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MAY 2017 B I R M A N E T A L . 1425 Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations CAMILLE BIRMAN CNRM, UMR 3589, Météo-France/CNRS, Toulouse, France FATIMA KARBOU CNRM, UMR 3589, Météo-France/CNRS, Saint Martin d’Hères, France JEAN-FRANÇOIS MAHFOUF CNRM, UMR 3589, Météo-France/CNRS, Toulouse, France MATTHIEU LAFAYSSE,YVES DURAND,GÉRALD GIRAUD, AND LAURENT MÉRINDOL CNRM, UMR 3589, Météo-France/CNRS, Saint Martin d’Hères, France LAURA HERMOZO CLS, Toulouse, France (Manuscript received 24 June 2016, in final form 20 February 2017) ABSTRACT A one-dimensional variational data assimilation (1DVar) 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 nu- merical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar 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 nu- merical snowpack model Crocus significantly reduces the bias and standard deviation with respect to in- dependent 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. 1. Introduction the snowpack and its evolution in time and for avalanche hazard forecasting. However, precipitation patterns are Reliable precipitation estimates are needed for many particularly variable in mountainous areas because of applications, including meteorology, hydrology, and the influence of altitude, orography, aspect, and the as- climate studies (Boone et al. 2004; Tapiador et al. 2012; sociated small spatial scale of convective events. Accu- Kucera et al. 2013; Kidd and Levizzani 2011; Valipour rate estimations of precipitation in mountains and of its et al. 2013; Valipour and Eslamian 2014; Valipour 2015, variability in space and time would ideally require a 2016). For instance, a good knowledge of precipitation dense rain gauge network combined with an effective in mountainous regions at appropriate spatial and tem- analysis method. Cokriging of precipitation with alti- poral scales is a prerequisite for accurate modeling of tude is one of the simplest and widely used methods, but it requires the precipitation to be strongly correlated Corresponding author e-mail: Camille Birman, camille.birman@ with altitude (Hevesi et al. 1992a,b). Such methods can meteo.fr be used for monitoring precipitation accumulation at DOI: 10.1175/JHM-D-16-0144.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/28/21 01:26 AM UTC 1426 JOURNAL OF HYDROMETEOROLOGY VOLUME 18 seasonal or annual time scales. For example, Prudhomme or greater over which the meteorological variables can be andReed(1999)used topographical data to improve the assumed to be uniform. The first analysis scheme, called mapping of extreme precipitation in a mountainous re- Système d’Analyse Fournissant des Renseignements gion of Scotland, using residual kriging with a regression Atmosphériques à la Neige (SAFRAN) is used to gen- method relating topographical variables to the median of erate relevant meteorological parameters that indirectly the annual daily precipitation. Mair and Fares (2011) govern energy and mass budgets of the snowpack, at compared different geostatistical methods to estimate massif scale, on an hourly time basis. Multilayer snow- rainfall and showed that the use of topographical in- pack simulations are performed using a physical model formation improved the simulated precipitation accu- called Crocus (Brun et al. 1992; Vionnet et al. 2012), mulating on a monthly time-scale basis. Schmidli et al. which employs SAFRAN atmospheric forcing outputs, (2002) studied the long-term variability of the pre- to generate simulations of the snowpack. Avalanche cipitation over the Alps during the period 1901–90, using hazard forecasting is then performed using a system data from rain gauges to produce an analysis at a spatial called Modèle Expert d’Aide à la Prévision du Risque resolution of 25 km and a monthly time step. They d’Avalanche (MEPRA; Giraud 1992) based on expert highlighted a climatological trend with an increase of analysis of the simulated snowpack mechanical proper- winter precipitation over the northern and western Alps ties. Raleigh et al. (2015) showed that errors associated and a decrease of precipitation in autumn in the south of with the atmospheric forcing had the largest impact on the Alps. Other techniques have been used that take ac- snowpack modeling uncertainties. Precipitation being count of the aspect of slopes with respect to atmospheric one of the most important atmospheric forcing variables, flow for daily analysis, but at a low temporal resolution itsestimationrequiresasmuchcareasthemodelingof (from seasonal to yearly accumulations; Daly et al. 1994; snowpack properties. Schwab 2000). However, these methods are not suitable SAFRAN has been widely used for the analysis of for the short-term monitoring of rapidly changing phe- precipitation in mountainous areas and has been found to nomena such as floods or for avalanche hazard forecasts. provide reliable precipitation estimates (Durand et al. Long-term series of reliable estimates of precipitation are 2009b). However, the model produces precipitation an- thus necessary at spatial and temporal resolutions that alyses only at massif scale and is not suitable to provide meet hydrological and snow study requirements. analyses on smaller areas or on a regular grid. For in- Methods have been developed to derive precipitation stance, the use of SAFRAN on high-elevation glacier using a set of a priori information from climatological sites requires the use of a postprocessing method to cor- data and currently available measurements (Guan et al. rect the bias of SAFRAN estimates with respect to in situ 2005; Kyriakidis et al. 2001; Gottardi et al. 2012). Other observations (e.g., Gerbaux et al. 2005). Moreover, the long-term precipitation databases have been constructed use of indirect observations such as radar or satellite es- for mountainous areas using atmospheric reanalysis out- timates is difficult with the optimal interpolation tech- puts of numerical weather prediction (NWP) models with nique. In the framework of the European Reanalysis and appropriate downscaling techniques to account for Observations for Monitoring (Euro4M) project, another orography. Crochet (2007), Crochet et al. (2007),and precipitation analysis system called MESCAN has been Durand et al. (2009a,b), for example, used the 40-yr Eu- built, also based on an optimal interpolation technique ropean Centre for Medium-Range Weather Forecasts using conventional surface observations to produce (ECMWF) Re-Analysis over the time period 1958–2002 temperature, relative humidity, and precipitation analysis to compute precipitation estimates over mountainous (Coustau et al. 2014; Soci et al. 2016). Simulations of snow areas. Crochet (2007) and Crochet et al. (2007) have and surface fluxes using the surface model Surface Ex- produced a 1-km-resolution precipitation analysis over ternalisée (SURFEX) forced by MESCAN analysis have Iceland accounting for flow dependency and orientation given satisfactory results overall. However, limitations of slopes with respect to the predominant wind. Durand appear over mountains since the specifications of error et al. (2009a,b) have generated an analysis of all relevant statistics and correlation length scale in the MESCAN meteorological parameters for snowpack modeling, in- analysis scheme are tuned for flat areas and are not ap- cluding rainfall and snowfall rates, and studied their propriate in complex terrain. The main purpose of the evolution in time over the French Alps. present development is to build a precipitation analysis To accurately simulate the evolution of snowpack over model having a quality similar to SAFRAN (at massif mountains at massif scale together with its mechanical scale) but that can also be easily adapted to provide stability, Météo-France has developed a chain of three precipitation analyses at smaller spatial scales. The sys- models (Durand et al. 1999). For this chain of models, a tem should be able to assimilate both conventional and massif is defined as a homogeneous area of about 500 km2 remote sensing data. In the case of indirect measurements Unauthenticated | Downloaded 09/28/21 01:26 AM UTC MAY 2017 B I R M A N E T A L . 1427 TABLE 1. Scores of the 1DVar precipitation analysis compared to observations not assimilated over the 23 massifs of the French Alps for the years 2012/13 and 2013/14. The values in boldface correspond to the best scores between Safran and 1DVar. The names of the massifs are indicated as well as if they are part of the northern (N) or southern (S) Alps. The scores are