of the assimilation window, which recommends using observations with a large period centred on the assimilation time. The positive impact of the assimilation of RASTA wind data is particularly evidenced for the IOP7a case since results indicate an improvement in the predicted wind at short-term ranges (2 and 3 h) and in the 11 h precipitation forecasts. However, in the 45-day cycled period, the comparison against other assimilated observations shows an overall neutral impact. Results are still encouraging since a slight positive improvement in the 5, 8 and 11 h precipitation forecasts was demonstrated.

The assimilation of wind profiler (profiler) observations has the potential of improving the model initial condition of dynamical field with its high spatial and temporal wind field information. In this study, the impacts of two kinds of wind observation operators, i.e., eastward and northward velocities (u and v; uv_scheme) and wind speed and direction (spd and dir; sd_scheme), on analyses and forecasts are discussed to determine the optimal observation operator for profiler data assimilation. To compare the performance of the two observation operators on profiler data assimilation, we carry out a series of single wind tests and a two-week period cycling assimilation and forecasting experiments. The single wind tests show that the adjustment of the two observation operators to the analyses of wind can be significantly different with large innovation (OMB) of dir. The uv_scheme adjusts the corresponding model variables by ingesting profiler information of u and v winds, so that the analyzed spd is indirectly affected by the observed dir. By contrast, the changes in observed dir will not affect the analyzed spd in sd_scheme, as the observed spd and dir are independently assimilated. The case diagnoses illustrate that the sd_scheme generates the best analysis of wind in RMSE when validating against either the profiler or the pilot observations. The improved analysis of winds in turn leads to better wind forecasts and precipitation prediction, confirming the advantage of sd_scheme. Results from the cycling experiments further demonstrate that, compared with the uv_scheme, the sd_scheme shows decent improvements in wind forecasts, thus leading to better precipitation forecast skills.

An ensemble Kalman filter for convective‐scale data assimilation (KENDA) has been developed for the COnsortium for Small‐scale MOdelling (COSMO) model. The KENDA system comprises a local ensemble transform Kalman filter (LETKF) and a deterministic analysis based on the Kalman gain for the analysis ensemble mean. The KENDA software suite includes tools for adaptive localization, multiplicative covariance inflation, relaxation to prior perturbations and adaptive observation errors. In the version introduced here, conventional data (radiosonde, aircraft, wind profiler, surface station data) are assimilated. Latent heat nudging of radar precipitation has also been added to the KENDA system to be applied to the deterministic analysis only or additionally to all ensemble members. The performance of different system components is investigated in a quasi‐operational setting using a basic cycling environment (BACY) for a period of six days with 24 h forecasts. For this period and an additional 28 day period, deterministic KENDA forecasts are compared with forecasts based on the observation nudging data assimilation scheme, which is currently operational at the German Weather Service (Deutscher Wetterdienst, DWD). For our experiments, lateral boundary conditions for the regional model are given by a global ensemble Kalman filter for the ICOsahedral Nonhydrostatic (ICON) model. The performance of the KENDA system proves overall to be superior to the forecast quality of the operational nudging scheme, in particular with regard to precipitation. Latent heat nudging improves precipitation forecasts in both systems and has slightly more benefit in combination with the LETKF than with observation nudging.

Abstract. The optimal spatial layout of a radar wind profiler (RWP) network for rainfall forecasting, especially over complex terrain, remains uncertain. This study explores the benefits of assimilating vertical wind measurements from various RWP network layouts into convective-scale numerical weather prediction (NWP) through Observing System Simulation Experiments (OSSEs). Synthetic RWP data were assimilated into the Weather Research and Forecasting (WRF) model using the National Severe Storms Laboratory three-dimensional variational data assimilation (DA) system for three southwest (SW)-type heavy rainfall events in the Beijing–Tianjin–Hebei region. Four types of DA experiments were conducted and compared: a control experiment (CTL) that assimilates data solely from the operational RWP network and three additional experiments incorporating foothill (FH), ridge (RD), and combined foothill–ridge (FH_RD) RWP network layouts. A detailed examination of the 21 July 2023 case reveals that the FH_RD experiment generally exhibits more skillful storm forecasts in terms of areal coverage, storm mode, and orientation, benefiting from refined mesoscale wind analysis. Particularly, in the RD experiment, RWP data assimilation notably reduces wind errors and improves the representation of mesoscale atmospheric features near the Taihang Mountains upstream of Beijing, crucial for convective initiation (CI). Aggregated score metrics across all cases also indicate that both FH and RD experiments offer substantial added value over the operational network alone. Further sensitivity experiments on vertical resolution and maximum detection height indicate that the RWP system configuration with the highest detection height achieves the best performance, while lower detection height degrades forecast quality. These findings highlight the importance of strategic RWP network placement along the Taihang Mountains' ridge and foothill for short-term quantitative precipitation forecast in the Beijing–Tianjin–Hebei region.

