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.