Academic literature on the topic 'Precipitation forecasting Africa'

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.

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.

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.

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.

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.

Abstract This work assimilates multisensor precipitation-sensitive microwave radiance observations into a storm-scale NASA Unified Weather Research and Forecasting (NU-WRF) Model simulation of the West African monsoon. The analysis consists of a full description of the atmospheric states and a realistic cloud and precipitation distribution that is consistent with the observed dynamic and physical features. The analysis shows an improved representation of monsoon precipitation and its interaction with dynamics over West Africa. Most significantly, assimilation of precipitation-affected microwave radiance has a positive impact on the distribution of precipitation intensity and also modulates the propagation of cloud precipitation systems associated with the African easterly jet. Using an ensemble-based assimilation technique that allows state-dependent forecast error covariance among dynamical and microphysical variables, this work shows that the assimilation of precipitation-sensitive microwave radiances over the West African monsoon rainband enables initialization of storms. These storms show the characteristics of continental tropical convection that enhance the connection between tropical waves and organized convection systems.

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.

Fall armyworm, Spodopterafrugiperda, is a crop pest native to the Americas, which has invaded and spread throughout sub-Saharan Africa within two years. Recent estimates of 20–50% maize yield loss in Africa suggest severe impact on livelihoods. Fall armyworm is still infilling its potential range in Africa and could spread to other continents. In order to understand fall armyworm’s year-round, global, potential distribution, we used evidence of the effects of temperature and precipitation on fall armyworm life-history, combined with data on native and African distributions to construct Species Distribution Models (SDMs). We also investigated the strength of trade and transportation pathways that could carry fall armyworm beyond Africa. Up till now, fall armyworm has only invaded areas that have a climate similar to the native distribution, validating the use of climatic SDMs. The strongest climatic limits on fall armyworm’s year-round distribution are the coldest annual temperature and the amount of rain in the wet season. Much of sub-Saharan Africa can host year-round fall armyworm populations, but the likelihoods of colonising North Africa and seasonal migrations into Europe are hard to predict. South and Southeast Asia and Australia have climate conditions that would permit fall armyworm to invade. Current trade and transportation routes reveal Australia, China, India, Indonesia, Malaysia, Philippines and Thailand face high threat of fall armyworm invasions originating from Africa.

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

The Caledon River Basin is located on one of the most water-scarce region on the African continent. The water resources of the Caledon River Basin play a pivotal role in socio-economic activities in both Lesotho and South Africa but the basin experiences recurrent severe droughts and frequent water shortages. The Caledon River is mostly used for commercial and subsistence agriculture, industrial and domestic supply. The resources are also important beyond the basin’s boundaries as the water is transferred to the nearby Modder River. The Caledon River is also a significant tributary to the Orange-Senqu Basin, which is shared by five southern African countries. However, the water resources in the basin are under continuous threat as a result of rapidly growing population, economic growth as well as changing climate, amongst others. It is therefore important that the hydrological regime and water resources of the basin are thoroughly evaluated and assessed so that they can be sustainably managed and utilised for maximum economic benefits. Climate change has been identified by the international community as one of the most prominent threats to peace, food security and livelihood and southern Africa as among the most vulnerable regions of the world. Water resources are perceived as a natural resource which will be affected the most by the changing climate conditions. Global warming is expected to bring more severe, prolonged droughts and exacerbate water shortages in this region. The current study is mainly focused on investigating the impacts of climate change on the water resources of the Caledon River Basin. The main objectives of the current study included assessing the past and current hydrological characteristics of the Caledon River Basin under current state of the physical environment, observed climate conditions and estimated water use; detecting any changes in the future rainfall and evaporative demands relative to present conditions and evaluating the impacts of climate on the basin’s hydrological regime and water resources availability for the future climate scenario, 2046-2065. To achieve these objectives the study used observed hydrological, meteorological data sets and the basin’s physical characteristics to establish parameters of the Pitman and WEAP hydrological models. Hydrological modelling is an integral part of hydrological investigations and evaluations. The various sources of uncertainties in the outputs of the climate and hydrological models were identified and quantified, as an integral part of the whole exercise. The 2-step approach of the uncertainty version of the model was used to estimate a range of parameters yielding behavioural natural flow ensembles. This approach uses the regional and local hydrological signals to constrain the model parameter ranges. The estimated parameters were also employed to guide the calibration process of the Water Evaluation And Planning (WEAP) model. The two models incorporated the estimated water uses within the basin to establish the present day flow simulations and they were found to sufficiently simulate the present day flows, as compared to the observed flows. There is an indication therefore, that WEAP can be successfully applied in other regions for hydrological investigations. Possible changes in future climate regime of the basin were evaluated by analysing downscaled temperature and rainfall outputs from a set of 9 climate models. The predictions are based on the A2 greenhouse gases emission scenario which assumes a continuous increase in emission rates. While the climate models agree that temperature, and hence, evapotranspiration will increase in the future, they demonstrate significant disagreement on whether rainfall will decrease or increase and by how much. The disagreement of the GCMs on projected future rainfall constitutes a major uncertainty in the prediction of water resources availability of the basin. This is to the extent that according to 7 out of 9 climate models used, the stream flow in four sub-basins (D21E, D22B, D23D and D23F) in the Caledon River Basin is projected to decrease below the present day flows, while two models (IPSL and MIUB) consistently project enhanced water resource availability in the basin in the future. The differences in the GCM projections highlight the margin of uncertainty involved predicting the future status of water resources in the basin. Such uncertainty should not be ignored and these results can be useful in aiding decision-makers to develop policies that are robust and that encompass all possibilities. In an attempt to reduce the known uncertainties, the study recommends upgrading of the hydrological monitoring network within the Caledon River Basin to facilitate improved hydrological evaluation and management. It also suggests the use of updated climate change data from the newest generation climate models, as well as integrating the findings of the current research into water resources decision making process.

Predicting the location and timing of rainfall events has important social and economic impacts. It is also important to have the ability to predict the amount of rainfall accurately. In operational centres forecasters use deterministic model output data as guidance for a subjective probabilistic rainfall forecast. The aim of this research is to determine the skill in an objective multi-model, multi-institute objective probabilistic forecast system. This was done by obtaining the rainfall forecast of two high-resolution regional models operational in South Africa. The first model is the Unified Model (UM) which is operational at the South African Weather Service. The UM contributed three members which differ in physics, data assimilation techniques and horisontal resolution. The second model is the Conformal-Cubic Atmospheric Model (CCAM) which is operational at the Council for Scientific and Industrial Research which in turn contributed two members to the ensemble system differing in horisontal resolution. A single-model ensemble was constructed for the UM and CCAM models respectively with each of the individual members having equal weights. The UM and CCAM single-model ensemble prediction models have been used in turn to construct a multi-model ensemble prediction system, using simple un-weighted averaging. The multi-model system was used to predict the 24-hour rainfall totals for three austral summer half-year seasons of 2006/07 to 2008/09. The forecast of this system was rigorously tested using observed rainfall data for the same period. From the multi-model system it has been found that the probabilistic forecast has good significant skill in predicting rainfall. The multi-model system proved to have skill and shows discrimination between events and non-events. This study has shown that it is possible to make an objective probabilistic rainfall forecast by constructing a multi-model, multi-institute system with high resolution regional models currently operational in South Africa. Thus, probabilistic rainfall forecasts with usable skill can be made with the use of a multi-model short-range ensemble prediction system over the South African domain. Such a system is not currently operational in South Africa. Copyright
Dissertation (MSc)--University of Pretoria, 2012.
Geography, Geoinformatics and Meteorology
Unrestricted

AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.

AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.