Jury, R.M., Pathack, B. and Parcker, B. (1999) Climatic Determinants and Statistical Prediction of Tropical Cyclone Days in the Southwest Indian Ocean. Journal of Climate, 12, 1738-1746.
https://doi.org/10.1175/1520-0442(1999)012<1738:CDASPO>2.0.CO;2

has been cited by the following article:

• TITLE: Seasonal Prediction of Tropical Cyclones and Storms over the Southwestern Indian Ocean Region Using the Generalized Linear Models

  AUTHORS: Kombo Hamad Kai, Yohanna Wilson Shaghude, Christian Bs Uiso, Agnes Laurent Kijazi, Sarah Osima, Sara Abdalla Khamis, Asya Omar Hamad

  KEYWORDS: Tropical Cyclones and Storms Frequency, Thermodynamic and Dynamic Models, Skill Scores TCs/TSs Variability and Verification, Leave One out Cross Validation

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.13 No.2, March 13, 2023

  ABSTRACT: Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.