Abstract. The optimal spatial layout for a radar wind profiler (RWP) network in rainfall forecasting, especially over complex terrain, remains uncertain. This study explores the benefits of assimilating vertical wind measurements from various RWP network layouts into convective-scale numerical weather prediction (NWP) through observing system simulation experiments (OSSEs). Synthetic RWP data were assimilated into the Weather Research and Forecasting (WRF) model using the National Severe Storms Laboratory three-dimensional variational data assimilation (DA) system for three southwest (SW)-type heavy rainfall events in the Beijing-Tianjin-Hebei region. Four types of DA experiments were conducted and compared: a control experiment (CTL) that assimilates data solely from the operational RWP network, and three additional experiments incorporating foothill (FH), ridge (RD), and combined foothill-ridge (FH_RD) RWP network layouts. A detailed examination of the 21 July 2023 case reveals that the FH_RD experiment generally exhibits more skillful storm forecasts in terms of areal coverage, storm mode, and orientation, benifiting from refined mesoscale wind analysis. Particularly, in the RD experiment, RWP data assimilation notably reduces wind errors and enhances mesoscale dynamics near the Taihang Mountains upstream of Beijing, crucial for convective initiation (CI). Aggregated score metrics across all cases also indicate that both FH and RD experiments offer substantial added value over the operational network alone. Further sensitivity experiments on vertical resolution and maximum detection height indicate that the RWP system configuration with the highest detection height achieves the best performance, while lower detection height degrades forecast quality. These findings highlight the importance of strategic RWP network placement along the Taihang Mountains' ridge and foothill for short-term quantitative precipitation forecast in the Beijing-Tianjin-Hebei region.

Abstract To improve the prediction of heavy rainfall in southern China during the prerainy season, horizontal wind data from wind profiling radars (WPRs) were assimilated in the partial-cycle data assimilation (DA) system based on a three-dimensional variational method. The analyses from the DA system are used as initial conditions for the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model. The impact of assimilating WPR data on the quantitative precipitation forecast (QPF) in southern China was evaluated over the period of the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014, by comparing the results of a control experiment with WPR data assimilated and a denial experiment without WPR data. The positive impact of WPR DA was significant on the forecasts of atmospheric variables in the vertical and diagnostic fields at the surface, especially those of surface wind fields in the 0–6-h range. The inclusion of WPR data also improved the QPF skill of light and heavy rainfall throughout the 12-h forecast period by reducing the predicted spurious precipitation (thereby alleviating overestimations and false alarms), with the largest improvement in 6-h heavy rainfall forecasts. WPR DA considerably alleviated the spinup problem, remarkably improving the QPF of heavy rainfall (especially extreme rainfall). The improved representation of wind and moisture at lower levels in the analyses due to WPR DA was the physical cause of the QPF improvement, as is illustrated using a case study.

Abstract During the first Wind Forecast Improvement Project (WFIP), new meteorological observations were collected from a large suite of instruments, including wind velocities measured on networks of tall towers provided by wind industry partners, wind speeds measured by cup anemometers mounted on the nacelles of wind turbines, and wind profiles by networks of Doppler sodars and radar wind profilers. Previous data denial studies found a significant improvement of up to 6% root mean squared error (RMSE) reduction for short‐term wind power forecasts due to the assimilation of all of these observations into the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) forecast model using a 3D variational data assimilation scheme. As a follow‐on study, we now investigate the impacts of assimilating into the RAP model either the additional remote sensing observations (sodars and radar wind profilers) alone or assimilating the industry‐provided in situ observations (tall towers and nacelle anemometers) alone, in addition to routinely available standard meteorological data sets. The more numerous tall tower/nacelle observations provide a relatively large improvement through the first 3 to 4 hours of the forecasts, which diminishes to a negligible impact by forecast hour 6. In comparison, the sparser vertical profiling sodars/radars provide an initially smaller impact that decays at a much slower rate, with a positive impact present through the first 12 hours of the forecast. Large positive assimilation impacts for both sets of instruments are found during daytime hours, while small or even negative impacts are found during nighttime hours.

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

Abstract. This study was performed in the framework of HyMeX (Hydrological cycle in the Mediterranean Experiment), which aimed to study the heavy precipitation that regularly affects the Mediterranean area. A reanalysis with a convective-scale model AROME-WMED (Application of Research to Operations at MEsoscale western Mediterranean) was performed, which assimilated most of the available data for a 2-month period corresponding to the first special observation period of the field campaign (Fourrié et al., 2019). Among them, observations related to the low-level humidity flow were assimilated. Such observations are important for the description of the feeding of the convective mesoscale systems with humidity (Duffourg and Ducrocq, 2011; Bresson et al., 2012; Ricard et al., 2012). Among them there were a dense reprocessed network of high-quality Global Navigation Satellite System (GNSS) zenithal total delay (ZTD) observations, reprocessed data from wind profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data. The aim of the paper is to assess the impact of the assimilation of these four observation types on the analyses and the forecasts from the 3 h forecast range (first guess) up to the 48 h forecast range. In order to assess this impact, several observing system experiments (OSEs) or so-called denial experiments, were carried out by removing one single data set from the observation data set assimilated in the reanalysis. Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts, as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality, but this impact is not statistically significant. The assimilation of the Spanish radar data improves the 3 h precipitation forecast quality as well as the short-term (30 h) precipitation forecasts, but this impact remains located over Spain. Moreover, marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data, as they represent a very small data set, mainly located over the sea.

