Feasibility Study of the Reconstruction of Historical Weather with Data Assimilation

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.

Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

“…Gray and red lines are the ensemble members and their mean, respectively. data integration in socio-hydrology mainly focused on parameter calibration and assumed no uncertainty in the input forcing (e.g., Barendrecht et al, 2019;Roobavannan et al, 2017;Ciullo et al, 2017;van Emmerik et al, 2014;Gonzales and Ajami, 2017). To deeply understand the sociohydrological processes, long-term historical analysis should be performed.…”

“…To deeply understand the sociohydrological processes, long-term historical analysis should be performed. Although there are many studies on the accu-rate reconstruction of the historical weather conditions (e.g., Toride et al, 2017), it may be necessary to tackle the uncertainty in hydrometeorological data sets used for the input forcing of the socio-hydrological models.…”

“…Socio-hydrology is an emerging research field in which twoway feedback between social and water systems is investigated (Sivapalan et al, 2012(Sivapalan et al, , 2014. Understanding complex socio-hydrological phenomena contributes to solving water crises around the world.…”

“…The other limitation of the data-driven approach is that the quantification of the causal relationship cannot be easily done by empirical data only (e.g., Dang and Konar, 2018). Considering the advantages and disadvantages of model and data, previous studies used social statistics to calibrate and validate their socio-hydrological models (e.g., Barendrecht et al, 2019;Roobavannan et al, 2017;Ciullo et al, 2017;van Emmerik et al, 2014;Gonzales and Ajami, 2017).…”

Socio-hydrologic data assimilation: Analyzing human-flood interactions by model-data integration

“…Although there are many studies on the accurate reconstruction of the historical weather condition (e.g., Toride et al 2017), it may be necessary to tackle with the uncertainty in hydrometeorological datasets used for the input forcing of the socio-hydrologic models.…”