Study On Data Fusion Of Multi-source Precipitable Water Vapor Base On Air And Ground Monitoring

Precipitable water vapor is among the key variables in the water and energy cycle,,and also a vital factor affecting the effects of surface radiation as a measure of water vapor content.It directly affects the vertical stability of the atmosphere,and it has significant effect in the study of climate change,precipitation forecasts,and atmospheric corrections.At present,the current PWV products are restricted by observation methods and inversion methods,which caused the restriction of temporal and spatial discontinuities,low precision,and rough spatial resolution.However,there is complementarity between the different data of air-ground monitoring.Data fusion technology can be used to generate a data set of PWV with temporal and spatial continuity and high precision,without changing the original observation conditions and inversion methods,which makes analyzing the temporal and spatial distribution characteristics of atmospheric precipitation easier,and it provides a data basis for artificial precipitation,disaster prevention and mitigation,and climate change research.Qinghai Province is located in a plateau climate-sensitive area with rich and complex topography.the climate change of Qinghai has significant impact on the climate change of its downstream and even the whole country.Due to the limitation of the geographical features of the area with high altitude and extremely cold,the ground stations are sparse and the observation data is far less than the east coast distraction.Therefore,one single data source cannot effectively analyze the temporal and spatial distribution characteristics of the local precipitable amount in a large range of time and space.This article selects Qinghai Province as the study area,based on multi-source air-to-ground water vapor monitoring data,as two widely used global PWV products(satellite-based MODIS data and reanalysis-based ERA5 data)and ground station data(GNSS data and Radiosonde data),apply the spatiotemporal fusion model ESTARFM and linear regression correction method to fusion and correct the precipitation data of Qinghai Province from 2001 to 2018,and use the fused data to study the temporal and spatial distribution characteristics of the precipitation in Qinghai Province.The results showed that:1.Evaluating reanalysis-based ERA5 data,satellite-based MODIS data,GNSS data with radiosonde data,the precision sequence from high to low is ground-based GNSS data,reanalysis-based ERA5 data,MODIS satellite data.The GNSS precipitation data has high accuracy,the mean square root error is below 3mm,the average relative error is 1.52?2.76 mm,and the correlation is about 0.9.The ERA5 data is overestimated in Qinghai Province,with an average deviation of 11% and mean square root error between 4.2 to 4.96 mm.The accuracy of MODIS data is low in Qinghai Province,which is underestimated at least 30%.The accuracy of each data set shows a high consistency over time,it all appears to be high in summer and autumn,low in winter and spring,and the error increases as the amount of precipitation rises.2.The constructed ESTARFM model has a good spatial fusion effect,which improves the spatial resolution,and also upgrades the accuracy of the MODIS precipitable product with merging the characteristics of different data.The high spatial resolution fusion data image provides detailed and reasonable spatial changes,which are usually consistent with the spatial changes estimated by the ERA5 reanalysis data before fusion.Although the accuracy of time fusion and radiosonde data is low,the trend is basically the same.3.Analyzing the temporal and spatial changes of the precipitation in Qinghai Province,we found that during the study period,the average annual precipitation in Qinghai Province was 1807.74 mm,and the inter-annual change rate was 18.235mm/a.The overall trend was upward fluctuations,and the change rate increase rapid after 2009,the annual average precipitation has increased obviously.The range of seasonal average precipitation is: summer>spring>autumn>winter,which also shows an increasing trend year by year,and the highest increase rate in summer is 8.817mm/a.The spatial distribution is majorly affected by the topography,showing a trend of decreasing with the increase in altitude.