A Comparison of GPS- and NWP-derived PW Data over the Korean Peninsula |
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Authors: | Ha-Taek KWON Eui-Hyun JUNG and Gyu-Ho LIM |
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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 |
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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. |
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Keywords: | GPS precipitable water numerical weather prediction model dry bias |
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