Impact of Sea Surface Temperature Anomalies on the Atlantic Tropical Storm Activity and West African Rainfall - Mo, Kingtse C.; Bell, Gerald D.; Thiaw, Wassila M. (Journal of the Atmospheric Sciences, 2001)
The association between rainfall over the Sahel and Sudan region and tropical storm activity in the Atlantic is examined using the NCEP–NCAR reanalysis and sea surface temperature anomalies (SSTAs) from 1949 to 1998. Evidence indicates that both are influenced by global SSTAs. The SSTA modes generating favorable atmospheric conditions for tropical storms to develop are also in favor of a wet rainfall season in the Sahel and Sudan region. The easterly waves over West Africa become tropical storms only if the atmospheric conditions over the Atlantic are favorable. These conditions are responses to SSTAs. In addition to ENSO, a multidecadal trend mode also plays a role. The positive phase of the trend mode features positive loadings in the North Pacific and the North Atlantic, and negative loadings over the three southern oceans. The positive (negative) phases of both modes are associated with increased (reduced) Atlantic tropical storm activity, and with wet (dry) West African monsoon seasons. The SSTAs over the tropical South Atlantic (S-ATL) are related to the rainfall dipole over West Africa, but the influence on tropical storms is not large. Warm (cold) SSTAs over the tropical North Atlantic enhance (suppress) the occurrence of tropical storms, but have little influence on rainfall over West Africa. The most prominent circulation features associated with the positive phases of SSTA modes are enhanced upper-level 200-hPa easterly winds and reduced vertical wind shear in the main development region of the tropical Atlantic, which are well-known features of active Atlantic tropical storm seasons. The associated low-level flow shows enhanced anomalous westerly winds across the Atlantic to Africa. That allows more moisture transport into Africa and, therefore, more rainfall.
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A New Multiple Imputation Approach Using Machine Learning to Enhance Climate Databases in Senegal - Touré, Mory; Klutse, Nana Ama Browne; Sarr, Mamadou Adama; Kenne, Annine Duclaire; Bhuiyanr, Md Abul Ehsan; Ndiaye, Ousmane; Badiane, Daouda; Thiaw, Wassila M.; Sy, Ibrahima; Mbow, Cheikh; Sall, Saïdou Moustapha; Gaye, Amadou Thierno (Research Square (Research Square), 2023)
Abstract This study aims at enhancing climate data in Senegal using information from the Global Surface Summary of the Day (GSOD). It uses data from 1991 to 2022 from major secondary synoptic stations in Senegal. These data are subject to missing values (data gaps). To address these gaps, multiple imputation was used based on three machine learning models: PMM (Predictive Mean Matching), RF (Random Forest), and NORM (Bayesian Linear Regression). The PMM model relies on averages of similar data, the RF model handles complex relationships between variables, even on an intra-seasonal scale, while the NORM model captures seasonal variations and extreme values. The results highlight the higher performance of the RF model in terms of accuracy and variance explanation compared to the others. The findings of this study open new avenues for informed decision-making in sectors such as agriculture and urban planning, where accurate climate data play a crucial role. However, while this study lays the groundwork for better utilization of climate data in Senegal, challenges persist, including the ongoing need to collect high-quality data and adapt models to data intricacies.
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