1998 Fiscal Year Final Research Report Summary
Studies on Method of Analysis for Regional Meteorology using Ground data and Satellite data
| Project/Area Number |
08456130
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
生物環境
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| Research Institution | HOKKAIDO UNIVERSITY |
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Principal Investigator |
HORIGUCHI Ikuo Faculty of Agr., Hokkaido Univ., Pro., 農学部, 教授 (10001439)
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| Co-Investigator(Kenkyū-buntansha) |
O Shuho Faculty of Agr., Hokkaido Univ., Inst., 農学部, 助手 (30301873)
MACHIMURA Takasi Faculty of Agr., Hokkaido Univ., Inst., 農学部, 助手 (30190383)
URANO Shinichi Faculty of Agr., Hokkaido Univ., Pro., 農学部, 教授 (40096780)
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| Project Period (FY) |
1996 – 1998
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| Keywords | Satellite Data / Regional Meteorology / Surface Temperature / Air Temperature Distribution / Wind Speed Distribution / GMS Data / NOAA Data / AMeDAS Data |
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| Research Abstract |
The studies were performed for the 2 items using satellite data and ground data ; the distributions of air temperature and the distributions of wind speed.
The distributions of air temperature in Liaoning Province, China were estimated using GMS data and the accuracy of air temperature were compared with our published data. The results are that the correlation coefficients between surface temperature derived from satellite and air temperature, and RMSE(Root Mean Square Error) are better at morning time and at night time than at day time. Especially, RMSE at 6-9 AM have the best.
Also, air temperature and daily mean air temperature were estimated using surface temperature measured on the ground and these accuracy were compared with our published data. The results are that the correlation coefficients between surface temperature measured on the ground and daily mean air temperature, and RMSE are better at morning time(6 AM) and at evening time(6 PM).
The measured wind speed in Toubetu and Shinsinotu, Hokkaido were compared with surface temperature derived from NOAA data. The result is that the correlation coefficients between surface temperature derived from satellite data and measured wind speed are better in May and June when measured wind speed are the strongest.
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