Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters

Sung-Min Kim,Hyun Mee Kim

doi:10.1080/16000870.2018.1492839

Abstract

In this study, the forecast sensitivity to error covariance parameters was calculated using the forecast sensitivity to observations (FSO) and was employed to adjust the observation error variance in the Korea Meteorological Administration (KMA) Unified Model (UM) four-dimensional variational (4DVAR) system. The error covariance adjustment parameters were estimated by applying a multiple linear regression method to the forecast error reduction (FER) and forecast sensitivity to error covariance parameters in July and August 2012. The adjustment parameters were applied for numerical weather prediction (NWP) in August 2012 using the KMA UM 4DVAR system to validate the adjusted observation error variance in the operational NWP. The results indicated that most observation error variances should be decreased to reduce the forecast error. By decreasing the observation error variance of Advanced Television and Infrared Observational Satellite Operational Vertical Sounder (ATOVS) data by 72.14% for the NWP of August 2012, the residual within the assimilation window (O-A) decreased by 10.62% and that in the 24–29 h forecast range (O-F) decreased by 5.4%; therefore, the analysis and forecast results verified with radiosonde, surface, and satellite observations showed more similar values to all those observations. The upper atmospheric O-F was reduced by approximately 2–3.5% verified by Advanced Microwave Sounding Unit-A, and 21.7% verified by Infrared Atmospheric Sounding Interferometer (IASI) and Atmospheric Infrared Sounder (AIRS) sensors. Therefore, the adjustment of ATOVS observation error variance using the forecast sensitivity to error covariance parameters was effective for reducing the forecast error in the KMA UM 4DVAR system.

Highlights

IntroductionData assimilation (DA) has been developed for complex systems, such as the variational data assimilation (DA), to assimilate the surface and atmospheric observations
In recent decades, data assimilation (DA) has been developed for complex systems, such as the variational DA, to assimilate the surface and atmospheric observations
The soi – sensitivities associated with SONDE and surface-specific humidity (i.e. SONDE_q and SURFACE_q) are relatively small, which may be attributable to the dry energy norm used to calculate de

Summary

Introduction

Data assimilation (DA) has been developed for complex systems, such as the variational DA, to assimilate the surface and atmospheric observations. The contribution of each observation to forecasts (i.e. observation impact) can be quantitatively estimated within a very short computation time using the adjoint-based forecast sensitivity to observation (FSO) method (Baker and Daley, 2000). The observation impact using the FSO has been used semi-operationally to determine observation types, locations, and variables that are beneficial or detrimental to forecasts (Langland and Baker, 2004; Cardinali, 2009; Gelaro and Zhu, 2009; Gelaro et al, 2010; Joo et al, 2013; Jung et al, 2013; Kim et al, 2013; Kim and Kim, 2013; Kim and Kim, 2014; Lorenc and Marriott, 2014; Kim et al, 2017; Kim and Kim, 2017)

Objectives

Results

Conclusion