Academic literature on the topic 'Precipitation forecasting Africa, Southern'
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Journal articles on the topic "Precipitation forecasting Africa, Southern"
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Ratnam, J. V., S. K. Behera, S. B. Ratna, C. J. de W. Rautenbach, C. Lennard, J. J. Luo, Y. Masumoto, K. Takahashi, and T. Yamagata. "Dynamical Downscaling of Austral Summer Climate Forecasts over Southern Africa Using a Regional Coupled Model." Journal of Climate 26, no. 16 (August 6, 2013): 6015–32. http://dx.doi.org/10.1175/jcli-d-12-00645.1.
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Abstract The prediction skill of dynamical downscaling is evaluated for climate forecasts over southern Africa using the Advanced Research Weather Research and Forecasting (WRF) model. As a case study, forecasts for the December–February (DJF) season of 2011/12 are evaluated. Initial and boundary conditions for the WRF model were taken from the seasonal forecasts of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) coupled general circulation model. In addition to sea surface temperature (SST) forecasts generated by nine-member ensemble forecasts of SINTEX-F, the WRF was also configured to use SST generated by a simple mixed layer Price–Weller–Pinkel ocean model coupled to the WRF model. Analysis of the ensemble mean shows that the uncoupled WRF model significantly increases the biases (errors) in precipitation forecasted by SINTEX-F. When coupled to a simple mixed layer ocean model, the WRF model improves the spatial distribution of precipitation over southern Africa through a better representation of the moisture fluxes. Precipitation anomalies forecasted by the coupled WRF are seen to be significantly correlated with the observed precipitation anomalies over South Africa, Zimbabwe, southern Madagascar, and parts of Zambia and Angola. This is in contrast to the SINTEX-F global model precipitation anomaly forecasts that are closer to observations only for parts of Zimbabwe and South Africa. Therefore, the dynamical downscaling with the coupled WRF adds value to the SINTEX-F precipitation forecasts over southern Africa. However, the WRF model yields positive biases (>2°C) in surface air temperature forecasts over the southern African landmass in both the coupled and uncoupled configurations because of biases in the net heat fluxes.
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Yuan, Xing, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan. "Probabilistic Seasonal Forecasting of African Drought by Dynamical Models." Journal of Hydrometeorology 14, no. 6 (November 22, 2013): 1706–20. http://dx.doi.org/10.1175/jhm-d-13-054.1.
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Abstract As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982–2007) seasonal hydrologic hindcasts run at 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode from observations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land–atmosphere coupling, is necessary.
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Stein, T. H. M., W. Keat, R. I. Maidment, S. Landman, E. Becker, D. F. A. Boyd, A. Bodas-Salcedo, G. Pankiewicz, and S. Webster. "An Evaluation of Clouds and Precipitation in Convection-Permitting Forecasts for South Africa." Weather and Forecasting 34, no. 1 (February 1, 2019): 233–54. http://dx.doi.org/10.1175/waf-d-18-0080.1.
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Abstract Since 2016, the South African Weather Service (SAWS) has been running convective-scale simulations to assist with forecast operations across southern Africa. These simulations are run with a tropical configuration of the Met Office Unified Model (UM), nested in the Met Office global model, but without data assimilation. For November 2016, convection-permitting simulations at 4.4- and 1.5-km grid lengths are compared against a simulation at 10-km grid length with convection parameterization (the current UM global atmosphere configuration) to identify the benefits of increasing model resolution for forecasting convection across southern Africa. The simulations are evaluated against satellite rainfall estimates, CloudSat vertical cloud profiles, and SAWS radar data. In line with previous studies using the UM, on a monthly time scale, the diurnal cycle of convection and the distribution of rainfall rates compare better against observations when convection-permitting model configurations are used. The SAWS radar network provides a three-dimensional composite of radar reflectivity for northeast South Africa at 6-min intervals, allowing the evaluation of the vertical development of precipitating clouds and of the timing of the onset of deep convection. Analysis of four case study days indicates that the 4.4-km simulations have a later onset of convection than the 1.5-km simulations, but there is no consistent bias of the simulations against the radar observations across the case studies.
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Dutra, E., F. Di Giuseppe, F. Wetterhall, and F. Pappenberger. "Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index." Hydrology and Earth System Sciences 17, no. 6 (June 28, 2013): 2359–73. http://dx.doi.org/10.5194/hess-17-2359-2013.
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Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.
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Glotfelty, Timothy, Diana Ramírez-Mejía, Jared Bowden, Adrian Ghilardi, and J. Jason West. "Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)." Geoscientific Model Development 14, no. 6 (June 3, 2021): 3215–49. http://dx.doi.org/10.5194/gmd-14-3215-2021.
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Abstract. Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and Community Land Model (CLM) LSMs in the Weather Research and Forecasting (WRF) model over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna land use and land cover (LULC) category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate responses. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near-surface temperature and precipitation. However, in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation changes. Differences in thermal changes between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates.
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MacLeod, David, and Cyril Caminade. "The Moderate Impact of the 2015 El Niño over East Africa and Its Representation in Seasonal Reforecasts." Journal of Climate 32, no. 22 (October 31, 2019): 7989–8001. http://dx.doi.org/10.1175/jcli-d-19-0201.1.
