Empirical statistical modeling of March-May rainfall prediction over southern nations, nationalities and people’s region of Ethiopia

Wondimu Tadiwos Hailesilassie, Gizaw Mengistu Tsidu

Research output: Contribution to journalArticle

Abstract

Statistical predictive models were developed to investigate how global rainfall predictors relate to the March-May (MAM) rainfall over Southern Nations, Nationalities and People's Region (SNNPR) of Ethiopia. Data utilized in this study include station rainfall data, oceanic and atmospheric indices. Because of the spatial variations in the interannual variability and the annual cycle of rainfall, an agglomerative hierarchical cluster analyses were used to delineate a network of 20 stations over study area into three homogeneous rainfall regions in order to derive rainfall indices. Time series generated from the delineated regions were later used in the rainfall/teleconnection indices analyses. The methods employed were correlation analysis and multiple linear regressions. The regression modes were based on the training period from 1987-2007 and the models were validated against observation for the independent verification period of 2008-2012. Results obtained from the analysis revealed that sea surface temperature (SST) variations were the main drivers of seasonal rainfall variability. Although SSTs account for the majority of variance in seasonal rainfall, a moderate improvement of rainfall prediction was achieved with the inclusion of atmospheric indices in prediction models. The techniques clearly indicate that the models were reproducing and describing the pattern of the rainfall for the sites of interest. For the forecast to become useful at an operational level, further development of the model will be necessary to improve skill and to determine the error bounds of the forecast.

Original languageEnglish
Pages (from-to)569-578
Number of pages10
JournalMausam
Volume66
Issue number3
Publication statusPublished - 2015

Fingerprint

Ethiopia
rainfall
prediction
predictions
modeling
forecasting
regression analysis
stations
sea surface temperature
education
inclusions
cycles
teleconnection
annual cycle

All Science Journal Classification (ASJC) codes

  • Atmospheric Science
  • Geophysics

Cite this

@article{a48ab3465843403ab9e349cf2341d732,
title = "Empirical statistical modeling of March-May rainfall prediction over southern nations, nationalities and people’s region of Ethiopia",
abstract = "Statistical predictive models were developed to investigate how global rainfall predictors relate to the March-May (MAM) rainfall over Southern Nations, Nationalities and People's Region (SNNPR) of Ethiopia. Data utilized in this study include station rainfall data, oceanic and atmospheric indices. Because of the spatial variations in the interannual variability and the annual cycle of rainfall, an agglomerative hierarchical cluster analyses were used to delineate a network of 20 stations over study area into three homogeneous rainfall regions in order to derive rainfall indices. Time series generated from the delineated regions were later used in the rainfall/teleconnection indices analyses. The methods employed were correlation analysis and multiple linear regressions. The regression modes were based on the training period from 1987-2007 and the models were validated against observation for the independent verification period of 2008-2012. Results obtained from the analysis revealed that sea surface temperature (SST) variations were the main drivers of seasonal rainfall variability. Although SSTs account for the majority of variance in seasonal rainfall, a moderate improvement of rainfall prediction was achieved with the inclusion of atmospheric indices in prediction models. The techniques clearly indicate that the models were reproducing and describing the pattern of the rainfall for the sites of interest. For the forecast to become useful at an operational level, further development of the model will be necessary to improve skill and to determine the error bounds of the forecast.",
author = "Hailesilassie, {Wondimu Tadiwos} and Tsidu, {Gizaw Mengistu}",
year = "2015",
language = "English",
volume = "66",
pages = "569--578",
journal = "Mausam",
issn = "0252-9416",
publisher = "India Meteorological Department",
number = "3",

}

Empirical statistical modeling of March-May rainfall prediction over southern nations, nationalities and people’s region of Ethiopia. / Hailesilassie, Wondimu Tadiwos; Tsidu, Gizaw Mengistu.

In: Mausam, Vol. 66, No. 3, 2015, p. 569-578.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Empirical statistical modeling of March-May rainfall prediction over southern nations, nationalities and people’s region of Ethiopia

AU - Hailesilassie, Wondimu Tadiwos

AU - Tsidu, Gizaw Mengistu

PY - 2015

Y1 - 2015

N2 - Statistical predictive models were developed to investigate how global rainfall predictors relate to the March-May (MAM) rainfall over Southern Nations, Nationalities and People's Region (SNNPR) of Ethiopia. Data utilized in this study include station rainfall data, oceanic and atmospheric indices. Because of the spatial variations in the interannual variability and the annual cycle of rainfall, an agglomerative hierarchical cluster analyses were used to delineate a network of 20 stations over study area into three homogeneous rainfall regions in order to derive rainfall indices. Time series generated from the delineated regions were later used in the rainfall/teleconnection indices analyses. The methods employed were correlation analysis and multiple linear regressions. The regression modes were based on the training period from 1987-2007 and the models were validated against observation for the independent verification period of 2008-2012. Results obtained from the analysis revealed that sea surface temperature (SST) variations were the main drivers of seasonal rainfall variability. Although SSTs account for the majority of variance in seasonal rainfall, a moderate improvement of rainfall prediction was achieved with the inclusion of atmospheric indices in prediction models. The techniques clearly indicate that the models were reproducing and describing the pattern of the rainfall for the sites of interest. For the forecast to become useful at an operational level, further development of the model will be necessary to improve skill and to determine the error bounds of the forecast.

AB - Statistical predictive models were developed to investigate how global rainfall predictors relate to the March-May (MAM) rainfall over Southern Nations, Nationalities and People's Region (SNNPR) of Ethiopia. Data utilized in this study include station rainfall data, oceanic and atmospheric indices. Because of the spatial variations in the interannual variability and the annual cycle of rainfall, an agglomerative hierarchical cluster analyses were used to delineate a network of 20 stations over study area into three homogeneous rainfall regions in order to derive rainfall indices. Time series generated from the delineated regions were later used in the rainfall/teleconnection indices analyses. The methods employed were correlation analysis and multiple linear regressions. The regression modes were based on the training period from 1987-2007 and the models were validated against observation for the independent verification period of 2008-2012. Results obtained from the analysis revealed that sea surface temperature (SST) variations were the main drivers of seasonal rainfall variability. Although SSTs account for the majority of variance in seasonal rainfall, a moderate improvement of rainfall prediction was achieved with the inclusion of atmospheric indices in prediction models. The techniques clearly indicate that the models were reproducing and describing the pattern of the rainfall for the sites of interest. For the forecast to become useful at an operational level, further development of the model will be necessary to improve skill and to determine the error bounds of the forecast.

UR - http://www.scopus.com/inward/record.url?scp=84951074075&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84951074075&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84951074075

VL - 66

SP - 569

EP - 578

JO - Mausam

JF - Mausam

SN - 0252-9416

IS - 3

ER -