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

Two momentum control variable schemes are typically used in most data assimilation systems: stream function and unbalanced velocity potential (ψ/χu scheme) and eastward and northward velocity (U/V scheme). The wind profiler radar (profiler) plays an important role in the expansion of meteorological observations networks. In this study, the impacts of two momentum control variable schemes on assimilating a dense wind profiler network are discussed based on the single profiler station observation tests and six convective rainfall events cycling experiments. Single profiler station observation tests indicate that profiler data assimilation is sensitive to control variables. The dynamical increments using U/V scheme contain more elaborate structure, which contributes to valuing the straightforward effects of profiler observations objectively. By contrast, unrealistic increments occur around the observation station in the experiment using ψ/χu scheme. Six continuous cycling experiments further demonstrate that, for convective rainfall events, experiment using U/V scheme leads to more skillful quantitative precipitation forecasts of convective rainfall. Diagnostic analysis results show that analysis using ψ/χu scheme prevents a good fitting to the profiler data, and accurate dynamical analysis containing abundant small-scale disturbances are the main reason for improving precipitation prediction of convective rainfall when U/V scheme is adopted.

The Hydrological Cycle in Mediterranean Experiment (HyMeX) is a 10-year international programme devoted to improving our understanding of the hydrological cycle in the Mediterranean area, with special emphasis on the predictability and evolution of high-impact weather events (Drobinski et al., 2014). Heavy precipitation is a major natural hazard in the Mediterranean. Daily surface rainfall greater than 100 mm is not uncommon for Mediterranean precipitation events, and often such amounts are recorded in only a few hours, associated with mesoscale convective systems (MCSs). The occurrence of these heavy precipitation amounts over small river catchments that are characteristic of the Mediterranean region often leads to devastating flash floods and flooding events. Each year, these heavy precipitation events (HPEs) result in up to hundreds of millions of euros in damages and many casualties. The distinctive topography and geographical location of the Mediterranean basin (Figure 1) make the region particularly prone to HPEs. Most of these events occur in autumn over the western Mediterranean when sea water is warmest and serves as an important heat and moisture source from which convective and baroclinic atmospheric systems can derive their energy. The steep orography surrounding the Mediterranean Sea aids in lifting the low-level, conditionally unstable air, thus initiating condensation and convection processes. The HyMeX observation strategy relies on (i) heavily instrumented Special Observation Periods (SOPs) of a few months to provide detailed and specific observations to study key processes, and (ii) longer observation periods repetitively or routinely collecting observations to monitor long-term water cycle processes and rare events such as flash flooding over a few specific instrumented watersheds. The first SOP was dedicated to heavy precipitation and flash floods. This major international field campaign took place from 5 September to 6 November 2012 over the northwestern Mediterranean Sea and its surrounding coastal regions in France, Italy and Spain. Ducrocq et al. (2014) gave a comprehensive description of the SOP1 observation strategy and execution, as well as the detailed list of deployed instruments. The observations collected by more than 200 research instruments constitute an unprecedented dataset. Uniquely in comparison to previous field experiments in the region, the atmosphere, ocean, and land surfaces were all sampled in order to study the interaction and feedbacks between the different components of the hydrological cycle during heavy precipitation and flash floods. As a substantial improvement to previous field campaigns dedicated to heavy precipitation (e.g. MAP field experiment in 1999: Bougeault et al., 2001), SOP1 collected observations also over the sea and of precipitating systems forming over the sea and affecting the coastal areas. A strong modelling component (ocean–atmosphere–hydrology, process–weather, prediction–climate models) was conceived from the beginning in HyMeX, which allowed use of the field campaign data for model validation, data assimilation, improving physical parametrizations and for advancing process understanding. This Special Issue presents a wide range of studies carried out over the last three years exploiting the exceptional dataset of observations and model output collected during SOP1. The special issue consists of a series of 31 articles. Some major results of these articles are summarized in the following sections. Section 2 presents advances in observations and their use in characterizing HPE, section 3 highlights the advances in process understanding, and section 4 details results about predictability of HPE from numerical weather prediction (NWP) and regional climate models. As a primary stage of analysis, the consistency between co-located observations has been assessed for different research instruments measuring winds and water vapour during SOP1 (Chazette et al. (this SI, pp 7–22), Saïd et al. (this SI, pp 23–42)) and satellite products (Rysman et al. (this SI, pp 43–55)). Chazette et al. (this SI, pp 7–22) compare water vapour observations from a ground-based water-vapour Raman lidar (Chazette et al., 2014), an airborne water-vapour lidar and boundary-layer pressurized balloons (Doerenbecher et al., 2016). Saïd et al. (this SI, pp 23–42) compare winds from 11 UHF and VHF wind profiler radars, balloon radiosoundings, in situ aircraft or boundary-layer pressurised balloons. Airborne cloud radar measurements are used to validate the Convective Overshooting (COV) diagnostic based on passive microwave measurements from the Microwave Humidity Sounder (MHS) in Rysman et al. (this SI, pp 43–55). Several studies take advantage of additional measurements from research instruments or availability of data from non-real-time networks to evaluate the performance and adequacy of current operational networks for describing the ambient low-level circulation and atmospheric water vapour. Bock et al. (this SI, pp 56–71) reprocessed more than 1000 ground-based Global Positioning System (GPS) receivers located in Spain, France and Italy to evaluate the accuracy of near-real-time E-GVAP GPS Zenith Total Delay (ZTD) data assimilated in operational NWP systems. They conclude that the mean differences between E-GVAP and reprocessed ZTD data are not negligible. On the other hand, the comparison of the integrated water vapour (IWV) from reprocessed GPS and radiosondes reveals no significant biases over night-time and small biases during daytime. This result gives high confidence in the quality of modern radiosonde systems in contrast to past experiments (e.g. AMMA: Agustí-Panareda et al., 2009). Saïd et al. (this SI, pp 23–42) examine how the wind field derived from UHF and VHF wind profiler radars deployed along the French coast is able to represent the low-level circulation over the northwestern Mediterranean Sea. They clearly show that the drastically different characteristics of the winds induced by the complex terrain of the region make it difficult to retrieve wind fields from the five coastal profiler radars. The study of Khodayar et al. (this SI, pp 72–85) on Intensive Observing Period IOP8 concludes that the variability of water vapour and low-level wind convergence is quite adequately sampled by the operational networks inland, but not over the sea where the spatial and temporal resolution of observations is undeniably insufficient to identify and locate moisture convergence areas. Radar observations collected during SOP1 (Bousquet et al., 2015) represent a valuable