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Abstract El Niño–Southern Oscillation (ENSO) has large socioeconomic impacts worldwide. The positive phase of ENSO, El Niño, has been linked to intense rainfall over East Africa during the short rains season (October–December). However, we show here that during the extremely strong 2015 El Niño the precipitation anomaly over most of East Africa during the short rains season was less intense than experienced during previous El Niños, linked to less intense easterlies over the Indian Ocean. This moderate impact was not indicated by reforecasts from the ECMWF operational seasonal forecasting system, SEAS5, which instead forecast large probabilities of an extreme wet signal, with stronger easterly anomalies over the surface of the Indian Ocean and a colder eastern Indian Ocean/western Pacific than was observed. To confirm the relationship of the eastern Indian Ocean to East African rainfall in the forecast for 2015, atmospheric relaxation experiments are carried out that constrain the east Indian Ocean lower troposphere to reanalysis. By doing so the strong wet forecast signal is reduced. These results raise the possibility that link between ENSO and Indian Ocean dipole events is too strong in the ECMWF dynamical seasonal forecast system and that model predictions for the East African short rains rainfall during strong El Niño events may have a bias toward high probabilities of wet conditions.
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Tompkins, Adrian M., and Laura Feudale. "Seasonal Ensemble Predictions of West African Monsoon Precipitation in the ECMWF System 3 with a Focus on the AMMA Special Observing Period in 2006." Weather and Forecasting 25, no. 2 (April 1, 2010): 768–88. http://dx.doi.org/10.1175/2009waf2222236.1.
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Abstract The West Africa monsoon precipitation of the ECMWF operational Seasonal Forecast System (SYS3) is evaluated at a lead time of 2–4 months in a 49-yr hindcast dataset, with special attention paid to the African Monsoon Multidisciplinary Analysis (AMMA) special observation period during 2006. In both the climatology and the year 2006 the SYS3 reproduces the progression of the West Africa monsoon but with a number of differences, most notably a southerly shift of the precipitation in the main monsoon months of July and August and the lack of preonset rainfall suppression and sudden onset jump. The model skill at predicting summer monsoon rainfall anomalies has increased in recent years indicating improvements in the ocean analysis since the 1990s. Examination of other model fields shows a widespread warm sea surface temperature (SST) bias exceeding 1.5 K in the Gulf of Guinea throughout the monsoon months in addition to a cold bias in the North Atlantic, which would both tend to enhance rainfall over the Gulf of Guinea coast at the expense of the monsoon rainfall over the Sahel. Seasonal forecasts were repeated for 2006 using the same release of the atmospheric forecast model forced by observed SSTs, and the monsoon rainfall reverts to its observed position, indicating the importance of the SST biases. A lack of stratocumulus off the west coast of Africa in SYS3 was hypothesized as a possible cause of the systematic rain and SST biases. Two more sets of ensembles were thus conducted with atmospheric model upgrades designed to tackle radiation, deep convection, and turbulence deficiencies. While these enhancements improve the simulation of stratocumulus significantly, it is found that the improvement in the warm SST bias is limited in scope to the southern cold tongue region. In contrast, the changes to the representation of convection cause an increase in surface downwelling shortwave radiation that, combined with latent heat flux changes associated with the wind stress field, increases the SST warm bias on and to the north of the equator. Thus, while the precipitation shortfall in the Sahel is reduced with the new physics, the overestimated rainfall of SYS3 in the coastal region is further enhanced, degrading the model systematic errors overall in the West Africa region. Finally, the difference in the systematic biases between the coupled and uncoupled systems was noted to be an impediment to the development of seamless forecasting systems.
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Paeth, Heiko, Robin Girmes, Gunter Menz, and Andreas Hense. "Improving Seasonal Forecasting in the Low Latitudes." Monthly Weather Review 134, no. 7 (July 1, 2006): 1859–79. http://dx.doi.org/10.1175/mwr3149.1.
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Abstract Seasonal forecast of climate anomalies holds the prospect of improving agricultural planning and food security, particularly in the low latitudes where rainfall represents a limiting factor in agrarian production. Present-day methods are usually based on simulated precipitation as a predictor for the forthcoming rainy season. However, climate models often have low skill in predicting rainfall due to the uncertainties in physical parameterization. Here, the authors present an extended statistical model approach using three-dimensional dynamical variables from climate model experiments like temperature, geopotential height, wind components, and atmospheric moisture. A cross-validated multiple regression analysis is applied in order to fit the model output to observed seasonal precipitation during the twentieth century. This model output statistics (MOS) system is evaluated in various regions of the globe with potential predictability and compared with the conventional superensemble approach, which refers to the same variable for predictand and predictors. It is found that predictability is highest in the low latitudes. Given the remarkable spatial teleconnections in the Tropics, a large number of dynamical predictors can be determined for each region of interest. To avoid overfitting in the regression model an EOF analysis is carried out, combining predictors that are largely in-phase with each other. In addition, a bootstrap approach is used to evaluate the predictability of the statistical model. As measured by different skill scores, the MOS system reaches much higher explained variance than the superensemble approach in all considered regions. In some cases, predictability only occurs if dynamical predictor variables are taken into account, whereas the superensemble forecast fails. The best results are found for the tropical Pacific sector, the Nordeste region, Central America, and tropical Africa, amounting to 50% to 80% of total interannual variability. In general, the statistical relationships between the leading predictors and the predictand are physically interpretable and basically highlight the interplay between regional climate anomalies and the omnipresent role of El Niño–Southern Oscillation in the tropical climate system.
