Insights from Forecast Demonstration Projects and Research Development Projects, training workshops, and symposia, conducted between 2000 and 2024 are summarized. The projects were organized by the Nowcasting and Mesoscale Research Working Group of the World Weather Research Programme of the World Meteorological Organization. The objective was to advance, promote, and build capacity in nowcasting and very short-range forecasting. The projects were associated with the Olympic Games, emergency management, and aviation services. They brought international experts together to work in a collaborative fashion. Extensive interaction with end users and decision-makers expanded and extended the scope of services from traditional weather hazards (heavy rain, wind, hail, lightning) to include specific user needs (e.g., visibility in complex terrain or airport runways, periods of calm winds or light rain, heat stress). Substantial progress has been made in many areas including advanced radar nowcasting algorithms, stochastic nowcasts, kilometric and hectometric numerical weather prediction models, blending of observations and models, and multimodel systems. Verification was a key and valuable component of the projects quantifying the results. Also, the types of services have expanded to include both summer and winter services, complex terrain and urban environments, air transport, air quality, hydrology, and health. Insights are presented in all aspects of nowcasting and very short-range forecasting from end-user decision-making, critical role of the forecaster, forecast systems (models, heuristics, observations), to science and knowledge gaps.

In summer, many sea-breeze fronts (SBFs) are observed propagating from the sea to inland areas. However, there has been an absence of in-depth studies on whether and how these SBFs alone initiate convection initiation (CI) inland. We selected an inland CI event that occurred near Beijing on 17 May 2019 to analyze how the SBF triggers CI during its inland progression. The 3-km continuously cycled analyses with 12-min updates, produced by assimilating observations from radar and dense surface networks, revealed that as the northwestward-moving SBF reached Beijing, it interacted with the warm and dry southerly flow, mountains, and city landscape. These interactions created local conditions of strong convergence and high humidity, conducive to CI. The mountains and cities blocked and changed the direction of winds behind the SBF from southeasterly to easterly, enhancing local convergence and moisture along with the westerly downslope flow from the mountains. Meanwhile, the reduction in wind speed allowed the wet, cold air mass behind the SBF to catch up with the enhanced convergence zone, enabling the air parcel to rise from the surface to the level of free convection (LFC), thereby triggering convection. The new storm then merged with the eastward-propagating convective systems from the western mountains to form the record-breaking heavy rainfall. Sensitivity studies were conducted to quantify the effects induced by mountains, cities, and both. It was found that mountains played a vital role in enhancing convergence by changing the wind direction of the SBF, while cities primarily contributed to slowing down the SBF, thereby aligning wind convergence with water vapor and enabling the moist air to be lifted to the LFC. SIGNIFICANCE STATEMENT: It is a common phenomenon worldwide that SBFs from the sea penetrate inland areas and interact with other mesoscale circulations to trigger CI and develop severe weather, leading to considerable loss of life and property. Most previous studies on SBF primarily focused on CI near the seashore, with inland SBFs farther from the coastline receiving less attention. Consequently, it remains a challenge to understand and predict whether and where SBFs trigger CI inland. Our study of a severe storm initiated by an SBF reveals that complex topography (mountains and city landscape) plays a key role in CI by altering the wind direction, wind speed, and moisture convergence of the SBF.

Explicit simulation of hailstorms remains a challenge partly due to the lack of accurate representations of both initial conditions and microphysical processes. Using a moderate hailstorm case that occurred in Beijing on 10 June 2016, the impact of the initial conditions on explicit hail prediction has been studied in Part I of this two-part work via high-resolution data assimilation. This Part II paper examines the role of improved graupel treatment by comparing the recently upgraded Thompson-Eidhammer microphysics scheme (MP38) with two previous versions. MP38 is a double- moment hail-aware scheme with the ability to additionally predict the graupel number concentration and density. This case study showed that the addition of these predictive variables improved the simulation of the mass-weighted mean diameter of hail and thereby reduced the overestimation of hail size. However, the hail size was significantly fi cantly underpredicted without the prediction of hail density, indicating that both quantities must be prognosed for skillful hail prediction. It was further shown that the revised graupel treatment also influenced fl uenced hailstorm dynamics. The smaller hail size in MP38 led to a stronger graupel melting process, which further promoted a stronger cold pool and downdraft. By assessing the efficiency fi ciency of the upgraded Thompson-Eidhammer microphysics scheme, the current study shed some light on the importance of the accurate representation of microphysical processes in numerical models for explicit hailstorm prediction.

