Impact of FORMOSAT-3/COSMIC GPS radio occultation and dropwindsonde data on regional model predictions during the 2007 Mei-yu season |
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Authors: | Fang-Ching Chien Ying-Hwa Kuo |
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Institution: | (1) Department of Earth Sciences, National Taiwan Normal University, No. 88, Section 4, Ting-Chou Road, Taipei, 116, Taiwan;(2) Mesoscale and Microscale Meteorology (MMM) Division, National Center for Atmospheric Research, Boulder, CO 80307-3000, USA |
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Abstract: | A study of the impact of FORMOSAT-3/COSMIC GPS radio occultation (RO) and dropwindsonde data on regional model simulations
for a 11-day period during the 2007 Mei-yu season is presented. The Weather Research and Forecasting (WRF) model and its three-dimensional
variation component, WRF-Var, are used for regional model predictions of heavy rainfall events in Taiwan. Without the use
of GPS RO and dropwindsonde data, pressure and relative humidity are, in general, underestimated by the model; temperature
predictions have a warm bias at the low level and a cold bias at the high level; and the east–west and north–south component
winds have positive and negative biases, respectively. Incorporating GPS RO data tends to improve the prediction for longer
integration. The assimilation of dropwindsonde data improves the forecast at the earlier time and at higher levels, and the
improvement decreases over time. The reason the dropwindsonde data produce a positive impact earlier and the GPS RO data later
is that there are few GPS RO observations in the fine domain. The large-scale simulation is first improved using the GPS RO
observations, and the resulting changes can have a positive impact on the mesoscale at the later time. The dropwindsonde observations
were taken inside the fine domain such that their impact can be detected early in the simulation. With both types of observation
included, the prediction shows even greater improvement. At the earlier forecast time, there is nearly no impact from GPS
and dropwindsonde data on rainfall forecasts. However, at the later integration time, the GPS data start to significantly
improve the rainfall forecast. The dropwindsonde data also provide a positive impact on rainfall forecasts, but it is not
as significant as that of the GPS data. |
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