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Trentini, Laura, Sara Dal Gesso, Marco Venturini, Federica Guerrini, Sandro Calmanti, and Marcello Petitta. "A Novel Bias Correction Method for Extreme Events." Climate 11, no. 1 (December 23, 2022): 3. http://dx.doi.org/10.3390/cli11010003.
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When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on human activities. However, the efficacy of most of the currently available methods is reduced in the case of extreme events because of the limited number of data for these low probability and high impact events. In this study, a novel bias correction methodology is proposed, which corrects the bias of extreme events. To do so, we extended one of the most popular bias correction techniques, i.e., quantile mapping (QM), by improving the description of extremes through a generalised extreme value distribution (GEV) fitting. The technique was applied to the daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7 and GCFS2.1. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction.
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Kuik, F., A. Lauer, J. P. Beukes, P. G. Van Zyl, M. Josipovic, V. Vakkari, L. Laakso, and G. T. Feig. "The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study." Atmospheric Chemistry and Physics Discussions 15, no. 5 (March 10, 2015): 7309–63. http://dx.doi.org/10.5194/acpd-15-7309-2015.
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Abstract. South Africa has one of the largest industrialized economies in Africa. Emissions of air pollutants are particularly high in the Johannesburg-Pretoria metropolitan area, the Mpumalanga Highveld and the Vaal Triangle, resulting in local air pollution. This study presents and evaluates a setup for conducting modeling experiments over southern Africa with the Weather Research and Forecasting model including chemistry and aerosols (WRF-Chem), and analyzes the contribution of anthropogenic emissions to the total black carbon (BC) concentrations from September to December 2010. The modeled BC concentrations are compared with measurements obtained at the Welgegund station situated ca. 100 km southwest of Johannesburg. An evaluation of WRF-Chem with observational data from ground-based measurement stations, radiosondes, and satellites shows that the meteorology is modeled mostly reasonably well, but precipitation amounts are widely overestimated and the onset of the wet season is modeled approximately 1 month too early in 2010. Modeled daily mean BC concentrations show a good temporal correlation with measurements, but the total BC concentration is underestimated in the model by up to 50%. Sensitivity studies with anthropogenic emissions of BC and co-emitted species turned off show that anthropogenic sources can contribute up to 100% to BC concentrations in the industrialized and urban areas, and anthropogenic BC and co-emitted species together up to 60% to PM1 levels. Particularly the co-emitted species contribute significantly to the aerosol optical depth (AOD). Furthermore, in areas of large scale biomass burning atmospheric heating rates are increased through absorption by BC up to about the 600 hPa level.
Dissertations / Theses on the topic "Precipitation forecasting Africa, Southern"
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Browne, Nana Ama Kum. "Model evaluation for seasonal forecasting over southern Africa." Doctoral thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10208.
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Includes bibliographical references.This study contributes to a broader effort of institutions toward improving seasonal forecasts over southern Africa. The primary objective is to understand where global models show shortcomings in their simulations, and how this impacts on their seasonal forecast skill. It is proposed that the skill of a model in simulating natural climate variability is an appropriate metric for a model's potential skill in seasonal forecasting. Thus the study investigates the performance of two global models in simulating the regional processes in relation to the processes variability, and how this is related to their forecast skill.
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McHugh, Maurice J. "Precipitation over Southern Africa and global-scale atmospheric circulation during Boreal Winter /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488191667182839.
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Dyson, Liesl Letitia. "A dynamical forecasting perspective on synoptic scale weather systems over southern Africa." Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-03272006-153324/.
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Shongwe, Mxolisi Excellent. "Performance of recalibration systems of general circulation model forecasts over southern Africa." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-07032007-102650.
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Marín, Saul. "The response of precipitation and surface hydrology to tropical macro-climate forcing in Colombia." Access citation, abstract and download form; downloadable file 15.62 Mb, 2004. http://wwwlib.umi.com/dissertations/fullcit/3131688.
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Beraki, Asmerom Fissehatsion. "ECHAM4.5 global circulation model as a seasonal forecasting system for southern Africa : coupled vs. uncoupled." Thesis, University of Pretoria, 2015. http://hdl.handle.net/2263/53535.
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The predictive skill of seasonal forecast arises from the slowly evolving climate processes where the signature, that noticeably influence the mean state of weather conditions, mainly resides in the ocean. The interaction of the ocean and atmosphere is therefore the minimum level of complexity required for seasonal timescale. The practice of contemporary seasonal prediction is presumably achievable with the use of two distinct GCM (Global Climate Model) configurations commonly referred to as one- and two-tiered forecasting systems based on the manner in which the atmosphere and ocean exchange information. One-tiered forecasting systems (Coupled climate models) are placed at the highest hierarchy in the science of numerical modelling in terms of complexity. They are hypothesized to represent the state of the art of seasonal forecasting which inherently renders them to be convenient for seasonal climate prediction purposes. Notwithstanding, it may be important to appraise whether or not two-tier forecasting systems (uncoupled models) offer comparable levels of skill that are currently attainable by state-of-the-art coupled climate models under a constrained computational resources environment. Such a restrictive environment is commonly found in developing countries such as South Africa. With this in mind, the study attempts to test the notion under a perfect model framework where the atmospheric global climate model is forced with the best estimate of predicted sea-surface temperature (SST), while the two systems are kept similar in all other aspects. The framework eliminates differences between the two forecasting systems due to model biases and in fact enables the discrimination of the role of coupling on seasonal forecast skill.
