A posteriori diagnostics of the impact of observations on the AROME‐France convective‐scale data assimilation system

Abstract

AROME-France is an operational convective-scale numerical weather prediction system which uses a 3D-Var assimilation scheme in order to determine its initial conditions. In addition to conventional and satellite observations, regional high-resolution observations are assimilated, such as screen-level observations, total zenith delays from ground-based GPS stations and radar measurements (radial winds and reflectivities). The impact of the various observation types on AROME-France analyses is assessed using an a posteriori diagnostic, the reduction of the estimation error variance. This diagnostic, estimated using a randomization technique, allows one to investigate observation impact depending on the control variable field, model levels, date, analysis time, and spatial scales considered. This highlights the importance of screen-level observations (2 m temperature and relative humidity, 10 m wind) for the lowest layers, of aircraft measurements for temperature and wind field analyses and of radar observations for wind and specific humidity in middle and high troposphere. Only the last mentioned observations appear to be informative regarding the horizontal length-scale below 200 km, while all observations are mostly informative for length-scales above 200 km. The results from two diagnostics, namely the variance reduction and the Degrees of Freedom for Signal (DFS), are compared and found to be largely similar. The variance reduction appears to be an interesting diagnostic to improve the understanding of how observations are used. 1 2 χ ) T R −1 (d − HB 1 2 χ).

Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

“…7 for the experiment OREFLEC. The RH field of the 3D-Var analysis is clearly constrained by the RH retrievals since nearly all points verify the following ranking: y These results are consistent with the recent computations of a posteriori diagnostics of the impact of observations on the analysis of the AROME 3D-Var assimilation system (Brousseau et al 2013). They computed the reduction of the estimation error variance and showed the large impact of the radar observations on the analyzed model fields, in particular that the relative humidity pseudo-observations retrieved from the radar reflectivities contribute the most to the variance reduction of specific humidity in the midatmosphere during precipitating periods.…”

Observation impact in a convective‐scale localized ensemble transform Kalman filter

“…The influence matrix is defined as the derivative (Jacobi matrix) of the analysis vector in observation space with respect to the vector of observations. A study by Brousseau et al (2013) using this method has shown consistency with results from error variance reduction (Desroziers et al, 2005). The limitation of these methods is that they only describe the analysis influence of observations, which is an interesting quantity itself and likely related to the forecast impact of observations.…”

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

“…The background error covariance matrices were specified through the use of an ensemble method (Brousseau et al, 2011). Data assimilated by the AROMEFrance DA system included observations from radiosondes, wind profilers, aircrafts, ships, buoys, automatic weather stations, satellites, GPS stations, and both Doppler radar wind velocity and radar reflectivity (Brousseau et al, 2014).…”