Establishment and analysis of global gridded Tm-Ts relationship model

In ground-based GPS meteorology, Tm is a key parameter to calculate the conversion factor that can convert the zenith wet delay（ZWD） to precipitable water vapor（PWV）. It is generally acknowledged that Tm is in an approximate linear relationship with surface temperature Ts, and the relationship presents regional variation. This paper employed sliding average method to calculate correlation coefficients and linear regression coefficients between Tm and Ts at every 2°× 2.5° grid point using Ts data from European Centre for Medium-Range Weather Forecasts（ECMWF） and Tm data from ＂GGOS Atmosphere＂, yielding the grid and bilinear interpolation-based Tm Grid model. Tested by Tm and Ts grid data, Constellation Observation System of Meteorology, Ionosphere, and Climate（COSMIC） data and radiosonde data, the Tm Grid model shows a higher accuracy relative to the Bevis Tm-Ts relationship which is widely used nowadays. The Tm Grid model will be of certain practical value in high-precision PWV calculation.

Application and Study of Precipitation Schemes in Weather Simulation in Summer and Winter over China

Through simulation of summer and winter precipitation cases in China, the cloud precipitation schemes of model were examined. Results indicate that it is discrepant between convective precipitation simulated by the Kain-Fritsch （KF） scheme and Betts-Miller （BM） scheme in summer, the former scheme is better than the latter in this case. The ambient atmosphere may be varied by different convective schemes. The air is wetter and the updraft is stronger in the KF scheme than in the BM scheme, which can induce the more grid scale precipitation in the KF scheme, i.e., the different cumulus schemes may have the different and important effect on the grid scale precipitation. However, there is almost no convective rain in winter in northern China, so the effect of cumulus precipitation on the grid scale precipitation can be disregarded. Therefore, the gird scale precipitation is primary in the winter of northern China.

How is the precipitation distributed vertically in arid mountain region of Northwest China?

Precipitation in the arid region of Northwest China(NWC)shows high spatial and temporal variability,in large part because of the region’s complex topography and moisture conditions.However,rain gauges in the area are sparse,and most are located at altitudes below 2000 m,which limits our understanding of precipitation at higher altitudes.Interpolated precipitation products and satellite-based datasets with high spatiotemporal resolution can potentially be a substitute for rain gauge data.In this study,the spatial and temporal properties of precipitation in the arid region of NWC were analyzed using two gridded precipitation products:SURF_CLI_CHN_PRE_DAY_GRID_0.5(CHN)and Tropical Rainfall Measuring Mission(TRMM)3 B43.The CHN and TRMM 3 B43 data showed that in summer,precipitation was more concentrated in southern Xinjiang than in northern Xinjiang,and the opposite was true in winter.The largest difference in precipitation between mountainous areas and plains appeared in summer.High-elevation areas with high precipitation showed more stable annual precipitation.Different sub-regions showed distinctive precipitation distributions with elevation,and both datasets showed that the maximum precipitation zone appeared at high altitude.

Evaluation of the ECMWF Precipitation Product over Various Regions of Iran

Easy access to accurate and reliable climate data is a crucial concern in hydrological modeling.In this regard,gridded climate data have recently been provided as an alternative to observational data.However,those data should be first evaluated and corrected to guarantee their validity and accuracy.This study offered a new approach to assess the ECMWF gridded precipitation data based on some indicators,including correlation coefficient(CC),normalized root-mean-square error(NRMSE),and absolute error(AE)in daily and monthly intervals(2007-2017)across different climatic and geographical areas of Iran.Besides,an artificial neural network(ANN)model was utilized to correct the ECMWF precipitation product.According to the results,NRMSE was less than 2(in 93%of stations)and 5(in63%of stations)on monthly and daily scales,respectively.Moreover,CC was above 0.6 in 58%and 94%of stations on daily and monthly scales,respectively.The AE values were from-0.5 to 0.5,in 80%(daily scale)and 50%(monthly scale)of stations.Having corrected the ECMWF precipitation product by ANN,the number of stations with NRMSE less than 5 increased from 63%to 74%on the daily timescale,whereas the number of stations with NRMSE less than 2 reached 95%from 93%on the monthly timescale.The results also showed that the number of stations with CC more than 0.6 increased from 58%to 87%on the daily timescale.