Due to the enormous computational resources required to develop and run an operational forecast system based on coupled models, their engagement for real-time forecasts has been negligible in South Africa. However, motivated by the recent advances in computing infrastructures in South Africa due to the establishment and maintenance of the Centre for High Performance Computing (CHPC) as well as international collaboration, the study pioneered in Africa the emergence of the South African Weather Service Coupled Model (SCM) also referred to as the ECHAM4.5-MOM3-SA. The model couples the ECHAM4.5 atmospheric general circulation model (AGCM) and Modular Ocean Model version 3 (MOM3) using the multiple program multiple data (MPMD) coupler paradigm. The model employs an atmospheric initialization strategy that is different from other models that couple the same atmosphere and ocean models. The study reveals that the South African coupled model has skill levels for ENSO (El Niño Southern Oscillation) forecasts comparable with other coupled models currently administered by international centres. Furthermore the model is also found to be skilful in predicting upper air dynamics, surface air temperature, rainfall and equatorial Indian Ocean Dipole (IOD).
In the two-tiered experiment, the AGCM is constrained by the lower boundary conditions derived from predicted SST anomalies of two ocean-atmosphere coupled general circulation models (CGCMs) combined through equal weighting. In addition, the SST uncertainty amplitude (lower and upper bounds) defined from this combination is also considered as separate forcing fields. As with the CGCM, the AGCM is initialized with the realistic state of the atmosphere and soil moisture. Results from hindcasts show that this optimized forecasting system demonstrates large-scale consistent skill improvements for surface temperature and rainfall totals relative to forcing the AGCM with persisted SST anomalies and the AMIP-2 (Atmospheric Model Intercomparison Project) type simulations. Model evaluation further reveals that the AGCM is able to forecast anomalous upper air atmospheric dynamics (circulation) over the tropics up to several months ahead. In addition, the contribution of the predicted SST, which is based on a multi-model approach, is shown to be of significant importance for optimized AGCM results. However, the AGCM appears to be weakly sensitive to soil moisture initialization which may suggest an internal weakness of the model. The study has addressed some optimization issues for atmospheric models and proposed an optimal AGCM configuration that can serve as baseline against which more advanced models can be tested.
Finally, the comparative experiments reveal that the GCM configurations widely differ in their performances and the superiority of one model over the other is mostly dependent on the ability to a priori determine an optimal global SST field for forcing the AGCM. In fact, the AGCM offers comparable predictive capabilities with the CGCM when the CGCMs skilful predicted SST evolution can in turn be used to force the AGCM. This finding supports the notion that the further enhancement of seasonal forecasting practices favours the use and further improvement of CGCMs (should computational resources be permissible) since the potential for further improvement of AGCM-based forecasts heavily depends on the improvement of CGCMs.Thesis (PhD)--University of Pretoria, 2015.
Geography, Geoinformatics and Meteorology
PhD
Unrestricted
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Chen, Chia-Jeng. "Hydro-climatic forecasting using sea surface temperatures." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/48974.
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A key determinant of atmospheric circulation patterns and regional climatic conditions is sea surface temperature (SST). This has been the motivation for the development of various teleconnection methods aiming to forecast hydro-climatic variables. Among such methods are linear projections based on teleconnection gross indices (such as the ENSO, IOD, and NAO) or leading empirical orthogonal functions (EOFs). However, these methods deteriorate drastically if the predefined indices or EOFs cannot account for climatic variability in the region of interest. This study introduces a new hydro-climatic forecasting method that identifies SST predictors in the form of dipole structures. An SST dipole that mimics major teleconnection patterns is defined as a function of average SST anomalies over two oceanic areas of appropriate sizes and geographic locations. The screening process of SST-dipole predictors is based on an optimization algorithm that sifts through all possible dipole configurations (with progressively refined data resolutions) and identifies dipoles with the strongest teleconnection to the external hydro-climatic series. The strength of the teleconnection is measured by the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate ensemble hydro-climatic forecasts. The dipole teleconnection method is applied to the forecasting of seasonal precipitation over the southeastern US and East Africa, and the forecasting of streamflow-related variables in the Yangtze and Congo Rivers. These studies show that the new method is indeed able to identify dipoles related to well-known patterns (e.g., ENSO and IOD) as well as to quantify more prominent predictor-predictand relationships at different lead times. Furthermore, the dipole method compares favorably with existing statistical forecasting schemes. An operational forecasting framework to support better water resources management through coupling with detailed hydrologic and water resources models is also demonstrated.
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Lazenby, Melissa J. "Evaluating model performance and constraining uncertainty using a processed-based framework for Southern African precipitation in historical and future climate projections." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/68382/.
