Academic literature on the topic 'Long-range weather forecasting Africa'
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Journal articles on the topic "Long-range weather forecasting Africa"
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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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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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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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Batté, Lauriane, Constantin Ardilouze, and Michel Déqué. "Forecasting West African Heat Waves at Subseasonal and Seasonal Time Scales." Monthly Weather Review 146, no. 3 (March 1, 2018): 889–907. http://dx.doi.org/10.1175/mwr-d-17-0211.1.
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Abstract Early indication of an increased risk of extremely warm conditions could help alleviate some of the consequences of severe heat waves on human health. This study focuses on boreal spring heat wave events over West Africa and the Sahel and examines the long-range predictability and forecast quality of these events with two coupled forecasting systems designed at Météo-France, both based on the CNRM-CM coupled global climate model: the operational seasonal forecasting System 5 and the experimental contribution to the World Weather Research Programme/World Climate Research Programme (WWRP/WCRP) subseasonal-to-seasonal (S2S) project. Evaluation is based on past reforecasts spanning 22 years, from 1993 to 2014, compared to reference data from reanalyses. On the seasonal time scale, skill in reproducing interannual anomalies of heat wave duration is limited at a gridpoint level but is significant for regional averages. Subseasonal predictability of daily humidity-corrected apparent temperature drops sharply beyond the deterministic range. In addition to reforecast skill measures, the analysis of real-time forecasts for 2016, both in terms of anomalies with respect to the reforecast climatology and using a weather-type approach, provides additional insight on the systems’ performance in giving relevant information on the possible occurrence of such events.
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BHOWMIK, S. K. ROY, ANUPAM KUMAR, and ANANDA K.DAS. "Real-time mesoscale modeling for short range prediction of weather over Maitri region in Antarctica." MAUSAM 62, no. 4 (December 16, 2021): 535–46. http://dx.doi.org/10.54302/mausam.v62i4.339.
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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.
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Tennant, Warren J., Zoltan Toth, and Kevin J. Rae. "Application of the NCEP Ensemble Prediction System to Medium-Range Forecasting in South Africa: New Products, Benefits, and Challenges." Weather and Forecasting 22, no. 1 (February 1, 2007): 18–35. http://dx.doi.org/10.1175/waf979.1.
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Abstract The National Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. The use of model-generated probability forecasts has a clear benefit in the skill of the 1–7-day forecasts. This is seen in the forecast probability distribution being more successful in spanning the observed space than a single deterministic forecast and, thus, substantially reducing the instances of missed events in the forecast. In addition, the probability forecasts generated using the EFS are particularly useful in estimating confidence in forecasts. During the second week of the forecast the EFS is used as a heads-up for possible synoptic-scale events and also for predicting average weather conditions and probability density distributions of some elements such as maximum temperature and wind. This paper assesses the medium-range forecast process and the application of the NCEP EFS at the South African Weather Service. It includes a description of the various medium-range products, adaptive bias-correction methods applied to the forecasts, verification of the forecast products, and a discussion on the various challenges that face researchers and forecasters alike.
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ABDELWAHAB, MM, A. SALAHELDIN, and Z. METWALLY. "A case of Khamsin type weather in north Africa." MAUSAM 36, no. 3 (April 6, 2022): 291–94. http://dx.doi.org/10.54302/mausam.v36i3.1912.
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In most cases, desert depressions over north Africa form in the lee of the Atlas mountains, Such occurrences are found when a north or northwest air stream from over the Atlantic moves toward the Atlas range, or when a northeasterly wind blows over the western Mediterranean towards these mountains, As shown in Fig. 1, these depressions may follow numerous tracks during their eastward movement. These depressions usually produce severe heat waves and sandstorms (EL Fandi, 1940, Soliman 1958). The phenomenon of Khamsin weather in spring is one of the main problems associated with weather analysis and forecasting in the area of north Africa. In recent years, several formulations of these types of desert depressions have been discussed from the point of view of their sources and supply of heat and energy.
The present paper is an attempt to identify the effect of cyc1ogenetic activity on the trajectory of these depressions.
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Mkuhlani, Siyabusa, Nkulumo Zinyengere, Naomi Kumi, and Olivier Crespo. "Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review." Open Life Sciences 17, no. 1 (January 1, 2022): 1398–417. http://dx.doi.org/10.1515/biol-2022-0507.
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Abstract Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy.
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Oosthuizen, Christiaan, Barend Van Wyk, Yskandar Hamam, Dawood Desai, and Yasser Alayli. "The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car." Energies 13, no. 8 (April 17, 2020): 1984. http://dx.doi.org/10.3390/en13081984.
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For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.
