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Proceedings Paper

Assimilation of microwave, infrared, and radio occultation satellite observations with a weather research and forecasting model for heavy rainfall forecasting
Author(s): Pakornpop Boonyuen; Falin Wu; Parwapath Phunthirawuth; Yan Zhao
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Paper Abstract

In this research, satellite observation data were assimilated into Weather Research and Forecasting Model (WRF) by using Three-dimensional Variational Data Assimilation System (3DVAR) to analyze its impacts on heavy rainfall forecasts. The weather case for this research was during 13-18 September 2015. Tropical cyclone VAMCO, forming in South China Sea near with Vietnam, moved on west direction to the Northeast of Thailand. After passed through Vietnam, the tropical cyclone was become to depression and there was heavy rainfall throughout the area of Thailand. Observation data, used in this research, included microwave radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A), infrared radiance observations from Infrared Atmospheric Sounding Interferometer (IASI), and GPS radio occultation (RO) from the COSMIC and CHAMP missions. The experiments were designed in five cases, namely, 1) without data assimilation (CTRL); 2) with only RO data (RO); 3) with only AMSU-A data (AMSUA); 4) with only IASI data (IASI); and 5) with all of RO, AMSU-A and IASI data assimilation (ALL). Then all experiment results would be compared with both NCEP FNL (Final) Operational Global Analysis and the observation data from Thai Meteorological Department weather stations. The experiments result demonstrated that with microwave (AMSU-A), infrared (IASI) and GPS radio occultation (RO) data assimilation can produce the positive impact on analyses and forecast. All of satellite data assimilations have corresponding positive effects in term of temperature and humidity forecasting, and the GPS-RO assimilation produces the best of temperature and humidity forecast biases. The satellite data assimilation has a good impact on temperature and humidity in lower troposphere and vertical distribution that very helpful for heavy rainfall forecast improvement.

Paper Details

Date Published: 19 October 2016
PDF: 10 pages
Proc. SPIE 10001, Remote Sensing of Clouds and the Atmosphere XXI, 100010M (19 October 2016); doi: 10.1117/12.2241786
Show Author Affiliations
Pakornpop Boonyuen, Beihang Univ. (China)
Falin Wu, Beihang Univ. (China)
Parwapath Phunthirawuth, Weather Forecast Bureau (Thailand)
Yan Zhao, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 10001:
Remote Sensing of Clouds and the Atmosphere XXI
Adolfo Comerón; Evgueni I. Kassianov; Klaus Schäfer, Editor(s)

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