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

Abstract

The extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing quantitative precipitation estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a vertical profile of reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient, and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example).This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some numerical weather prediction (NWP) models is now comparable with the radar one, and their dynamical core and microphysics schemes allow the production of realistic simulations of VPRs.The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high-resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from an NWP model are used to derive radar QPEs.The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6 h accumulations compared to the operational QPEs, showing the relevance of this new approach.

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

“…The use of spatialised satellite retrievals (Margulis et al, 2019;Cluzet et al, 2020) to better constrain snow cover variability, or a finer correction of meteorological forcings using radar precipitation data (e.g. Birman et al, 2017;Le Bastard et al, 2019) in combination with higher resolution NWP models and their ensemble counterparts, might be a solution.…”

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

“…There, simulation accuracy may be more limited by snow-related processes, such as wind drift and uncertain physical processes resulting in snow cover variability, than by meteorological errors. The use of spatialized satellite retrievals (Margulis et al, 2019;Cluzet et al, 2020) to better constrain snow cover variability, or a finer correction of meteorological forcings using radar precipitation data (e.g., Birman et al, 2017;Le Bastard et al, 2019) in combination with higher-resolution NWP models and their ensemble counterparts, might be a solution.…”

W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

“…In order to define fog events, the visibility at or near surface height must be known. Though there has been work done to classify the visibility from radar reflectivity (Li, 2015), which was done with a plan position indicator (PPI) scanning strategy, the lowest gates still suffered from quality issues due to ground clutter. The most reliable way to measure the visibility is with a visibility meter.…”