To describe the evolution of atmospheric processes and rainfall forecast in Tanzania, the Advanced Weather Research and Forecasting (WRF-ARW) model was used. The principal objectives of this study were 1) the understa...To describe the evolution of atmospheric processes and rainfall forecast in Tanzania, the Advanced Weather Research and Forecasting (WRF-ARW) model was used. The principal objectives of this study were 1) the understanding of mesoscale WRF model and adapting the model for Tanzania;2) to conduct numerical experiments using WRF model with different convective parameterization schemes (CP’s) and investigate the impact of each scheme on the quality of rainfall forecast;and 3) the investigation of the capability of WRF model to successfully simulate rainfall amount during strong downpour. The impact on the quality of rainfall forecast of six CP’s was investigated. Two rainy seasons, short season “Vuli” from October to December (OND) and long season “Masika” from March to May (MAM) were targeted. The results of numerical experiments showed that for rainfall prediction in Dar es Salaam and (the entire coast of the Indian Ocean), GD scheme performed better during OND and BMJ scheme during MAM. Results also showed that NC scheme should not be used, which is in agreement to the fact that in tropics rainfall is from convective activities. WRF model to some extent performs better in the cases of extreme rainfall.展开更多
Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial...Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.展开更多
Under Arctic warming,near-surface energy transfers have significantly changed,but few studies have focused on energy exchange over Arctic glacier due to limitations in available observations.In this study,the atmosphe...Under Arctic warming,near-surface energy transfers have significantly changed,but few studies have focused on energy exchange over Arctic glacier due to limitations in available observations.In this study,the atmospheric energy exchange processes over the Arctic glacier surface were analyzed by using observational data obtained in summer 2019 in comparison with those over the Arctic tundra surface.The energy budget over the glacier greatly differed from that over the tundra,characterized by less net shortwave radiation and downward sensible heat flux,due to the high albedo and icy surface.Most of the incoming solar radiation was injected into the glacier in summer,leading to snow ice melting.During the observation period,strong daily variations in near-surface heat transfer occurred over the Arctic glacier,with the maximum downward and upward heat fluxes occurring on 2 and 6 July 2019,respectively.Further analyses suggested that the maximum downward heat flux is mainly caused by the strong local thermal contrast above the glacier surface,while the maximum upward heat transfer cannot be explained by the classical turbulent heat transfer theory,possibly caused by countergradient heat transfer.Our results indicated that the near-surface energy exchange processes over Arctic glacier may be strongly related to local forcings,but a more in-depth investigation will be needed in the future when more observational data become available.展开更多
文摘To describe the evolution of atmospheric processes and rainfall forecast in Tanzania, the Advanced Weather Research and Forecasting (WRF-ARW) model was used. The principal objectives of this study were 1) the understanding of mesoscale WRF model and adapting the model for Tanzania;2) to conduct numerical experiments using WRF model with different convective parameterization schemes (CP’s) and investigate the impact of each scheme on the quality of rainfall forecast;and 3) the investigation of the capability of WRF model to successfully simulate rainfall amount during strong downpour. The impact on the quality of rainfall forecast of six CP’s was investigated. Two rainy seasons, short season “Vuli” from October to December (OND) and long season “Masika” from March to May (MAM) were targeted. The results of numerical experiments showed that for rainfall prediction in Dar es Salaam and (the entire coast of the Indian Ocean), GD scheme performed better during OND and BMJ scheme during MAM. Results also showed that NC scheme should not be used, which is in agreement to the fact that in tropics rainfall is from convective activities. WRF model to some extent performs better in the cases of extreme rainfall.
基金This research was supported by grants from the Polish National Science Centre(project numbers 2015/19/B/ST10/02158 and 2017/27/B/ST10/00297)The computations were partly performed in the PoznańSupercomputing and Networking Center(Grant No.331)We would like to thank the Polish Institute of Meteorology and Water Management-National Research Institute,for providing the radar-derived products.
文摘Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.
基金Supported by the National Key Research and Development Program of China(2022YFC2807203 and 2022YFC3702001-03)Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(2019QZKK0105)+1 种基金National Natural Science Foundation of China(41830968)Planning Project of Institute of Atmospheric Physics,Chinese Academy of Sciences(E268091801).
文摘Under Arctic warming,near-surface energy transfers have significantly changed,but few studies have focused on energy exchange over Arctic glacier due to limitations in available observations.In this study,the atmospheric energy exchange processes over the Arctic glacier surface were analyzed by using observational data obtained in summer 2019 in comparison with those over the Arctic tundra surface.The energy budget over the glacier greatly differed from that over the tundra,characterized by less net shortwave radiation and downward sensible heat flux,due to the high albedo and icy surface.Most of the incoming solar radiation was injected into the glacier in summer,leading to snow ice melting.During the observation period,strong daily variations in near-surface heat transfer occurred over the Arctic glacier,with the maximum downward and upward heat fluxes occurring on 2 and 6 July 2019,respectively.Further analyses suggested that the maximum downward heat flux is mainly caused by the strong local thermal contrast above the glacier surface,while the maximum upward heat transfer cannot be explained by the classical turbulent heat transfer theory,possibly caused by countergradient heat transfer.Our results indicated that the near-surface energy exchange processes over Arctic glacier may be strongly related to local forcings,but a more in-depth investigation will be needed in the future when more observational data become available.
