Climate Prediction and Applications Center

Nairobi, Kenya

Climate Prediction and Applications Center

Nairobi, Kenya
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Gitau W.,University of Nairobi | Gitau W.,Climate Prediction and Applications Center | Gitau W.,University of Burgundy | Ogallo L.,Climate Prediction and Applications Center | And 2 more authors.
International Journal of Climatology | Year: 2013

The aim of this study was to derive components of the intraseasonal rainfall variations from the daily rainfall in the Equatorial Eastern Africa region and assess their spatial coherence, a pointer to their potential predictability. Daily rainfall observations from 36 stations distributed over Equatorial Eastern Africa and extending from 1962 to 2000 were used. The March to May and October to December periods commonly referred to as the long and short rainfall seasons respectively were considered. Seasonal and intraseasonal statistics at the local (station) level were first defined. The stations were also grouped into near-homogeneous (sub-regional) zones based on daily rainfall. Similarly, seasonal and intraseasonal statistics were then derived at sub-regional level using three different approaches. Inter-station correlation coefficients of the intraseasonal statistics at local levels were finally computed and plotted as box-plots.For the two rainfall seasons, the two statistics showing the highest spatial coherence were the seasonal rainfall totals and the number of the wet days at sub-regional level. The local variance explained for these two variables, as an average over all the sub-regions, was more than 40%. At the bottom of the hierarchy were the mean rainfall intensity and frequency of dry spells of 5 days or more which showed the least coherence, with the local variance explained being less than 10% in each season. For each of the intraseasonal components of daily rainfall considered, the short rainfall season statistics were more coherent compared to the long rainfall season. Lag-correlations with key indices depicting sea-surface temperatures in the Pacific and Indian Oceans showed that the hierarchy between the rainfall statistics in the strength of the teleconnections reflected that of spatial coherence. © 2012 Royal Meteorological Society.

Gitau W.,University of Nairobi | Camberlin P.,University of Burgundy | Ogallo L.,Climate Prediction and Applications Center | Okoola R.,University of Nairobi
International Journal of Climatology | Year: 2015

Despite earlier studies over various parts of the world including equatorial Eastern Africa (EEA) showing that intraseasonal statistics of wet and dry spells have spatially coherent signals and thus greater predictability potential, no attempts have been made to identify the predictors for these intraseasonal statistics. This study therefore attempts to identify the predictors (with a 1-month lead time) for some of the subregional intraseasonal statistics of wet and dry spells (SRISS) which showed the greatest predictability potential during the short rainfall season over EEA. Correlation analysis between the SRISS and seasonal rainfall totals on one hand and the predefined predictors on the other hand were initially computed and those that were significant at 95% confidence levels retained. To identify additional potential predictors, partial correlation analyses were undertaken between SRISS and large-scale oceanic and atmospheric fields while controlling the effects of the predefined predictors retained earlier. Cross-validated multivariate linear regression (MLR) models were finally developed and their residuals assessed for independence and for normal distribution. Four large-scale oceanic and atmospheric predictors with robust physical/dynamical linkages with SRISS were identified for the first time. The cross-validated MLR models for the SRISS of wet spells and seasonal rainfall totals mainly picked two of these predictors around the Bay of Bengal. The two predictors combined accounted for 39.5% of the magnitude of the SST changes between the July-August and October-November-December periods over the Western Pole of the Indian Ocean Dipole, subsequently impacting EEA rainfall. MLR models were defined yielding cross-validated correlations between observed and predicted values of seasonal totals and number of wet days ranging from 0.60 to 0.75, depending on the subregion. MLR models could not be developed over a few of the subregions suggesting that the local factors could have masked the global and regional signals encompassed in the additional potential predictors. © 2014 Royal Meteorological Society.

Omondi P.,Climate Prediction and Applications Center | Ogallo L.A.,Climate Prediction and Applications Center | Anyah R.,University of Connecticut | Muthama J.M.,University of Nairobi | Ininda J.,University of Nairobi
International Journal of Climatology | Year: 2013

