Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to ...Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.展开更多
Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).Th...Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM)data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA)Land Data Assimilation System(CLDAS)precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.展开更多
Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and ...Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and highquality land assimilation products prior to 2008.To address this issue,this paper proposes a deep learning model based on the Hybrid Attention Transformer(HAT),aiming to improve the downscaling accuracy of high speed winds in the CLDAS2.010-m wind field from 6.25 to 1 km by(1)incorporating digital elevation information(DEM),(2)enhancing the loss function,and(3)employing a prediction error method.We utilized data in 2020–2021 for training and validation,and data in 2019 for testing,conducted ablation experiments to verify the effectiveness of each module,while comparing the results with those of the traditional bilinear interpolation method and the UNET model coupled with a dual cross-attention mechanism.The ablation experiment results indicate that in terms of wind speed categories,HAT with DEM performs the best for wind speeds below level 3 on the Beaufort scale,while HAT with DEM,loss function,and prediction error improvements excels for wind speeds above level 4.Specifically,for wind speeds above level 6,the HAT with all the three improvement measures achieves decent results,with mean absolute error(MAE),probability of detection(POD),and threat score(TS)of 0.825 m s-1,0.813,and 0.607,respectively,when evaluated against CLDAS3.0 as the ground truth.The model performs better in March–May and November,while its performance is the weakest in June–August;it also performs better during the day than at night and shows suboptimal performance over the plains.The model is closer to the ground truth in reconstructing the structural details of wind fields and outperforms the annual average during most high wind weather events,indicating better predictive capability and adaptability for such events.Overall,the HAT with all the three proposed improvements demonstrates significant progress in downscaling predictions of high winds and provides insights into generation of high-resolution historical meteorological gridded data.展开更多
基金Supported by the National Key Research and Development Program of China(2018YFC1506601)National Natural Science Foundation of China(91437220)+1 种基金China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002 and GYHY201206008)China Meteorological Administration“Meteorological Data Quality Control and Multi-source Data Fusion and Reanalysis”project。
文摘Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.
基金Supported by the National Natural Science Foundation of China(42030611)National Key Research and Development Program of China(2023YFC3007502)+1 种基金Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(2019QZKK0105)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_1301)。
文摘Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM)data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA)Land Data Assimilation System(CLDAS)precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.
基金Supported by the Project of“Advanced Research on Civil Space Technology during the 14th Five-Year Plan”of National Meteorological Information Centre of China Meteorological Administration(NMICJY202305)National Natural Science Foundation of China(42205153,42430602,and 92037000)。
文摘Due to lack of a dense network of ground observations in China before 2008,the China Meteorological Administration's Land Data Assimilation System(CLDAS)faces challenges in directly generating high-resolution and highquality land assimilation products prior to 2008.To address this issue,this paper proposes a deep learning model based on the Hybrid Attention Transformer(HAT),aiming to improve the downscaling accuracy of high speed winds in the CLDAS2.010-m wind field from 6.25 to 1 km by(1)incorporating digital elevation information(DEM),(2)enhancing the loss function,and(3)employing a prediction error method.We utilized data in 2020–2021 for training and validation,and data in 2019 for testing,conducted ablation experiments to verify the effectiveness of each module,while comparing the results with those of the traditional bilinear interpolation method and the UNET model coupled with a dual cross-attention mechanism.The ablation experiment results indicate that in terms of wind speed categories,HAT with DEM performs the best for wind speeds below level 3 on the Beaufort scale,while HAT with DEM,loss function,and prediction error improvements excels for wind speeds above level 4.Specifically,for wind speeds above level 6,the HAT with all the three improvement measures achieves decent results,with mean absolute error(MAE),probability of detection(POD),and threat score(TS)of 0.825 m s-1,0.813,and 0.607,respectively,when evaluated against CLDAS3.0 as the ground truth.The model performs better in March–May and November,while its performance is the weakest in June–August;it also performs better during the day than at night and shows suboptimal performance over the plains.The model is closer to the ground truth in reconstructing the structural details of wind fields and outperforms the annual average during most high wind weather events,indicating better predictive capability and adaptability for such events.Overall,the HAT with all the three proposed improvements demonstrates significant progress in downscaling predictions of high winds and provides insights into generation of high-resolution historical meteorological gridded data.
