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.
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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 15, no. 15 (August 12, 2015): 8809–30. http://dx.doi.org/10.5194/acp-15-8809-2015.
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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 temporal correlation of 0.66 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 can contribute 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 an altitude of about 600hPa.
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Cloux, Sara, Daniel Garaboa-Paz, Damián Insua-Costa, Gonzalo Miguez-Macho, and Vicente Pérez-Muñuzuri. "Extreme precipitation events in the Mediterranean area: contrasting two different models for moisture source identification." Hydrology and Earth System Sciences 25, no. 12 (December 20, 2021): 6465–77. http://dx.doi.org/10.5194/hess-25-6465-2021.
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Abstract. Concern about heavy precipitation events has increasingly grown in the last years in southern Europe, especially in the Mediterranean region. These occasional episodes can result in more than 200 mm of rainfall in less than 24 h, producing flash floods with very high social and economic losses. To better understand these phenomena, a correct identification of the origin of moisture must be found. However, the contribution of the different sources is very difficult to estimate from observational data; thus numerical models are usually employed to this end. Here, we present a comparison between two methodologies for the quantification of the moisture sources in two flooding episodes that occurred during October and November 1982 in the western Mediterranean area. A previous study, using the online Eulerian Weather Research and Forecasting (WRF) Model with water vapor tracer (WRF-WVT) model, determined the contributions to precipitation from moisture evaporated over four different sources: (1) the western Mediterranean, (2) the central Mediterranean, (3) the North Atlantic Ocean and (4) the tropical and subtropical Atlantic and tropical Africa. In this work we use the offline Lagrangian FLEXPART-WRF model to quantify the role played by these same sources. Considering the results provided by WRF-WVT as “ground truth”, we validated the performance of the FLEXPART-WRF. Results show that this Lagrangian method has an acceptable skill in identifying local (western Mediterranean) and medium-distance (central Mediterranean and North Atlantic) sources. However, remote moisture sources, like tropical and subtropical areas, are underestimated by it. Notably, for the October event, the tropical and subtropical area reported a relative contribution 6 times lower than with the WRF-WVT. In contrast, FLEXPART-WRF overestimates the contribution of some sources, especially from North Africa. These over- and underestimates should be taken into account by other authors when drawing conclusions from this widely used Lagrangian offline analysis.
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Al-Najjar, Hassan, Gokmen Ceribasi, Emrah Dogan, Mazen Abualtayef, Khalid Qahman, and Ahmed Shaqfa. "Stochastic time-series models for drought assessment in the Gaza Strip (Palestine)." Journal of Water and Climate Change 11, S1 (October 19, 2020): 85–114. http://dx.doi.org/10.2166/wcc.2020.330.
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Abstract The Eastern Mediterranean region of the Middle East and North Africa (MENA) is experiencing patterns of major drought due to the effects of rising temperatures and falling precipitation levels. The multiscale drought evaluation Standardized Precipitation Evapotranspiration Index (SPEI) reveals evolving and severe drought from North Africa and the Sinai desert toward the Middle East. While there has been a period without drought between 1970 and 1990, the severity and frequency of drought increased considerably after 1990. Current drought conditions in the Eastern Mediterranean region of MENA are moderate to severe with a 60–100% likelihood of occurrence, according to time parameters. The Gaza Strip is especially vulnerable to the consequences of increasing drought because it is situated in the vicinity of the Sinai Desert; therefore, a downscaled study of drought in the region is essential to implement mitigation measures for the sustainable management and planning of coastal aquifer and agricultural activities in the Gaza Strip. Considerable availability of precipitation time series from various meteorological stations helped provide a local drought study for the Gaza Strip, in accordance with the Standardized Precipitation Index (SPI). The stochastic time-series model of (4,0,1) (5,1,1)12 shows a robust simulator for modeling and forecasting the future trend of precipitation at the nine meteorological stations. In terms of correlation accuracy, the model achieves a correlation (r) of approximately 93–97% in the calibration range and a correlation (r) of about 92–99% in the validation range. In terms of measuring the difference between the values, the root mean squared error (RMSE) of the model results shows that the RMSE was between 7–21 in the calibration range and 11–21 in the validation range. The model reveals a slightly stable trend in precipitation patterns at the northern meteorological stations of Beit Hanon, Beit Lahia, Shati, and Remal. However, declining precipitation tendency was recorded at the southern meteorological stations of Mughraka, Nussirat, Beir Al-Balah, Khanyounis, and Rafah. The SPI-based drought assessment implies that the precipitation annual threshold levels at SPI = 0 drop territorially from 474 mm in the north to about 250 mm in the south of the Gaza Strip. In this study, a representative 12-month local scale SPI12 at an annual precipitation threshold level of 370 mm was formulated to address the drought conditions in the Gaza Strip. Standing on the outputs of the local SPI12 scale might signify that the region of the Gaza Strip risks drought status with an incidence likelihood varying from 8% in the north to 100% in the south. Regular drought is prevalent in the northern governorates, but the hazards of extreme and severe drought are high in the southern areas with an incidence risk of about 83%. Sequentially, southern governorates of Rafah and Khanyounis experience chronic annual drought, while the return period of drought is reported to be every 9–12 years in the northern governorates of the Gaza Strip. The rain-fed years of 1998 and 2010 reported the worst periods of drought, while the period of 2016 showed a good droughtless water balance. Overall, the no-drought status might define the prospective conditions in the governorates of North Gaza, Gaza, and central Gaza over the next 20 years, while Rafah and Khanyounis are anticipated to be under normal to severe drought conditions.
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González-Rojí, Santos J., Martina Messmer, Christoph C. Raible, and Thomas F. Stocker. "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1." Geoscientific Model Development 15, no. 7 (April 7, 2022): 2859–79. http://dx.doi.org/10.5194/gmd-15-2859-2022.
