Evaluation On The Products Of CMA Land Data Assimiltion System Around Qinghai-Tibeat Plateau

Soil temperature,humidity,and precipitation are important parameters in the study of Land Surface Process Models,they can reflect the situation of surface moisture balance and also be an important indicator to monitor the soil degradation.The Qinghai-Xizang Plateau affects the Asian monsoon and the global atmospheric circulation through a series of atmospheric and hydrological processes directly and indirectly.However,the type of the surface is complex,the environment is harsh,in addition the region has the widest distribution and the largest thickness of permafrost compared with the middle and low latitudes,which lead to the lack of long-term site observations.Currently,the domestic CMA Land Data Assimilation System(CLDAS)can provide real-time high-resolution service,which has a high accuracy.This article uses the observational data of the Qinghai-Xizang Plateau and its surrounding sites and a number of foreign products(GLDAS-NOAH,TRMM3B42)to evaluate the soil temperature,moisture,and precipitation of the first and second editions of CLDAS for 2013-2015,respectively.The results show that:(l)GLDAS-NOAH(Global Land Data Assimilation System(GLDAS))and CLDAS-V1.0(CMA Land Data Assimilation System Version1.0)were better in the four sites of Ando,Nagqu,Nierong and Sta-ave(whose underlying surface is attributed to Kobayashi alpine meadow),while in Bangor(whose underlying surface is attributed to Grassy alpine grassland),Jiali(whose underlying surface is attributed to Subalpine evergreen leaves Shrub)and Biru(whose underlying surface is attributed to Subalpine evergreen leaf shrubs),the quality of the merged data performs less well,and the Ali station(whose underlying surface is attributed to Dwarf shrub desert)is the site with the largest deviation between the merged data and the in-situ soil moisture data.Through the analysis of the variation of the whole day,it is discovered that there is a significant diurnal variation of soil moisture.Specific performance is shown as follows:from 14:00 to 20:00,the merged products are relatively low of the whole day.The quality of the two merged products has the lowest score in the middle and the late of August and mid-October in 2013.By further analysis,it is found that when the precipitation intensity increases sharply,the quality of the two merged data became worse.For the two merged products,the quality in the Qinghai-Xizang Plateau decreases from southeast region to northwest region.The GLDAS-NOAH precipitation data performed best overall,but the merged precipitation data scored high on most sites in eastern Sichuan,and its applicability was the best.(2)The precipitation data of CLDAS-v1.0 and GLDAS-NOAH are consistent with the overall trend of time variation observed by site stations.The precipitation data of TRMM3B42 is the worst in the winter and spring,with the maximum being around January-Febraary,overestimating the entire spring and Precipitation in winter.With increasing altitude,the three types of data are closer to site data.In addition,the three data overestimates the frequency of small precipitation events as a whole,while underestimating the frequency of occurrence of heavy rainfall events.(3)The correlation(both for soil temperature and soil moisture)between the CLDAS-V2.0 and in-situ observations is better than that of GLDAS-NOAH.The correlation between the land model products(both for CLDAS-V2.0 and GLDAS-NOAH)and in-situ observations in wet season is larger than that in dry season,and shows a decreasing trend with soil depth.The deviation of soil moisture between the CLDAS-V2.0 and in-situ observations is slightly larger than that of GLDAS-NOAH.In addition,in shallow soil layers,the mean relative error(MRE)of soil moisture between the model products(both for CLDAS-V2.0 and GLDAS-NOAH)and in-situ observations during dry season is larger than that of the wet season.The root mean square error(RMSE)between CLDAS-V2.0 soil temperature product and in-situ observations in wet season is larger than that of the dry season,while this inclination of RMSE is opposite for GLDAS-NOAH temperature product during the wet and dry season.However,the two model products show a lager fluctuation in deep soil layers relative to in-situ observations both in soil temperature and soil moisture.At last,the two model products(CLDAS-V2.0 and GLDAS-NOAH)can not reproduce the significantly "lag" features that the observed soil temperature and soil moisture show a delayed change with the soil depth.And the daily Peak/valley values' appear time of the soil temperature and soil moisture will show a "lag" feature after the precipitation happened which the two model products can not describe it.