Linkages between dominant spatio-temporal decadal rainfall variability modes and the global sea surface temperature (SST) modes are investigated over East Africa region for the period 1950-2008. Singular value decomposition (SVD) and canonical correlation analysis (CCA) techniques are employed to examine potential linkages and predictability of decadal rainfall variability over the region. When the ten-year periodicity is filtered out from the observed monthly rainfall data, distinct decadal rainfall regimes are exhibited in the time series of mean seasonal rainfall anomalies. Spectral density analysis of rainfall time series showed dominance of a ten-year periodicity, significant at 95% confidence level. The Principal Component Analysis (PCA) results yielded nine and seven homogeneous decadal rainfall zones for long rains; March-May (MAM), and the short rains: October-December (OND) seasons, respectively. The third season of June-August (JJA) which is mainly experienced in western and coastal sub-regions had eight homogenous zones delineated. Results show that the leading three SVD-coupled modes explain greater than 75% of the squared covariance between the two fields. The first SVD mode for Indian, Atlantic and Pacific Oceans contributed to 50, 43 and 38% of the total square covariance for MAM season, respectively. The same mode accounted for 65, 48 and 40% for OND rainfall season, respectively. For the JJA season, mode one contributed to about 61, 39 and 42% of the variance. The study showed that forcing of decadal rainfall over the region is associated with El Niño mode that is prominent over the Pacific Ocean, while Indian Ocean dipole is the leading mode over the Indian Ocean basin. An inter-hemispheric dipole mode that is common during ENSO was a prominent feature in the Atlantic Ocean forcing regional decadal rainfall. The high variability of these modes highlighted the significant roles of all the global oceans in forcing decadal rainfall variability over the region. In addition, results from multiple linear regression model showed substantial variation of the model prediction skill of the decadal rainfall variability modes within various homogenous zones and for different seasons © 2012 Royal Meteorological Society.

Githeko A.K.,Kenya Medical Research Institute | Ogallo L.,Climate Prediction and Applications Center | Lemnge M.,National Institute for Medical Research | Okia M.,Uganda Ministry of Health | Ototo E.N.,Kenya Medical Research Institute
Malaria Journal | Year: 2014

Background: Malaria epidemics remain a serious threat to human populations living in the highlands of East Africa where transmission is unstable and climate sensitive. An existing early malaria epidemic prediction model required further development, validations and automation before its wide use and application in the region. The model has a lead-time of two to four months between the detection of the epidemic signal and the evolution of the epidemic. The validated models would be of great use in the early detection and prevention of malaria epidemics. Methods. Confirmed inpatient malaria data were collected from eight sites in Kenya, Tanzania and Uganda for the period 1995-2009. Temperature and rainfall data for the period 1960-2009 were collected from meteorological stations closest to the source of the malaria data. Process-based models were constructed for computing the risk of an epidemic in two general highland ecosystems using temperature and rainfall data. The sensitivity, specificity and positive predictive power were used to validate the models. Results: Depending on the availability and quality of the malaria and meteorological data, the models indicated good functionality at all sites. Only two sites in Kenya had data that met the criteria for the full validation of the models. The additive model was found most suited for the poorly drained U-shaped valley ecosystems while the multiplicative model was most suited for the well-drained V-shaped valley ecosystem. The +18°C model was adaptable to any of the ecosystems and was designed for conditions where climatology data were not available. The additive model scored 100% for sensitivity, specificity and positive predictive power. The multiplicative model had a sensitivity of 75% specificity of 99% and a positive predictive power of 86%. Conclusions: The additive and multiplicative models were validated and were shown to be robust and with high climate-based, early epidemic predictive power. They are designed for use in the common, well- and poorly drained valley ecosystems in the highlands of East Africa. © 2014 Githeko et al.; licensee BioMed Central Ltd.

Awange J.L.,Curtin University Australia | Anyah R.,University of Connecticut | Agola N.,Doshisha University | Forootan E.,University of Bonn | Omondi P.,Climate Prediction and Applications Center
Water Resources Research | Year: 2013

The changing climatic patterns and increasing human population within the Lake Victoria Basin (LVB), together with overexploitation of water for economic activities call for assessment of water management for the entire basin. This study focused on the analysis of a combination of available in situ climate data, Gravity Recovery And Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM) observations, and high resolution Regional Climate simulations during recent decade(s) to assess the water storage changes within LVB that may be linked to recent climatic variability/changes and anomalies. We employed trend analysis, principal component analysis (PCA), and temporal/spatial correlations to explore the associations and covariability among LVB stored water, rainfall variability, and large-scale forcings associated with El-Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Potential economic impacts of human and climate-induced changes in LVB stored water are also explored. Overall, observed in situ rainfall from lake-shore stations showed a modest increasing trend during the recent decades. The dominant patterns of rainfall data from the TRMM satellite estimates suggest that the spatial and temporal distribution of precipitation have not changed much during the period of 1998-2012 over the basin consistent with in situ observations. However, GRACEderived water storage changes over LVB indicate an average decline of 38.2 mm/yr for 2003-2006, likely due to the extension of the Owen Fall/Nalubale dam, and an increase of 4.5 mm/yr over 2007-2013, likely due to two massive rainfalls in 2006-2007 and 2010-2011. The temporal correlations between rainfall and ENSO/IOD indices during the study period, based on TRMM and model simulations, suggest significant influence of large-scale forcing on LVB rainfall, and thus stored water. The contributions of ENSO and IOD on the amplitude of TRMM-rainfall and GRACE-derived water storage changes, for the period of 2003-2013, are estimated to be ∼2.5 cm and ∼1.5 cm, respectively. © 2013. American Geophysical Union. All Rights Reserved.

