Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

S Federico

doi:10.5194/amt-6-3563-2013

Abstract

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

Highlights

Modern numerical weather prediction (NWP) data assimilation systems use information from a range of sources to provide the best estimate of the atmospheric state at a given time
This paper shows the development of a three-dimensional stand-alone data assimilation system tailored for the Regional Atmospheric Modeling System (RAMS; Cotton et al, 2003; Pielke, 2002)
Similar considerations apply for the meridional wind com- the analyses effectively reduces the error (37 % of the value ponent (Fig. 6b), whose RMSE_f. This (RMSE_f) is ∼ 1.0 m s−1 lower than 35 at the analysis time) after one-hour forecast

Summary

Introduction

Modern numerical weather prediction (NWP) data assimilation systems use information from a range of sources to provide the best estimate of the atmospheric state (i.e. the analysis) at a given time. Federico: Implementation of a 3D-Var system standard atmospheric variables, such as radiances, and they include the imposition of dynamic balance either by the model itself (4D-Var) or through the explicit use of balance equations In recent years, these advantages have fostered the implementation of variational data assimilation systems in limited area models (Zou et al, 1995; Barker et al, 2004; Huang et al, 2009; Zupanski et al, 2005). Even though the incremental formulation of the cost function might not be suitable for convective-scales because of the linearization required in its formulation, recent studies show the applicability of this approach to the convective scale for the WRF (Weather Research and Forecasting) model (Sun et al, 2012; Wang et al, 2013) Another difference between the 3D-Var and RAMDAS is that the former uses the zonal and meridional wind components as control variables while RAMDAS uses the velocity potential and stream function. The paper is divided as follows: Sect. 2 provides details about the data assimilation system; Sect. 3 shows the numerical experiment set-up; Sect. 4 gives the results of the application to the short-term wind forecast; and Sect. 5 gives conclusions

The data assimilation system

Conclusions

Radiosondes

Findings

Wind profilers