dataset that is used to develop and evaluate novel radar-based products for research and operational activities. Bousquet et al. (this SI, pp 86–94) assess the quality of real-time multiple-Doppler radar winds retrieved from the French operational network radars over southern France with the radial velocity measurements collected by an airborne cloud radar during SOP1. The mean difference between the two three-dimensional (3D) cloud wind fields is close to zero and confirms that multiple-Doppler radar wind observations are accurate enough to be used for operational purposes such as NWP model verification. Another radar-based product, namely a hydrometeor classification algorithm, is evaluated in Ribaud et al. (this SI, pp 95–107). They propose an original method to derive 3D hydrometeor fields associated with convective systems from the multi-frequency single-radar hydrometeor classification. Wolfensberger et al. (this SI, pp 108–124) develop a new algorithm to automatically detect the melting layer based on polarimetric radar scans. Raupach and Berne (this SI, pp 125–137) present a new approach for the spatial interpolation of experimental raindrop size distribution (DSD) spectra from a network of disdrometers and a weather radar. Besson et al. (this SI, pp 138–152) show that the refractivity measurements collected from some weather radars during SOP1 compare well with observations from surface weather stations, and are able to capture the diurnal cycle and the low-level conditions during the pre-convection period. The field campaign observations have been extensively used to assess the quality of NWP analyses and forecasts, as well as high-resolution research or regional climate model simulations, before they were exploited to document SOP1 conditions and advance process understanding. In particular, the real-time HyMeX-SOP dedicated version of the convection-permitting AROME NWP system (AROME-WMED: Fourrié et al., 2015) is thoroughly compared to different datasets in Bock et al. (this SI, pp 56–71), Chazette et al. (this SI, pp 7–22), Di Girolamo et al. (this SI, pp 153–172), Duffourg et al. (this SI, pp 259–274), Rainaud et al. (this SI, pp 173–187) and Saïd et al. (this SI, pp 23–42). The characteristics of the IOPs together with the large-scale circulation that prevailed during SOP1 are presented in Ducrocq et al. (2014). IOPs over Spain and Italy are more specifically described in Jansà et al. (2014) and Ferretti et al. (2014), respectively. Monthly precipitation totals from surface stations were well above the corresponding climatology for most regions in October and November 2012, and were near average in September. The overall temporal distribution of HPE is closely related to the Atlantic weather regimes. Rysman et al. (this SI, pp 43–55) shows that convective activity during SOP1, based on the Deep Convection (DC) and COV diagnostics from MHS over the Mediterranean area (including over the sea), was not notably different from the last 12 years, except for some instances in the second half of the SOP period with COV occurrence reaching two to four times the average values. Bock et al. (this SI, pp 56–71) use reprocessed GPS data to examine the spatial and temporal variability of IWV during SOP1. They point out a high temporal variability in the moisture content, with higher content at Mediterranean coastal sites which are under the influence of strong low-level inland advection of moisture. IWV peaks are often observed shortly before precipitation. Saïd et al. (this SI, pp 23–42) highlight the high spatial variability of low-level winds along the northwestern coast, observed by the mesoscale wind profiler radar networks, strongly linked to the complex mountainous coasts and islands and their role in the low-level circulation. The mistral and the tramontane, two regional northerly and northwesterly dry winds which often blow in the northwestern Mediterranean basin, are examples of interactions of the large-scale flow with orography. During autumn, as in SOP1, the second prevailing wind of the northwestern basin is onshore wind, favouring heavy precipitation (Ricard et al., 2012). Di Girolamo et al. (this SI, pp 153–172) study three transition events from mistral/tramontane to southerly marine flow in southern France during SOP1. Low-level wind reversals are found to have a strong impact on water vapour transport, leading to a large variability of the water vapour vertical and horizontal distributions. A noticeable result is that the increase/decrease in water vapour mixing ratio within the boundary layer may be abrupt and marked during these transition periods, with values increasing or decreasing by a factor of 2–4 within 1 h. The high variability of water vapour associated with low-level winds impacts the air–sea fluxes. Rainaud et al. (this SI, pp 173–187) show that the Gulf of Lion is the area with the highest variability of air–sea fluxes during SOP1, due to the prevailing strong dry regional winds (mistral/tramontane). Another remarkable result of this study is that even though some HPEs occur without significant air–sea fluxes, all strong air–sea exchange episodes include, or occur just 1 or 2 days before, HPEs. A major ingredient in HPE is the conditionally unstable, low-level marine flow impinging on the mountainous coastal regions bordering the western Mediterranean Sea, associated with lifting that leads to the onset of deep convection at the same place. The lifting mechanisms mostly result from the interactions of the low-level circulation with the orography, which greatly depends upon the local configuration of the terrain. However, a noteworthy outcome of many of the IOP studies carried out over Spain, France or Italy is that these cases present common characteristics (Figure 1). In addition to the direct role of orographic lifting, they frequently highlight features such as the presence of pre-existing convergence lines (dynamically or orographically induced), the role of a cold pool (possibly, but not necessarily, resulting from evaporative cooling) and contributions of topographical flows (e.g. gap winds and barrier jets). Based on IOP18, IOP19 and pre-SOP1 events over northeastern Italy (NEI), Davolio et al. (this SI, pp 188–205) identify two different dynamical behaviours of the flow impinging on the Alpine orography, associated with two different patterns of heavy precipitation. All the events present a similar initial phase, forced by the advancing synoptic disturbance and characterized by a weak low-level wind coming from the Adriatic Sea (sirocco wind) blocked by the Alps and deflected as an easterly/northeasterly barrier wind over the NEI plain. Then, different interactions with the orography produce two different evolutions: (i) flow-over conditions progressively establish themselves, the barrier wind disappears and the orographically forced uplift of the impinging flow produces intense orographic precipitation over the Alps, with embedded convective activity; (ii) the uplift over the pre-existing cold air over the NEI plain (blocking) is able to initiate convection well upstream of the orography where the unstable incoming flow is forced to rise over the layer characterized by the barrier wind. Blocked flow conditions persist and the convergence line between the sirocco wind and the barrier wind further triggers convection. The precipitation affects the NEI plain or even the coastal area, far from the Alps. Scheffknecht et al. (this SI, pp 206–221) explicitly highlight the role of the deflection of flow around the mountainous islands by studying the localized convective system of IOP15c, which led to severe flooding over southern Corsica. They clearly identify the splitting of the northerly low-level wind around the Corsican orography and the resulting convergence at the southern of the as key mechanisms for this Heavy precipitation only when the between the and western of the flow was and when gap winds from the to et al. (this SI, pp study the role of in initiating convection for two from SOP1. the first the flow marine winds low-level convergence for the