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Abstract. The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation, and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia, and Brazil. The temporal correlation of simulated monthly accumulated precipitation against in situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e. elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but enforces a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and, finally, precipitation. Hence, this setup provides the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered a biodiversity hotspot of Peru. The robustness of this particular configuration of the model is backed up by similar results obtained during wet climate conditions observed in 2012.
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Tao, Zhining, Scott A. Braun, Jainn J. Shi, Mian Chin, Dongchul Kim, Toshihisa Matsui, and Christa D. Peters-Lidard. "Microphysics and Radiation Effect of Dust on Saharan Air Layer: An HS3 Case Study." Monthly Weather Review 146, no. 6 (June 2018): 1813–35. http://dx.doi.org/10.1175/mwr-d-17-0279.1.
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A Saharan air layer (SAL) event associated with a nondeveloping African easterly wave (AEW) over the main development region of the eastern Atlantic was sampled by the NASA Global Hawk aircraft on 24–25 August 2013 during the NASA Hurricane and Severe Storm Sentinel (HS3) campaign and was simulated with the NASA Unified Weather Research and Forecasting (NU-WRF) Model. Airborne, ground-based, and spaceborne measurements were used to evaluate the model performance. The microphysical and radiative effects of dust and other aerosols on the SAL structure and environment were investigated with the factor-separation method. The results indicate that relative to a simulation without dust–radiative and microphysical impacts, Saharan dust and other aerosols heated the SAL air mainly through shortwave heating by the direct aerosol–radiation (AR) effect, resulting in a warmer (up to 0.6 K) and drier (up to 5% RH reduction) SAL and maintaining the strong temperature inversion at the base of the SAL in the presence of predominant longwave cooling. Radiative heating of the dust accentuated a vertical circulation within the dust layer, in which air rose (sank) in the northern (southern) portions of the dust layer. Furthermore, above and to the south of the dust layer, both the microphysical and radiative impacts of dust tended to counter the vertical motions associated with the Hadley circulation, causing a small weakening and southward shift of convection in the intertropical convergence zone (ITCZ) and reduced anvil cloud to the north. Changes in moisture and cloud/precipitation hydrometeors were largely driven by the dust-induced changes in vertical motion. Dust strengthened the African easterly jet by up to ~1 m s−1 at the southern edge of the jet, primarily through the AR effect, and produced modest increases in vertical wind shear within and in the vicinity of the dust layer. These modulations of the SAL and AEW environment clearly contributed to the nondevelopment of this AEW.
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Hu, Zhiyuan, Jianping Huang, Chun Zhao, Qinjian Jin, Yuanyuan Ma, and Ben Yang. "Modeling dust sources, transport, and radiative effects at different altitudes over the Tibetan Plateau." Atmospheric Chemistry and Physics 20, no. 3 (February 7, 2020): 1507–29. http://dx.doi.org/10.5194/acp-20-1507-2020.
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Abstract. Mineral dust plays an important role in the climate of the Tibetan Plateau (TP) by modifying the radiation budget, cloud macro- and microphysics, precipitation, and snow albedo. Meanwhile, the TP, with the highest topography in the world, can affect intercontinental transport of dust plumes and induce typical distribution characteristics of dust at different altitudes. In this study, we conduct a quasi-global simulation to investigate the characteristics of dust source contribution and transport over the TP at different altitudes by using a fully coupled meteorology–chemistry model, the Weather Research and Forecasting model with chemistry (WRF-Chem), with a tracer-tagging technique. Generally, the simulation reasonably captures the spatial distribution of satellite-retrieved dust aerosol optical depth (AOD) at different altitudes. Model results show that dust particles are emitted into atmosphere through updrafts over major desert regions and then transported to the TP. The East Asian dust (mainly from the Gobi and Taklamakan deserts) is transported southward and is lifted up to the TP, contributing a mass loading of 50 mg m−2 at a height of 3 km and 5 mg m−2 at a height of 12 km over the northern slope of the TP. Dust from North Africa and the Middle East are concentrated over both of the northern and southern slopes below 6 km, where mass loadings range from 10 to 100 and 1 to 10 mg m−2 below 3 km and above 9 km, respectively. As the dust is transported to the north and over the TP, mass loadings are 5–10 mg m−2 above a height of 6 km. The dust mass flux carried from East Asia to the TP is 7.9 Tg yr−1, mostly occurring at heights of 3–6 km. The dust particles from North Africa and the Middle East are transported eastward following the westerly jet and then are carried into the TP at the west side with dust mass fluxes of 7.8 and 26.6 Tg yr−1, respectively. The maximum mass flux of the North African dust mainly occurs at 0–3 km (3.9 Tg yr−1), while the Middle Eastern dust occurs at 6–9 km (12.3 Tg yr−1). The dust outflow occurs on the east side (−17.89 Tg yr−1) and south side (−11.22 Tg yr−1) of the TP, with a peak value (8.7 Tg yr−1) at 6–9 km. Moreover, the dust (by mass) is concentrated within the size range of 1.25–5.0 µm and the dust (by particle number) is concentrated in the size range of 0.156–1.25 µm. Compared with other aerosols, the dust contributes to more than 50 % of the total AOD over the TP. The direct radiative forcing induced by the dust is −1.28 W m−2 at the top of the atmosphere (cooling), 0.41 W m−2 in the atmosphere (warming), and −1.68 W m−2 at the surface (cooling). Our quantitative analyses of the dust contributions from different source regions and the associated radiative forcing can help us to better understand the role of dust on the climate over the TP and surrounding regions.