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This thesis develops an innovative process-based analysis of contemporary model performance of precipitation over southern Africa. This region is typically understudied and not fully understood due to the complexity of various influences and drivers of precipitation. Historical simulations of precipitation are assessed including principal drivers, sources of biases and dominant modes of interannual variability. The South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the south-west Indian Ocean, is evaluated as the feature of interest in historical simulations. Most CMIP5 models simulate an SIOCZ feature, but are typically too zonally oriented and discontinued between land and the adjacent Indian Ocean. Excessive precipitation over the continent is likely associated with excessively high low-level moisture flux around the Angola Low, which is almost entirely due to model circulation biases. Drivers of precipitation over southern Africa include three dominant moisture flux transport pathways which originate from flow around the SIOHP and SAOHP and monsoon winds. Interannual variability in the SIOCZ is shown by a clear dipole pattern, indicative of a northeast-southwest movement of the SIOCZ. Drivers of this shift are significantly related to the El Niño Southern Oscillation and the subtropical Indian Ocean dipole in observations. However models do not capture these teleconnections well, limiting confidence in model representation of variability. A large majority of the population rely heavily on precipitation over southern Africa for agricultural purposes. Therefore spatial and temporal changes in precipitation are crucial to identify and understand with intentions to ultimately provide useful climate information regarding water security over the region. Key climate change signals over southern Africa are established in this thesis (OND and DJF), in which the dominant regional mechanisms of precipitation change over southern Africa are quantified. Robustness and credibility of these changes are additionally quantified. The most notable projected change in precipitation over southern Africa is the distinct drying signal evident in the pre-summer season (OND). This has the implication of delaying the onset of the rainy season affecting planting and harvesting times. Future projections of the SIOCZ are determined, which indicate a northward shift of approximately 200km. A dipole pattern of precipitation wetting/drying is evident, where wetting occurs to the north of the climatological axis of maximum rainfall, hence implying a northward shift of the ITCZ, consistent with the SIOCZ shift. Using a decomposition method it is established that ΔP's dipole pattern emerges largely from the dynamic component, which holds most uncertainty, particularly over the south-west Indian Ocean. Changes in precipitation over land are not solely driven by dynamical changes but additionally driven by thermodynamic contributions, implying projected changes over land and ocean regions require different approaches. SST patterns of warming over the Indian Ocean corroborate the warmest-get-wetter mechanism driving wetting over the south-west Indian Ocean, which is robust in both key seasons. Coherent model behaviour is understood via across model correlation plots of principal components, whereby patterns of coherent warming patterns are identified. Composite analyses of diagnostic variables across models illustrate patterns driving projected precipitation changes. Drying is more robust over land than over the south-west Indian Ocean. Clear robust drying signal in OND, however magnitude is uncertain. Drivers of uncertainty include SST pattern changes, which modulate atmospheric circulation patterns. Therefore reductions in uncertainty rely on the accurate representation of these processes within climate models to become more robust. There is a desire from both climate scientists and policy-makers to reduce uncertainty in future projections. No one particular methodology is unanimously agreed upon, however one approach is analysed in this thesis. Uncertainties of future precipitation projections are addressed using a process-based model ranking framework. Several metrics most applicable to southern African climate are selected and ranked, which include aspects of both mean state and variability. A sensitivity test via a Monte Carlo approach is performed for various sub-samples of “top” performing models within the CMIP5 model dataset. Uncertainty is significantly reduced when particular sub-sets of “top” performing models are selected, however only for austral summer over the continent. The result has the implication that potential value is established in performing a process-based model ranking over southern Africa. However additional investigation is required before such an approach may become viable and sufficiently credible and robust. Reductions in model spread are additionally established in SIOCZ projections, whereby model processes of change exhibit agreement, despite differing initial SIOCZ conditions. Therefore model process convergence and coherence is established with respect to projected changes in the SIOCZ, irrespective of initial climatology biases.
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Erasmus, Magdel. "Formation and Development of Tropical Temperate Troughs across Southern Africa as Simulated by a State-of-the-art Coupled Model." Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/73478.
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A Tropical Temperate Trough (TTT) is a type of weather system that links the tropics and the extra-tropics across southern Africa. TTT events have been studied statistically in detail, but very little research has been done to study this phenomenon dynamically and especially on a seasonal scale. This study therefore focuses on the predictability of the characteristics of TTTs across southern Africa on a seasonal scale, by using a state-of-the-art seasonal forecasting model, namely the GloSea5 developed by the UK Met Office. Gridded hindcast data for the months of November, December, January and February from 1996/1997 to 2009/2010 are compared to observed data. The different ensemble members of the GloSea5 model (with lead-times of 1 week up to 2 months) are first compared separately to the observed data, after which the model average, with a 0-month, a 1-month and a 2-month lead-time, is calculated and also compared to the observed dataset.
TTT events have distinctive characteristics during the formation and the development phases. Most prominent of these characteristics are the cloud bands associated with these weather systems, which have a north-west to south-east orientation and move from west to east across southern Africa. To identify the TTTs, daily outgoing long-wave radiation values are processed by a Meteorological Robot (MetBot), with a strict criterion to identify the cloud bands that characterise these systems. The MetBot’s algorithm produces the information needed to further investigate the different characteristics of TTTs, such as the frequency, the location and the intensity of these systems. Analysis of the MetBot output includes calculating the Root Mean Square Error, the percentage error and in some cases the percentage deviation of the number of cloud bands, as well as the anchor point, the centroid position, the area, the tilt and the minimum and maximum OLR values of the cloud bands.
This investigation revealed that the characteristics of TTT events can to some extent be predicted on a seasonal scale for the summer rainfall season of southern Africa. The model used in this study fared particularly well with a 1-month lead-time forecast (compared to a 0-month and a 2-month lead-time forecast). The intensity and the location of the cloud bands associated with TTT events are forecast with a smaller percentage error than the frequency of these systems, as the frequency of TTTs tend to be significantly under-predicted by the model. For some predicted quantities, such as the area of the cloud bands, a bias-adjustment is necessary which produces significantly better results with smaller percentage errors. In the conclusions, suggestions are made on possible future studies, and how to develop this study further to create seasonal forecasts with higher skill with special regards to TTT events.Dissertation (MSc)--University of Pretoria, 2019.
