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This study employs the regional Climate-Weather Research and Forecasting model (CWRF) to first investigate the primary physical mechanisms causing biases in simulating summer precipitation over the Yangtze River Basin (YRB), and then enhance its predictive ability through an optimal multi-physics ensemble approach. The CWRF 30-km simulations in China are compared among 28 combinations of varying physics parameterizations during 1980–2015. Long-term average summer biases in YRB precipitation are remotely correlated with those of large-scale circulations. These teleconnections of biases are highly consistent with the observed correlation patterns between interannual variations of precipitation and circulations, despite minor shifts in their primary action centers. Increased YRB precipitation aligns with a southward shifted East Asian westerly jet, an intensified low-level southerly flow south of YRB, and a south-eastward shifted South Asian high, alongside higher moisture availability over YRB. Conversely, decreased YRB precipitation corresponds to an opposite circulation pattern. The CWRF control configuration using the ensemble cumulus parameterization (ECP), compared to other cumulus schemes, best captures the observed YRB precipitation characteristics and associated circulation patterns. Coupling ECP with the Morrison or Morrison-aerosol microphysics and the CCCMA or CAML radiation schemes enhances the overall CWRF skills. Compared to the control CWRF, the ensemble average of these skill-enhanced physics configurations more accurately reproduces YRB summer precipitation's spatial distributions, interannual anomalies, and associated circulation patterns. The Bayesian Joint Probability calibration to these configurations improves the ensemble's spatial distributions but compromises its interannual anomalies and teleconnection patterns. Our findings highlight substantial potential for refining the representation of climate system physics to improve YRB precipitation prediction. This is notably achieved by realistically coupling cumulus, microphysics, and radiation processes to accurately capture circulation teleconnections. Further enhancements can be achieved by optimizing the multi-physics ensemble among skill-enhanced configurations. [ABSTRACT FROM AUTHOR]

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On the evening of 30 April 2021, a severe hailstorm swept across eastern China, causing catastrophic gale and damaging hailstones. This hailstorm event was directly caused by two mesoscale convective systems associated with strong squall lines, with mid-level cold advection from the northeast cold vortex, and strong low-level convergence associated with the low-level vortex and wind shear line. Double nesting of the high-resolution weather research and forecasting model (9–1 km) is utilized to simulate this hailstorm with five microphysics schemes. The radar-based maximum estimated size of hail (MESH) algorithm, differential reflectivity and fractions skill scores were used to quantitatively evaluate the precision. All schemes basically captured the two squall lines that swept through eastern China, although they appeared one or two hours earlier than observation. Particularly, Goddard and Thompson performed better in the MESH swath and fractions skill scores among the five different schemes. However, Thompson most realistically captured the reflectivity pattern, intensity and vertical structure of mesoscale convective systems. Its high-reflectivity column corresponded to the maximum center of the hail mixing ratio within the updraft region, which is consistent with the characteristics of a pulse-type hailstorm in its mature phase. [ABSTRACT FROM AUTHOR]

The effectiveness of urban hydrological processes in mitigating the urban heat island (UHI) effect and human thermal stress in the megacity of Nanjing during an extreme heat wave event (6th–10th August 2013) was assessed using Weather Research and Forecasting Single‐Layer Urban Canopy Models. The inclusion of urban hydrological processes improved model performance, with more reasonable diurnal cycles and smaller mean errors, root mean square errors, and normalized root mean square errors for meteorological variables. Through evaporative cooling, urban hydrological processes can greatly increase specific and relative humidity, while reducing near‐surface and surface temperatures, wind speed, and planetary boundary layer (PBL) height, and the cooling and wetting effects could affect the entire PBL, especially in low‐intensity residential areas. Urban hydrological processes can effectively mitigate both the near‐surface and surface UHI effect. The city‐wide mitigation effectiveness of near‐surface UHI ranged between 0.9°C and 1.1°C throughout the day, while the city‐wide mitigation effectiveness of surface UHI at noon reached ∼5°C. The maximum reduction of near‐surface and surface UHI in low‐intensity residential areas reached 1.3°C and 10.0°C, respectively. Changes in heat stress indices indicate that the cooling effect improves human thermal comfort at night, while the increased humidity outweighs the cooling effect and exacerbates human thermal discomfort during daytime. The city‐wide thermal stress increased by up to 0.4°C, 0.2°C, and 0.5°C during daytime and decreased by up to 0.4°C, 0.3°C, and 0.6°C at night for wet‐bulb globe temperature, apparent temperature, and humidity index, respectively. Key Points: The cooling and wetting effects of urban hydrological processes are most significant during daytime and in low‐intensity residential areasUrban hydrological processes effectively mitigate both the near‐surface and surface urban heat island effectUrban hydrological processes improve human thermal comfort at night but exacerbate it during daytime under heat‐wave conditions [ABSTRACT FROM AUTHOR]

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities. [ABSTRACT FROM AUTHOR]

This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes. [ABSTRACT FROM AUTHOR]

This study evaluates the effects of major cumulus parameterization closures on summer precipitation simulations over the U.S. Atlantic Coasts and Gulf of Mexico. A series of mesoscale regional climate model simulations using an Ensemble Cumulus Parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation are conducted and compared to determine the systematic errors and relative performances of individual and combined closures in capturing precipitation spatiotemporal variations. The results show that closure algorithms largely affect precipitation's geographic distribution, frequency and intensity, and diurnal cycle. The quasi-equilibrium and total instability adjustment closures simulate widespread wet biases, while the instability tendency closure produces systematic dry biases. Two closure algorithms based on the average vertical velocity at the cloud base and column moisture convergence complementarily reproduce the observed precipitation pattern and amount, and capture the frequency of heavy rainfall events better than other closures. In contrast, the instability tendency closures are better at capturing the diurnal phase but yield much larger deficits in amount. Therefore, cloud base vertical velocity and moisture convergence may be the primary factors controlling precipitation seasonal mean and daily variation, while the instability tendency may play a critical role in regulating the diurnal cycle phase. [ABSTRACT FROM AUTHOR]

This study comprehensively evaluates the effects of twelve cumulus parameterization (CUP) schemes on simulations of 1993 and 2008 Central US summer floods using the regional climate-weather research and forecasting model. The CUP schemes have distinct skills in predicting the summer mean pattern, daily rainfall frequency and precipitation diurnal cycle. Most CUP schemes largely underestimate the magnitude of Central US floods, but three schemes including the ensemble cumulus parameterization (ECP), the Grell-3 ensemble cumulus parameterization (G3) and Zhang-McFarlane-Liang cumulus parameterization (ZML) show clear advantages over others in reproducing both floods location and amount. In particular, the ECP scheme with the moisture convergence closure over land and cloud-base vertical velocity closure over oceans not only reduces the wet biases in the G3 and ZML schemes along the US coastal oceans, but also accurately reproduces the Central US daily precipitation variation and frequency distribution. The Grell (GR) scheme shows superiority in reproducing the Central US nocturnal rainfall maxima, but others generally fail. This advantage of GR scheme is primarily due to its closure assumption in which the convection is determined by the tendency of large-scale instability. Future study will attempt to incorporate the large-scale tendency assumption as a trigger function in the ECP scheme to improve its prediction of Central US rainfall diurnal cycle. [ABSTRACT FROM AUTHOR]