Mesoscale Precipitation Research Articles

Objective Verification of the Weather Prediction Center’s Mesoscale Precipitation Discussions

Abstract The Weather Prediction Center (WPC) issues Mesoscale Precipitation Discussions (MPDs) to highlight regions where heavy rainfall is expected to pose a threat for flash flooding. Issued as short-term guidance, the MPD consists of a graphical depiction of the threat area and a technical discussion of the forecasted meteorological and hydrological conditions conducive to heavy rainfall and the potential for a flash flood event. MPDs can be issued either during or in anticipation of an event and typically are valid for up to 6 h. This study presents an objective verification of WPC’s MPDs issued between 2016 and 2022, complete with a climatology, false alarm analysis, and contingency table-based skill scores (e.g., critical success index and fractional coverage). Regional and seasonal differences become evident when MPDs are assessed based on these groupings. MPDs improved in basic skill scores between 2016 and 2020, with a slight decline in scores for 2021 and 2022. The false alarm ratio of MPDs has decreased between 2016 and 2021. The most dramatic improvement over the period occurs in the MPDs in the winter season (December, January, and February) and along the West Coast (primarily atmospheric river events). The accuracy of MPDs in this group has quadrupled when measured by fractional coverage, and the false alarm rate is approximately one-fifth of the 2016 value. Skill during active monsoon seasons tends to decrease, partially due to the large size of MPDs issued for monsoon-related flash flooding events.

Case Studies of the Microphysical and Kinematic Structure of Summer Mesoscale Precipitation Clouds over the Eastern Tibetan Plateau

Case Studies of the Microphysical and Kinematic Structure of Summer Mesoscale Precipitation Clouds over the Eastern Tibetan Plateau

Ensemble-based analysis of heavy rainfall–snowfall associated with mesoscale precipitation bands within an extratropical cyclone over northeastern China

This paper investigates the key synoptic-scale factors that affected the forecasting of mesoscale rainfall and snowfall and their associated uncertainties in a heavy rain–snow event in northeastern China on 18–20 November 2020, using ensemble-based sensitivity analysis based on global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. The heavy precipitation event was attributed to an extratropical cyclone and experienced two stages, with the snowfall stage having a better precipitation forecast skill than the rainfall stage. The mesoscale rainfall and snowfall were caused by a mesoscale rainband over Liaoning Province and two mesoscale snowbands over Heilongjiang Province, respectively, and they showed some differences with respect to their forecast skill and related key synoptic-scale factors contributing to the precipitation centers. The precipitation amount in the two different stages was correlated significantly with the midlevel trough and sensitive to the location and intensity of the low-level vortex (surface cyclone), and particularly the low-level jets and the associated water vapor transport. However, some differences were confirmed in the two different stages: the weaker midlevel trough and accompanying weaker low-level temperature trough in the rainfall stage were related to increased precipitation because the midlevel trough was far away from the control area, while the stronger midlevel trough and accompanying stronger low-level temperature trough were associated with increased precipitation in the snowfall stage. In addition to the synoptic-scale low-level jet (SLLJ), the precipitation in the rainfall stage was also affected by a boundary layer jet (BLJ) over the ocean, while only SLLJs were present in the snowfall stage. The uncertainty of the precipitation forecast was derived mainly from the uncertainty in the strength and location of the SLLJs and BLJ. Notably, the intensity of northeasterly winds west of the low-level vortex may affect the predictability of heavy snowfall.

The NOAA Weather Prediction Center’s Use and Evaluation of Experimental Warn-on-Forecast System Guidance

