Abstract
There is a large amount of documented weather information all over the world, including Asia (e.g., old diaries, log books, etc.). The ultimate goal of this study is to reconstruct historical weather by deriving total cloud cover (TCC) from historically documented weather records and to assimilate them using a general circulation model and a data assimilation scheme. Two experiments are performed using the Global Spectral Model and an ensemble Kalman filter: 1) a reanalysis data experiment and 2) a ground observation data experiment, for 18 synthesized observation stations in Japan according to the Historical Weather Data Base. By assuming that weather records can be converted into three TCC categories, the synthetic observation data of daily TCC are created from reanalysis data, with a large observation error of 30%, and by classifying ground observation data into the three categories. Compared with the simulation without assimilation of any observation, the results of the reanalysis data experiment show improvements, not only in TCC but also in other meteorological variables (e.g., humidity, precipitation, precipitable water, wind, and pressure). For specific humidity at 2 m above the surface, the monthly averaged root-mean-square error is reduced by 18%–22% downstream of the assimilated region. The results of the ground observation data experiment are not as successful as a result of additional error sources, indicating the bias needs to be handled correctly. By showing improvements with the loosely classified cloud information, the feasibility of the developed model to be applied for historical weather reconstruction is confirmed.
Current affiliation: Department of Civil and Environmental Engineering, University of California, Davis, Davis, California.
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