Abstract The impact of land variables on temperature forecasts in atmospheric cycling is often underestimated or overlooked. This oversight primarily occurs due to the abundance of meteorological measurements available for assimilation and partly because soil states are assumed to be quickly reset by atmospheric forcing, such as precipitation, justifying no spin‐ups or no updates of soil states during cycling. In this study, by updating soil moisture every 6 hr using different analysis data sets for May 2019, considerable discrepancies were found, highlighting large uncertainties in soil moisture analysis. Different soil moisture analyses produced systematically different temperature forecasts, with errors growing over cycles to be comparable to a typical error magnitude of 2‐m temperature observations (∼2°K). This study demonstrates that temperature forecasts are significantly influenced by whether and how soil moisture is updated, not only near the surface but also up to the low‐mid troposphere and throughout the cycles.

The impact of land variables on temperature forecasts in atmospheric cycling is often underestimated or overlooked. This oversight primarily occurs due to the abundance of meteorological measurements available for assimilation and partly because soil states are assumed to be quickly reset by atmospheric forcing, such as precipitation, justifying no spin-ups or no updates of soil states during cycling. In this study, by updating soil moisture every 6 hours using different analysis datasets for May 2019, considerable discrepancies were found, highlighting large uncertainties in soil moisture analysis. Different soil moisture analyses produced systematically different temperature forecasts, with errors growing over cycles to be comparable to a typical error magnitude of 2-m temperature observations (~2ºK). This study demonstrates that temperature forecasts are significantly influenced by whether and how soil moisture is updated, not only near the surface but also up to the low-mid troposphere and throughout the cycles.

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

A record-breaking precipitation event, with a maximum 24-h (1-h) precipitation of 624 mm (201.9 mm) observed at Zhengzhou Weather Station, occurred in Henan Province, China, in July 2021. However, all global operational forecast models failed to predict the intensity and location of maximum precipitation for this event. The unexpected heavy rainfall caused 398 deaths and 120.06 billion RMB of economic losses. The high-societal-impact of this event has drawn much attention from the research community. This article provides a research review of the event from the perspectives of observations, analysis, dynamics, predictability, and the connection with climate warming and urbanization. Global reanalysis data show that there was an anomalous large-scale circulation pattern that resulted in abundant moisture supply to the region of interest. Three mesoscale systems (a mesoscale low pressure system, a barrier jet, and downslope gravity current) were found by recent high-resolution model simulation and data assimilation studies to have contributed to the local intensification of the rainstorm. Furthermore, observational analysis has suggested that an abrupt increase in graupel through microphysical processes after the sequential merging of three convective cells contributed to the record-breaking precipitation. Although these findings have aided in our understanding of the extreme rainfall event, preliminary analysis indicated that the practical predictability of the extreme rainfall for this event was rather low. The contrary influences of climate warming and urbanization on precipitation extremes as revealed by two studies could add further challenges to the predictability. We conclude that data sharing and collaboration between meteorological and hydrological researchers will be crucial in future research on high-impact weather events.

Convection is the main contributor to heavy rainfall over China's Yangtze‐Huai River Basin (YHRB) during Meiyu season; however, the mechanisms of convection initiation (CI) associated with the Meiyu front are still not well understood. In this study, a large set of 86,099 CI events, identified from composite reflectivity data in YHRB over six Meiyu seasons, were used to investigate the characteristics of the spatiotemporal distribution of CI in YHRB. The result showed that the overwhelming majority of CI events (∼90%) occurred in the region of existing stratiform clouds. Meanwhile, CI tended to concentrate in mountainous areas and exhibited two triggering modes. By relating the CI events with an objective analysis of ERA5 reanalysis data, it was also revealed that the characteristics of CI occurrence varied with patterns of Meiyu circulation and their interactions with local topography, and the warm air advection pattern dominated the Meiyu CI. We further illustrated that CI on the plains occurred with a morning peak corresponding to environments of high 0–3 km shear (SHR3) and low most unstable convective available potential energy (MUCAPE), while the CI near or over mountains had an afternoon peak corresponding to low SHR3 and high MUCAPE environments.