convection the flow around the and the resulting convergence is the for the second Another influence of the orography is also on by and Davolio (this SI, pp which the of the and islands as a the rainfall distribution over and Duffourg et al. (this SI, pp study the mesoscale convective systems that over the sea and heavy precipitation over the French coastal region during The convective systems are during their stage over the by and conditionally unstable air carried by a to low-level The low-level wind convergence in this flow is the to initiate and the of convective to the convective The convergence line when a surface in the of the associated with the of an In evaporative low-level convergence and thus This is found to a key role in the deep convection embedded within the associated with the et al. (this SI, pp compare this Mediterranean to deep Mediterranean also associated with heavy precipitation and over the same region and the same of the associated heavy rainfall was by two different (i) deep convection for and (ii) associated with precipitation for the second the most of SOP1, was also characterized by an associated with a and low-level convergence within which an over the coast et al., 2014). et al. (this SI, pp identify two mechanisms leading to heavy precipitation in this (i) a wind convergence line over the sea to the convective and (ii) the interaction of the low-level winds with the orography the heavy precipitation. During SOP1, more than instruments were deployed over southern France to the activity associated with convective systems. the exceptional the 3D of the with a large of recorded during SOP1 et al., Ribaud et al. (this SI, pp the between activity and for the convective line 3D wind and hydrometeor observations from the weather radars. The results that the of from the convective area the area of the is at the of a activity the of from the of the convective area the of the field within this may also have the leading to a On the the of the system over a small may have strongly the resulting in intense activity at high et al. (this SI, pp further study the impact of terrain on precipitation from observations of over southern They show that the orography and the rainfall a role in the rainfall and in the associated processes. The above the of the local other processes, such as or with the near the A low-level flow is a common ingredient for HPEs. The of the moisture over the Mediterranean Sea is quite and and from (i) from the Mediterranean Sea, (ii) from the Atlantic and from Chazette et al. (this SI, pp highlight a of high water-vapour content air associated with the of a and strong low-level winds over the Mediterranean within which during et al. (this SI, pp identify for IOP8 two moisture in the Atlantic for the of the and from the Mediterranean Sea for the et al. (this SI, pp study more specifically the variability of IWV over and particularly the IWV distribution between the and and associated mesoscale processes as well as the strong of IWV in the of the and of the on the upstream coast only small were spatial are more for the of deep convection in the of the than at the to for heavy precipitation have been in years and significant has been the of convection-permitting NWP systems. However, the accuracy of is insufficient to the in of and of rainfall and flash Several studies in this special issue at improving the systems and studying the processes and modelling components that influence The of of new of observations has been by the of which observations from model as a first et al. (this SI, pp develop a weather radar for a wide of such as by with The performance of the with a has been evaluated measurements at and from the French operational weather radar network and also for and The results show a consistency between observations and their but with a in observations by and the by a et al. (this SI, pp study the impact on quality of the of additional SOP1 and satellite In a but small impact is The of the impact depends on the weather that the location of the as well as their with routinely data areas. Scheffknecht et al. (this SI, pp 206–221) study for the to initial conditions and model of convection-permitting They a high to initial of the initial conditions the and location of the On the decreasing the from to not clearly the and Davolio (this SI, pp also a weak impact of a higher horizontal resolution 2 on precipitation or of Rainaud et al. (this SI, pp 173–187) evaluate the low-level atmospheric from over sea the and and measurements during SOP1. A is except during strong mistral/tramontane wind due to (i) a in the and (ii) an of the heat in these cold and dry strong wind by the et al. (this SI, pp the impact of the sea on the low-level fluxes during of the convection-permitting atmospheric the (i) without on the (ii) with and with model output as show that the location of the heavy precipitation on the French is when a is used due to the impact on the resulting in a of the surface wind of the upstream low-level et al. (this SI, pp make use of the SOP1 dataset of HPEs to evaluate the of a operational convection-permitting prediction A result is that a convection-permitting with a few can a with many more data is found not as as a surface be et al. (this SI, pp evaluate and compare precipitation from two convection-permitting systems over the of SOP1. The predictability of heavy precipitation of is found to be strongly related to how the the surface also by Duffourg et al. (this SI, pp and et al. (this SI, pp et al. (this SI, pp study the mechanisms that the predictability over the Atlantic and Mediterranean regions from to the beginning of October during SOP1 when was around the A major outcome about the HPE predictability is that the of interaction between the and the Atlantic the of and thus the conditions over the Mediterranean. forced at the by have been regional climate and a period the SOP1 period within the et al., with the of these regional climate observations and NWP or research models. Khodayar et al. (this SI, pp a comparison of regional climate and convection-permitting NWP on the from the regional climate capture the occurrence of the precipitation they produce notably precipitation totals and than the NWP models. convective events are not well by the regional climate models. The differences with the NWP to in the physical parametrizations variability and vertical distribution of than in the initial or boundary et al. (this SI, pp evaluate two regional climate precipitation datasets over They that of the most intense HPEs were also observed HPEs for most the and they study the between differences and precipitation differences over the years and for SOP1 events. This Special Issue the range of advances have in the understanding and predictability of HPEs. SOP1 a remarkable and dataset that has been to make advances in process understanding, rise to new for heavy precipitation events in the western Mediterranean. has also been in and of NWP and regional climate such as convection-permitting prediction systems or regional models. more studies and are in the coming The of observations from the research instruments deployed during SOP1, such as from the or the airborne is and further advances in understanding and modelling of heavy precipitation events. The role of the Mediterranean Sea and the and of water vapour the precipitating systems be with more integrated to the at The regional climate and more specifically the convection-permitting climate be further of and from Several studies also at improving the key physical parametrizations that strongly influence the of deep convection at and process modelling and modelling in its studies at in the initial by data of new observation or data The of these studies is to and NWP prediction to more of heavy precipitation and of its As for previous major field it take years to the field campaign dataset that long-term the The HyMeX programme is by a large of and are on the HyMeX The field campaign SOP1 was by and The also and the as well as all the that have to the quality of the articles and of this Special

