Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale numerical weather prediction analyses and forecasts of heavy precipitation events

Mary Borderies,Olivier Caumont,Nadia Fourrié,Julien Delanoë,Pascal Marquet,Véronique Ducrocq

doi:10.5194/nhess-19-907-2019

Abstract

Abstract. This article investigates the potential of W-band radar reflectivity to improve the quality of analyses and forecasts of heavy precipitation events in the Mediterranean area. The “1D+3DVar” assimilation method, operationally employed to assimilate ground-based precipitation radar data in the Météo-France kilometre-scale numerical weather prediction (NWP) model AROME, has been adapted to assimilate the W-band reflectivity measured by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere) during a 2-month period over the Mediterranean area. After applying a bias correction, vertical profiles of relative humidity are first derived via a 1-D Bayesian retrieval, and then used as relative humidity pseudo-observations in the 3DVar assimilation system of AROME. The efficiency of the 1-D Bayesian method in retrieving humidity fields is assessed using independent in-flight humidity measurements. To complement this study, the benefit brought by consistent thermodynamic and dynamic cloud conditions has been investigated by separately and jointly assimilating the W-band reflectivity and horizontal wind measurements collected by RASTA in the 3 h 3DVar assimilation system of AROME. The data assimilation experiments are conducted for a single heavy precipitation event and then also for 32 cases. Results indicate that the W-band reflectivity has a larger impact on the humidity, temperature and pressure fields in the analyses compared to the assimilation of RASTA wind data alone. Besides, the analyses get closer to independent humidity observations if the W-band reflectivity is assimilated alone or jointly with RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more rapidly as the forecast range increases when compared to the assimilation of RASTA wind data alone. Generally, the joint assimilation of the W-band reflectivity with wind data results in the best improvement in the rainfall precipitation forecasts. Consequently, results of this study indicate that consistent thermodynamic and dynamic cloud conditions in the analysis leads to an improvement of both model initial conditions and forecasts. Even though to a lesser extent, the assimilation of the W-band reflectivity alone also results in a slight improvement of the rainfall precipitation forecasts.

Highlights

Kilometre-scale numerical weather prediction (NWP) models are able to explicitly resolve convection, and to represent clouds and precipitation with a reasonable degree of realism (Gustafsson et al, 2018)
The primary objective of this article was to assess the impact of the assimilation of W-band radar reflectivity in a kilometre-scale NWP model, and to improve analyses and short-term forecasts of heavy precipitation events in the Mediterranean area
The W-band reflectivity measurements collected by the RASTA airborne Doppler W-band radar during the HyMeX-SOP1 were assimilated into the 3 h 3D variational (3DVar) assimilation system of the NWP model AROME

Summary

Introduction

Kilometre-scale numerical weather prediction (NWP) models are able to explicitly resolve convection, and to represent clouds and precipitation with a reasonable degree of realism (Gustafsson et al, 2018). Doppler radar observations are well suited to initialize these NWP models because they provide wind and reflectivity measurements at a comparable spatial and temporal resolution. Observations from ground-based precipitation radars are operationally assimilated in many kilometre-scale NWP models to initialize precipitation areas (Gustafsson et al, 2018). Ground-based precipitation radars have a very poor sensitivity to clouds. The increased number of Doppler cloud-profiling radars (Wolde and Pazmany, 2005; Delanoë et al, 2013, 2016; Illingworth et al, 2015; Chahat et al, 2016) is extremely appealing in data assimilation to initialize kilometre-scale NWP models in cloudy areas.

Objectives

Results

Conclusion