(Tenki is the bulletin journal of the Meteorological Society of Japan in Japanese.)
TENKI, Vol. 57, No. 1, pp. 5-17, 2010
Sadao SAITOH* and Akira SHIBATA**
* Japan Aerospace Exploration Agency (Present affiliation: Japan Meteorological Agency, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan).
** Meteorological Research Institute (Present affiliation: Japan Aerospace Exploration Agency).
(Received 3 March 2009; Accepted 14 October 2009)
All weather sea surface wind (AWSSW) is estimated from AMSR-E 6.925 and 10.65 GHz horizontal brightness temperature on the earth observation satellite Aqua. AWSSW can estimate wind speed even in rainy condition. >From this research, it is revealed that AWSSW can estimate wind storm around typhoon and that its operational use can be expected.
(Tenki is the bulletin journal of the Meteorological Society of Japan in Japanese.)
TENKI, Vol. 57, No. 5, pp. 289-303, 2010
This study analyzed the long-term changes in the frequency of heavy snow events, which are defined for three time scales (1-, 3-, 7-days) and three threshold values (2-, 5-, 10-year return periods), and their relation to winter (from December to February) temperature at the surface and in upper levels, in and around Niigata prefecture. This analysis is based on daily snowfall data observed at 13 railway stations from 1960/1961 winter to 2008/2009 winter.
The frequency of 1-, 3-, and 7- days snowfall events over the 2-, 5-, 10-year return periods was high from 1960/1961 winter to 1985/1986 winter and low after 1986/1987 winter. For the frequency of 1-, 3-, and 7- days snowfall events over the 2-year return periods, a discontinuous change between 1985/1986 winter and 1986/1987 winter is detected. This frequency shows a negative correlation with the winter mean temperature at surface, and becomes lower (higher) as winter mean temperature increases (decreases). Although it shows a negative correlation with winter mean temperature below the 350hPa level, the correlation becomes weaker with height. Furthermore, it decreases (increases) by 0.4-0.5 times/year, when winter mean temperature at Niigata increases (decreases) one degree.
(Tenki is the bulletin journal of the Meteorological Society of Japan in Japanese.)
TENKI, Vol. 57, No. 7, pp. 449-462, 2010
The accuracy of extreme value analysis in estimating the return periods of record highest rainfalls was examined with a focus on the influence of sample variability. A series of Monte Carlo simulations was applied to an annual maximum value (AMV) analysis based on the generalized extreme value distribution (GEV) and a peaks over threshold (POT) analysis based on the generalized Pareto distribution (GPD), by generating sample data corresponding to these distributions. It was found that the shape parameter κ had a key role controlling the accuracy of estimation, which was improved by using the average value of κ over stations, rather than calculating the κ value for each station, unless the true value of κ was not too variable among stations. On the other hand, no significant difference was found in the accuracy of AMV analysis and POT analysis. Based on these results, return periods of record highest rainfalls were calculated using three kinds of climatic data in Japan, with attention to differences according to methods and data sources.