Omondi P.,Climate Prediction and Applications Center | Awange J.L.,Curtin University Australia | Awange J.L.,Karlsruhe Institute of Technology | Ogallo L.A.,Climate Prediction and Applications Center | And 2 more authors.
Journal of Hydrology | Year: 2012

Detailed knowledge about the long-term interface of climate and rainfall variability is essential for managing agricultural activities in Eastern African countries. To this end, the space-time patterns of decadal rainfall variability modes over East Africa and their predictability potentials using Sea Surface Temperature (SST) are investigated. The analysis includes observed rainfall data from 1920 to 2004 and global SSTs for the period 1950-2004. Simple correlation, trend and cyclical analyses, Principal Component Analysis (PCA) with VARIMAX rotation and Canonical Correlation Analysis (CCA) are employed. The results show decadal signals in filtered observed rainfall record with 10 years period during March-May (MAM) and October-December (OND) seasons. During June-August (JJA), however, cycles with 20 years period are common. Too much/little rainfall received in one or two years determines the general trend of the decadal mean rainfall. CCA results for MAM showed significant positive correlations between the VARIMAX-PCA of SST and the canonical component time series over the central equatorial Indian Ocean. Positive loadings were spread over the coastal and Lake Victoria regions while negative loading over the rest of the region with significant canonical correlation skills. For the JJA seasons, Atlantic SSTs had negative loadings centred on the tropical western Atlantic Ocean associated with the wet/dry regimes over western/eastern sectors. The highest canonical correlation skill between OND rainfall and the Pacific SSTs showed that El Niño/La Niña phases are associated with wet/dry decades over the region. © 2012 Elsevier B.V.

Camberlin P.,CNRS Biogeosciences Laboratory | Gitau W.,University of Nairobi | Oettli P.,University of Tokyo | Ogallo L.,Climate Prediction and Applications Center | Bois B.,CNRS Biogeosciences Laboratory
Climate Research | Year: 2014

Downscaling seasonal rainfall predictions to daily time-scale, for crop yield simulation for instance, can be performed using stochastic generators (SGs). The spatial interpolation of the SG parameters is required to generate rainfall time-series at ungauged places. A methodology is defined which makes use of topography to interpolate these parameters, in a region with a rugged terrain covering Kenya and north-eastern Tanzania. A first-order Markov chain was used to model rainfall occurrence, and a gamma distribution was used to model amounts. The 2 para - meters of the Markov models, p01 and p11, and the 2 parameters of the gamma distribution are computed at 121 stations. The Kolmogorov-Smirnov test for goodness-of-fit shows that 88% (99%) of the stations and months have their dry (wet) spell frequencies successfully reproduced by first-order Markov chains, and two-third of the stations have their daily amounts satisfactorily fitted by the gamma distribution. Local regression, using elevation as the predictor and weighting stations according to distance from the target pixel and to environmental variables, is used to interpolate the 4 SG para meters. Cross-validation indicates that distance-weighted regression provides good estimates, but the inclusion of topographical variables (aspect in particular) improves the results further. The final maps show a strong orographic control of both the Markov and gamma parameters. However, while elevation has an effect on rainfall occurrence, rainfall intensity is more strongly related to local slope aspect, with eastward to southeastward oriented foothills and coastlines displaying the highest gamma scale values. These results suggest that a statistical disaggregation of daily rainfall is improved by taking explicitly into account topography through its effect on the spatial distribution of SG parameters. © Inter-Research 2014.