This study examines use of experimental Warn-on-Forecast System (WoFS) guidance for short-term flash flood prediction at the NOAA Weather Prediction Center’s Meteorological Watch (Metwatch) desk. The WoFS guidance provides storm-scale ensemble forecasts for individual thunderstorms out to six hours and has previously shown great promise in its predictive skill for heavy rainfall events. Its operational utility was examined during 2019 and 2020 in a formal collaboration between Warn-on-Forecast scientists and Metwatch meteorologists. During that time, Metwatch meteorologists integrated real-time WoFS guidance into their Mesoscale Precipitation Discussion forecast processes and provided evaluations via a post-event survey. The survey queried impacts of WoFS guidance on their situational awareness, workload, and confidence, and Metwatch meteorologists also reported subjective assessments of model performance. Survey results highlighted the importance of viewing consistency in WoFS guidance across runs and agreement between WoFS guidance with conceptual models, other numerical weather prediction guidance, and observations. The use of WoFS tended to either maintain or slightly increase Metwatch meteorologists’ workload, while also increasing their confidence (notably for events perceived as better predicted). Of the different forecast attributes evaluated, Metwatch meteorologists reported convective mode as the attribute best predicted by WoFS. Use of WoFS guidance supported Mesoscale Precipitation Discussion decision making, including the placement and spatial extent of the product and the level of specificity provided about the related flash flood threat(s).

Multiscale Processes Leading to Heavy Precipitation in the Eastern Nepal Himalayas

Abstract The processes underlying heavy rainfall in the higher elevations of the Himalayas are still not well known despite their importance. Here, we examine the detailed process causing a heavy rainfall event, observed by our rain gauge network in the Rolwaling valley, eastern Nepal Himalayas, using ERA5 and a regional cloud-resolving numerical simulation. Heavy precipitation (112 mm day−1) was observed on 8 July 2019 at Dongang (2790 m above sea level). Most of the precipitation (81 mm) occurred during 1900–2300 local time (LT). The synoptic-scale environment is characterized by a monsoon low pressure system (LPS) over northeastern India. The LPS lifted moisture upward from the lower troposphere and then horizontally transported it into the eastern Nepal Himalayas within the middle troposphere, increasing the content of the water vapor around Dongang. A mesoscale convective system passed over Dongang around the time of the intense precipitation. The numerical simulation showed that surface heat fluxes prevailed under the middle tropospheric (∼500 hPa) southeasterly flow associated with the LPS around a mountain ridge on the upwind side of Dongang until 1900 LT, enhancing convective instability. Topographic lifting led to the release of the enhanced instability, which triggered the development of a mesoscale precipitation system. The southeasterly flow pushed the precipitation system northward, which then passed over Dongang during 2000–2200 LT, resulting in heavy precipitation. Thus, we conclude that the heavy precipitation came from the multiscale processes such as three-dimensional moisture transport driven by the LPS and the diurnal variation in heat fluxes from the land surface. Significance Statement Precipitation in the Himalayas is closely related to the hydrological cycle, floods, and landslide disasters in South Asia. Thus, elucidating the features of precipitation in the Himalayas is important. This study explored multiscale processes leading to a heavy precipitation event that was observed on 8 July 2019 at Dongang in the Rolwaling valley of the eastern Nepal Himalayas. We identified new processes producing heavy precipitation in the Himalayas: the three-dimensional synoptic-scale moisture transport driven by a monsoon low pressure system and the effect of the diurnal variation in heat fluxes from the land surface on the development and movement of a mesoscale precipitation system causing heavy precipitation. These findings broaden our understanding of heavy precipitation in the Himalayas.

The history of UK weather forecasting: the changing role of the central guidance forecaster. Part 6: the late twentieth century: forecasting smaller‐scale features

During the final two decades of the twentieth century, major strides had been made in forecasting synoptic-scale features such as fronts and cyclones. However, the forecasting of embedded smaller-scale features which could produce significant weather remained challenging. In addition, forecasting convection remained highly problematic. New conceptual models were developed, which when used in conjunction with satellite and radar imagery, assisted forecasters in both the understanding and short-period prediction of such features. A non-hydrostatic mesoscale computer model gradually became integrated into operational use by forecasters, providing some additional guidance on mesoscale precipitation distribution as well as boundary layer phenomena.

