Comparison between 3D-Var and 4D-Var data assimilation methods for the simulation of a heavy rainfall case in central Italy

Vincenzo Mazzarella,Giorgio Budillon,Rossella Ferretti,Ida Maiello,Vincenzo Capozzi

doi:10.5194/asr-14-271-2017

Abstract

Abstract. This work aims to provide a comparison between three dimensional and four dimensional variational data assimilation methods (3D-Var and 4D-Var) for a heavy rainfall case in central Italy. To evaluate the impact of the assimilation of reflectivity and radial velocity acquired from Monte Midia Doppler radar into the Weather Research Forecasting (WRF) model, the quantitative precipitation forecast (QPF) is used.The two methods are compared for a heavy rainfall event that occurred in central Italy on 14 September 2012 during the first Special Observation Period (SOP1) of the HyMeX (HYdrological cycle in Mediterranean EXperiment) campaign. This event, characterized by a deep low pressure system over the Tyrrhenian Sea, produced flash floods over the Marche and Abruzzo regions, where rainfall maxima reached more than 150 mm 24 h−1.To identify the best QPF, nine experiments are performed using 3D-Var and 4D-Var data assimilation techniques. All simulations are compared in terms of rainfall forecast and precipitation measured by the gauges through three statistical indicators: probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR). The assimilation of conventional observations with 4D-Var method improves the QPF compared to 3D-Var. In addition, the use of radar measurements in 4D-Var simulations enhances the performances of statistical scores for higher rainfall thresholds.

Highlights

The aim of the modern data assimilation systems is to provide the best estimate of the initial conditions, a requirement for an accurate weather forecast, through the use of huge amount of data acquired in situ or by remote-sensing
The results suggested that the assimilation of radial velocity and reflectivity with 4D-Var system improved the quantitative precipitation forecast (QPF) skills for a short range forecast
The observed rainfall peaks are the result of intense convective phenomena, occurred over a restricted area and in a very short time lapse, for which Weather Research Forecasting (WRF) model fails in capturing the small-scale rainfall variability and the effect of data assimilation is not ideal (Liu et al, 2013)

Summary

Introduction

The aim of the modern data assimilation systems is to provide the best estimate of the initial conditions, a requirement for an accurate weather forecast, through the use of huge amount of data acquired in situ or by remote-sensing. The classical assimilation schemes, such as optimum interpolation (OI) or successive correction method (SCM), have been replaced by modern techniques with variational approach, i.e. three dimensional and four-dimensional variational data assimilation methods (3D-Var and 4D-Var). Both methods are implemented in the Weather Research Forecasting (WRF) model (Skamarock et al, 2008). The use of tangent linear and adjoint models (Errico, 1997; Errico et al, 1993; Erico and Reader, 1999) produces the propagation of analysis increment over the assimilation window and greater computational resources compared to 3D-Var are needed. The WRF 4D-Var system uses physical and dynamical constraints to enhance the balance of analysis field producing initial conditions that contain convective-scale balance, these constrains are not used

Objectives

Results

Conclusion