The influence of microphysical processes on surface wind speeds is primarily indirect, primarily occurring through downdraft cooling and latent heat variations. The Weather Research and Forecasting (WRF) model was ado...
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The influence of microphysical processes on surface wind speeds is primarily indirect, primarily occurring through downdraft cooling and latent heat variations. The Weather Research and Forecasting (WRF) model was adopted in this study to simulate a squall line that occurred in South China from May 6 to 7, 2023, during which the strongest observed surface wind reached 40.1 m s-1. This study analyzed the impact of microphysical processes on the strong surface winds of the squall line. The results showed that all four microphysics schemes-Thompson, Thompson aerosol-aware (Thompson-A), Morrison, and WSM6-could reproduce the strong winds of the squall line. The Thompson scheme was best suited for simulating the extremely high winds associated with the squall line process selected for this experiment. This scheme generated the strongest winds, largely owing to a concentrated precipitation area that produced strong negative buoyancy in localized regions. This effect was further intensified by high rates of rain evaporation and precipitation particle drag, which reduced downdraft interference and concentrated strong winds in specific areas. Additionally, the smaller raindrop sizes in both the Thompson and Thompson-A schemes enhanced evaporation, increased cooling, and strengthened downdrafts. However, the Thompson scheme achieved greater wind intensities than the Thompson-A scheme because of higher localized evaporation rates and a more focused precipitation distribution. In contrast, the Morrison and WSM6 schemes resulted in more dispersed cooling effects and weaker surface winds owing to broader evaporation areas and increased downdraft interference. However, in the Thompson-A scheme, the actual evaporation was somewhat weaker, likely influenced by the environmental temperature, which, along with the broader precipitation distribution, resulted in weaker surface winds than in the Thompson scheme. The Morrison scheme showed interference among downdrafts as they spread at
Machine learning methods, represented by deep learning, are rapidly changing and enhancing the process and results of the Numerical Weather Prediction (NWP) model. To better explore the application potential of the ma...
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ISBN:
(纸本)9798350385113;9798350385106
Machine learning methods, represented by deep learning, are rapidly changing and enhancing the process and results of the Numerical Weather Prediction (NWP) model. To better explore the application potential of the machine learning method in the microphysical parameterization schemes (MPS) for NWP model, this study introduces a deep learning based cloud microphysical processes scheme. The scheme includes two steps: data processing and machine learning. The data process step in our scheme is to prepare data for the machine learning method, where the data is extracted and refined from NWP outputs. Subsequent to this process, a one-dimensional dense convolutional neural network (1DD-CNN) is applied to rigorously analyze the data and boost the predictive precision and computational speed of cloud microphysical processes. Empirical results validate the scheme's efficacy in advancing meteorological predictive capabilities.
作者：
Gao, ShiboMin, JinzhongNanjing Univ Informat Sci & Technol
Key Lab Meteorol DisasterMinist Educ KLME Joint Int Res Lab Climate & Environm Change ILCEC Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing Jiangsu Peoples R China
A mesoscale convective system (MCS) over East China on June 5, 2009 was thoroughly analyzed using an Advanced Regional Prediction System (ARPS) ensemble square root filter (EnSRF) system. The analyzed reflectivity str...
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A mesoscale convective system (MCS) over East China on June 5, 2009 was thoroughly analyzed using an Advanced Regional Prediction System (ARPS) ensemble square root filter (EnSRF) system. The analyzed reflectivity structure, location and intensity compared well with observation, and were substantially better than an experiment without radar data assimilation. The cold pool and wind speed in the convective regions were strengthened. With improved initial conditions, the impact of single-moment (SM), double-moment (DM) and triple-moment (TM) microphysics parameterization (MP) schemes on ensemble forecasts of MCSs was evaluated. The use of multi-moment (MM) MP schemes showed some improvements in neighborhood ensemble probability for reflectivity and precipitation. Quantitative reflectivity and precipitation forecast skills were also improved in MM forecasts, with those of the TM forecast the best.
A squall line occurred in East China during 12 July 2014 was simulated with the Weather Research and Forecasting (WRF) model using spectral bin and two-moment bulk microphysical parameterization scheme, respectively. ...
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A squall line occurred in East China during 12 July 2014 was simulated with the Weather Research and Forecasting (WRF) model using spectral bin and two-moment bulk microphysical parameterization scheme, respectively. Comparative study showed that significant differences existed in the dynamic, thermodynamic and microphysical structures of squall line between bulk and bin simulation results. The bulk scheme produced a well-organized but shorter radar structure while bin scheme simulated scattered but stronger radar echo which was more consistent with observation. Bulk scheme had a better performance in predicting the strong rainfall areas and amount. The strong rear-to-front (RTF) inflow and convective updrafts were identified in bulk scheme by comparison with weak RTF and updrafts in bin scheme. In addition, bulk simulated a deeper cold pool than bin. Much higher cloud droplet number concentration was simulated by bulk scheme, while higher raindrop mass and number concentration was generated by bin scheme. Detailed analysis and sensitivity tests are needed in future to further investigate the possible mechanisms that responsible for the distinctive results.
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