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

Kipkogei, Oliver; Bhardwaj, Amit; Kumar, Vinay; Ogallo, Laban; Opijah, Franklin; Mutemi, Joseph; Krishnamurti, T. N.

doi:10.1007/s00703-015-0430-0

Cited by 8 publications

(9 citation statements)

References 26 publications

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“…Models show a spread in capability (Indeje et al 2000;Mutemi et al 2007;Kipkogei et al 2016), with the short rains generally being better represented (Shongwe et al 2011). Like HadGEM3-GC2 (see Fig.…”

Section: Pan-africanmentioning

confidence: 99%

Evaluating Climate Models with an African Lens

James

Washington

Abiodun

et al. 2018

Bulletin of the American Meteorological Society

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Section: Pan-africanmentioning

confidence: 99%

Evaluating Climate Models with an African Lens

James

Washington

Abiodun

et al. 2018

Bulletin of the American Meteorological Society

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“…Consequently, the use of outputs from RCMs has become a vital and effective tool for studying regional climate change and variability due to their capability of capturing finer spatial and temporal scales that are required for climate change impacts and adaptation studies (Jones et al 2004;Pal et al 2007;Wang et al 2004). Previous studies have focused on assessing the performance of RCMs over the East African region (e.g., Sun et al 1999a, b;Anyah andSemazzi 2006, 2007;Diro et al 2012;Endris et al 2016Endris et al , 2013Kipkogei et al 2016;Ogwang et al 2014Ogwang et al , 2015Ogwang et al , 2016Opijah et al 2014;Segele et al 2009) as well as over the Lake Victoria basin (e.g., Anyah 2005;Sabiiti 2008;Thiery et al 2015;Williams et al 2015), but very few studies have been focused on national scale especially over Uganda (e.g., Mugume et al 2017a, b;Nandozi et al 2012;Nimusiima et al 2014), and in addition, these studies are largely based on the outputs from a single RCM and one observational dataset.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of rainfall simulations over Uganda in CORDEX regional climate models

Kisembe

Favre

Dosio

et al. 2018

Theor Appl Climatol

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The study evaluates the ability of ten regional climate models (RCMs) to simulate the present-day rainfall over Uganda within the Coordinated Regional Downscaling Experiment (CORDEX) for the period 1990-2008. The models' ability to reproduce the space-time variability of annual, seasonal, and interannual rainfall has been diagnosed. A series of metrics have been employed to quantify the RCM-simulated rainfall pattern discrepancies and biases compared to three gridded observational datasets. It is found that most models underestimate the annual rainfall over the country; however, the seasonality of rainfall is properly reproduced by the RCMs with a bimodal component over the major part of the country and a unimodal component over the north. Models reproduce the interannual variability of the dry season (December-February) but fail with the long and short rains seasons even if the ENSO and IOD signal is correctly simulated by most models. In many aspects, the UQAM-CRCM5 RCM is found to perform best over the region. Overall, the ensemble mean of the ten RCMs reproduces the rainfall climatology over Uganda with reasonable skill.

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“…As in our current study, Kipkogei et al (2016) use the TIGGE multi-model ensemble to assess skill over the Greater Horn of Africa. They also study four TIGGE models: ECMWF (Europe), NCEP (US), UKMET (UK), and CPTEC (Brazil) and show that the skill, as measured by spatial correlations and the equitable threat score (ETS; also a metric of the spatial pattern of rainfall), is the highest for a weighted bias-corrected multi-model average (super-ensemble), followed by the UKMET model, uncorrected for biases.…”

Section: Accepted Articlementioning

confidence: 99%

“…Second, our choice of temporal and spatial scales is motivated by real-world water management challenges, and we examine skill separately for individual catchments (see Figure 2) as well as average the results regionally. Compared to Kipkogei et al (2016) who validate spatial rainfall patterns daily…”

Section: Accepted Articlementioning

confidence: 99%

“…These ensemble forecasts are publicly available in real‐time or near real‐time and can be directly fed into hydrological models or used in real‐time to produce alerts and guidance to farmers, reservoir managers, and governments. While a few studies have examined the skill of these models for Africa (Kipkogei et al., 2016; Mutemi et al., 2007), the utility of this suite of model output has not yet been fully assessed for hydrological applications over Ethiopia. In this study, we analyze operational forecasts from four forecast centers (NCEP, UKMET, ECMWF, and Canada) obtained through the THORPEX International Grand Global Ensemble (TIGGE; Bougeault et al., 2010; Swinbank et al., 2016) archive.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling

Stellingwerf

Riddle

Hopson

et al. 2021

Earth and Space Science

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Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe and the United States) and a multi-model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias-corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events.The skill of the multi-model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0-5 day model accumulation than for the first 24 hours, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias-correction. Compared to individual models, the multi-model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 hour lead times.

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

Cited by 8 publications

References 26 publications

Evaluating Climate Models with an African Lens

Evaluating Climate Models with an African Lens

Evaluation of rainfall simulations over Uganda in CORDEX regional climate models

Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling

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