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Improving multimodel medium range forecasts over the Greater Horn of Africa using the FSU superensemble

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This study makes use of the WMO’s multimodel data set called THORPEX integrated grand global ensemble (TIGGE) towards the construction of multimodel superensemble forecasts covering a period of 10 days. The goal of this study is to explore the forecast skill for precipitation forecasts over the Greater Horn of Africa (this is a consortium of 11 countries). The multimodels include forecast data set from a suite of models that include: The European Centre for Medium Range Weather Forecasts (ECMWF), the National Centre for Environmental Prediction (NCEP), the Center for Weather Forecast and Climatic Studies (CPTEC) and the United Kingdom Meteorological Office (UKMO). After performing a training phase for the superensemble weights covering the previous 450 days of October, November and December months of 2008–2012, forecasts of precipitation were prepared for the multimodel superensemble. These covered day 1 to day 10 of forecasts over the region. Various skill metrics were prepared to validate the forecast rainfall against the tropical rainfall measuring mission (TRMM) observed rainfall data. This study shows that the construction of the multimodel superensemble was a worthwhile effort since it provided the best overall skills for the RMS errors, the spatial correlations and the equitable threat scores and their bias errors for precipitation forecasts from day 1 to day 10 over all of the countries covered by the Greater Horn of Africa. The best among the member model was the UKMO model. This study strongly suggests the usefulness of a product such as the multimodel superensemble for improved precipitation forecasts over East Africa.

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This work was supported by NSF Grant No. UFSU0004. This research study was also supported by IGAD Climate Prediction and Applications Centre (ICPAC) for the support of the principal author while he was visiting Florida State University. We wish to acknowledge the THORPEX Integrated Grand Global Ensemble (TIGGE) for providing the forecast data used in this paper.

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Correspondence to O. Kipkogei.

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Responsible Editor: J.-F. Miao.



The Equitable Threat Score (Schaefer 1990) computes the skill in forecasting the area of precipitation amounts over any specified preset value. It is expressed as

$${\text{ETS}} = \frac{{\left( {H - {\text{CH}}} \right)}}{{\left( {F + O - H - {\text{CH}}} \right)}}$$

In Eq. 4, O is the number of grid points that observe more than the threshold, F is the number of grid boxes that forecast more than the threshold, H is the number of grid points that correctly forecast more than the threshold and CH is the expected number of hits in a random forecast of F points for O observed points, which is equal to

$${\text{CH}} = \frac{{\left( {F \times O} \right)}}{T}$$

In Eq. 5, T is the total number of grid boxes inside the domain to be validated.

ETS is a good estimate to measure precipitation skills. The higher it is the better the score for that given threshold. It varies from a small negative number (−1/3) to 1, with the latter representing a perfect score.

Bias score on the other hand is the ratio of the forecast to observed area (points) of rainfall amounts over any given thresholds (Anthes 1983). It is defined as

$${\text{BIAS}} = \frac{F}{O}$$

Bias score gives an indication whether the model is under forecasting or over forecasting. A model that consistently remains near a bias of 1.0 is a good one.

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Kipkogei, O., Bhardwaj, A., Kumar, V. et al. Improving multimodel medium range forecasts over the Greater Horn of Africa using the FSU superensemble. Meteorol Atmos Phys 128, 441–451 (2016).

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