This study quantitatively examines the contribution of assimilating observations in the regions with different dynamic instabilities to the analysis and prediction of an extreme rainstorm event in Fujian Province of China. The wind profiling radar (WPR) observations are classified into two groups, i.e., strong and weak instability areas (SIA and WIA), according to their local dynamic instability identified by the ensemble spread. Their performance of assimilation and prediction in terms of the wind and precipitation are evaluated and compared in detail. The results show that the wind analysis error by assimilating all of the WPR observations can be reduced by about 30%. In particular, the wind analysis errors by only assimilating the observations in the SIA are about 12% lower than those in the WIA. They are related to the existence of the low-level horizontal wind shear with strong instability in the SIA. The case study shows that the assimilation of observations in the SIA can effectively correct the wind fields on the two sides of the wind shear line, producing an improved precipitation forecast compared to observation assimilation in the WIA.

The accuracy of ERA5 reanalysis datasets and their applicability in the coastal area of Bohai Bay are crucial for weather forecasting and environmental protection research. However, synthesis evaluation of ERA5 in this region remains lacking. In this study, using a tropospheric wind profile radar (CFL-06L) placed in coastal Huanghua city, the deviations of ERA5 reanalysis data are assessed from the ground to an altitude of 5 km. The results indicate that the wind speed of ERA5 reanalysis data exhibits good consistency from the surface to the tropospheric level of about 5 km, with R2 values ranging from 0.5 to 0.85. The lowest mean wind speed error, less than 3 m/s, occurs in the middle layer, while larger errors are observed at the surface and upper layers. Specifically, at 150 m, the R2 is as low as 0.5, with numerous outliers around 5000 m. Seasonal analysis shows that the ERA5 wind field performs best in summer and worst in autumn and winter, especially at lower levels affected by circulation systems, high stratus clouds, and aerosols, with errors reaching up to 10 m/s. Further analysis of extreme weather events, such as heavy rain; hot, dry winds; and snowstorms, reveals that the effects of sea-land winds and strong convective systems significantly impact the observation of wind profiles and the assimilation of reanalysis data, particularly under the constrain of boundary layer height. Additionally, we also find that the transition of sea-land breeze is capable of triggering the nighttime low-level jet, thereby downward transporting the aloft ozone to the ground and resulting in an abnormal increase in the surface ozone concentration. The study provides a scientific basis for improving meteorological forecasting, optimizing wind energy resource utilization, and formulating environmental protection policies, highlighting its significant scientific and practical application value.

