Precipitation analysis over the French Alps using a variational approach and study of potential added value of ground based radar observations

Precipitation analysis over the French Alps using a variational approach and study of potential... AbstractA 1-D variational (1D-VAR) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain-gauges and precipitation estimates from weather radars with background information from short range Numerical Weather Prediction (NWP) forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012-2013 and 2013-2014). It is shown that the 1D-Var model allows an effective assimilation of measurements of different types, including rain-gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Precipitation analysis over the French Alps using a variational approach and study of potential added value of ground based radar observations

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
eISSN
1525-7541
D.O.I.
10.1175/JHM-D-16-0144.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA 1-D variational (1D-VAR) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain-gauges and precipitation estimates from weather radars with background information from short range Numerical Weather Prediction (NWP) forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012-2013 and 2013-2014). It is shown that the 1D-Var model allows an effective assimilation of measurements of different types, including rain-gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Mar 6, 2017

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