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Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences 18, no. 2 (February 18, 2014): 611–20. http://dx.doi.org/10.5194/hess-18-611-2014.
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Abstract. The humanitarian crises caused by the recent droughts (2008–2009 and 2010–2011) in East Africa have illustrated that the ability to make accurate drought forecasts with sufficient lead time is essential. The use of dynamical model precipitation forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for March–May and October–December rain seasons when evaluated against measurements from the available in situ stations from East Africa. The forecast for October–December rain season has higher skill than for the March–May season. ECMWF forecasts add value to the consensus forecasts produced during the Greater Horn of Africa Climate Outlook Forum (GHACOF), which is the present operational product for precipitation forecast over East Africa. Complementing the original ECMWF precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.
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Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 8, 2013): 10209–30. http://dx.doi.org/10.5194/hessd-10-10209-2013.
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Abstract. The humanitarian crisis caused by the recent droughts (2008–2009 and 2010–2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The October–December rain season has higher skill that the March–May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of the drought event.
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Zhande, Ethel S. S., and S. B. Jonnalagadda. "Precipitation chemistry in Zimbabwe, Southern Africa." Journal of Environmental Science and Health . Part A: Environmental Science and Engineering and Toxicology 31, no. 5 (May 1996): 977–93. http://dx.doi.org/10.1080/10934529609376401.
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Tennant, Warren. "Numerical forecasting of monthly climate in southern Africa." International Journal of Climatology 19, no. 12 (October 1999): 1319–36. http://dx.doi.org/10.1002/(sici)1097-0088(199910)19:12<1319::aid-joc381>3.0.co;2-9.
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Cook, Benjamin I., Gordon B. Bonan, and Samuel Levis. "Soil Moisture Feedbacks to Precipitation in Southern Africa." Journal of Climate 19, no. 17 (September 1, 2006): 4198–206. http://dx.doi.org/10.1175/jcli3856.1.
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Abstract The effects of increased soil moisture on wet season (October–March) precipitation in southern Africa are investigated using the Community Climate System Model version 3 (CCSM3). In the CTRL case, soil moisture is allowed to interact dynamically with the atmosphere. In the MOIST case, soil moisture is set so that evapotranspiration is not limited by the supply of water. The MOIST scenario actually results in decreased precipitation over the region of perturbed soil moisture, compared to CTRL. The increased soil moisture alters the surface energy balance, resulting in a shift from sensible to latent heating. This manifests in two ways relevant for precipitation processes. First, the shift from sensible to latent heating cools the surface, causing a higher surface pressure, a reduced boundary layer height, and an increased vertical gradient in equivalent potential temperature. These changes are indicative of an increase in atmospheric stability, inhibiting vertical movement of air parcels and decreasing the ability of precipitation to form. Second, the surface changes induce anomalous surface divergence and increased subsidence. This causes a reduction in cloud cover and specific humidity above 700 hPa and results in a net decrease of column-integrated precipitable water, despite the increased surface water flux, indicating a reduction in moisture convergence. Based on this and a previous study, soil moisture may act as a negative feedback to precipitation in southern Africa, helping to buffer the system against any external forcing of precipitation (e.g., ENSO).
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Shongwe, M. E., G. J. van Oldenborgh, B. J. J. M. van den Hurk, B. de Boer, C. A. S. Coelho, and M. K. van Aalst. "Projected Changes in Mean and Extreme Precipitation in Africa under Global Warming. Part I: Southern Africa." Journal of Climate 22, no. 13 (July 1, 2009): 3819–37. http://dx.doi.org/10.1175/2009jcli2317.1.
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Abstract This study investigates likely changes in mean and extreme precipitation over southern Africa in response to changes in radiative forcing using an ensemble of global climate models prepared for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Extreme seasonal precipitation is defined in terms of 10-yr return levels obtained by inverting a generalized Pareto distribution fitted to excesses above a predefined high threshold. Both present (control) and future climate precipitation extremes are estimated. The future-to-control climate ratio of 10-yr return levels is then used as an indicator for the likely changes in extreme seasonal precipitation. A Bayesian approach to multimodel ensembling is adopted. The relative weights assigned to each of the model simulations is determined from bias, convergence, and correlation. Using this method, the probable limits of the changes in mean and extreme precipitation are estimated from their posterior distribution. Over the western parts of southern Africa, an increase in the severity of dry extremes parallels a statistically significant decrease in mean precipitation during austral summer months. A notable delay in the onset of the rainy season is found in almost the entire region. An early cessation is found in many parts. This implies a statistically significant shortening of the rainy season. A substantial reduction in moisture influx from the southwestern Indian Ocean during austral spring is projected. This and the preaustral spring moisture deficits are possible mechanisms delaying the rainfall onset in southern Africa. A possible offshore (northeasterly) shift of the tropical–temperate cloud band is consistent with more severe droughts in the southwest of southern Africa and enhanced precipitation farther north in Zambia, Malawi, and northern Mozambique. This study shows that changes in the mean vary on relatively small spatial scales in southern Africa and differ between seasons. Changes in extremes often, but not always, parallel changes in the mean precipitation.
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Lizcano, Gil, and Martin Todd. "Non–ENSO control on southern Africa precipitation variability." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1826 (January 15, 2005): 61–62. http://dx.doi.org/10.1098/rsta.2004.1477.
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The objective of this study is to quantify the nature of the mechanisms of southern Africa rainfall variability, unrelated to the El Niño Southern Oscillation, by means of a simple empirical composite analysis, as a baseline for a more detailed study.
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Ratnam, J. V., S. K. Behera, Y. Masumoto, and T. Yamagata. "Remote Effects of El Niño and Modoki Events on the Austral Summer Precipitation of Southern Africa." Journal of Climate 27, no. 10 (May 9, 2014): 3802–15. http://dx.doi.org/10.1175/jcli-d-13-00431.1.