Abstract A wind profiler network with a total of 65 profiling radar systems was operated by the China Meteorological Observation Center (MOC) of the China Meteorological Administration (CMA) until July 2015. In this study, a quality control procedure is constructed to incorporate the profiler data from the wind-profiling network into the local data assimilation and forecasting systems. The procedure applies a blacklisting check that removes stations with gross errors and an outlier check that rejects data with large deviations from the background. As opposed to the biweight method, which has been commonly implemented in outlier elimination for univariate observations, the outlier elimination method is developed based on the iterated reweighted minimum covariance determinant (IRMCD) for multivariate observations, such as wind profiler data. A quality control experiment is performed separately for subsets containing profiler data tagged with/without rain flags in parallel every 0000 and 1200 UTC from 20 June to 30 September 2015. The results show that with quality control, the frequency distributions of the differences between the observations and the model background meet the requirements of a Gaussian distribution for data assimilation. A further intensive assessment of each quality control step reveals that the stations rejected by the blacklisting contained poor data quality and that the IRMCD rejects outliers in a robust and physically reasonable manner. Detailed comparisons between the IRMCD and the biweight method are performed, and the IRMCD is demonstrated to be more efficient and more comprehensive regarding the dataset used in this study.

Meiyu-front rainstorm is one of the main disastrous weather events in summer in East China. In this study, seven assimilation experiments of multi-type observation data such as wind profile data, microwave radiometer data and radiosonde sounding data are designed to forecast the Meiyu-front rainstorm on 15 June 2020. The results show that the seven experiments can basically simulate the orientation of rain belt. The comprehensive experiment which assimilates all types of observations performs the best in simulating the location of heavy rainstorm and shows good performance in simulating the precipitation above moderate rain. For the comprehensive experiment, the forecast deviation of rainstorm and heavy rainstorm is small, and the equitable threat score has also been greatly improved compared with other experiments. It is found that the convective available potential energy is enhanced after the assimilation of surface observation data. In addition, the wind convergence and water vapor transportation are modified after assimilating wind profile data. Accordingly, the precipitation efficiency is improved in the comprehensive experiment. The profiles of pseudo-equivalent potential temperature, vorticity and divergence show that, the assimilation of new-types observation data from wind profiler radar and microwave radiometer increases the instability of atmospheric stratification and enhances the ascending motion in the heavy precipitation center. The above results show that the introduction of various some new-type data before the numerical simulation can reduce the forecast deviation. In addition, the combined assimilation of microwave radiometer and sounding data presents better performance than single data assimilation, which indicates that data mutual complementation is essential to improving forecast accuracy.

