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A Comparison of GPS- and NWP-derived PW Data over the Korean Peninsula
Authors:Ha-Taek KWON  Eui-Hyun JUNG and Gyu-Ho LIM
Institution:School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--172, Korea
Abstract:Precipitable Water (PW) derived from Global Positioning System (GPS) measurements and numerical weather prediction (NWP) model analysis data were compared to further evaluate the efficacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.
Keywords:GPS  precipitable water  numerical weather prediction model  dry bias
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