Academic literature on the topic 'Long-range weather forecasting Africa, Southern'

Abstract. Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December–February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

Abstract Seasonal multimodel forecasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used to drive a malaria model and create reforecasts of malaria incidence for Botswana, in southern Africa, in a unique integration of a fully dynamic, process-based malaria model with an ensemble forecasting system. The forecasts are verified against a 20-yr malaria index and compared against reference simulations obtained by driving the malaria model with data from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Performance assessment reveals skill in the DEMETER-driven malaria forecasts for prediction of low (below the lower tercile), above-average (above the median), and high (above the upper tercile) malaria events, with the best results obtained for low malaria events [relative operating characteristics (ROC) area = 0.84, 95% confidence interval = 0.63–1.0]. For high malaria events, the DEMETER-driven malaria forecasts are skillful, but the forecasting system performs poorly for those years that it predicts the highest probabilities of a high malaria event. Potential economic value analysis demonstrates the potential value for the DEMETER-driven malaria forecasts over a wide range of user cost-loss ratios, which is primarily due to the ability of the system to save on the cost of action in low malaria years.

Abstract. Subsistence farming in Southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo basin using a set of climate change projections from several regional climate model downscalings. Furthermore the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future as they can more often lead to informed decision making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models, of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts given that both indicators can be skilfully predicted for the December-to-February season, at least two months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

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.

Abstract. Characterising the dynamics of landscape scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and northern and southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP dataset, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 and 65–77% respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35% over the Northern Africa region to 89% over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America, and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 Peloponnese wildfires within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS), as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring System (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions datasets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET AOD indicates that the former is overestimated by ∼ 20–30%, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those currently implemented as part of the Monitoring Atmospheric Composition and Climate (MACC) programme within the CAMS.

Abstract. Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.

Abstract Periods of low convective activity over southern Africa during the peak rainy season from December to February are known to be due to the northeastward displacement of the tropical temperate trough (TTT) systems from the landmass. In this study, using Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) data, the authors show that the displacement of the TTT systems during long periods of low convective activity has origins in the Northern Hemisphere. Using standardized area-averaged outgoing longwave radiation (OLR) daily anomalies over southern Africa, long periods of low convective activity are defined as periods of positive OLR anomalies lasting consecutively for 5 or more days with a standard deviation of 1 or more. An eddy streamfunction anomaly composite of the periods of low convective activity shows an upper-level anomalous wave originating in the Northern Hemisphere and extending to southern Africa from the eastern Pacific and displacing the tropical–extratropical cloud bands from the southern African landmass into the southwestern Indian Ocean. The wave train is also seen to generate an anticyclonic anomaly over southern Africa, resulting in suppressed convective activity. Understanding the causes of the long periods of low convective activity will help in improving their predictability and also the predictability of seasonal rainfall over southern Africa.

The main objective of this paper is to implement Polar WRF model for the Maitri (Lat. 70° 45 S, Long. 11° 44 E) region at the horizontal resolution of 15 km using initial and boundary conditions of the Global Forecast System T-382 operational at the India Meteorological Department (IMD). The study evaluates the performance of the model using the conventional approach of case studies. The results of the case studies illustrated in this paper reveal that the model is capable of capturing synoptic and meso-scale weather systems. Forecast fields are consistent with the corresponding analysis fields. Synoptic charts of mean sea level pressure prepared by the Weather Service of South Africa at Pretoria are used for the model validation. The model derived meteograms of mean sea level pressure are compared against the corresponding observations. The study demonstrates the usefulness of the forecast products for short range forecasting of weather over the Maitri region. The forecast outputs are made available in the real-time mode in the national web site of IMD www.imd.gov.in. The study is expected to benefit weather forecasters at Maitri.

