Radiative cooling scale method for correcting hourly surface air temperature error in numerical weather prediction models

Abstract

AbstractGlobally, a variety of numerical weather prediction (NWP) models are employed by national agencies. However, the derived outputs from these models are generally inadequate for precise cultivation management due to bias error. This study uses observational data acquired over several months to develop an equation for correcting surface air temperature (SAT) error in NWP model outputs. With the proposed method, model outputs are converted into potential temperature to cancel the influence of differences between computational elevation and real elevation. The potential temperature errors in the NWP model outputs are corrected using a linear regression equation in which a radiative cooling scale (RCS) serves as the independent variable. The RCS is computed using air temperature observed at a nearby meteorological observation station, together with upper‐level pressure data from the NWP model. Versions of the correction models are developed for specific situations: day, night, and upper‐level meteorology, classified into six groups. In an application of the proposed method, the root mean square error (RMSE) of the corrected SAT output from a global spectral model operated by the Japan Meteorological Agency (JMA) was 1.2–1.6 K; the RMSE of the corrected SAT output from a nonhydrostatic model operated by the JMA was 1.2–1.5 K. The proposed method corrected the NWP model SAT output errors to a level of accuracy similar to that of data corrected using the Kalman filtering technique without the requirement for constant observation at the target site.