Impact of Gaussian Transformation on Cloud Cover Data Assimilation for
Historical Weather Reconstruction
Abstract
Old descriptive diaries are important sources of daily weather
conditions before modern instrumental measurements became available. A
previous study demonstrated the potential of reconstructing historical
weather at high temporal resolution by assimilating cloud cover
converted from descriptive diaries. However, cloud cover often exhibits
a non-Gaussian distribution, which violates a basic assumption of most
data assimilation schemes. In this study, we applied a Gaussian
transformation (GT) approach for cloud cover data assimilation and
conducted observing system simulation experiments (OSSEs) using 15
randomly selected observation points over Japan. We performed
experiments to assimilate cloud cover with large observational errors
using the Global Spectral Model (GSM) and a local ensemble transform
Kalman filter (LETKF). Without GT, temperature and zonal and meridional
wind exhibited deterioration compared to the experiment assimilating no
observations. By contrast, the 2-month root mean square error (RMSE) of
zonal wind, meridional wind, temperature, and specific humidity at
mid-troposphere were improved by 8.7%, 5.1%, 4.2%, and 1.4%,
respectively, through GT. Among two-dimensional variables, the 2-month
RMSE of total cloud cover, surface pressure, rainfall, and downward
solar radiation were improved by 2.2%, 5.2%, 27.6%, and 4.3%,
respectively. We further demonstrated that the effect of GT was more
pronounced on clear days. Our results show the potential of GT in
high-resolution historical weather reconstruction using old descriptive
diaries.