Geography, Geoinformatics and Meteorology
MSc
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Tirivarombo, Sithabile. "Climate variability and climate change in water resources management of the Zambezi River basin." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002955.
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Water is recognised as a key driver for social and economic development in the Zambezi basin. The basin is riparian to eight southern African countries and the transboundary nature of the basin’s water resources can be viewed as an agent of cooperation between the basin countries. It is possible, however, that the same water resource can lead to conflicts between water users. The southern African Water Vision for ‘equitable and sustainable utilisation of water for social, environmental justice and economic benefits for the present and future generations’ calls for an integrated and efficient management of water resources within the basin. Ensuring water and food security in the Zambezi basin is, however, faced with challenges due to high variability in climate and the available water resources. Water resources are under continuous threat from pollution, increased population growth, development and urbanisation as well as global climate change. These factors increase the demand for freshwater resources and have resulted in water being one of the major driving forces for development. The basin is also vulnerable due to lack of adequate financial resources and appropriate water resources infrastructure to enable viable, equitable and sustainable distribution of the water resources. This is in addition to the fact that the basin’s economic mainstay and social well-being are largely dependent on rainfed agriculture. There is also competition among the different water users and this has the potential to generate conflicts, which further hinder the development of water resources in the basin. This thesis has focused on the Zambezi River basin emphasising climate variability and climate change. It is now considered common knowledge that the global climate is changing and that many of the impacts will be felt through water resources. If these predictions are correct then the Zambezi basin is most likely to suffer under such impacts since its economic mainstay is largely determined by the availability of rainfall. It is the belief of this study that in order to ascertain the impacts of climate change, there should be a basis against which this change is evaluated. If we do not know the historical patterns of variability it may be difficult to predict changes in the future climate and in the hydrological resources and it will certainly be difficult to develop appropriate management strategies. Reliable quantitative estimates of water availability are a prerequisite for successful water resource plans. However, such initiatives have been hindered by paucity in data especially in a basin where gauging networks are inadequate and some of them have deteriorated. This is further compounded by shortages in resources, both human and financial, to ensure adequate monitoring. To address the data problems, this study largely relied on global data sets and the CRU TS2.1 rainfall grids were used for a large part of this study. The study starts by assessing the historical variability of rainfall and streamflow in the Zambezi basin and the results are used to inform the prediction of change in the future. Various methods of assessing historical trends were employed and regional drought indices were generated and evaluated against the historical rainfall trends. The study clearly demonstrates that the basin has a high degree of temporal and spatial variability in rainfall and streamflow at inter-annual and multi-decadal scales. The Standardised Precipitation Index, a rainfall based drought index, is used to assess historical drought events in the basin and it is shown that most of the droughts that have occurred were influenced by climatic and hydrological variability. It is concluded, through the evaluation of agricultural maize yields, that the basin’s food security is mostly constrained by the availability of rainfall. Comparing the viability of using a rainfall based index to a soil moisture based index as an agricultural drought indicator, this study concluded that a soil moisture based index is a better indicator since all of the water balance components are considered in the generation of the index. This index presents the actual amount of water available for the plant unlike purely rainfall based indices, that do not account for other components of the water budget that cause water losses. A number of challenges were, however, faced in assessing the variability and historical drought conditions, mainly due to the fact that most parts of the Zambezi basin are ungauged and available data are sparse, short and not continuous (with missing gaps). Hydrological modelling is frequently used to bridge the data gap and to facilitate the quantification of a basin’s hydrology for both gauged and ungauged catchments. The trend has been to use various methods of regionalisation to transfer information from gauged basins, or from basins with adequate physical basin data, to ungauged basins. All this is done to ensure that water resources are accounted for and that the future can be well planned. A number of approaches leading to the evaluation of the basin’s hydrological response to future climate change scenarios are taken. The Pitman rainfall-runoff model has enjoyed wide use as a water resources estimation tool in southern Africa. The model has been calibrated for the Zambezi basin but it should be acknowledged that any hydrological modelling process is characterised by many uncertainties arising from limitations in input data and inherent model structural uncertainty. The calibration process is thus carried out in a manner that embraces some of the uncertainties. Initial ranges of parameter values (maximum and minimum) that incorporate the possible parameter uncertainties are assigned in relation to physical basin properties. These parameter sets are used as input to the uncertainty version of the model to generate behavioural parameter space which is then further modified through manual calibration. The use of parameter ranges initially guided by the basin physical properties generates streamflows that adequately represent the historically observed amounts. This study concludes that the uncertainty framework and the Pitman model perform quite well in the Zambezi basin. Based on assumptions of an intensifying hydrological cycle, climate changes are frequently expected to result in negative impacts on water resources. However, it is important that basin scale assessments are undertaken so that appropriate future management strategies can be developed. To assess the likely changes in the Zambezi basin, the calibrated Pitman model was forced with downscaled and bias corrected GCM data. Three GCMs were used for this study, namely; ECHAM, GFDL and IPSL. The general observation made in this study is that the near future (2046-2065) conditions of the Zambezi basin are expected to remain within the ranges of historically observed variability. The differences between the predictions for the three GCMs are an indication of the uncertainties in the future and it has not been possible to make any firm conclusions about directions of change. It is therefore recommended that future water resources management strategies account for historical patterns of variability, but also for increased uncertainty. Any management strategies that are able to satisfactorily deal with the large variability that is evident from the historical data should be robust enough to account for the near future patterns of water availability predicted by this study. However, the uncertainties in these predictions suggest that improved monitoring systems are required to provide additional data against which future model outputs can be assessed.