AbstractLong-term variations in fire weather conditions are examined throughout Australia from gridded daily data from 1950 to 2016. The McArthur forest fire danger index is used to represent fire weather conditions throughout this 67-yr period, calculated on the basis of a gridded analysis of observations over this time period. This is a complementary approach to previous studies (e.g., those based primarily on model output, reanalysis, or individual station locations), providing a spatially continuous and long-term observations-based dataset to expand on previous research and produce climatological guidance information for planning agencies. Long-term changes in fire weather conditions are apparent in many regions. In particular, there is a clear trend toward more dangerous conditions during spring and summer in southern Australia, including increased frequency and magnitude of extremes, as well as indicating an earlier start to the fire season. Changes in fire weather conditions are attributable at least in part to anthropogenic climate change, including in relation to increasing temperatures. The influence of El Niño–Southern Oscillation (ENSO) on fire weather conditions is found to be broadly consistent with previous studies (indicating more severe fire weather in general for El Niño conditions than for La Niña conditions), but it is demonstrated that this relationship is highly variable (depending on season and region) and that there is considerable potential in almost all regions of Australia for long-range prediction of fire weather (e.g., multiweek and seasonal forecasting). It is intended that improved understanding of the climatological variability of fire weather conditions will help lead to better preparedness for risks associated with dangerous wildfires in Australia.

Dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg for the Degree of Master of Science
Various techniques and procedures suited to monthly forecasting are investigated and tested. These include using the products generated by atmospheric general circulation models during a 17-year hindcast experiment, and downscaling the forecast circulation to regional rainfall in South Africa using circulation indices and canonical correlation analysis. The downscaling methods are evaluated using the cross-validation technique. Various model forecast bias-correction methods and skill-enhancing ensemble techniques are employed to improve the 30-day prognosis of the model. Forecasts from the general circulation model and each technique are evaluated. Those demonstrating reasonable skill over the southern Africa region, and which are feasible when considering available resources, are adopted into a strategy which can be used operationally to produce monthly outlooks. Various practical issues regarding the operational aspects of long-term forecasting are also discussed.
Andrew Chakane 2019