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Abstract Remote effects modulating the austral summer precipitation over southern Africa during El Niño/El Niño Modoki events are investigated by analyzing the observed events during December–February of the years from 1982/83 to 2010/11. Based on the composite analyses, it is found that southern Africa experiences significantly below normal precipitation during El Niño events compared to El Niño Modoki events. During these latter events, precipitation anomalies are not so significant although southern Africa as a whole receives below normal precipitations. The differences in the spatial distribution of precipitation over southern Africa are seen to be related to the sea surface temperature (SST) anomalies of the equatorial Pacific through atmospheric teleconnections. The low-level (850 hPa) Matsuno–Gill response to anomalously high precipitation over the Pacific during El Niño events results in an anomalous anticyclone extending from the equatorial to the subtropical South Indian Ocean. These anomalous anticyclonic winds weaken the tropical moisture flow into the southern Africa landmass. Rossby wave activity flux analysis of the upper-level (300 hPa) circulation shows an anomalous tropospheric stationary wave from the Pacific propagating toward southern Africa and maintaining an anomalous anticyclone over southern Africa. The anomalous Matsuno–Gill response and the anomalous tropospheric stationary wave response are intense during El Niño events, causing drought over southern Africa. During El Niño Modoki events, these processes are weaker compared to El Niño events.
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Tiriolo, Luca, Rosa Claudia Torcasio, Stefania Montesanti, and Stefano Federico. "Verification of a Real Time Weather Forecasting System in Southern Italy." Advances in Meteorology 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/758250.
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This paper shows the performance of an operational forecasting system, based on the regional atmospheric modeling system (RAMS), at 3 km horizontal resolution over southern Italy. The model is initialized from the 12 UTC operational analysis/forecasting cycle of the European Centre for Medium range Weather Forecasts (ECMWF). The forecast is issued for the following three days. The performance is evaluated for a whole year for the surface parameters: temperature, relative humidity, wind speed and direction, and precipitation. The verification has been performed against SYNOP stations over southern Italy. A dense non-GTS network over Calabria is used for precipitation. Results show that RMSE is about 2-3 K for temperature, 12–16% for relative humidity, 2.0–2.8 m/s for wind speed, and 55–75° for wind direction, the performance varying with the season and with the forecasting time. The error increases between the first and third forecast days. The verification of the rainfall forecast shows that the model underestimates the area of the precipitation. The model output statistics (MOS) is applied to all parameters but precipitation. Results show that the MOS reduces the RMSE by 0–30%, depending on the forecasting time, on the season and on the meteorological parameter.
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Ratnam, J. V., Takeshi Doi, Willem A. Landman, and Swadhin K. Behera. "Seasonal Forecasting of Onset of Summer Rains over South Africa." Journal of Applied Meteorology and Climatology 57, no. 12 (December 2018): 2697–711. http://dx.doi.org/10.1175/jamc-d-18-0067.1.
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AbstractIn this study, we attempted to forecast the onset of summer rains over South Africa using seasonal precipitation forecasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), seasonal forecasting system. The precipitation forecasts of the 12-member SINTEX-F2 system, initialized on 1 August and covering the period 1998–2015, were used for the study. The SINTEX-F2 forecast precipitation was also downscaled using dynamical and statistical techniques to improve the spatial and temporal representation of the forecasts. The Weather Research and Forecasting (WRF) Model with two cumulus parameterization schemes was used to dynamically downscale the SINTEX-F2 forecasts. The WRF and SINTEX-F2 precipitation forecasts were corrected for biases using a linear scaling method with a 31-day moving window. The results indicate the onset dates derived from the raw and bias-corrected model precipitation forecasts to have realistic spatial distribution over South Africa. However, the forecast onset dates have root-mean-square errors of more than 30 days over most parts of South Africa except over the northeastern province of Limpopo and over the Highveld region of Mpumalanga province, where the root-mean-square errors are about 10–15 days. The WRF Model with Kain–Fritsch cumulus scheme (bias-corrected SINTEX-F2) has better performance in forecasting the onset dates over Limpopo (the Highveld region) compared to other models, thereby indicating the forecast of onset dates over different regions of South Africa to be model dependent. The results of this study are important for improving the forecast of onset dates over South Africa.
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Kopij, G. "Avian Diversity along a Precipitation Gradient in Southern Africa." Zoodiversity 55, no. 1 (2021): 17–24. http://dx.doi.org/10.15407/zoo2021.01.017.
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In April 2013, a transect c. 1100 km long with 100 points arranged in 5 sections was designed in northern Namibia between Ruacana Watefall (W) and Kongola (E) to study avian diversity in relation to gradual changes in precipitation (c. 350 mm in W to c.600 mm in E). In total, 81 bird species were recorded in all 100 points, but in particular section, the numbers were low, ranging from 22 to 38 species. In overall, the most frequent and most numerous (dominant) were the following species: Cape Turtle Dove, Blue Waxbill, Grey-headed Sparrow, Laughing Dove and Fork-tailed Drongo. Together they comprised 42.2 % of all individuals recorded. All of them, except for the Fork-tailed Drongo, were granivores. Only nine species were recorded in ten or more points. Besides the above-mentioned dominant species, the following other were in this group: Black-chested Prinia, Black-throated Canary, Pied Crow and White-browed Scrub Robin. In particular section the number of dominant species ranged from 5 to 7, without a gradient. Two species, the Cape Turtle Dove and Blue Waxbill, remained dominant in all fi ve sections. Th e Laughing Dove and Fork-tailed Drongo — in four sections, and the Grey-headed Sparrow — in three sections. Although, no gradient in species diversity, evenness, and dominance structure was recorded, signifi cant diff erences between two most western and two most eastern sections were apparent. Two species were more frequent and numerous in the eastern than in the western sections: White-browed Scrub Robin and Cape Turtle Dove; while the following species were more frequent and numerous in the western than the eastern sections: African Palm Swift , Black-chested Prinia, Black-throated Canary, Blue Waxbill, Grey-headed Sparrow and Pied Crow.
