• Aucun résultat trouvé

Using historical raingage data to adjust a global rainfall reanalysis over Africa

N/A
N/A
Protected

Academic year: 2021

Partager "Using historical raingage data to adjust a global rainfall reanalysis over Africa"

Copied!
2
0
0

Texte intégral

(1)

HAL Id: hal-02607858

https://hal.inrae.fr/hal-02607858

Submitted on 16 May 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Using historical raingage data to adjust a global rainfall reanalysis over Africa

L. Lebecherel, Vazken Andréassian, Olivier Delaigue, M. Riffard-Chenet

To cite this version:

L. Lebecherel, Vazken Andréassian, Olivier Delaigue, M. Riffard-Chenet. Using historical raingage data to adjust a global rainfall reanalysis over Africa. EGU General Assembly 2018, Apr 2018, Vienna, Austria. pp.1, 2018. �hal-02607858�

(2)

5. Conclusion & Perspectives 1. Introduction & Objective

Using historical raingage data to adjust a global rainfall reanalysis over Africa

Laure Lebecherel 1 , Vazken Andréassian 1 , Olivier Delaigue 1 and Marine Riffard-Chenet 2

1

Irstea , HYCAR Research Unit, Antony, France (laure.lebecherel@irstea.fr ; webgr.irstea.fr) ;

2

Tractebel Engineering (Coyne-et-Bellier), Gennevilliers, France

• In raingage-poor regions, global rainfall reanalysis products can constitute a valuable source of precipitation information for hydrological applications. Indeed, the spatial and time scales of reanalysis data sets are compatible with those of hydrological models.

• Unfortunately, global rainfall reanalyses often present large biases which limit their use without a preliminary adjustment :

• The climatic adjustment of the ERA-Interim reanalysis improves the correlation between ground measurements and reanalysis.

• The method of climatic adjustment of the rainfall ERA- Interim reanalysis will now be extended at the daily time step.

• This method will be validated at catchment scale with GR4J hydrological model.

2. Study area and data

www.irstea.fr

ERA-Interim reanalysis rainfall Tractebel monthly raingage dataset

The ECMWF ERA-Interim reanalysis provides long rainfall time series from 1979. Over Africa, there is enough overlap with monthly ground measurements to allow for calibrating an error-adjustment model.

This work aims to propose a climatic adjustment of the ERA-interim rainfall reanalysis based on Tractebel’s historical raingage dataset over Africa at the monthly time scale. The method consists in building a seasonal intensity-dependent error correction curve using all data points where ground measurements are available.

3. Methodology of the climatic adjustment of the ERA-Interim rainfall reanalysis

4. Seasonal correction curve estimation and application

• An essential hypothesis of the method is that the reanalysis error relative to rainfall intensity is stable in time.

• To have enough data to build the climatic adjustment, one correction curve is estimated for each month and each region.

16 groups of country and a totally of 58 “climate zones” are created (based on world map of Köppen-Geiger climate classification (Peel et al., 2007)).

Peel, M. C., B. L. Finlayson, et T. A. McMahon (2007), Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11(5), 1633-1644, doi: 10.5194/hess-11-1633-2007.

Estimation of the correction curve for each month Application of the correction curve for each month

EGU General Assembly 2018 Vienna 8–13 April 2018

Climate zone 14.04.C

Global

Performance

• Root mean square error values of ERA-Interim reanalysis rainfall are reduced with the climatic adjustment method (except for climate zone 15.02.A).

• For some climate zones, the reduction of errors is large (especially for climate zones 12.01.A and B).

• Pearson correlation coefficients are also improved with the climatic adjustment for all climate zones.

Correction curve:

P.raingage/P.era = f(P.era)

Références

Documents relatifs

Bias adjustment in WFDEI was undertaken on the monthly average scale for a number of hydrological variables necessary to calculate evapotranspira- tion, soil moisture, and runoff

Thirty year Inter annual variability of Rainfall and NDVI showed over six selected sites in the Sahel region ((a) Burkina Faso and (b) Niger), the Sudanian Savannah region ((c)

Detailed intercompar- isons of the conventional data or quality control criteria used in each reanalysis are beyond the scope of this review; how- ever, four of the reanalyses

This study highlights how CMIP5 models represent the annual cycle of rainfall characteristics over West Africa, focusing on the timing of the monsoon season (onset, cessation),

Observed (grey areas) and estimated surface soil moisture (black thick lines) during 2006 over three sites (Wankama, Agoufou and Nalohou) using API model and ground-based

The intrinsic uncertainty of the analysis system, due to the spatial and temporal interpolation steps, is first assessed over the whole period 1958-2008 by comparing Safran outputs

Evolution of weekly temperature data assimilation statistics from in situ profiles, function of the depth averaged over the whole Mediterranean basin : number of observations

experiments with respect to All run for all Safran variables: liquid precipitation (R), solid precipitation (S), mean air temperature (T), specific humidity (SH), wind speed (W),