Development of a Pressure–Precipitation Transmitter

Abstract

A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

Insurance Loss Dependence on Typhoon Maximum Wind Speed, Translation Speed and Size over Japan

“…Risk management in disaster insurance is generally conducted using naturalcatastrophe risk-analysis models (CAT models; Foote et al 2017), which are stochastic models based on weather generators (Wilby et al 2004;Ailliot et al 2015;Inatsu et al 2019). The typhoon model is a CAT model generating hundreds of thousands of hypothetical typhoons over an approximately 10,000-year long period based on observations, which are superimposed on the distribution and vulnerability of exposure assets to estimate the insurance losses.…”