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Lim Kam Sian, Kenny Thiam Choy, Jianhong Wang, Brian Odhiambo Ayugi, Isaac Kwesi Nooni, and Victor Ongoma. "Multi-Decadal Variability and Future Changes in Precipitation over Southern Africa." Atmosphere 12, no. 6 (June 9, 2021): 742. http://dx.doi.org/10.3390/atmos12060742.
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The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student’s t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century.
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Cook, Kerry H. "A Southern Hemisphere Wave Response to ENSO with Implications for Southern Africa Precipitation." Journal of the Atmospheric Sciences 58, no. 15 (August 2001): 2146–62. http://dx.doi.org/10.1175/1520-0469(2001)058<2146:ashwrt>2.0.co;2.
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Wichern, Florian, and Rainer Georg Joergensen. "Soil Microbial Properties Along a Precipitation Transect in Southern Africa." Arid Land Research and Management 23, no. 2 (April 8, 2009): 115–26. http://dx.doi.org/10.1080/15324980902817071.
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De Sales, Fernando, Gregory S. Okin, Yongkang Xue, and Kebonye Dintwe. "On the effects of wildfires on precipitation in Southern Africa." Climate Dynamics 52, no. 1-2 (March 16, 2018): 951–67. http://dx.doi.org/10.1007/s00382-018-4174-7.
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Hoell, Andrew, Andrea E. Gaughan, Shraddhanand Shukla, and Tamuka Magadzire. "The Hydrologic Effects of Synchronous El Niño–Southern Oscillation and Subtropical Indian Ocean Dipole Events over Southern Africa." Journal of Hydrometeorology 18, no. 9 (September 1, 2017): 2407–24. http://dx.doi.org/10.1175/jhm-d-16-0294.1.
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Abstract Southern Africa precipitation during December–March (DJFM), the height of the rainy season, is closely related with two modes of climate variability, El Niño–Southern Oscillation (ENSO) and the subtropical Indian Ocean dipole (SIOD). Recent research has found that the combined effects of ENSO and SIOD phasing are linked with changes to the regional southern Africa atmospheric circulation beyond the individual effects of either ENSO or SIOD alone. Here, the authors extend the recent research and examine the southern Africa land surface hydrology associated with the synchronous effects of ENSO and SIOD events using a macroscale hydrologic model, with particular emphasis on the evolution of the hydrologic conditions over three critical Transfrontier Conservation Areas: the Kavango–Zambezi Conservation Area, the Greater Limpopo Transfrontier Park, and the Kgalagadi Transfrontier Park. A better understanding of the climatic effects of ENSO and SIOD phase combinations is important for regional-scale transboundary conservation planning, especially for southern Africa, where both humans and wildlife are dependent on the timing and amount of precipitation. Opposing ENSO and SIOD phase combinations (e.g., El Niño and a negative SIOD or La Niña and a positive SIOD) result in strong southern Africa climate impacts during DJFM. The strong instantaneous regional precipitation and near-surface air temperature anomalies during opposing ENSO and SIOD phase combinations lead to significant soil moisture and evapotranspiration anomalies in the year following the ENSO event. By contrast, when ENSO and SIOD are in the same phase (e.g., El Niño and a positive SIOD or La Niña and a negative SIOD), the southern Africa climate impacts during DJFM are minimal.
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Purich, Ariaan, Tim Cowan, Seung-Ki Min, and Wenju Cai. "Autumn Precipitation Trends over Southern Hemisphere Midlatitudes as Simulated by CMIP5 Models." Journal of Climate 26, no. 21 (October 16, 2013): 8341–56. http://dx.doi.org/10.1175/jcli-d-13-00007.1.
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Abstract In recent decades, Southern Hemisphere midlatitude regions such as southern Africa, southeastern Australia, and southern Chile have experienced a reduction in austral autumn precipitation; the cause of which is poorly understood. This study focuses on the ability of global climate models that form part of the Coupled Model Intercomparison Project phase 5 to simulate these trends, their relationship with extratropical and subtropical processes, and implications for future precipitation changes. Models underestimate both the historical autumn poleward expansion of the subtropical dry zone and the positive southern annular mode (SAM) trend. The multimodel ensemble (MME) is also unable to capture the spatial pattern of observed precipitation trends across semiarid midlatitude regions. However, in temperate regions that are located farther poleward such as southern Chile, the MME simulates observed precipitation declines. The MME shows a strong consensus in twenty-first-century declines in autumn precipitation across southern Chile in both the medium–low and high representative concentration pathway (RCP) scenarios and across southern Africa in the high RCP scenario, but little change across southeastern Australia. Projecting a strong positive SAM trend and continued subtropical dry-zone expansion, the models converge on large SAM and dry-zone-expansion-induced precipitation declines across southern midlatitudes. In these regions, the strength of future precipitation trends is proportional to the strength of modeled trends in these phenomena, suggesting that unabated greenhouse gas–induced climate change will have a large impact on austral autumn precipitation in such midlatitude regions.
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Li, Lu, Cosmo S. Ngongondo, Chong-Yu Xu, and Lebing Gong. "Comparison of the global TRMM and WFD precipitation datasets in driving a large-scale hydrological model in southern Africa." Hydrology Research 44, no. 5 (October 13, 2012): 770–88. http://dx.doi.org/10.2166/nh.2012.175.
