雷达资料同化对提高数值天气预报准确率具有重要意义。针对2016年7月5-6日武汉一次梅雨期暴雨过程,采用改进的雷达资料同化方案STMAS(Space and Time Multiscale Analysis System)同化雷达径向速度和反射率因子。通过与LAPS(Local Analy...雷达资料同化对提高数值天气预报准确率具有重要意义。针对2016年7月5-6日武汉一次梅雨期暴雨过程,采用改进的雷达资料同化方案STMAS(Space and Time Multiscale Analysis System)同化雷达径向速度和反射率因子。通过与LAPS(Local Analysis and Prediction System)方案的结果对比初始动力场、水汽条件、热力场、预报天气形势、降水和雷达回波等的差异,并着重分析了STMAS方案对初始场及降水预报的改进及其原因。结果表明:(1)同化雷达径向速度时,STMAS方案在三维变分基础上引入连续方程做强约束条件,对初始场中动力场改善效果较为明显。(2)同化雷达反射率因子时,STMAS方案增加利用雷达回波直接调整湿度步骤,强迫雷达回波高于阈值区域饱和,使得初始场的水汽条件更加充沛,对流不稳定能量更大。(3)由于STMAS方案初始场的改善,使得预报场中高低空天气系统配置较好,最终使得预报的雨带和强降水落区在位置和强度上更接近实况,其中100 mm以上强降水预报能力尤为突出。展开更多
将多重网格策略引入NLS-3DVar(Non-linear Least Squares-based on Three-dimensional Variational Data Assimilation,非线性最小二乘三维变分同化)方法,进而应用于2400多个国家级气象观测站逐时气温数据和NCEP再分析气温数据的融合,...将多重网格策略引入NLS-3DVar(Non-linear Least Squares-based on Three-dimensional Variational Data Assimilation,非线性最小二乘三维变分同化)方法,进而应用于2400多个国家级气象观测站逐时气温数据和NCEP再分析气温数据的融合,得到中国区域空间分辨率1°×1°,时间分辨率为6小时的气温融合产品。分别从单重网格(分辨率1°×1°)和双重网格(分辨率由2°×2°到1°×1°)利用2014年1~12月(4、5月除外)的独立检验数据考察NLS-3DVar气温融合产品质量,验证基于多重网格策略的NLS-3DVar方法的优越性。在单重网格下,与广泛应用于气象行业的Cressman插值产品(均方根误差和相关系数的年平均值分别为1.961°C d^(-1)和0.924)相比,NLS-3DVar产品全年始终具有最小的均方根误差和最大的相关系数,年平均值分别为1.915°C d^(-1)和0.929;站点间误差分析进一步表明,NLS-3DVar产品在大多数检验站点精度更高,在新疆、甘肃、云南、陕西等地区尤为突出;加入双重网格策略的NLS-3DVar产品与单重网格的NLS-3DVar产品误差对比显示,均方根误差年平均值分别为1.649°C d^(-1)和1.711°C d^(-1),相关系数年平均值分别为0.970和0.968,二者在均方根误差和相关系数的表现上都极为相似,即双重网格NLS-3DVar气温产品尽管对观测数据采取了稀疏化处理,但依旧维持了原有的产品精度,并且在计算效率上提高了1倍多。而与同样在双重网格下基于多尺度的STMAS(Space–Time Multiscale Analysis System)算法相比,双重网格的NLS-3DVar产品在产品精度上同样占据优势,在计算效率上单位时次耗时与STMAS算法几乎相当。展开更多
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
文摘雷达资料同化对提高数值天气预报准确率具有重要意义。针对2016年7月5-6日武汉一次梅雨期暴雨过程,采用改进的雷达资料同化方案STMAS(Space and Time Multiscale Analysis System)同化雷达径向速度和反射率因子。通过与LAPS(Local Analysis and Prediction System)方案的结果对比初始动力场、水汽条件、热力场、预报天气形势、降水和雷达回波等的差异,并着重分析了STMAS方案对初始场及降水预报的改进及其原因。结果表明:(1)同化雷达径向速度时,STMAS方案在三维变分基础上引入连续方程做强约束条件,对初始场中动力场改善效果较为明显。(2)同化雷达反射率因子时,STMAS方案增加利用雷达回波直接调整湿度步骤,强迫雷达回波高于阈值区域饱和,使得初始场的水汽条件更加充沛,对流不稳定能量更大。(3)由于STMAS方案初始场的改善,使得预报场中高低空天气系统配置较好,最终使得预报的雨带和强降水落区在位置和强度上更接近实况,其中100 mm以上强降水预报能力尤为突出。
文摘将多重网格策略引入NLS-3DVar(Non-linear Least Squares-based on Three-dimensional Variational Data Assimilation,非线性最小二乘三维变分同化)方法,进而应用于2400多个国家级气象观测站逐时气温数据和NCEP再分析气温数据的融合,得到中国区域空间分辨率1°×1°,时间分辨率为6小时的气温融合产品。分别从单重网格(分辨率1°×1°)和双重网格(分辨率由2°×2°到1°×1°)利用2014年1~12月(4、5月除外)的独立检验数据考察NLS-3DVar气温融合产品质量,验证基于多重网格策略的NLS-3DVar方法的优越性。在单重网格下,与广泛应用于气象行业的Cressman插值产品(均方根误差和相关系数的年平均值分别为1.961°C d^(-1)和0.924)相比,NLS-3DVar产品全年始终具有最小的均方根误差和最大的相关系数,年平均值分别为1.915°C d^(-1)和0.929;站点间误差分析进一步表明,NLS-3DVar产品在大多数检验站点精度更高,在新疆、甘肃、云南、陕西等地区尤为突出;加入双重网格策略的NLS-3DVar产品与单重网格的NLS-3DVar产品误差对比显示,均方根误差年平均值分别为1.649°C d^(-1)和1.711°C d^(-1),相关系数年平均值分别为0.970和0.968,二者在均方根误差和相关系数的表现上都极为相似,即双重网格NLS-3DVar气温产品尽管对观测数据采取了稀疏化处理,但依旧维持了原有的产品精度,并且在计算效率上提高了1倍多。而与同样在双重网格下基于多尺度的STMAS(Space–Time Multiscale Analysis System)算法相比,双重网格的NLS-3DVar产品在产品精度上同样占据优势,在计算效率上单位时次耗时与STMAS算法几乎相当。
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.