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. July 2014.
South Africa’s climate is highly variable, implying that the national agricultural sector should make provision to have early warning services in place in order to reduce the risks of disasters. More than 70% of natural disasters worldwide are caused by weather and climate or weather and climate related hazards. Reliable Seasonal Climate Forecasting (SCF) for South Africa would have the potential to be of great benefit to users in addressing disaster risk reduction. A disaster is a serious disruption of the functioning of a community or a society, causing widespread human, material, economic or environmental losses, which exceed the ability of the affected community or society to cope when using their own resources. The negative impacts on agricultural production in South Africa due to natural disasters including disasters due to increasing climate variability and climate change are critical to the sector. The hypothesis assumed in the study is the improved early warning service and better SCF dissemination lead to more effective and better decision making for subsequent disaster risk reduction in the agricultural sector. The most important aspect of knowledge management in early warning operations is that of distributing the most useful service to the target group that needs it at the right time. This will not only ensure maximum performance of the entity responsible for issuing the early warnings, but will also ensure the maximum benefit to the target group. South Africa is becoming increasingly vulnerable to natural disasters that are afflicted by localised incidents of seasonal droughts, floods and flash floods that have devastating impacts on agriculture and food security. Such disasters might affect agricultural production decisions, as well as agricultural productivity. Planting dates and plant selection are decisions that depend on reliable and accurate meteorological and climatological knowledge and services for agriculture. Early warning services that could be used to facilitate informed decision making includes advisories on iv future soil moisture conditions in order to determine estimated planting times, on future grazing capacity, on future water availability and on forecasts of the following season’s weather and climate, whenever that is possible. The involvement of government structures, obviously, is also critical in immediate responses and long term interventions. The importance of creating awareness, of offering training workshops on climate knowledge and SCF, and of creating effective early warning services dissemination channels is realized by government. This is essential in order to put effective early warning services in place as a disaster-risk coping tool. Early warning services, however, can only be successful if the end-users are aware of what early warning systems, structures and technologies are in place, and if they are willing that those issuing the early warning services become involved in the decision-making process. Integrated disaster-risk reduction initiatives in government programmes, effective dissemination structures, natural resource-management projects and communityparticipation programmes are only a few examples of actions that will contribute to the development of effective early warning services, and the subsequent response to and adoption of the advices/services strategies by the people most affected. The effective distribution of the most useful early warning services to the end-user, who needs it at the right time through the best governing structures, may significantly improve decision making in the agricultural, food security and other water-sensitive sectors. Developed disaster-risk policies for extension and farmers as well as other disaster prone sectors should encourage self-reliance and the sustainable use of natural resources, and will reduce the need for government intervention. The SCF producers (e.g. the South African Weather Service (SAWS)) have issued new knowledge to intermediaries for some years now, and it is important to determine whether this knowledge has been used in services, and if so whether these services were applied effectively in coping with disaster-risks and in disaster v reduction initiatives and programmes. This study for that reason also intends to do an evaluation of the knowledge communication processes between forecasters, and intermediaries at national and provincial government levels. It therefore, aims to assess and evaluate the current knowledge communication structures within the national agricultural sector, seeking to improve disaster-risk reduction through effective early warning services. A boundary organisation is an organization which crosses the boundary between science, politics and end-users as they draw on the interests and knowledge of agencies on both sides to facilitate evidence base and socially beneficial policies and programmes. Reducing uncertainty in SCF is potentially of enormous economic value especially to the rural communities. The potential for climate science to deliver reduction in total SCF uncertainty is associated entirely with the contributions from internal variability and model uncertainty. The understanding of the limitations of the SCFs as a result of uncertainties is very important for decision making and to end-users during planning. Disappointing, however, is that several studies have shown a fairly narrow group of potential users actually receive SCFs, with an even a smaller number that makes use of these forecasts In meeting the objectives of the study the methodology to be followed is based on knowledge communication. For that reason two types of questionnaires were drafted. Open and closed questionnaires comprehensively review the knowledge, understanding, interpretation of SCFs and in early warning services distribution channels. These questionnaires were administered among the SCF producers and intermediaries and results analysed. Lastly the availability of useful SCFs knowledge has important implications for agricultural production and food security. Reliable and accurate climate service, as one of the elements of early warning services, will be discussed since they may be used to improve agricultural practices such as crop diversification, time of planting vi and changes in cultivation practices. It was clear from the conclusions of the study that critical elements of early warning services need to receive focused attention such as the SCF knowledge feedback programme should be improved by both seasonal climate producers and intermediaries, together with established structures through which reliable, accurate and timely early warning services can be disseminated. Also the relevant dissemination channels of SCFs are critical to the success of effective implementation of early warning services including the educating and training of farming communities. The boundary organisation and early warning structures are important in effective implementation of risk reduction measures within the agricultural sector and thus need to be prioritised. Enhancing the understandability and interpretability of SCF knowledge by intermediaries will assist in improving action needed to respond to SCFs. Multiple media used by both SCF producers and intermediaries in disseminating of SCFs should be accessible by all users and end-users. The Government should ensure that farming communities are educated, trained and well equipped to respond to risks from natural hazards.

Weather-related delays in the aviation sector will always occur, however, through effective delay management and improved weather forecasting, the impact and duration of delays can be reduced. The research examined the type of weather that caused departure delays, due to adverse weather at the departure station, namely O. R. Tambo International Airport (ORTIA), over the period 2010 to 2013. It was found that the most significant weather that causes such delays are thunderstorms, followed by fog. Other noteworthy elements are rainfall, without the influence of other weather elements, and icing. It was also found that the accuracy of a weather forecast does not impact on the number of departure delays, and thus departure delays due to weather at the departure station are largely unavoidable. However, the length and impact of such delays can be reduced through improved planning. The study highlights that all weather-related delays can be reduced by improved weather forecasts, effective assessment of the weather forecast, and collaborative and timely decision making. A weather impact index system was designed for ORTIA and recommendations for delay reductions are made.
Geography
M. Sc. (Geography)