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This paper provides a comparison of two widely used global precipitation datasets in southern Africa: Tropical Rainfall Measuring Mission (TRMM) and Water and Global Change (WATCH) Forcing Data (WFD). We also evaluate the performance of the water and snow balance modelling system (WASMOD-D) in a water balance simulation of 22 gauged basins over the southern Africa region. Water balance for southern Africa was simulated using the two global datasets as input with regionalized model parameter values. The results reveal that the special variation patterns of mean annual precipitation from TRMM and WFD datasets and temporal changing trend are similar in southern Africa. However, they are quite different in terms of probability distributions. Simulation of WASMOD-D based on two datasets in southern Africa results in model performances of above 0.5 for Nash–Sutcliffe (NS) values, below 10% for volume error (VE) values and a good reproduction of the observed flow duration curves for the majority of the basins. Finally, WFD data which have been bias corrected were observed to outperform TRMM data in southern Africa. The approaches and results described in this study contribute to the limited literature on regional hydrological modelling in the data-scarce region of southern Africa.
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Kalu, Ikechukwu, Christopher E. Ndehedehe, Onuwa Okwuashi, and Aniekan E. Eyoh. "Assessing Freshwater Changes over Southern and Central Africa (2002–2017)." Remote Sensing 13, no. 13 (June 29, 2021): 2543. http://dx.doi.org/10.3390/rs13132543.
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In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions.
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Cattani, Elsa, Olivia Ferguglia, Andrés Merino, and Vincenzo Levizzani. "Precipitation Products’ Inter–Comparison over East and Southern Africa 1983–2017." Remote Sensing 13, no. 21 (November 3, 2021): 4419. http://dx.doi.org/10.3390/rs13214419.
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During recent decades East Africa (EA) and Southern Africa (SA) have experienced an intensification of hydrological hazards, such as floods and droughts, which have dramatically affected the population, making these areas two of the regions of the African continent most vulnerable to these hazards. Thus, precipitation monitoring and the evaluation of its variability have become fundamentally important actions through the analysis of long-term data records. In particular, satellite-based precipitation products are often used because they counterbalance the sparsity of the rain gauge networks which often characterize these areas. The aim of this work is to compare and contrast the capabilities of three daily satellite-based products in EA and SA from 1983 to 2017. The selected products are two daily rainfall datasets based on high-resolution thermal infrared observations, TAMSAT version 3 and CHIRPS, and a relatively new global product, MSWEP version 2.2, which merges satellite-based, rain gauge and re-analysis precipitation data. The datasets have been directly intercompared, avoiding the traditional rain gauge validation. This is done by means of pairwise comparison statistics at 0.25° spatial resolution and daily time scale to assess rain–detection and quantitative estimate capabilities. Monthly climatology and spatial distribution of seasonality are analyzed as well. The time evolution of the statistical indexes has been evaluated in order to analyze the stability of the rain detection and estimation performances. Considerable agreement among the precipitation products emerged from the analysis, in spite of the differences occurring in specific situations over complex terrain, such as mountainous and coastal regions and deserts. Moreover, the temporal evolution of the statistical indices has demonstrated that the agreement between the products improved over time, with more stable capabilities in identifying precipitating days and estimating daily precipitation starting in the second half of the 1990s.
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Zwane, Nosipho, Henerica Tazvinga, Christina Botai, Miriam Murambadoro, Joel Botai, Jaco de Wit, Brighton Mabasa, Siphamandla Daniel, and Tafadzwanashe Mabhaudhi. "A Bibliometric Analysis of Solar Energy Forecasting Studies in Africa." Energies 15, no. 15 (July 29, 2022): 5520. http://dx.doi.org/10.3390/en15155520.
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Solar energy forecasting is considered an essential scientific aspect in supporting efforts to integrate solar energy into power grids. Moreover, solar energy forecasting plays an essential role in mitigating greenhouse gas emissions and conserving energy for future use. This study conducted a bibliometric analysis to assess solar energy forecasting research studies evolution at the continental (Africa) and southern Africa levels. Key aspects of analysis included (i) scientific research trends, (ii) nature of collaboration networks, (iii) co-occurrence of keywords and (iv) emerging themes in solar energy forecasting over the last two decades, between the years 2000–2021. The results indicate that solar energy forecasting research has, on average, expanded by 6.4% and 3.3% in Africa and southern Africa, respectively. Based on the study context, solar energy forecasting research only gained momentum in 2015, peaking in 2019, but it is generally still subtle. The scientific mapping illustrated that only South Africa ranks among the leading countries that have produced high numbers of published documents and also leads in contributions to the research area in both Africa and southern Africa. Three emerging topics were identified from the thematic map analysis—namely, “solar irradiance”, “artificial intelligence” and “clear sky”, which implies that researchers are paying attention to solar irradiance, using modelling techniques that incorporate machine learning techniques. Overall, this study contributes to scientific information on the potential bankability of renewable energy projects that could assist power utilities, governments and policymakers in Africa to enforce the green economy through accelerated decarbonisation of the energy systems and building relationships with developed countries for support and better transitioning to solar energy. From a Water–Energy–Food nexus perspective, the results of this work could assist the scientific community in Africa to take advantage of the inherent interconnectedness of water, energy and food resources, whilst also advancing the use of integrated solutions to shape the focus of solar energy research into a more systems thinking and transdisciplinary approach involving the interconnected primary resources and stakeholders pursuit of the Sustainable Development Goals.
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Misra, Vasubandhu. "The Influence of Pacific SST Variability on the Precipitation over Southern Africa." Journal of Climate 16, no. 14 (July 15, 2003): 2408–18. http://dx.doi.org/10.1175/2785.1.
