Quantitative precipitation forecasting in numerical weather prediction (NWP) models rely on physical parameterization schemes. However, these schemes involve considerable uncertainties due to limited knowledge of the mechanisms involved in the precipitating process, ultimately leading to degraded precipitation forecasting skills. To address this issue, our study proposes using a Swin-Transformer based deep learning (DL) model to quantitatively map fundamental variables solved by NWP models to precipitation maps. Our results show that the DL model effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation-triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Planetary boundary layer (PBL) modeling is a primary contributor to uncertainties in a numerical weather prediction model due to difficulties in modeling the turbulent transport of surface fluxes. The Weather Research and Forecasting model (WRF) has included many PBL schemes which may feature a non-local transport component driven by super-grid eddies or a one-and-half order turbulence closure model. In the present study, a turbulent kinetic energy (TKE)-based turbulence closure model is integrated into the non-local Asymmetric Convective Model version 2 (ACM2) PBL scheme and implemented in WRF. Non-local transport is modeled the same as ACM2 using the transilient matrix method. The new TKE-ACM2 PBL scheme is evaluated by comparing it with high spatiotemporal Doppler LiDAR observations in Hong Kong over 30 days each for summer and winter seasons to examine its capability in predicting the vertical structures of winds. Scatter plots of measured versus simulated instantaneous wind speeds show that TKE-ACM2 is able to reduce the root mean square error and mean bias and improve the index of agreement, especially at the urban observational site. The diurnal evolution of monthly averaged wind profiles suggests TKE-ACM2 can better match both the magnitudes and vertical gradients, revealing its superiority compared to ACM2 at stable atmospheric conditions. Other meteorological parameters including the potential temperature profiles, PBL heights, and surface wind speeds have also been investigated with references to various sources of observations.

Ziping Zuoa, Jimmy C.H. Funga,b, Zhenning Lia,*, Yiyi Huangd, Mau Fung, Wonga, Alexis K.H. Laua,c, Xingcheng Luea Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinab Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinac Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Chinad Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USAe Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, ChinaCorresponding author: Zhenning Li,[email protected] worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late 21st century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer (June–September) daytime and nighttime temperatures in the 2040s will be 0.7–0.8 °C and 0.9–1.1 °C, respectively. In the 2090s, the mean difference will be 0.5–3.1 °C and 0.7–3.4 °C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. Compared with those in the 2010s, hot extremes are expected to be more frequent, intense, extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4 °C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2–0.4 °C in all three scenarios. In the 2090s, the increase in these values will be 23 days and 36.0 °C in SSP5-8.5. Moreover, a 10-year extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September in the 2090s.SIGNIFICANCE STATEMENTPearl River Delta (PRD) has been experiencing record-shattering heatwaves in recent years. This study aims to investigate the future trends of summer heatwaves in the PRD by modeling three future scenarios including a sustainable scenario, an intermediate scenario, and a worst-case scenario. Except the sustainable scenario, summer temperatures in the intermediate and worst-case scenarios will keep increasing, and heatwaves will become more frequent, intense, extensive, and longer-lasting. In the worst-case scenario, extreme heat events that occurred once in 10 years in the 2010s will shorten to once a month in the 2090s. A better understanding of heatwave trends will benefit implementing climate mitigation methods, urban planning, and improving social infrastructure.

Previous studies on South China’s convective precipitation forecast focused on the effects of multi-scale dynamics and microphysics parameterizations. However, how the uncertainty in aerosol data might cause errors in quantitative precipitation forecast (QPF) has yet to be investigated. In this case study, we estimate the impact of aerosol uncertainties on the QPF for South China’s severe convection using convection-permitting simulations. The variability range of aerosol concentrations is estimated with past observation for the pre-summer months. Simulation results suggest that the rainfall pattern and intensity change notably when aerosol concentrations are varied. The simulation with low aerosol concentrations produces the most intense precipitation, approximately 50\% stronger than the high-concentration simulation. Decreasing aerosol hygroscopicity also increases precipitation intensity, especially in pristine clouds. The aerosol uncertainty changes alter the number of cloud condensation and ice nuclei, which modifies the altitude and amount of latent heating and thereby modulates convection.

Recent worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late 21st century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer daytime and nighttime temperatures in the 2040s will be 0.7–0.8 °C and 0.9–1.1 °C, respectively. In the 2090s, they will be 0.5–3.1 °C and 0.7–3.4 °C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. Compared with those in the 2010s, hot extremes are expected to be more frequent, more intense, more extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4 °C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2–0.4 °C in all three scenarios. In the 2090s, the increase in these values will be 23 days and 36.0 °C in SSP5-8.5. Moreover, a 10-year extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September.