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

Daoud, Aurélien Ben; Sauquet, Éric; Bontron, Guillaume; Obled, Charles; Lang, Michel

doi:10.1016/j.atmosres.2015.09.015

Cited by 63 publications

(82 citation statements)

References 49 publications

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“…For precipitation amount, the best four-predictor set is similar except that the occurrence of the previous day Occ−1 is replaced by the 700 hPa air temperature T 700 . Note that the selection of predictors R 700 , W 700 , and T 700 is consistent with results of several past studies in the region (e.g., Ben Daoud et al, 2016).…”

Section: Datasupporting

confidence: 76%

“…Statistical downscaling models (SDMs) have been widely used to generate local weather scenarios for past or future climates from outputs of climate models (e.g., Wilby et al, 1999;Hanssen-Bauer et al, 2005;Boé et al, 2007;Lafaysse et al, 2014) and to produce local weather forecasts from outputs of numerical weather prediction models (e.g., Obled et al, 2002;Gangopadhyay et al, 2005;Marty et al, 2012;Ben Daoud et al, 2016). For recent years, they have also been used to reconstruct past weather conditions from atmospheric reanalysis data (e.g., Kuentz et al, 2015;Caillouet et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Chardon

Hingray

Favre

2018

Hydrol. Earth Syst. Sci.

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Abstract. Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

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Section: Datasupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Chardon

Hingray

Favre

2018

Hydrol. Earth Syst. Sci.

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“…The succession of steps (1) SANDHY (Radanovics et al, 2013;Ben Daoud et al, 2016), (2) subselection with additional analogue levels , (3) bias correction (see above), and (4) Schaake shuffle (see above) is called SCOPE and is summarized by Fig. B1.…”

Section: Use Of Median Event Characteristicsmentioning

confidence: 99%

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

Caillouet¹,

Vidal²,

Sauquet³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatiotemporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

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“…Most of them use a unique analogy distance computed from one or several predictors to find the most relevant analogues, on the contrary to SANDHY that uses a stepwise approach where analogue subsets are sequentially refined with respect to different predictors. The SANDHY method is described by four analogy levels optimized by Ben Daoud et al ( , 2016 on the Seine and Saône basins and summarized in Table 1. The predictand considered is daily precipitation, as the initial aim of this method was quantitative precipitation forecast.…”

Section: Sandhymentioning

confidence: 99%

“…Compared to previous downscaling studies, this experiment has three main combined specificities: (1) the 20CR reanalysis is downscaled for the whole of mainland France and Corsica; (2) it is performed with SANDHY (Stepwise ANalogue Downscaling method for HYdrology, Ben Daoud et al, , 2016Radanovics et al, 2013), a statistical downscaling method locally optimized over 608 zones paving France to take into account regional climate features; (3) the downscaling method provides 25-member ensemble reconstructed fields to reflect the uncertainty in the downscaling step.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

Caillouet¹,

Vidal²,

Sauquet³

et al. 2016

Clim. Past

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Abstract. This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards.The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over-and under-estimated over the whole of France.Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T ). Comparisons to the Safran reanalysis over 1959-2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies.The probabilistic downscaling of 20CR over the period 1871-2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.

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

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

Cited by 63 publications

References 49 publications

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

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