East African Rainfall and the Tropical Circulation/Convection on Intraseasonal to Interannual Timescales

Mutai, Charles; Ward, Martin

doi:10.1175/1520-0442(2000)013<3915:earatt>2.0.co;2

Cited by 251 publications

(199 citation statements)

References 58 publications

Supporting

Mentioning

183

Contrasting

Order By: Relevance

“…Similar results are seen in previous studies such as Hastenrath (1995), Kumar (1999), Mutai and Ward (2000) and Nicholson (2014). There exist less predictability of MAM rainfall because of the influence of local features and processes not well simulated by models.…”

Section: Overall Forecast Skillsupporting

confidence: 89%

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Ogutu

Franssen

Supit

et al. 2016

Intl Journal of Climatology

View full text Add to dashboard Cite

This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of precipitation (tp), near-surface air temperature (tas) and surface downwelling shortwave radiation (rsds). We validated the 30-year (1981-2010) hindcast version of S4 against the WFDEI reanalysis (WATCH Forcing Data ERA-Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score (RPSS) and the relative operating curve skill score (ROCSS) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM, JJA and OND).S4 exhibits a wet bias in OND, a dry bias in MAM and a mix of both in JJA. Temperature biases are similar in all seasons, constant with lead-time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills.Predictability of the three variables varies with season, location and lead-time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead-time compared to MAM and JJA. Upper-and lower-tercile tp forecasts are 20-80% better than climatology. Temperature forecasts are skillful for at least 3 months lead-time and they are 40-100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El-Niño Southern Oscillation (ENSO)-related anomalous years with region-dependent skill.

show abstract

Section: Overall Forecast Skillsupporting

confidence: 89%

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Ogutu

Franssen

Supit

et al. 2016

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…In contrast, for short-rains season in October-December, in which rainfall peaked in December, R 2 values decreased and were very low in December. From the constructed rainfall maps and the analysis of temporal rainfall variability, precipitation patterns regimes agreed well with results from equatorial East African studies showing that the rainfall is abundant in most areas for the long-rains season [17], while short-rains season reveal more interannual variability [18]. High and low R 2 values corresponded with the temporal patterns in rainfall variability.…”

Section: Discussionsupporting

confidence: 80%

Mapping Precipitation, Temperature, and Evapotranspiration in the Mkomazi River Basin, Tanzania

Mmbando

Kleyer

2018

Climate

View full text Add to dashboard Cite

Abstract:It is still a challenge to provide spatially explicit predictions of climate parameters in African regions of complex relief, where meteorological information is scarce. Here we predict rainfall, temperature, and reference evapotranspiration (ET o ) for the southern Mkomazi River Basin in Northeastern Tanzania, East Africa, by means of regression-based, digital elevation models (DEM) at 90 m spatial-resolution and geographic information systems (GIS) techniques. We mapped rainfall for the period 1964-2010. The models accounted for orographic factors which strongly influenced the spatial variability of rainfall in the region. According to orography, the area was divided into three zones for modelling rainfall: windward, leeward, and transition zone. Rainfall indicates high spatial and temporal variability dominated by equatorial East-African climate circulation systems. Maximum and minimum temperatures were modelled for the period 1989-1994, the models accounted only for the altitude gradient. Mean temperature was calculated by arithmetic mean of maximum and minimum temperatures maps in ArcGIS. ET o was estimated in ArcGIS following the method described by Hargreaves and Samani. The maps were made on a monthly basis for rainfall, ET o , and mean, maximum, and minimum temperatures. The obtained maps are useful for the purpose of agriculture, ecological, and water resources management.

show abstract

“…The figure also indicates the domains selected to study specific monsoonal regions (black boxes), namely the North American monsoon (e.g., Adams and Comrie, 1997;Vera et al, 2006), the South American monsoon (e.g., Vera et al, 2006;Marengo et al, 2012;Carvalho and Cavalcanti, 2016), the West Africa monsoon (e.g., Nicholson, 2013), the East African monsoon (e.g., Nairobi, 1979;Mutai and Ward, 2000), the Indian monsoon (e.g., Webster et al, 1998;Krishnamurthy and Shukla, 2000;Prasad, 2005), the East Asia monsoon (e.g., Wang et al, 2001;Yihui and Chan, 2005), and the Northern Australia monsoon (e.g., Hendon and Liebmann, 1990).…”

Section: The Onset Of the Rainy Seasonmentioning

confidence: 99%

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

et al. 2017

View full text Add to dashboard Cite

Sub-seasonal to seasonal (S2S) retrospective forecasts from three global coupled models are used to evaluate the predictability of the onset and demise dates of the rainy season over monsoonal regions. The onset and demise dates of the rainy season are defined using only precipitation data. The forecasts of the onset and demise dates of the rainy season are based on a hybrid methodology that combines observations and simulations. Although skillful model precipitation predictions remain challenging in many regions, our results show that they are skillful enough to identify onset and demise dates of the rainy season in many monsoon regions at sub-seasonal (∼30 days) lead-times in retrospective forecasts. We verify sub-seasonal prediction skill for the onset and demise dates of the rainy season over South America, East Asia, and Northern Australia. However, we find low prediction skill for the onset and demise of the rainy season on sub-seasonal scales over the Indian monsoon region. This information would be valuable to sectors related to water management.

show abstract

East African Rainfall and the Tropical Circulation/Convection on Intraseasonal to Interannual Timescales

Cited by 251 publications

References 58 publications

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Mapping Precipitation, Temperature, and Evapotranspiration in the Mkomazi River Basin, Tanzania

Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

Contact Info

Product

Resources

About