Forootan E.,University of Bonn | Rietbroek R.,University of Bonn | Kusche J.,University of Bonn | Sharifi M.A.,University of Tehran | And 4 more authors.
Remote Sensing of Environment | Year: 2014

Extracting large scale water storage (WS) patterns is essential for understanding the hydrological cycle and improving the water resource management of Iran, a country that is facing challenges of limited water resources. The Gravity Recovery and Climate Experiment (GRACE) mission offers a unique possibility of monitoring total water storage (TWS) changes. An accurate estimation of terrestrial and surface WS changes from GRACE-TWS products, however, requires a proper signal separation procedure. To perform this separation, this study proposes a statistical approach that uses a priori spatial patterns of terrestrial and surface WS changes from a hydrological model and altimetry data. The patterns are then adjusted to GRACE-TWS products using a least squares adjustment (LSA) procedure, thereby making the best use of the available data. For the period of October 2002 to March 2011, monthly GRACE-TWS changes were derived over a broad region encompassing Iran. A priori patterns were derived by decomposing the following auxiliary data into statistically independent components: (i) terrestrial WS change outputs of the Global Land Data Assimilation System (GLDAS); (ii) steric-corrected surface WS changes of the Caspian Sea; (iii) that of the Persian and Oman Gulfs; (iv) WS changes of the Aral Sea; and (v) that of small lakes of the selected region. Finally, the patterns of (i) to (v) were adjusted to GRACE-TWS maps so that their contributions were estimated and GRACE-TWS signals separated. After separation, our results indicated that the annual amplitude of WS changes over the Caspian Sea was 152. mm, 101. mm over both the Persian and Oman Gulfs, and 71. mm for the Aral Sea. Since January 2005, terrestrial WS in most parts of Iran, specifically over the center and northwestern parts, exhibited a mass decrease with an average linear rate of ~. 15. mm/yr. The estimated linear trends of groundwater storage for the drought period of 2005 to March 2011, corresponding to the six main basins of Iran: Khazar, Persian and Oman Gulfs, Urmia, Markazi, Hamoon, and Srakhs were -6.7, -6.1, -11.2, -9.1, -3.1, and -4.2. mm/yr, respectively. The estimated results after separation agree fairly well with 256 in-situ piezometric observations. © 2013 Elsevier Inc.

Omondi P.,Climate Prediction and Applications Center | Awange J.L.,Curtin University Australia | Ogallo L.A.,Climate Prediction and Applications Center | Ininda J.,University of Nairobi | Forootan E.,University of Bonn
Advances in Water Resources | Year: 2013

Influence of low frequency global Sea Surface Temperatures (SSTs) modes on decadal rainfall modes over Eastern Africa region is investigated. Fore-knowledge of rainfall distribution at decadal time scale in specific zones is critical for planning purposes. Both rainfall and SST data that covers a period of 1950-2008 were subjected to a 'low-pass filter' in order to suppress the high frequency oscillations. VARIMAX-Rotated Principal Component Analysis (RPCA) was employed to delineate the region into decadal rainfall zones while Singular Value Decomposition (SVD) techniques was used to examine potential linkages of these zones to various areas of the tropical global oceans. Ten-year distinct decadal signals, significant at 95% confidence level, are dominant when observed in-situ rainfall time series are subjected to spectral analysis. The presence of variability at El Niño Southern Oscillation (ENSO)-related timescales, combined with influences in the 10-12. year and 16-20. year bands were also prevalent. Nine and seven homogeneous decadal rainfall zones for long rainfall season i.e. March-May (MAM) and the short rainfall season i.e. October-December (OND), respectively, are delineated. The third season of June-August (JJA), which is mainly experienced in western and Coastal sub-regions had eight homogenous zones delineated. The forcing of decadal rainfall in the region is linked to the equatorial central Pacific Ocean, the tropical and South Atlantic Oceans, and the Southwest Indian Ocean. The high variability of these modes highlighted the significant roles of all the global oceans in forcing decadal rainfall variability over the region. © 2013 Elsevier Ltd.

Ogallo L.,Climate Prediction and Applications Center
Procedia Environmental Sciences | Year: 2010

The economies of Africa, however, are predominantly dependent on rainfed agriculture and the associated industries. Current and future sustainable socio-economic development of the African nations will therefore heavily depend on the ability to cope with the current climate variability as well as adaptation to future climate changes. Some lessons and experiences that have been gained from many years of operations at the IGAD(Intergovernmental Authority on Development) Climate Prediction and Applications Centre (ICPAC) on issues related to mainstreaming climate change/variability information into the planning/policy development in the Greater Horn of Africa (GHA) are described. A short summary of the lessons and experiences from the Southern African Development Community (SADC) Drought Monitoring Centre (DMC-SADC); and the African Centre of Meteorological Applications for Development (ACMAD) is provided. © 2010 Published by Elsevier.

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