Abstract A summer convective precipitation case, occurring in eastern China on 16–17 July 2020, is selected to investigate the impact of joint assimilation of ground-based profiling platforms and weather radars on forecasting convective storms using observational system simulation experiments (OSSEs). The simulated profiling platforms include the Doppler wind lidar (DWL), a wind profiler (WP), and a microwave radiometer (MWR). Results show that joint assimilation of WP and radar data produces a better analysis of convective dynamical structure than joint assimilation of DWL and radar data, since WP detects deeper layer winds. Joint assimilation of MWR and radar data enables rapid adjustment of temperature and humidity and thus, avoids the potential errors introduced by the latent heat term of the radar diabatic initialization in the early stage. Profiling observations in a horizontal spacing of 80 km provide fewer benefits for convective forecasting, while reducing the spacing to 40 km can dramatically improve model analysis and forecasts. Joint assimilation of multiple profiling observations in a 20-km horizontal spacing with radar data exhibits a beneficial synergistic effect and mitigates “the ramp-down issue” during the forecast stage. Assimilating profiling observations with an update interval less than 30 min does not have as pronounced an effect on convective forecasts as horizontal spacing. Furthermore, assimilating profiling observations at a 20-km horizontal spacing can obtain accurate mesoscale background environment and forecast storms with an ability comparable to radar data assimilation. This work emphasizes the need to consider implementing a joint mesoscale detection system that incorporates weather radars and profiling observations for leveraging convective storm forecasting.

Abstract During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

The Australian Government Bureau of Meteorology completed the installation of a network of 9 new wind profiling radars across mainland Australia in 2017, which complement an existing network of 5 profilers and 5 research systems. This results in a network of 14 operational, and 19 total, profilers across Australia and Davis Station in Antarctica. Four of the new profilers are higher power stratospheric tropospheric systems, designed to measure winds from near ground level to the tropopause, and maintain the upper air network in Australia where sonde launches are no longer available. Wind measurements in the near field of the radar are demonstrated to be both possible and accurate by comparison with co-located radiosondes. Quality control procedures producing winds of sufficient accuracy for presentation to forecasters and ingestion into global numerical weather prediction models are described. The Australian network data are available on the global telecommunications system and are currently being ingested into all major models. First results from impact studies on forecast error reduction in the Australian Community Climate and Earth Systems Simulator show remote stations have the greatest impact.

Abstract. To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models.The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO's 300 m tower equipped at six levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and six temperature (T) and relative humidity (RH) sensors; and approximately 60 radiosonde launches.In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers (MWRs) as well as virtual temperature (Tv) measured by co-located wind profiling radars equipped with radio acoustic sounding systems (RASSs). Results indicate a mean absolute error (MAE) in the temperature retrieved by the microwave radiometers below 1.5 K in the lowest 5 km of the atmosphere and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 K in the layer of the atmosphere covered by these measurements (up to approximately 1.6–2 km). We also investigated the benefit of the vertical velocity correction applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data.Water vapor density (WVD) profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding and finding a mean absolute error below 0.5 g m−3 in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. An encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.

Abstract The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

Abstract The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development. Significance Statement NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Since 1 July 2018, the GRAPES (Global/Regional Assimilation and PrEdiction System) global 4‐dimensional variational (4D‐Var) data assimilation system has been in operation at the China Meteorological Administration (CMA). In this study, the GRAPES global 4D‐Var data assimilation system is comprehensively introduced. This system applies the non‐hydrostatic global tangent‐linear model (TLM) and the adjoint model (ADM) for the first time. The use of a digital filter as a weak constraint is achieved. A series of linear physical processes is developed, including vertical diffusion, subgrid‐scale orographic parametrization, large‐scale condensation, and cumulus convection parametrization. The vertical diffusion and subgrid‐scale orographic schemes are used in the operational suite and the linear convection parametrization and large‐scale condensation scheme remain under assessment. The Lanczos and conjugate gradient (Lanczos‐CG) algorithm and the limited‐memory Broyden‐Fletcher‐Goldfarb‐Shanno (L‐BFGS) algorithm are also developed. In terms of computational optimization, the total computational time of the GRAPES global TLM and ADM is approximately threefold that of the GRAPES global nonlinear model (NLM). Before it became operational, a one‐year retrospective trial was performed on the GRAPES global 4D‐Var data assimilation system. The entire system was stable, and the analysis and forecasting performances were significantly better than those of the 3D‐Var data assimilation system, especially in the Southern Hemisphere.

Abstract. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two sources are of the order of 1 and 2 m s−1, respectively, and time–height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s−1. The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. The results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.

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

Abstract In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection‐permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10 and 12 November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection‐permitting DA including novel developments in the assimilation of observations such as cloud‐affected satellite radiances in visible channels, ground‐based profiling networks, aircraft data, and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focused on key future challenges including the handling of multiscales (synoptic‐, meso‐, and convective‐scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short‐range forecasts and nowcasts of hazardous weather.

This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15–19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the storm transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the Ionian Sea into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the eye development, and the occurrence of warm rain processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the rainbands during the TLC phase, indicate weak convection and the presence of supercooled cloud droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass.