Intercomparison of the impact of INSAT-3D atmospheric motion vectors in 3DVAR and hybrid ensemble-3DVAR data assimilation systems during Indian summer monsoon

Rekha Bharali Gogoi,Arup Boroghain,Govindan Kutty

doi:10.1007/s00704-021-03649-2

Abstract

The impact of observations in a data assimilation (DA) may depend on various factors, and one aspect that can affect the impact is the specification of the background error covariance matrix. The present study compares the impact of INSAT-3D atmospheric motion vector (AMV) observations in traditional three-dimensional variational (3DVAR) DA system and hybrid ensemble transform Kalman filter (ETKF)-3DVAR DA system (HYBRID) available in Weather Research and Forecast (WRF) modeling system. The objective of the study is to understand how the impact of INSAT-3D AMV observations differ when assimilated using 3DVAR and HYBRID DA systems. The DA experiments are conducted over a ~4-week period of Indian summer monsoon rainfall of July 2016. Four sets of experiments are performed with and without INSAT-3D AMV in both the DA systems. The domain-wide verification with respect to radiosonde observations reveals that forecasts in HYBRID experiments are more accurate than 3DVAR experiments, in general. Geographical distribution depicts the positive impacts of INSAT-3D AMV observations across the domain in both 3DVAR and HYBRID DA systems. The AMV observations show a larger relative impact in HYBRID than in 3DVAR. The relative improvement in HYBRID with AMV DA compared to 3DVAR is 77% and 71% for wind and tropical temperature. The skill scores for quantitative evaluation of precipitation forecast indicate a modest improvement in rainfall for HYBRID run, and incorporating the AMV observation does not considerably enhance the skill of 24-h and 48-h rainfall forecast.

Highlights

The importance of early warnings from the Numerical Weather Prediction (NWP) model has increased greatly in the last few decades as it plays an important role in mitigating the damages due to natural disasters like floods, thunderstorms, heavy rains, tropical cyclones, etc
The data assimilation (DA) cycling experiments are performed for the ~4 week period of July 2016 and a 48 h model forecast is generated from each analysis
The results indicate that 3DVAR analysis fits more closely with the observations than HYBRID analysis

Summary

Introduction

The importance of early warnings from the Numerical Weather Prediction (NWP) model has increased greatly in the last few decades as it plays an important role in mitigating the damages due to natural disasters like floods, thunderstorms, heavy rains, tropical cyclones, etc. Data assimilation (DA) is a scientific method obtaining a very precise initial state of combining the background forecast and the observations (Daley 1991). Previous studies have shown that in the presence of flow evolving BEC in HYBRID DA system the observations are effectively assimilated as compared to traditional 3DVAR DA systems, and it is expected that the impact of INSAT-3D AMV observations may vary in different DA systems. The present study attempts to quantify the impact of assimilation of INSAT-3D AMV using the advanced HYBRID DA system using a ~ 4 weeks period of July 2016 using a limited area model. The specific objective of the study is to understand how different or similar the impact of INSAT-3D AMV observations by 3DVAR as compared to that assimilated by HYBRID DA system.

Objectives

Results

Conclusion