The purpose of this study is to diagnose mesoscale factors responsible for the formation and development of an extreme rainstorm that occurred on 20 July 2021 in Zhengzhou, China. The rainstorm produced 201.9 mm of rainfall in 1 h, breaking the record of mainland China for 1-h rainfall accumulation in the past 73 years. Using 2-km continuously cycled analyses with 6-min updates that were produced by assimilating observations from radar and dense surface networks with a four-dimensional variational (4DVar) data assimilation system, we illustrate that the modification of environmental easterlies by three mesoscale disturbances played a critical role in the development of the rainstorm. Among the three systems, a mesobeta-scale low pressure system (mesolow) that developed from an inverted trough southwest of Zhengzhou was key to the formation and intensification of the rainstorm. We show that the rainstorm formed via sequential merging of three convective cells, which initiated along the convergence bands in the mesolow. Further, we present evidence to suggest that the mesolow and two terrain-influenced flows near the Taihang Mountains north of Zhengzhou, including a barrier jet and a downslope flow, contributed to the local intensification of the rainstorm and the intense 1-h rainfall. The three mesoscale features coexisted near Zhengzhou in the several hours before the extreme 1-h rainfall and enhanced local wind convergence and moisture transport synergistically. Our analysis also indicated that the strong midlevel south/southwesterly winds from the mesolow along with the gravity-current-modified low-level northeasterly barrier jet enhanced the vertical wind shear, which provided favorable local environment supporting the severe rainstorm.

This study presents a multiscale four-dimensional variational data assimilation (MS-4DVar) scheme that aims to assimilate multiscale information from conventional and radar observations. The MS-4DVar scheme separately assimilates conventional and radar data in different outer loop iterations of an incremental 4DVar with varied resolutions in the tangent linear and adjoint models (TLM/ADM) and time window lengths in the 4DVar. The MS-4DVar scheme was evaluated through a series of single observation tests and several cycled assimilation and forecasting experiments for a real squall-line case. Our results indicated that different TLM/ADM resolutions and time window lengths applied to the conventional and radar observations improved the multiscale analysis. In addition, the MS-4DVar scheme was more efficient than the common 4DVar because of the low-resolution TLM/ADM used for conventional data and the shortened time window length for radar data. Verification of the squall-line forecasts suggested that the MS-4DVar scheme improved the hourly accumulated precipitation and radar reflectivity forecast skills and reduced the forecast errors of both large-scale environmental and convective-scale states. Further diagnosis revealed that the improvement of precipitation forecast skill was attributable to the stronger cold pool, deeper saturated water vapor layer, and stronger updraft of the simulated squall-line system, as well as a more favorable convective environment.

Hailstones have large damage potential; however, their explicit prediction remains quite challenging. The uncertainty in a model's initial condition and microphysics are two of the significant contributors to the challenge. This two-part study aims to investigate the impacts of improved initial condition and microphysics on hail prediction for a moderate hailstorm that occurred in Beijing on 10 June 2016. In the first part, the role of initial conditions on hail prediction is explored by assimilating high-density observations into a numerical model with a recently developed explicit hail microphysics scheme. High-resolution and high-frequency observations from radar and surface networks are assimilated using the Weather Research and Forecasting (WRF) Model's three-dimensional variational data assimilation (3DVAR) system. The role of the initial conditions in improving explicit hail prediction with two different planetary boundary layer (PBL) schemes, the Yonsei University (YSU) scheme and the Mellor-Yamada-Janjic (MYJ) scheme, is then examined. Results indicate that the data assimilation significantly improves the hail size and location prediction for both PBL schemes by reducing errors in surface wind, temperature, and moisture fields. It is also shown that the improved analyses of low-level and midlevel vertical wind shear, resulting mainly from radar data assimilation, are pivotal to the improvement of hailstorm prediction with the YSU scheme, while the improved analysis of thermodynamic field resulting from the assimilation of both radar and surface data plays a more important role with the MYJ scheme. The results of this work shed light on the influence of data assimilation and provide insights on explicit hail predictability with respect to model initial conditions.

