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

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.

Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

“…The method developed in this study to retrieve VPRs is based on the Bayesian approach used by Kummerow et al (1996Kummerow et al ( , 2001 in the Goddard profiling algorithm (GPROF). This was also used by Caumont et al (2010), Augros et al (2018) and Borderies et al (2018Borderies et al ( , 2019 for the validation and assimilation of radar reflectivity and dual-polarisation observations in the French high-resolution model AROME. In the same way, we use here a large database made of simulated profiles VPR mod in the vicinity of the considered radar pixel p i to find the most probable VPR (POVPR(p i )) given the observed apparent VPR rad .…”

“…Many national weather services have implemented a vertical profile of reflectivity (VPR) correction that either uses a climatological profile obtained from a large number of radar observations over a period of time (Andrieu and Creutin, 1995;Vignal et al, 1999;Borga et al, 2000;Seo et al, 2000;Germann and Joss, 2002;Kirstetter et al, 2010) or uses an idealised profile adjusted in real time (Kitchen et al, 1994;Tabary, 2007) with a pixel-wise approach or not. In both correction methods, the VPR can only be retrieved to represent the volume of atmosphere sampled by the radar and thus cannot provide information on the vertical structure of the precipitation in shielded areas.…”

The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6

“…DA is a key ingredient to the initial value problem of NWP (Bauer et al, 2015), as the frequent assimilation of high-quality observations helps adjust the NWP model towards the true atmospheric state. Recent advancements in observation systems and high-performance computing have brought progress for DA of new observations (Carlin et al, 2017;Kwon et al, 2018;Borderies et al, 2019;Federico et al, 2017, Mazzarella et al, 2017. GPS measurements of ZTDs are an especially interesting observation type, since they can sample the Integrated Water Vapour (IWV) amount at minute temporal-resolution .…”

Direct 4D‐Var assimilation of space‐borne cloud radar reflectivity and lidar backscatter. Part I: Observation operator and implementation

“…A similar technique has been used by Borderies et al . (2019) to show improvements to precipitation forecasts in a regional NWP model from assimilating airborne cloud radar data.…”