A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis

Abstract. The July 2021 flood in central Europe was one of the five costliest disasters in Europe in the last half century, with an estimated total damage of EUR 32 billion. The aim of this study is to analyze and assess the flood within an interdisciplinary approach along its entire process chain: the synoptic setting of the atmospheric pressure fields, the processes causing the high rainfall totals, the extraordinary streamflows and water levels in the affected catchments, the hydro-morphological effects, and the impacts on infrastructure and society. In addition, we address the question of what measures are possible to generate added value to early response management in the immediate aftermath of a disaster. The superposition of several factors resulted in widespread extreme precipitation totals and water levels well beyond a 100-year event: slow propagation of the low pressure system Bernd, convection embedded in a mesoscale precipitation field, unusually moist air masses associated with a significant positive anomaly in sea surface temperature over the Baltic Sea, wet soils, and steep terrain in the affected catchments. Various hydro-morphodynamic processes as well as changes in valley morphology observed during the event exacerbated the impact of the flood. Relevant effects included, among many others, the occurrence of extreme landscape erosion, rapidly evolving erosion and scour processes in the channel network and urban space, recruitment of debris from the natural and urban landscape, and deposition and clogging of bottlenecks in the channel network with eventual collapse. The estimation of inundation areas as well as the derived damage assessments were carried out during or directly after the flood and show the potential of near-real-time forensic disaster analyses for crisis management, emergency personnel on-site, and the provision of relief supplies. This study is part one of a two-paper series. The second part (Ludwig et al., 2022) puts the July 2021 flood into a historical context and into the context of climate change.

From Intra-plant to Regional Scale: June Temperatures and Regional Climates Directly and Indirectly Control Betula nana Growth in Arctic Alaska

Tundra shrubs reflect climate sensitivities in their growth-ring widths, yet tissue-specific shrub chronologies are poorly studied. Further, the relative importance of regional climate patterns that exert mesoscale precipitation and temperature influences on tundra shrub growth has been explored in only a few Arctic locations. Here, we investigate Betula nana growth-ring chronologies from adjacent dry heath and moist tussock tundra habitats in arctic Alaska in relation to local and regional climate. Mean shrub and five tissue-specific ring width chronologies were analyzed using serial sectioning of above- and below-ground shrub organs, resulting in 30 shrubs per site with 161 and 104 cross sections from dry and moist tundra, respectively. Betula nana growth-ring widths in both habitats were primarily related to June air temperature (1989–2014). The strongest relationships with air temperature were found for ‘Branch2’ chronologies (dry site: r = 0.78, June 16, DOY = 167; moist site: r = 0.75, June 9, DOY = 160). Additionally, below-ground chronologies (‘Root’ and ‘Root2’) from the moist site were positively correlated with daily mean air temperatures in the previous late-June (‘Root2’ chronology: r = 0.57, pDOY = 173). Most tissue-specific chronologies exhibited the strongest correlations with daily mean air temperature during the period between 8 and 20 June. Structural equation modeling indicated that shrub growth is indirectly linked to regional Arctic and Pacific Decadal Oscillation (AO and PDO) climate indices through their relation to summer sea ice extent and air temperature. Strong dependence of Betula nana growth on early growing season temperature indicates a highly coordinated allocation of resources to tissue growth, which might increase its competitive advantage over other shrub species under a rapidly changing Arctic climate.

Case Study of Mesoscale Precipitation Areas within the Comma Head of an Extratropical Cyclone

On 12–13 February 2016, a record-breaking rain–snow event during the passage of an extratropical cyclone occurred in Shandong Province, China, in which the 24 h precipitation totals at 48 of 123 national meteorological stations in Shandong Province broke their historical records for the month of February, and a further 25 stations recorded their second-largest February totals. This paper investigates the evolution of the mesoscale precipitation areas and the mechanisms responsible for the formation, organization, and maintenance of the mesoscale precipitation areas, using FY-2G satellite data, Doppler radar observations, and a Weather Research and Forecasting (WRF) model numerical simulation at 4 km grid spacing. The main results show that the comma head cloud of the cyclone developed from four echo strips. Intense precipitation was related to the mesoscale elongated precipitation areas (EPAs) of reflectivity >30 dBZ within the stratiform clouds. The formation and development of the EPAs coincided with the activities of a low-level shear line and an associated increase in frontogenesis. The simulated EPAs occurred in an environment of conditional instability (CI), inertial instability (II), and conditional symmetric instability (CSI). In the initial stage of the elongated rainfall areas (ERAs), rainfall was initiated by the frontal forcing in the presence of elevated CI, and II was generated by upright convection. During the development stage of the ERAs, the CI was absent, and condensational heating was enhanced. II occurred in the absence of upright convection, and it seems likely that the presence of II is a diabatic signature of the precipitation itself. Upper-level II intensified the convective systems by enhancing outflow aloft, and II caused the ERAs to organize. Thus, II played an important role in the organization and maintenance of the ERAs. The frontogenesis provided the dynamic condition for the release of the instability. Enhanced CSI and II intensified slantwise convection, and combining with enhanced frontogenesis, intensified the ERAs. The echo, ascent, and frontogenesis in snowfall areas were weaker than those in rainfall areas.

