A Linear Regression of Differential PWV Calibration Model to Improve the Accuracy of MODIS NIR All-Weather PWV Products Based on Ground-Based GPS PWV Data

Abstract

Precipitable water vapor (PWV) products derived from the near-infrared (NIR) channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, onboard Aqua and Terra satellites, were calibrated using a linear differential PWV (LinearDP) calibration model based on GPS-retrieved PWV observations. All MODIS NIR PWV pixels were classified into two groups according to the cloud mask of each pixel. For each group, MODIS NIR PWV products and ground-based PWV data from 453 GPS sites in Australia from January 2017 to December 2018 were utilized to determine the differential PWV by subtracting GPS PWV from MODIS PWV. Then, empirical regression relationship between the differential PWV data and the MODIS PWV products was developed using a linear regression approach. The LinearDP model coefficients were independently obtained from each month for each group. The period for model validation spans from January to December in 2019. Comparison of calibrated MODIS NIR PWV versus GPS-derived PWV over Australia showed that the root-mean-square error (RMSE) of Aqua has reduced 42.61% for clear group, 41.43% for cloudy group, and 41.45% for both clear and cloudy groups; and has respectively reduced 53.76%, 37.03%, and 39.33% for Terra. By comparing against ERA5 PWV data, the RMSE reduced 37.21%–43.14% for Aqua and 38.73%–53.87% for Terra. The improvement of MODIS NIR PWV products is further validated in China, with an RMSE reduction of 24.53%–31.78% for Aqua and 28.26%–38.69% for Terra against reference PWV from 214 GPS stations. The mean bias was reduced to −0.415–0.752 mm in Australia and to −0.382–2.013 mm in China.

Keywords

• GPS
• lineardp
• moderate resolution imaging spectroradiometer (MODIS)
• near-infrared
• precipitable water vapor (PWV)