This paper presents a multiscale hybrid ensemble-variational (EnVar) data assimilation strategy with an hourly rapid update aiming to improve analysis of convection via radar observations and of convective environment via conventional observations. In this multiscale hybrid EnVar strategy, the ensemble members are updated by assimilating conventional data using an EnKF to provide the hybrid EnVar with flow-dependent background error covariance (BEC). A two-step approach is employed in the hybrid EnVar to achieve improved multiscale analysis by assimilating radar data and conventional data, respectively, in two successive steps. This two-step procedure enables the applications of different BEC tuning factors and different hybrid weights for radar and conventional observations. In addition, this study also examines the impacts of the flow-dependent BEC generated with and without radar data assimilation in EnKF on the performance of hybrid EnVar analysis and ensuing convective forecasting. The multiscale hybrid EnVar strategy was first evaluated through a comparison with 3DVar and EnKF using a convective rainfall case. Quantitative verifications for both precipitation and environmental variables demonstrated that the hybrid EnVar system with an optimal multiscale configuration outperformed both the 3DVar and EnKF. The multiscale hybrid EnVar strategy was then evaluated through a series of sensitivity experiments. It was shown that the two-step assimilation strategy outperformed the one-step for both the precipitation and environmental variables, and the ensemble BEC generated without radar data assimilation led to improved hybrid EnVar analysis over that with radar data assimilation by better representing uncertainties in convective environment and reducing spurious spatial and multivariate correlations.

A convective system was initiated under weak forcing on the plains near Beijing on the afternoon of 26 June 2009 and developed into a squall line that resulted in heavy precipitation. Prediction of the convective initiation (CI) of the system was challenging due to the lack of precursors detectable by conventional observation networks. In this study, we investigated the CI mechanism using high‐resolution analyses obtained from the Variational Doppler Radar Analysis System via the assimilation of radar and dense surface observations. It was found that the CI was resulted from the interactions of a local cold air mass and the outflows from a weakened storm propagating from the nearby mountains. The cold air mass built up an unstable condition and veered air flow to form a local convergence zone in which moisture was accumulated. New convection was triggered as a result of strengthened convergence when the outflows from the dissipating storm approached the existing convergence zone. To confirm the critical role played by the local cold air mass, sensitivity experiments were conducted using WRF‐FDDA data assimilation and forecast system. The results suggested that the local cold air mass along with the southerly environmental wind and storm outflows exerted a dominant influence on the CI.

Towards the Next Generation Operational Meteorological Radar 1 2 Mark Weber, Kurt Hondl, Nusrat Yussouf , Youngsun Jung, Derek Stratman, Bryan 3 Putnam, Xuguang Wang, Terry Schuur, Charles Kuster, Yixin Wen, Juanzhen Sun, Jeff 4 Keeler, Zhuming Ying, John Cho, James Kurdzo, Sebastian Torres, Chris Curtis, David 5 Schvartzman, Jami Boettcher, Feng Nai, Henry Thomas, Dusan Zrnić, Igor Ivić, 6 Djordje Mirković, Caleb Fulton, Jorge Salazar, Guifu Zhang, Robert Palmer, Mark 7 Yeary, Kevin Cooley, Michael Istok, and Mark Vincent 8

The evolution of a heavy rainfall event that occurred on 19 August 2014 in northern Taiwan is investigated using observed data and analyzed using a newly developed system, namely, IBM_VDRAS. This system is based on a four-dimensional (4D) Variational Doppler Radar Assimilation System (VDRAS) that is capable of assimilating radar observations and surface station data over a complex terrain by adopting the Immersed Boundary Method (IBM). This event has precipitating processes and track different from those frequently observed in northern Taiwan. From the surface observations and the high spatiotemporal resolution analysis fields generated by IBM_VDRAS, it has been found that the rainfall process started with two single convective cells triggered by the interaction between land–sea breeze and terrain in two different cities (Taoyuan and Taipei). The outflow of one of the convective cells developed in Taoyuan City at an earlier time along with the outflow of another convective system that developed in the Taipei Basin; the former provided favorable conditions to intensify the latter. The enhanced major convective cell moved to the Taipei City metropolitan area and produced 80-mm precipitation within approximately 2.5 h. The kinematic, thermodynamic, and microphysical fields of the convective cells were analyzed in detail to explain the mechanisms that helped maintain the structure of the rainfall system. Sensitivity experiments of quantitative precipitation forecast revealed that the terrains prevent the location of major rainfall from shifting outside of the Taipei Basin. By assimilating the surface data, the model can better predict the rainfall position.