A bias correction method for precipitation through recognizing mesoscale precipitation systems corresponding to weather conditions

Accurate estimations of local precipitation are necessary for assessing water resources and water-related disaster risks. Numerical models are typically used to estimate precipitation, but biases can result from insufficient resolution and incomplete physical processes. To correct these biases, various bias correction methods have been developed. Recently, bias correction methods using machine learning have been developed for improved performance. However, estimating local hourly precipitation characteristics remains difficult due to the nonlinearity of precipitation. Here, we focused on precipitation systems that could be reproduced by numerical models, and estimated the spatial distribution of local precipitation by recognizing the relationship between simulated and observed precipitation with a resolution of 0.06 degrees using a machine learning method. We subsequently applied a quantile mapping method to modify the precipitation amounts. Validation showed that our method could significantly reduce bias in numerical simulations, especially the spatial distribution of hourly precipitation frequency. However, the bias in the temporal distribution of hourly precipitation did not improve. Spatial autocorrelation analysis showed that this method can predict precipitation systems with spatial scales of 2500 to 40000 km2, which are associated with large-scale disturbances (e.g., cold fronts, warm fronts, and low-pressure systems). The high accuracy of these estimates indicates that the spatial distribution of hourly precipitation frequency is strongly dependent on precipitation systems with these spatial scales. Accordingly, our method shows that the relationship between the spatial distribution of precipitation systems and local precipitation is strong, and by recognizing this relationship, the spatial distribution of local hourly precipitation can be accurately estimated.

Measured and modeled vertical structure of precipitation during mixed-phase event near the West Coast of the Antarctic Peninsula

Precipitation structures are easy to detect, however, the mesoscale atmospheric processes which they reflect are challenging to understand in Polar Regions and hard to model numerically. Currently, the spatial distribution of precipitation can be tracked at the resolution of minutes and seconds. For this purpose, the researchers at the Ukrainian Antarctic Akademik Vernadsky station employ several near-ground measurement systems and the Micro Rain Radar for remote vertical measurements. Measurements show stochastic precipitation variability caused by turbulence, precipitation bands related to the atmospheric processes of its formation, phase transition (melting) zones, and wind shears. The time scale of bands in the stratiform precipitation typically varied in the range of 5—15 minutes and corresponded to the 2—15 km spatial scale of atmospheric circulations according to the modeled parameters of the atmosphere. The Polar Weather Research and Forecast (Polar WRF) model was used to reveal the general atmospheric conditions. We also tested and evaluated its ability to reproduce small structures. A simple method based on typical model variables is proposed to identify the precipitation melting layer in the simulation data, similar to that determined by radars. The results were satisfyingly consistent with the position of the 0 °C isotherm in the model and with the radar measurements. In addition, the method highlighted supercooled mixed-phase precipitation. Modeling showed good results for large-scale processes like atmospheric fronts and general air mass features in the case study. However, even at the 1 km resolution the simulation reproduced thin mesoscale precipitation features smoothly, which sometimes looks unrealistic. As for other precipitation peculiarities, like band inclination, melting layer position, and mixed-phase zones, the Polar WRF model demonstrates high consistency with observations. The model can describe the atmospheric conditions except for the investigation of precipitation-initiating mechanisms, which still is a challenge for modeling at a small scale.

