Mapping Weather-Type Influence on Senegal Precipitation Based on a Spatial–Temporal Statistical Model

Henning W. Rust (1) , Mathieu Vrac (2, 3) , Benjamin Sultan (4) , Matthieu Lengaigne (4)

1 Institut für Meteorologie [Berlin]
2 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
3 ESTIMR - Extrèmes : Statistiques, Impacts et Régionalisation
4 LOCEAN - Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques

Senegal is particularly vulnerable to precipitation variability. To investigate the influence of large-scale circulation on local-scale precipitation, a full spatial–statistical description of precipitation occurrence and amount for Senegal is developed. These regression-type models have been built on the basis of daily records at 137 locations and were developed in two stages: (i) a baseline model describing the expected daily occurrence probability and precipitation amount as spatial fields from monsoon onset to offset, and (ii) the inclusion of weather types defined from the NCEP–NCAR reanalysis 850-hPa winds and 925-hPa relative humidity establishing the link to the synoptic-scale atmospheric circulation. During peak phase, the resulting types appear in two main cycles that can be linked to passing African easterly waves. The models allow the investigation of the spatial response of precipitation occurrence and amount to a discrete set of preferred states of the atmospheric circulation. As such, they can be used for drought risk mapping and the downscaling of climate change projections. Necessary choices, such as filtering and scaling of the atmospheric data (as well as the number of weather types to be used), have been made on the basis of the precipitation models' performance instead of relying on external criteria. It could be demonstrated that the inclusion of the synoptic-scale weather types lead to skill on the local and daily scale. On the interannual scale, the models for precipitation occurrence and amount capture 26% and 38% of the interannual spatially averaged variability, corresponding to Pearson correlation coefficients of rO = 0.52 and ri = 0.65, respectively.

Mots clés: Africa; Monsoons; Rainfall; Interannual variability; Seasonal cycle

Domaines: Géophysique [physics.geo-ph]

Henning W. Rust, Mathieu Vrac, Benjamin Sultan, Matthieu Lengaigne. Mapping Weather-Type Influence on Senegal Precipitation Based on a Spatial–Temporal Statistical Model. Journal of Climate, 2013, 26 (20), pp.8189-8209. ⟨10.1175/jcli-d-12-00302.1⟩. ⟨hal-01495317⟩