A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

Xuguang Wang,Govindan Kutty

doi:10.1155/2015/280546

Abstract

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.

Highlights

Modern data assimilation systems in various numerical weather forecast centers assimilate millions of observations each day from in situ and remote sensing platforms
For a given set of observations, how different or similar is the impact of the data assimilated by Gridpoint Statistical Interpolation (GSI) 3DVar compared to that assimilated by GSI 3DEnsVar? For a given data assimilation (DA) method, how different or similar is the impact of Radiosonde relative to Advanced Microwave Sounding Unit (AMSU) observations? How is the relative difference of impacts between Radiosonde and AMSU dependent on the DA method? Apart from these, the present study identifies shortcomings of GSI based 3DVar in optimal utilization of observations as compared to 3DEnsVar DA system
The root-mean-square error (RMSE) for 72-hour forecast lead time is verified with respect to the European Centre for Medium Range Weather Forecasting (ECMWF) analysis

Summary

Introduction

Modern data assimilation systems in various numerical weather forecast centers assimilate millions of observations each day from in situ and remote sensing platforms. Beginning in May 22, 2012, the NCEP operational Global Data Assimilation System (GDAS) has transitioned from 3DVar to a 3DVar based ensemble-variational (3DEnsVar) hybrid data assimilation system (e.g., [30, 31]) In this system, the flow dependent background error covariance from an EnKF [26] is incorporated in the GSI variational minimization using the extended control variable method [28]. The current study seeks to answer such question by comparing the impact of observations assimilated by the GSI based 3DVar and 3DEnsVar. The OSE was conducted to evaluate the impact of observations on forecasts out to 5day lead times. The present study compares the impacts of the observations from these two observing platforms assimilated by GSI based 3DVar and 3DEnsVar on NCEP Global Forecast System (GFS).

The Data Assimilation Systems

Experimental Design

Results

Conclusion and Discussion