A record‐breaking rainfall event was initiated between the urban heat island (UHI) and a small trumpet‐shaped mountain (Huadu Mountain) to its north in Guangzhou City at midnight of May 6, 2017. Numerically simulating the convection initiation (CI) was challenging due to insufficient model resolution and inaccurate boundary layer parameterization, this study therefore examined the pre‐convective mesoscale processes based on their four‐dimensional analyses obtained through data assimilation of radar and surface observations using the four‐dimensional Variational Doppler Radar Analysis System (VDRAS). Results suggested that the Huadu Mountain to the north of Guangzhou City played a crucial role in the CI through orographic blocking and nighttime cooling. Dynamically, the mountain blocked the upstream airflow causing an accumulation of water vapor in boundary layers; thermally, the large near‐surface temperature gradients between the mountain and its southern foot area induced northerly downslope winds and low‐level convergences and updrafts. The downslope winds enhanced the accumulation of boundary‐layer water vapor, which was then transported to higher altitudes over the CI region by the updrafts, thus resulting in the formation and growth of cloud water above the altitude of 1 km. In addition to the mountain, the UHI also played an important role by increasing the magnitude of the low‐level convergence and influencing its horizontal distribution. These findings urge attentions to the critical roles of small‐scale orography and its interaction with urban underlying surface in initiating local rainstorm events.

Twenty-one runners died of hypothermia during the 100 km Ultramarathon Mountain race in Baiyin, Gansu Province on 22 May 2021. The hypothermia was caused by a combination of low temperatures, precipitation, and high winds associated with a typical large-scale cold front passing by the race site that morning. Based on historical hourly records of 13 meteorological surface stations over the past six years, temperature (3.0°C) and apparent temperature (−5.1°C) at 1200 LST as well as gust wind speed (11.2 m s−1) at 1100 LST on the day of the tragedy were found to be within the top or bottom 5th percentile for the month of May. The precipitation was only moderate at this time, but when temperature lower than 3.0°C, gust wind speed greater than 11.2 m s−1, and precipitation greater than 0.1 mm for any adjacent three hours were combined together, 1200 LST 22 May fell within the top 0.1% of cases. The European Centre for Medium-range Weather Forecasting model produced reasonably good forecasts of the low temperature and high wind one day and seven days before the event, respectfully. Based on this study, lessons that can be learned from this tragedy are summarized from an academic perspective: Hazard and impact forecasts of high-impact weather events should be developed to increase the value of weather forecasts. Probability forecasts should be issued by government weather agencies and communicated well to the public. And more importantly, knowledge of how to evaluate the impact of weather should be delivered to the public in the future. We would like to extend our deepest condolences to the families and loved ones of the people who lost their lives in this tragedy, including 21 runners and one officer. May our efforts honor those who lost their lives by highlighting the value of weather forecasting and calling for greater action in the future.

Smartphones are increasingly being equipped with atmospheric measurement sensors providing huge auxiliary resources for global observations. Although China has the highest number of cell phone users, there is little research on whether these measurements provide useful information for atmospheric research. Here, for the first time, we present the global spatial and temporal variation in smartphone pressure measurements collected in 2016 from the Moji Weather app. The data have an irregular spatiotemporal distribution with a high density in urban areas, a maximum in summer and two daily peaks corresponding to rush hours. With the dense dataset, we have developed a new bias-correction method based on a machine-learning approach without requiring users' personal information, which is shown to reduce the bias of pressure observation substantially. The potential application of the high-density smartphone data in cities is illustrated by a case study of a hailstorm that occurred in Beijing in which high-resolution gridded pressure analysis is produced. It is shown that the dense smartphone pressure analysis during the storm can provide detailed information about fine-scale convective structure and decrease errors from an analysis based on surface meteorological-station measurements. This study demonstrates the potential value of smartphone data and suggests some future research needs for their use in atmospheric science.