Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction

RUIZ, JUAN; LIEN, GUO-YUAN; KONDO, KEIICHI; OTSUKA, SHIGENORI; MIYOSHI, TAKEMASA

Nonlinear Processes in Geophysics

European Geophysical Union

Año: 2021 vol. 28 p. 615 - 626

Non-Gaussian forecast error is a challenge forensemble-based data assimilation (DA), particularly for morenonlinear convective dynamics. In this study, we investigate the degree of the non-Gaussianity of forecast error distributions at 1 km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with theSCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (local ensemble transformKalman filter) assimilating phased array radar observationsevery 30 s. The results show that non-Gaussianity developsrapidly within convective clouds and is sensitive to the DAfrequency and the number of assimilated observations. Thenon-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 min to 30 s, particularly forvertical velocity and radar reflectivity