Daily quantitative precipitation forecasts based on the analogue method: Improvements and application to a French large river basin

Highlights

• A precipitation forecast method based on analogues is adapted to a large river basin.

• Temperature is introduced to take into account seasonal effects on precipitation.

• Vertical velocity is introduced to better characterise large-scale vertical motion.

• The upgraded analogue method is then applied in operational forecasting context.

• A substantial increase in skill is obtained until the lead-time of three days.

Abstract

This paper presents some improvements of a probabilistic quantitative precipitation forecasting method based on analogues, formerly developed on small basins located in South-Eastern France. The scope is extended to large scale basins mainly influenced by frontal systems, considering a case study area related to the Saône river, a large basin in eastern France. For a given target situation, this method consists in searching for the most similar situations observed in a historical meteorological archive. Precipitation amounts observed during analogous situations are then collected to derive an empirical predictive distribution function, i.e. the probabilistic estimation of the precipitation amount expected for the target day. The former version of this forecasting method (Bontron, 2004) has been improved by introducing two innovative variables: temperature, that allows taking seasonal effects into account and vertical velocity, which enables a better characterization of the vertical atmospheric motion. The new algorithm is first applied in a perfect prognosis context (target situations come from a meteorological reanalysis) and then in an operational forecasting context (target situations come from weather forecasts) for a three years period. Results show that this approach yields useful forecasts, with a lower false alarm rate and improved performances from the present day D to day D + 2.

Introduction

Many water-related stakeholders need quantitative precipitation forecasts as reliable as possible to anticipate discharges in river basins several hours or days ahead. For example, French operational flood forecasting services require precipitation forecasts several days ahead to anticipate flood risks (Lacaze et al., 2008), and hydroelectricity power producers need accurate and reliable precipitation forecasts to anticipate the discharge evolution along the regulated rivers and their tributaries in order to perform their activities and to provide safety control (Bompart et al., 2009).

Nowadays forecast uncertainties related to meteorological predictions and implied by modelling processes are more and more taken into account, leading to probabilistic quantitative precipitation forecasts (PQPFs) that provide ensemble forecasts. In particular, this kind of forecast enables to evaluate the risk of extreme events. At least, two approaches for producing PQPFs are commonly used: (i) regional ensemble weather forecasts based on dynamical approaches (e.g. COSMO-LEPS, Marsigli et al., 2005; PEARP, Thirel et al., 2008; EFAS, Thielen et al., 2009; ECMWF-ENS, Miller et al., 2010), (ii) statistical approaches based on a search for analogues (e.g. Obled et al., 2002, Hamill and Whitaker, 2006, Messner and Mayr, 2010, Marty et al., 2012).

The principle of analogue methods (AMs) is to use deterministic meteorological outputs of a target day D as an input for a statistical search of similar past days in terms of general circulation patterns. Similarity is measured on a set of relevant predictor variables computed by Numerical Weather Prediction (NWP) models. Finally the analogue situations selected are used to produce a sample of observed daily precipitations, which provides a probabilistic forecast for the day D.

The AMs assume a relationship between large-scale variables (predictors) and a local-scale target variable (predictand). Different predictors can be found in the literature. Table 1 presents a short description of a selection of representative models developed to predict precipitation fields under various climates. Note that this table does not include methods using combination of predictors in linear regression to estimate predictands. One can note that the number of predictors may differ from one region to another. Timbal et al. (2008) suggest defining combinations of predictors adapted for each season and each climatic region.

The predictors can be divided into two categories. Most AM developments deal with one or more low flow atmospheric fields, i.e. descriptors of the meteorological state of the atmosphere at synoptic scale and a set of geopotential heights is usually chosen. The number of geopotential heights and their corresponding pressure level varies from one study to another: Altava-Ortiz et al. (2006) used three geopotential heights at 500, 850 and 1000 hPa to estimate precipitation amounts for one major heavy rainfall event in Catalonia (Spain) while Diomede et al. (2008) selected the geopotential at 500 hPa to estimate precipitation in Northern Italy. Following Bontron and Obled (2005), Horton et al. (2012) and Chardon et al. (2014) selected analogous situations based on geopotential heights at 500 and 1000 hPa-level in Switzerland and in France, respectively.

The second category of predictors is made of moisture related variables with among them, the large-scale precipitation outputs PRCP from NWP models (e.g. Diomede et al., 2014, Turco et al., 2011). Themeßl et al. (2011) found PRCP the most important predictors for local precipitation far beyond the other variables related to the state of the atmosphere. However Dayon et al. (2015) have compared different versions of AM and pointed out large possible biases in an application to France, when PRCP is used. In Catalonia, Barrera et al. (2007) added the humidity at 1000 hPa to the set of predictors suggested formerly by Altava-Ortiz et al. (2006). Similarly Bliefernicht and Bárdossy (2007) selected three predictors (two geopotential heights and the moisture flux at 700 hPa defined by the product of the specific humidity at 700 hPa and the westerly wind) to predict rainfall in the Rhine basin in Germany. Bontron (2004) and later Gibergans-Báguena and Llasat (2007) and Diomede et al. (2008) tested different combinations of variables as predictors. Diomede et al. (2008) showed that geopotential at 500 hPa and vertical velocity at 700 hPa are the most relevant set of predictors to estimate precipitation in Northern Italy while Bontron (2004) highlighted that the best moisture related variable to be used in addition to synoptic scale variables to estimate precipitation in South-Eastern France is a combination of relative humidity and total column precipitable water. More exhaustively, Gibergans-Báguena and Llasat (2007) have identified 7 variables among a list of 33 thermodynamic descriptors and the AM combining these 7 variables to geopotential fields as predictors outperforms the other tested AMs. These works show that improved analogues sorting technique can be expected by incorporating new levels of analogy (i.e. incorporating new predictors and related similarity criteria). Note that analogy between meteorological situations can be also used in a post-processing of precipitation forecasts to improve the prediction skill of NWP models (e.g. Wang and Fan, 2009).

We will focus here on the application of AMs, and more specifically on one of the reference PQPF methods already used in France in an operational context and suggested by Bontron and Obled (2005) following the work of Bontron (2004). This former method was developed considering small basins located in South-Eastern France, subject to flash-flood events. The aim of this paper is to extend the scope of this AM, considering large French river basins under oceanic influences. This study presents then two improvements of the reference method and compares the relative performances of the latter with two alternatives, on the large French basin of the Saône river. We consider two contexts: (i) a “perfect prognosis context” where the meteorological fields of the target day D are directly built with observed data or re-analysed by a NWP model; (ii) an “operational forecast” context where the meteorological fields are provided by an operational NWP model, including forecast errors.

Section 2 presents the study area and the datasets used, and Section 3 introduces the scores selected for the evaluation of probabilistic forecasts. Section 4 gives the main principles of the benchmark method for searching analogues, based on a two-step procedure, plus two alternatives adding supplementary explanatory variables, air temperature and vertical air motion. The main results of the comparison of the methods are analysed in the fifth section, considering the “perfect prognosis” and the “operational forecast” contexts. The last section draws general conclusions.