Books on the topic "Precipitation forecasting Africa, Southern"
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Centre, Alberta River Forecast. Water supply outlook for southern and central Alberta. Edmonton, Alta: Alberta Environment, Water Resources Management Services, 1986.
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United Nations. Economic Commission for Africa. Economic and social conditions in southern Africa 2002: Economic impact of environmental degradation in southern Africa. Addis Ababa, Ethiopia: Economic Commission for Africa, 2002.
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Mandaza, Ibbo. Southern Africa in the year 2000: An overview and research agenda. Harare: SAPES Books, 1993.
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J, Vale Peter C., Swatuk Larry A. 1957-, and Odén Bertil 1939-, eds. Theory, change, and Southern Africa's future. Houndmills, Basingstoke, Hampshire: Palgrave, 2001.
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G, Maasdorp G., ed. Can South and Southern Africa become globally competitive economies? New York: St. Martin's Press, 1996.
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Tjønneland, Elling Njål. Southern Africa after apartheid: The end of apartheid, future regional cooperation, and foreign aid. Fantoft, Norway: Chr. Michelsen Institute, Dept. of Social Science and Development, 1992.
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1957-, Swatuk Larry A., and Shaw Timothy M, eds. Prospects for peace and development in southern Africa in the 1990s: Canadian and comparative perspectives. Lanham, Md: University Press of America, 1991.
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Municipal Development Programme for Eastern and Southern Africa. and Zimbabwe. Ministry of Local Government, Public Works, and National Housing., eds. Report on Ministers' Conference on Urban and Peri-Urban Agriculture in Eastern and Southern Africa: Prospects for Food Security and Growth. Harare, Zimbabwe: Municipal Development Partnership, 2003.
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L, O'Brien Karen, and Vogel Coleen, eds. Coping with climate variability: The use of seasonal climate forecasts in Southern Africa. Aldershot, England: Ashgate, 2003.
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(Editor), Karen O'Brien, and Coleen Vogel (Editor), eds. Coping With Climate Variability: The Use of Seasonal Climate Forecasts in Southern Africa (Ashgate Studies in Environmental Policy and Practice). Ashgate Publishing, 2003.
Find full textBook chapters on the topic "Precipitation forecasting Africa, Southern"
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Olaniyan, Olumide A., Vincent O. Ajayi, Kamoru A. Lawal, and Ugbah Paul Akeh. "Impact of Moisture Flux and Vertical Wind Shear on Forecasting Extreme Rainfall Events in Nigeria." In African Handbook of Climate Change Adaptation, 1127–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_98.
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AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.
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Olaniyan, Olumide A., Vincent O. Ajayi, Kamoru A. Lawal, and Ugbah Paul Akeh. "Impact of Moisture Flux and Vertical Wind Shear on Forecasting Extreme Rainfall Events in Nigeria." In African Handbook of Climate Change Adaptation, 1–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42091-8_98-1.
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AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.
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Mapani, Benjamin, Rosemary Shikangalah, Isaac Mapaure, and Aansbert Musimba. "Dichrostachys cinerea Growth Rings as Natural Archives for Climatic Variation in Namibia." In African Handbook of Climate Change Adaptation, 2433–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_257.
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AbstractGlobal Circulation Models (GCMs) are used to forecast climate change in Southern Africa, and the evidence shows that the region is going to warm up by up to 2° by the year 2050. Namibia is one of the driest countries in Southern Africa and is at a high risk of becoming much drier than current situation by 57%. Very few studies have been carried out in Southern Africa to show actual impacts of climate change. Practical applicability of GCMs at a local spatial scale remains limited due to the coarse nature of the models. Hence, improvement of the GCMs must begin with better understanding of the local microclimates and how they respond to regional circulation patterns. In many regions of Southern Africa, the lack of potential tools to access old climatic records precludes the estimation of climate trends beyond 100 years. In spite of these impediments, there are areas with excellent tree species such as Dichrostachys cinerea that are able to be used as climatic archives for specific time periods. In this chapter, the study shows that the combination of tree ring chronologies and precipitation records is a powerful methodology in climate modeling in the southern hemisphere and reveals nuances that show climate change. The evaluation of data from tree rings coupled with precipitation trends reveals signals that show that climate has indeed been changing over the past ten decades and will have a negative impact on livelihoods. These data can now be used in predictive models that can be used to project future scenarios and assist policy makers and planners to see how climate will evolve in the next 50–60 years.
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Mapani, Benjamin, Rosemary Shikangalah, Isaac Mapaure, and Aansbert Musimba. "Dichrostachys cinerea Growth Rings as Natural Archives for Climatic Variation in Namibia." In African Handbook of Climate Change Adaptation, 1–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42091-8_257-1.