High-resolution bias-corrected precipitation data over South Siberia, Russia

The accuracy of global hydrometeorological data is important for regional and global climate studies. We compared the ERA5 reanalysis monthly precipitation data of European Centre for Medium-range Weather Forecasts over South Siberia against observed precipitation data records from 132 ground weather stations for 1979–2015. The ERA5 reanalysis provides detailed fields reasonably reproducing features of mesoscale precipitation structures related to topography and synoptic-scale patterns. The linear correlation coefficients between reanalysis and weather station data are high but mean values are biased. The mean absolute error varies from −23 mm to 90 mm. A bias correction procedure was suggested to improve the precipitation data quality and reduce mean values error. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Raw ERA5 data were scaled according to the derived scale coefficient, resulting in monthly maps of corrected reanalysis for South Siberia (CRSS). Multiply validation of bias correction was performed on the control test sets. The CRSS data fit the observation data very well. The mean error does not exceed 7 mm, the mean absolute error maximal value is 44 mm, and the mean relative error is less than 23%. The maximal biases are typical for mountain areas. The statistical downscaling methods applied in this study can be easily transferred to other regions where long-term data sets of observed precipitation are available. The obtained results are promising since the proposed simple correction scheme is more accurate and robust in reproducing monthly - precipitation values than global reanalysis data. CRSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CRSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results.

Assessment of GPM IMERG satellite precipitation estimation and its dependence on microphysical rain regimes over the mountains of south-central Chile

Satellite data provide crucial information for those places lacking precipitation observations from ground-based sensors, especially over oceans, mountain regions, or developing countries. This is the case over much of South America, including Chile, a country with complex topography that has limited long-term precipitation records and high-elevation data, and no operational weather radars. This study focuses on investigating the skill of the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrieval for GPM (IMERG: version 6) quantitative precipitation estimation (QPE). IMERG is assessed against ground-based observations from two field campaigns that took place near 36°S: The Chilean Coastal Orographic Precipitation Experiment (CCOPE; winter 2015), which collected data over the coastal mountain range, and The Chilean Orographic and Mesoscale Precipitation Study (ChOMPS; winter 2016), which collected observations in a transect from the coast to the Andes. To characterize how IMERG performance depends on microphysical regime, we used data from profiling radars and rain gauge measurements to classify rainfall into regimes including “ice-initiated rain” and “warm rain”, characterized by the presence or absence of a well-defined melting layer respectively. Rain gauge data was used to evaluate performance of IMERG QPE overall and for these two regimes. IMERG depicts the general spatial pattern of observed orographic enhancement but highly underestimates the magnitude of this enhancement. At higher elevations during CCOPE, IMERG underestimated the total amount of rainfall by 50%, while during ChOMPS the underestimation was by 16%. For CCOPE, at higher elevation sites, IMERG underestimated ice-initiated rain by 30% and underestimated warm rain by 70%. For ChOMPS, the underestimation at the Andes site was 33% for ice-initiated rain and 50% for warm rain. IMERG QPE for both field campaigns showed larger underestimations for warm rain periods and at higher elevations than for ice-initiated rain periods. Documenting how IMERG performance varies with terrain and microphysical regime may help guide improvements to satellite-based QPE.

Heavy Winter Precipitation Events with Extratropical Cyclone Diagnosed by GPM Products and Trajectory Analysis

Heavy precipitation events were identified during the cold seasons of 2014–2019 using two-day accumulated precipitation data at 137 stations of the Japan Meteorological Agency. The mechanisms for producing heavy precipitation regarding the structure of an occluding extratropical cyclone were analyzed using the products of the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite and trajectory analysis on the European Centre for Medium-range Weather Forecasts atmospheric reanalysis data. Upper-ranked events with heavy precipitation were predominantly caused by extratropical cyclones and were in mature stages. In the top 50 ranked events, three south-coast cyclones were nominated, and the relationships between developing the mesoscale precipitation system and airstreams were intensively diagnosed. Hourly precipitation changes at stations that recorded heavy precipitation were primarily affected by a combination of the warm conveyor belt (WCB), cold conveyor belt (CCB), and dry intrusion (DI). Wide-ranging stratiform precipitation east of the cyclone center was composed of low-level WCB over the CCB and upper WCB, and convective clouds around the cyclone center were associated with the upper DI over the WCB that provided an extreme precipitation rate at the surface, including the formation of a band-shaped precipitation system. Convective cloud activities also contributed to moist air advection over the stationary stratiform precipitation areas recognized as the upper WCB. DPR products also identified deep stratiform precipitation in the cloud-head area behind the cyclone center with mid-level (near-surface) latent heat release (absorption) with increased potential vorticity along the CCB, making feedback intensification of the cyclone possible.

