%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Louvet, S.
%A Sultan, Benjamin
%A Janicot, Serge
%A Kamsu-Tamo, P. H.
%A Ndiaye, O.
%T Evaluation of TIGGE precipitation forecasts over West Africa at intraseasonal timescale
%D 2016
%L fdi:010068091
%G ENG
%J Climate Dynamics
%@ 0930-7575
%K West Africa ; Numerical prediction models ; Intra-seasonal variability ; Onset ; TIGGE forecasts
%K AFRIQUE DE L'OUEST ; AFRIQUE SUBSAHARIENNE
%M ISI:000381227100003
%N 1-2
%P 31-47
%R 10.1007/s00382-015-2820-x
%U https://www.documentation.ird.fr/hor/fdi:010068091
%> https://www.documentation.ird.fr/intranet/publi/2016/10/010068091.pdf
%V 47
%W Horizon (IRD)
%X The study evaluated for the first time the ability of meteorological models of TIGGE to forecast the main features of the West African monsoon rainfall. Seven numerical models were retained over the 2008-2012 period and compared to satellite rainfall estimates. We focused on the seasonal cycle and in particular on the onset of the rainy season and on the intra-seasonal variability that are both of high importance for agriculture, water management and health sectors. We found that the seasonal latitudinal shift of the ITCZ is rather well predicted in terms of amplitude and timing by the different models although there is a systematic northward drift in the ITCZ latitude from the lead-times 1- to 10-day. Although the onset date of rainfall varies a lot according to the different definition in the literature, we also found good performance of TIGGE forecasts in predicting the onset date of the monsoon. The analysis of intra-seasonal variability revealed that the skill of TIGGE forecasts is decreasing with the lead-time from 1-to 15-day and the performance of the ensemble mean of all models overcomes the one of any individual models. Overall criteria used in this study (intra-seasonal fluctuations, onset and seasonal cycles), the skill of UKMO and ECMWF models is better than any other model. Based on such analysis it is likely than an ensemble mean based only on these two models would be more skillful than the ensemble mean based on the seven models. TIGGE forecasts represent a promising step towards the delivery of useful climate information to end-users of key sectors such as agriculture, water management, health and public safety.
%$ 072 ; 021 ; 020