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Abstract This study is an analysis of AGCM model results to understand the dynamics of the response of precipitation over southern Africa (SA) to anomalies in the sea surface temperature (SST) over the Pacific Ocean. The pattern of interannual precipitation anomaly over SA and its temporal variations are quite similar in both the ensemble mean of the control (where AGCM is forced with observed SSTs in all ocean basins) and experimental runs (where AGCM is forced with seasonally varying climatological SST over the Pacific Ocean). However, the amplitude of the variability is found to be relatively reduced in the experimental runs. This is shown to be a result of the modulation of the Walker circulation by the variability of Pacific Ocean SST. The regional teleconnection pattern between the dominant mode of SA precipitation variability and SST anomalies over the eastern Indian Ocean is also influenced by the variations in Pacific SST. The nature of the teleconnection between SA precipitation and eastern Indian SST is apparent only when the Pacific SST variability is excluded. This is corroborated from observations as well.
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Li, Ji, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao. "Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model." Hydrology and Earth System Sciences 21, no. 2 (March 2, 2017): 1279–94. http://dx.doi.org/10.5194/hess-21-1279-2017.
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Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1–15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.
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Muofhe, Tshimbiluni Percy, Hector Chikoore, Mary-Jane Morongwa Bopape, Nthaduleni Samuel Nethengwe, Thando Ndarana, and Gift Tshifhiwa Rambuwani. "Forecasting Intense Cut-Off Lows in South Africa Using the 4.4 km Unified Model." Climate 8, no. 11 (November 7, 2020): 129. http://dx.doi.org/10.3390/cli8110129.
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Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They occur during all the above and often result in floods and snowfalls during the winter months, disrupting economic activities and causing extensive damage to infrastructure. This paper examines the evolution and circulation patterns associated with cases of severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service (SAWS) to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA Interim re-analyses and GPM precipitation at 24-hour timesteps. We present five recent severe COLs, which occurred between 2016 and 2019, that had high impact and found a higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM simulated the precipitation differently during the different stages of development and location of the systems. This is mainly due to inaccurate placing of COL centers. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.
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de Coning, E., and E. R. Poolman. "South African Weather Service operational satellite based precipitation estimation technique: applications and improvements." Hydrology and Earth System Sciences Discussions 7, no. 6 (November 12, 2010): 8837–71. http://dx.doi.org/10.5194/hessd-7-8837-2010.
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Abstract. Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The availability of the local version of the Unified Model and the Meteosat Second Generation satellite data make these products ideal components of precipitation measurement in data sparse regions such as Africa. In this article the local version of the Hydroestimator (originally from NOAA/NESDIS) is discussed as well as its applications for precipitation measurement in this region. Hourly accumulations of the Hydroestimator are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system. However, the Hydroestimator is by no means a perfect representation of the real rainfall. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field which can feed into the South African Flash Flood Guidance system. This new product should provide a more accurate and comprehensive input to the Flash Flood Guidance systems in South Africa as well as southern Africa. In this way the southern African region where data is sparse and very few radars are available can have access to more accurate flash flood guidance.
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Kalognomou, Evangelia-Anna, Christopher Lennard, Mxolisi Shongwe, Izidine Pinto, Alice Favre, Michael Kent, Bruce Hewitson, et al. "A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa." Journal of Climate 26, no. 23 (December 2013): 9477–506. http://dx.doi.org/10.1175/jcli-d-12-00703.1.
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The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ~50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989–2008). Results are compared against a number of observational datasets.In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Niño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.
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Hudak, D. R., and R. List. "Precipitation Development in Natural and Seeded Cumulus Clouds in Southern Africa." Journal of Applied Meteorology 27, no. 6 (June 1988): 734–56. http://dx.doi.org/10.1175/1520-0450(1988)027<0734:pdinas>2.0.co;2.
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Layberry, R., D. R. Kniveton, M. C. Todd, C. Kidd, and T. J. Bellerby. "Daily Precipitation over Southern Africa: A New Resource for Climate Studies." Journal of Hydrometeorology 7, no. 1 (February 1, 2006): 149–59. http://dx.doi.org/10.1175/jhm477.1.
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Abstract This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed.
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Blamey, R. C., and C. J. C. Reason. "The Role of Mesoscale Convective Complexes in Southern Africa Summer Rainfall." Journal of Climate 26, no. 5 (February 27, 2013): 1654–68. http://dx.doi.org/10.1175/jcli-d-12-00239.1.
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Abstract A combination of numerous factors, including geographic position, regional orography, and local sea surface temperatures, means that subtropical southern Africa experiences considerable spatial and temporal variability in rainfall and is prone to both frequent flooding and drought events. One system that may contribute to rainfall variability in the region is the mesoscale convective complex (MCC). In this study, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data is used to document the precipitation produced by MCCs over southern Africa for the 1998–2006 period. Most of the rainfall associated with MCCs is found to occur over central Mozambique, extending southward to eastern South Africa. High precipitation totals associated with these systems also occur over the neighboring southwest Indian Ocean, particularly off the northeast coast of South Africa. MCCs are found to contribute up to 20% of the total summer rainfall (November–March) in parts of the eastern region of southern Africa. If the month of March is excluded from the analysis, then the contribution increases up to 24%. In general, the MCC summer rainfall contribution for most of the eastern region is approximately between 8% and 16%. Over the western interior and Botswana and Namibia, the MCC contribution is much less (<6%). It is also evident that there is considerable interannual variability associated with the contribution that these systems make to the total warm season rainfall.
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Dezfuli, Amin K., Charles M. Ichoku, George J. Huffman, Karen I. Mohr, John S. Selker, Nick van de Giesen, Rebecca Hochreutener, and Frank O. Annor. "Validation of IMERG Precipitation in Africa." Journal of Hydrometeorology 18, no. 10 (October 1, 2017): 2817–25. http://dx.doi.org/10.1175/jhm-d-17-0139.1.