One‐dimensional maximum‐likelihood estimation for spaceborne precipitation radar data assimilation

AbstractSpaceborne precipitation radar such as Global Precipitation Measurement (GPM)/dual‐frequency precipitation radar (DPR) provides valuable observations of precipitation systems in three dimensions. Assimilation of GPM/DPR data is becoming an important technique for improving the accuracy of forecasting to complement scarce ground‐based observations. This study presents a new, one‐dimensional maximum‐likelihood estimation (1D‐MLE) method developed by the authors that enables the estimation of relative humidity profiles according to a non‐Gaussian probability density function. By assimilating the estimated relative humidity profiles using a four‐dimensional variational (4D‐Var) method, mesoscale precipitation forecasts by the Japan Meteorological Agency (JMA) have been considerably improved. The displacement of forecast precipitation during a severe weather event, in particular, is improved significantly. It was found that forecasting accuracy was maintained for a narrow GPM/DPR swath and low revisit frequency by repeating the assimilation–forecast cycle. Since the effectiveness was confirmed, the JMA began assimilating GPM/DPR data using the 1D‐MLE approach from March 2016.

Early Detection of Convective Echoes and Their Development Using a Ka-Band Radar Network

AbstractCumulonimbus clouds, which cause local heavy rainfall and urban floods, can develop within 20 min after being detected by operational centimeter-wavelength (X-, C-, or S-band) weather radars. To detect such clouds with greater lead times, Ka-band radars at a wavelength of 8.6 mm together with operational X-band radars were used in this study. The vertically averaged radar reflectivity (VAR) of convective echoes detected by the Ka-band and X-band radars were defined as mesoscale cloud echoes (MCEs) and mesoscale precipitation echoes (MPEs), respectively. The time series of each echo was analyzed by an echo tracking algorithm. On average, MCEs that developed into MPEs (denoted as developed MCEs) were detected 17 min earlier than the MPEs and 33 min earlier than the peak time of the area-averaged VAR (VARa) for MPEs. Some MCEs dissipated without developing into MPEs (denoted as non-developed MCEs). There were statistically significant differences between the developed and non-developed MCEs in terms of the maximum VARa values, maximum MCEs areas, and increase amounts of the VARa values and MCE areas for the first 6–12 min after their detection. Among these indicators, the maximum VARa for the first 9 min showed the most significant differences. Therefore, an algorithm for predicting MCE development using this indicator is discussed.

Observations and Predictability of a High-Impact Narrow Cold-Frontal Rainband over Southern California on 2 February 2019

AbstractShort-duration, high-intensity rainfall in Southern California, often associated with narrow cold-frontal rainbands (NCFR), threaten life and property. While the mechanisms that drive NCFRs are relatively well understood, their regional characteristics, specific contribution to precipitation hazards, and their predictability in the western United States have received little research attention relative to their impact. This manuscript presents observations of NCFR physical processes made during the Atmospheric River Reconnaissance field campaign on 2 February 2019 and investigates the predictability of the observed NCFR across spatiotemporal scales and forecast lead time. Dropsonde data collected along transects of an atmospheric river (AR) and its attendant cyclone during rapid cyclogenesis, and radiosonde observations during landfall 24 h later, are used to demonstrate that a configuration of the Weather Research and Forecasting (WRF) Model skillfully reproduces the physical processes responsible for the development and maintenance of the impactful NCFR. Ensemble simulations provide quantitative uncertainty information on the representation of these features in numerical weather prediction and instill confidence in the utility of WRF as a forecast guidance tool for short- to medium-range prediction of mesoscale precipitation processes in landfalling ARs. This research incorporates novel data and methodologies to improve forecast guidance for NCFRs impacting Southern California. While this study focuses on a single event, the outlined approach to observing and predicting high-impact weather across a range of spatial and temporal scales will support regional water management and hazard mitigation, in general.

