WRF‐based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis

Xun Wang,Vanessa Tolksdorf,Dieter Scherer,Marco Otto

doi:10.1002/joc.6686

Abstract

AbstractThe High Asia Refined analysis (HAR) is a regional atmospheric data set generated by dynamical downscaling of the Final operational global analysis (FNL) using the Weather Research and Forecasting (WRF) model. It has been successfully and widely utilized. A new version (HAR v2) with longer temporal coverage and extended domains is currently under development. ERA5 reanalysis data is used as forcing data. This study aims to find the optimal set‐up for the production of the HAR v2 to provide similar or even better accuracy as the HAR. First, we conducted a sensitivity study, in which different cumulus, microphysics, planetary boundary layer, and land surface model schemes were compared and validated against in situ observations. The technique for order preference by similarity to the ideal solution (TOPSIS) method was applied to identify the best schemes. Snow depth in ERA5 is overestimated in High Mountain Asia (HMA) and causes a cold bias in the WRF output. Therefore, we used Japanese 55‐year Reanalysis (JRA‐55) to correct snow depth initialized from ERA5 based on the linear scaling approach. After applying the best schemes identified by the TOPSIS method and correcting the initial snow depth, the model performance improves. Finally, we applied the improved set‐up for the HAR v2 and computed a one‐year run for 2011. Compared to the HAR, the HAR v2 has a better representation of air temperature at 2 m. It produces slightly higher precipitation amounts, but the spatial distribution of seasonal mean precipitation is closer to observations.

Highlights

High Mountain Asia (HMA) is a geographic region that includes the Tibetan Plateau (TP) and its surrounding mountain ranges, such as the Himalayas, the Karakoram, the Tian Shan, and so on
The physical parameterization schemes (PPSs) used in the High Asia Refined analysis (HAR) might not be suitable for the HAR v2, and the first objective of the current study is to investigate the sensitivity of simulated total precipitation (Prcp) and air temperature at 2 m above ground (T2) to different cumulus (CU), microphysics (MP), planetary boundary layer (PBL) and land surface model (LSM) schemes
We propose a bias correction method for initial snow depth and snow water equivalent based on the concept of linear scaling approach

Summary

| INTRODUCTION

High Mountain Asia (HMA) is a geographic region that includes the Tibetan Plateau (TP) and its surrounding mountain ranges, such as the Himalayas, the Karakoram, the Tian Shan, and so on. The High Asia Refined analysis (HAR, Maussion et al, 2011; 2014) is a regional atmospheric data set generated by dynamical downscaling using the Weather Research and Forecasting (WRF) model version 3.3.1 (Skamarock and Klemp, 2008) as RCM. The HAR provides detailed and accurate gridded climate data for HMA region It has been comprehensively analysed, especially in terms of precipitation and atmospheric water transport (Maussion et al, 2014; Curio et al, 2015; Pritchard et al, 2019; Li et al, 2020) and has been successfully applied in many research fields, such as glacier mass balance modelling (Mölg et al, 2014), snow and energy balance modelling (Huintjes et al, 2015), and so forth. Final objective is to apply the best PPSs identified by the TOPSIS method and the snow correction approach as the final set-up for the HAR v2, and to compare the two versions of the HAR

| METHODOLOGY

| RESULTS

| DISCUSSION

Findings

| CONCLUSIONS