Full textAbstract:
AbstractGlobal Circulation Models (GCMs) are used to forecast climate change in Southern Africa, and the evidence shows that the region is going to warm up by up to 2° by the year 2050. Namibia is one of the driest countries in Southern Africa and is at a high risk of becoming much drier than current situation by 57%. Very few studies have been carried out in Southern Africa to show actual impacts of climate change. Practical applicability of GCMs at a local spatial scale remains limited due to the coarse nature of the models. Hence, improvement of the GCMs must begin with better understanding of the local microclimates and how they respond to regional circulation patterns. In many regions of Southern Africa, the lack of potential tools to access old climatic records precludes the estimation of climate trends beyond 100 years. In spite of these impediments, there are areas with excellent tree species such as Dichrostachys cinerea that are able to be used as climatic archives for specific time periods. In this chapter, the study shows that the combination of tree ring chronologies and precipitation records is a powerful methodology in climate modeling in the southern hemisphere and reveals nuances that show climate change. The evaluation of data from tree rings coupled with precipitation trends reveals signals that show that climate has indeed been changing over the past ten decades and will have a negative impact on livelihoods. These data can now be used in predictive models that can be used to project future scenarios and assist policy makers and planners to see how climate will evolve in the next 50–60 years.
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Gwimbi, Patrick. "A Review of Tropical Cyclone Idai Forecasting, Warning Message Dissemination and Public Response Aspects of Early Warning Systems in Southern Africa." In Sustainable Development Goals Series, 37–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74262-1_3.
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Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation, 1613–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.
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AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.
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Irenge, Dianah I., Nnenesi A. Kgabi, and Sunday A. Reju. "Analysis of drought frequency and intensity using standard precipitation index." In Water Security and Climate Adaptation in Southern Africa, 103–27. AOSIS, 2021. http://dx.doi.org/10.4102/aosis.2020.bk205.06.
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Kgabi, Nnenesi A., Sunday A. Reju, and Dianah I. Irenge. "Water security and extreme precipitation indices of selected towns in Namibia." In Water Security and Climate Adaptation in Southern Africa, 61–78. AOSIS, 2021. http://dx.doi.org/10.4102/aosis.2020.bk205.04.
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Alemaw, Berhanu F. "Geomorphic Modelling Application for Geospatial Flood Hazards and Flash Flood Thresholds Forecasting." In Advances in Geospatial Technologies, 285–303. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3440-2.ch018.
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In this chapter, a geomorphic modelling is presented and as a tool for geospatial flood hazard and flash flood thresholds forecasting in drainage basins. The flash flood thresholds have been estimated in terms of flash flood guidance values for the various tributary watersheds of a drainage basin considered. It has been demonstrated using the Limpopo drainage basin in southern Africa. This transboundary basin was chosen because of its importance to water supply for the growing population and water demands in its four riparian states. The basin is also subject to frequent flood and drought hazards. Even though, well established hydrological and flood frequency models do exist for flood forecasting, the purpose of this manuscript is to produce indicative flash flood guidance from a drainage basin of diverse regional development and intensive catchment land-use land cover dynamics by shading light on the geospatial portrayal of flood producing determinants. This will be important in lieu of the need for designing flood forecasting and flood early warning systems for this basin which is subject to frequent flooding hazards. Recommendations on flood forecasting and mitigation of flood hazards is provided considering the technical, human capital and institutional challenges that exist in this part of Africa.
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Alemaw, Berhanu F., and Thebeyame Ronald Chaoka. "Climate Change Impact on the Water Resources of the Limpopo Basin." In Advances in Geospatial Technologies, 177–200. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3440-2.ch012.
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This chapter aims to evaluate the impacts of climate change on both hydrologic regimes and water resources of the Limpopo River Basin in southern Africa. Water resources availability in the basin, in terms of, seasonal and annual runoff (R), soil moisture (S) and actual evapotranspiration (Ea) is simulated and evaluated using the hydrological model, HATWAB. These water balances were computed from precipitation (P), potential evapotranspiration (Ep) and other variables that govern the soil-water-vegetation-atmospheric processes at 9.2km latitude/ longitude gird cells covering the basin. The 1961-90 simulated mean annual runoff reveals mixed patterns of high and low runoff across the region. Although relatively small changes in runoff simulations are prevalent among the three climate change scenarios, generally the OSU simulated relatively high runoff compared to the UKTR and HADCM2 GCMs.
Conference papers on the topic "Precipitation forecasting Africa, Southern"
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Ncane, Z. P., and A. K. Saha. "Forecasting Solar Power Generation Using Fuzzy Logic and Artificial Neural Network." In 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). IEEE, 2019. http://dx.doi.org/10.1109/robomech.2019.8704737.
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Nair, Sailen, William Becerra Gonzalez, and James Braid. "Battery Monitoring and Energy Forecasting for an Off-Grid Solar Photovoltaic Installation." In 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). IEEE, 2019. http://dx.doi.org/10.1109/robomech.2019.8704728.
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Onaolapo, A. K., R. Pillay-Carpanen, D. G. Dorrell, and E. E. Ojo. "A Comparative Evaluation of Conventional and Computational Intelligence Techniques for Forecasting Electricity Outage." In 2021 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). IEEE, 2021. http://dx.doi.org/10.1109/saupec/robmech/prasa52254.2021.9377243.
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Chen, Qin, and Komla Folly. "Effect of Input Features on the Performance of the ANN-based Wind Power Forecasting." In 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). IEEE, 2019. http://dx.doi.org/10.1109/robomech.2019.8704725.
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