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Abstract Understanding of hydroclimatic processes in Africa has been hindered by the lack of in situ precipitation measurements. Satellite-based observations, in particular, the TRMM Multisatellite Precipitation Analysis (TMPA) have been pivotal to filling this void. The recently released Integrated Multisatellite Retrievals for GPM (IMERG) project aims to continue the legacy of its predecessor, TMPA, and provide higher-resolution data. Here, IMERG-V04A precipitation data are validated using in situ observations from the Trans-African Hydro-Meteorological Observatory (TAHMO) project. Various evaluation measures are examined over a select number of stations in West and East Africa. In addition, continent-wide comparisons are made between IMERG and TMPA. The results show that the performance of the satellite-based products varies by season, region, and the evaluation statistics. The precipitation diurnal cycle is relatively better captured by IMERG than TMPA. Both products exhibit a better agreement with gauge data in East Africa and humid West Africa than in the southern Sahel. However, a clear advantage for IMERG is not apparent in detecting the annual cycle. Although all gridded products used here reasonably capture the annual cycle, some differences are evident during the short rains in East Africa. Direct comparison between IMERG and TMPA over the entire continent reveals that the similarity between the two products is also regionally heterogeneous. Except for Zimbabwe and Madagascar, where both satellite-based observations present a good agreement, the two products generally have their largest differences over mountainous regions. IMERG seems to have achieved a reduction in the positive bias evident in TMPA over Lake Victoria.
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de Coning, E., and E. Poolman. "South African Weather Service operational satellite based precipitation estimation technique: applications and improvements." Hydrology and Earth System Sciences 15, no. 4 (April 6, 2011): 1131–45. http://dx.doi.org/10.5194/hess-15-1131-2011.
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Abstract. Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 daily gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The local version of the Unified Model and the Meteosat Second Generation satellite data are ideal components of precipitation estimation in data sparse regions such as Africa. In South Africa hourly accumulations of the Hydroestimator (originally from NOAA/NESDIS) are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system, especially in regions which are not covered by radar. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field. The new product was tested over a two year period and provides a more accurate and comprehensive input to the Flash Flood Guidance systems in the data sparse southern Africa. Future work will include updating the period over which bias corrections were calculated.
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Avila, Ana Maria Heuminski de, and Andrea de Oliveira Cardoso. "Application of statistical correction in extended weather forecasting in the southern region of Brazil." Revista Brasileira de Meteorologia 27, no. 4 (December 2012): 388–94. http://dx.doi.org/10.1590/s0102-77862012000400002.
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Adverse weather conditions in critical periods of vegetative plant growth affect crop productivity, being a fundamental parameter for yield forecast. An increase in weather forecasting accuracy may be obtained by applying statistical correction to remove model bias. This study used statistical correction of ensemble forecasting with the atmospheric general circulation model (Center for Weather Forecasting and Climate Studies/Center for Ocean - Land - Atmosphere Studies - CPTEC/COLA) by mean error removal for three cities in the South of Brazil. Comparisons were made between corrected and original precipitation forecasts, and between these and data observed at their respective meteorological stations. Results showed that the applied statistical correction method may improve forecasting performance in some situations and that the term of forecast present high accuracy, indicating the importance of ensemble forecasting as an auxiliary tool in agricultural crop monitoring.
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Sulaiman, Junaida Binti, Herdianti Darwis, and Hideo Hirose. "Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 6 (November 20, 2014): 999–1006. http://dx.doi.org/10.20965/jaciii.2014.p0999.
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Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d), we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. In this study, a method to forecast monthly extreme precipitation using artificial neural networks (ANNs) is assessed using past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia. The use of combined inputs (MX5d with SOI, MJO, and DMI) is proposed to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four cases of data are sampled representing two major seasonal variations in Malaysia. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. ANNs are widely applied in the hydrology field because of their nonlinear ability in predicting nonstationary and seasonal data. In this paper, we have compared ANNs with seasonal autoregressive integrated moving average (ARIMA) and regression analysis using out-of-sample test data. The results for Kuantan indicate that seasonal ARIMA is the best method to forecast extreme precipitation when MX5d lags are used as input. For Kota Bharu, ANN exhibits better generalization ability than ARIMA and regression analysis when dual inputs (lagged MX5d and lagged global climate indices) are utilized in the model.
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Lü, A., S. Jia, W. Zhu, H. Yan, S. Duan, and Z. Yao. "El Niño-Southern Oscillation and water resources in the headwaters region of the Yellow River: links and potential for forecasting." Hydrology and Earth System Sciences 15, no. 4 (April 21, 2011): 1273–81. http://dx.doi.org/10.5194/hess-15-1273-2011.
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Abstract. This research explores the rainfall-El Niño-Southern Oscillation (ENSO) and runoff-ENSO relationships and examines the potential for water resource forecasting using these relationships. The Southern Oscillation Index (SOI), Niño1.2, Niño3, Niño4, and Niño3.4 were selected as ENSO indicators for cross-correlation analyses of precipitation and runoff. There was a significant correlation (95% confidence level) between precipitation and ENSO indicators during three periods: January, March, and from September to November. In addition, monthly streamflow and monthly ENSO indictors were significantly correlated during three periods: from January to March, June, and from October to December (OND), with lag periods between one and twelve months. Because ENSO events can be accurately predicted one to two years in advance using physical modeling of the coupled ocean-atmosphere system, the lead time for forecasting runoff using ENSO indicators in the Headwaters Region of the Yellow River could extend from one to 36 months. Therefore, ENSO may have potential as a powerful forecasting tool for water resources in the headwater regions of Yellow River.