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

Abstract

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 to produce 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 a 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.

Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

“…The parts of the radar data processing which could influence the value of this radar QPE bias was also pointed out: the VPR processing which bring the radar measurements down to a horizontal reference level at the altitude of the radar or of the bottom of the melting layer, and the uniform calibration of the single radar QPEs by hourly rain gauge data, which shift this reference level to the mean altitude of the rain gauges used. These results confirm the interest of works in progress supported by Météo-France and IGE to bring information on VPR below the altitude of measurement of the current radars in mountain, for example by integrating radar measurements from the bottom of a valley, or by using 3D analysis of the AROME NWP model at all altitudes [20]. Calibration procedures using rain gauges measurements and varying in space could also be interesting when the precipitation fields are not independent from the relief, or when these fields depend on local bias [21].…”

Microphysical Origin of Raindrop Size Distributions During the Indian Monsoon

“…Therefore, raindrops growth is gradual in stratiform rain and the reflectivity gradients are stronger in convective rain than in the stratiform rain (as noted by G. Liu & Fu, 2001, Fu & Liu, 2001, and S. Das & Maitra, 2016). In the presence of the reflectivity gradient, a measurement of reflectivity at 2 km altitude cannot accurately estimate surface rain, irrespective of the ZR relationship's accuracy (Bastard et al., 2019).…”

Reply on RC1

“…This method is similar to the most resembling column (MRC) method used by Borderies et al (2018) to calibrate and validate the RASTA cloud radar observation operator. It also includes an altitude-dependent weighting function (equation ( 8)) as was used in Le Bastard et al (2019), which puts a larger weight on the bins at a lower height. In this equation, Height is the height of the reflectivity bin and Altmax is the maximum altitude considered which for this study was set to 5000 m.…”