Evaluation of a Customized Variable‐Resolution Global Model and its Application for High‐Resolution Weather Forecasts in East Asia

AbstractThe performance of a variable‐resolution global model, based on the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) framework and with customized 160‐to‐1 km resolution grid mesh, was tested by simulating idealized flow fields as well as forecasting the evolution of actual weather systems. The mesh contains five levels of refinement, with 20, 15, 9, 3, and 1 km resolution covering central to East Asia, central to southern China, southeastern China, and Greater Bay Area/Hong Kong, respectively. Using a shallow‐water solver and MPAS‐A's solver, the mesh was evaluated against standard circularly refined meshes in simulating idealized steady‐state flows. Conservation properties and error growths in the 160‐to‐1 km and counterpart simulations were fairly comparable. By perturbing the steady flow, realistic baroclinic wave evolutions could be captured. Initialized by Global Forecast System (GFS), parallel experiments were further conducted with new‐Tiedtke (nTDK), Kain‐Fritsch (KF), and Tiedtke (TDK) cumulus schemes and a convection‐permitting suite (CP). Experiments showed that the model can give reasonable 5‐day outlooks and evolution of synoptic‐scale weather systems typically found in East Asia in various seasons. In particular, it can reproduce the mesoscale precipitation related to the Meiyu rainband (cold fronts) in summer (winter). When compared with station data, promising skills in predicting local temperature, humidity, and wind changes were found. It also performed slightly better using nTDK, KF, and TDK schemes, than adopting CP. Overall, by capturing multiscale features concurrently, these experiments gave reasonable global, regional, and local weather predictions, thereby demonstrating the practicality of using customized variable‐resolution meshes for high‐resolution short‐range weather forecasts under MPAS‐A framework.

ИССЛЕДОВАНИЕ РАДИОЛОКАЦИОННЫХ ХАРАКТЕРИСТИК ОБЛАЧНЫХ СИСТЕМ ПРИ ВЫПАДЕНИИ СИЛЬНЫХ ДОЖДЕЙ

The paper analyzes radar characteristics of mesoscale precipitation systems associated with heavy precipitation in Western Urals. The characteristics under study, obtained from the Doppler weather radar, include the maximum height of the radar echo of clouds and precipitation, meteorological phenomena, speed and direction of the radar echo movement. An attempt was made to classify mesoscale precipitation systems with respect to their horizontal scales and geometrical features as well as precipitation patterns. Statistical features of radar characteristics of cloud systems causing heavy rain were calculated taking into account their classification by horizontal scales. Radar meteorological characteristics were obtained from instruments in the Izhevsk city and Ufa city for the warm period of 2016–2017. The results can be used during nowcasting of heavy precipitation events.

Mesoscale precipitation systems and their role in the rapid development of a monsoon depression over the Bay of Bengal

AbstractThe structure of mesoscale precipitation within monsoon depressions is still not as well‐known as the synoptic‐scale composite cloud and precipitation structure. Here, using observational data from multi‐satellite sensors and a cloud‐resolving regional model, we investigate the three‐dimensional structure of mesoscale precipitation systems in the different stages of the life cycle of a monsoon depression. Effects of latent heating from the precipitation systems and the Bay of Bengal (BoB) on the development of the monsoon depression are also evaluated in sensitivity experiments with the model. A typical monsoon depression developed on 17 August 2016 over the BoB. In the rapid development phase, satellite observations reveal mesoscale convective systems with deep convective precipitation cells and stratiform precipitation near the head of the BoB. Extremely deep and intense convective cells appear along a ring‐like rain band when a closed cyclonic circulation becomes obvious around the northernmost part of the BoB. The deep convection appears frequently, particularly along the western‐to‐southwestern side of the low, a convergence‐prone area between northerlies with air masses of large convective available potential energy (CAPE) that makes up the western–southwestern part of the closed cyclonic circulation, and strong monsoon westerlies with relatively stable air masses to the south of the low. Sensitivity experiments reveal that both cloud/precipitation processes and evaporation from the BoB are essential for the rapid development of the monsoon depression over the BoB. Evaporation from the BoB adds a large amount of moisture to the atmospheric boundary layer near the low. Southwesterlies and southerlies on the eastern side of the low draw in warm, humid boundary‐layer air to the closed cyclonic circulation, which maintains a large‐CAPE environment in the low and enhances deep convection. A possible positive feedback process including moist convection that leads to the rapid intensification of monsoon depressions over the BoB is discussed.