陆面同化及再分析降水资料在内蒙古地区的适用性

Validation of land data assimilation and reanalysis precipitation datasets over Inner Mongolia

利用1982—2018年内蒙古地区115个气象站点的月降水观测资料评估了FLDAS、ERA5、CRA40/Land和GLDAS 4种陆面同化及再分析降水资料在内蒙古地区的可靠性。结果表明:(1)4种降水资料均能较好地表征降水量在内蒙古地区从东北向西部递减和冬季降水少、夏季降水多的时空变化特征。(2)相关系数、绝对平均误差、均方根误差和纳什效率系数的计算结果表明,4种降水资料在夏季表现最优、冬季表现最差,在东部半湿润区和半干旱区好于西部干旱区和极干旱区。(3)ERA5资料在绝大多数时间对内蒙古的降水有高估现象,GLDAS却对降水存在低估,尤其是在冬季,GLDAS对固态降水几乎没有观测能力,而新发布的FLDAS资料表现较好。总体来看,相对于ERA5和GLDAS,FLDAS和CRA40/Land降水资料与观测值之间的差别最低,有着最优的统计特征。

Monthly precipitation data observed at 115 weather stations in Inner Mongolia during 1982-2018 was compared with land data assimilation and reanalysis precipitation datasets[Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS),the Fifth Generation of ECMWF Reanalysis(ERA5),China Meteorological Administration,Global Land surface Reanalysis(CRA40/Land),and Global Land Data Assimilation System(GLDAS)].The reliability of the four precipitation datasets was analyzed and accuracy of monthly variation was also evaluated.The results showed that the four precipitation datasets captured features of precipitation distribution.Precipitation levels decreased from the northeast to west and were lower in winter and higher in summer.By analyzing correlation coefficients,mean absolute errors,mean bias errors,root mean square errors,and Nash-Sutcliffe efficiency coefficients,the four precipitation datasets showed the best performance in summer and the worst performance in winter,and better values in eastern semi-humid and semiarid areas than western arid and extremely arid areas.Compared with ERA5 and GLDAS,FLDAS,and CRA40/Land showed the lowest differences from observed values and the best statistical characteristics.ERA5 data mostly overestimated precipitation in Inner Mongolia during most of the study period,while GLDAS underestimated it.Particularly in winter,GLDAS showed almost no observation capability for snow precipitation,while the newly released FLDAS data performed better.Overall,FLDAS and CRA40/Land precipitation datasets performed best with better statistical power.