Detection and forecast of the rainy seasons, application over Analamanga

Changes like decrease of the rainfall amount or late rainy seasons are observed over Madagascar nowadays due to the climate changes.To tackle the negative impacts of climate changes, this paper aims to detect rainy seasons period (onset dates and offset dates) in order to forecast rainy season and set calendar.Climate variables, from 1979 to 2021, such as the daily precipitations, the temperatures, the Dipole Index Mode and the Oceanic Nio Indexes) were used.The detection of the rainy seasons was done by the mean of the accumulation of the anomalies of the daily mean of the precipitations.Modelling and forecasting stages were made by using Fuzzy Logic with triangular membership functions.The skill scores were the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the RMSE and MAE scaled by the standard deviation and the average of the onset and demise dates of the rainy seasons.Findings show that the investigated region was subdivided into two regions: A, where the climatological wet spell starts between 25 th October and 12 th November, and B, where the climatological wet spell starts between November 16 and 20.The best forecasting models were observed with values of RMSE (MAE) between 0.52 and 0.80 (0.04 and 0.36).Moreover, scaled by the standard deviation (average), these skill scores are less than 1 (have values between 0.05% and 7.30%).

Authors

Publication Details

Journal
International Journal of Engineering Sciences and Technologies
Published
2024-11-01
DOI
https://doi.org/10.70961/hrfe7516
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection and forecast of the rainy seasons, application over Analamanga

RAZANATOMPOHARIMANGA Niry Havana
International Journal of Engineering Sciences and Technologies
Precipitation Measurement and Analysis
article

Detection and forecast of the rainy seasons, application over Analamanga

RAZANATOMPOHARIMANGA Niry Havana
article en

Abstract

Changes like decrease of the rainfall amount or late rainy seasons are observed over Madagascar nowadays due to the climate changes.To tackle the negative impacts of climate changes, this paper aims to detect rainy seasons period (onset dates and offset dates) in order to forecast rainy season and set calendar.Climate variables, from 1979 to 2021, such as the daily precipitations, the temperatures, the Dipole Index Mode and the Oceanic Nio Indexes) were used.The detection of the rainy seasons was done by the mean of the accumulation of the anomalies of the daily mean of the precipitations.Modelling and forecasting stages were made by using Fuzzy Logic with triangular membership functions.The skill scores were the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the RMSE and MAE scaled by the standard deviation and the average of the onset and demise dates of the rainy seasons.Findings show that the investigated region was subdivided into two regions: A, where the climatological wet spell starts between 25 th October and 12 th November, and B, where the climatological wet spell starts between November 16 and 20.The best forecasting models were observed with values of RMSE (MAE) between 0.52 and 0.80 (0.04 and 0.36).Moreover, scaled by the standard deviation (average), these skill scores are less than 1 (have values between 0.05% and 7.30%).

International Journal of Engineering Sciences and TechnologiesVol. 2(Issue 1)
Climate action
Openalex Percentile: Top 72%
Precipitation Measurement and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.