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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African medium-range ensemble flood forecast system." Hydrology and Earth System Sciences 19, no. 8 (August 3, 2015): 3365–85. http://dx.doi.org/10.5194/hess-19-3365-2015.
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Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.
Dissertations / Theses on the topic "Long-range weather forecasting Africa"
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Moatshe, Peggy Seanokeng. "Verification of South African Weather Service operational seasonal forecasts." Pretoria: [S.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-08112009-131703.
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Lee, Jae-Won. "Long-range variability and predictability of the Ozark Highlands climate elements /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842546.
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Lemke, Benjamin D. "Long-range forecasting in support of operations in the Horn of Africa." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5110.
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Approved for public release; distribution is unlimitedOver the past several decades, the Horn of Africa (HOA) has experienced recurring climate variations, including droughts and floods that have devastated the region's livelihoods and prompted increased investment in strategies to minimize the negative effects of climate variations. These preventative strategies include the enactment of early warning systems, such as the Famine Early Warning System Network, and military commands such as U.S. Africa Command. If these organizations are to be successful, they must account for the many climate factors that affect Africa, including seasonal climate variations and climate change. Thus, skillful long-range forecasts, especially of precipitation, have become increasingly valuable in planning the operations of these organizations. In this study, we focused on assessing the potential for predicting HOA precipitation rate (PR) during the October-November rain season at lead times of several seasons. We correlated HOA PR and remote climate variables, and discovered a strong potential for skillful long-range forecasts of HOA PR using sea surface temperatures (SST) near New Zealand, the Philippines, and Namibia as predictors. Our forecast methods included deterministic (tercile matching, linear regression, optimal climate normals) and probabilistic (composite analysis) methods. Our verification metrics showed a definite improvement in forecast skill over existing long-range forecasts based on long-term means, and indicated that our forecasting methods have the potential to improve the planning of military and non-military operations in the HOA.
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Vavae, Hilia. "A simple forecasting scheme for predicting low rainfalls in Funafuti, Tuvalu." The University of Waikato, 2008. http://hdl.handle.net/10289/2435.
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The development of some ability for forecasting low rainfalls would be helpful in Tuvalu as rainwater is the only source of fresh water in the country. The subsurface water is brackish and saline so the entire country depends totally on rainwater for daily domestic supplies, agricultural and farming activities. More importantly, these atolls are often influenced by droughts which consequently make inadequate drinking water an issue. A simple graph-based forecasting scheme is developed and presented in this thesis for forecasting below average mean rainfall in Funafuti over the next n-month period. The approach uses precursor ocean surface temperature data to make predictions of below average rainfall for n = 1, 2 12. The simplicity of the approach makes it a suitable method for the country and thus for the Tuvalu Meteorological Service to use as an operational forecasting tool in the climate forecasting desk. The graphical method was derived from standardised monthly rainfalls from the Funafuti manual raingauge for the period January 1945 to July 2007. The method uses lag-1 and-lag 2 NINO4 sea surface temperatures to define whether prediction conditions hold. The persistence of predictability tends to be maintained when the observed NINO4 ocean surface temperatures fall below 26.0oC. Although the developed method has a high success probability of up to 80 percent, this can only be achieved when conditions are within the predictable field. A considerable number of below average rainfall periods are not within the predictable field and therefore cannot be forecast by this method. However, the graphical approach has particular value in warning when an existing drought is likely to continue.
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Malin, Melissa L. "Teleconnection pattern impacts on intra-seasonal climate variability in United States winters." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 244 p, 2009. http://proquest.umi.com/pqdweb?did=1891555391&sid=3&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Madadgar, Shahrbanou. "Towards Improving Drought Forecasts Across Different Spatial and Temporal Scales." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/1516.
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Recent water scarcities across the southwestern U.S. with severe effects on the living environment inspire the development of new methodologies to achieve reliable drought forecasting in seasonal scale. Reliable forecast of hydrologic variables, in general, is a preliminary requirement for appropriate planning of water resources and developing effective allocation policies. This study aims at developing new techniques with specific probabilistic features to improve the reliability of hydrologic forecasts, particularly the drought forecasts. The drought status in the future is determined by certain hydrologic variables that are basically estimated by the hydrologic models with rather simple to complex structures. Since the predictions of hydrologic models are prone to different sources of uncertainties, there have been several techniques examined during past several years which generally attempt to combine the predictions of single (multiple) hydrologic models to generate an ensemble of hydrologic forecasts addressing the inherent uncertainties. However, the imperfect structure of hydrologic models usually lead to systematic bias of hydrologic predictions that further appears in the forecast ensembles. This study proposes a post-processing method that is applied to the raw forecast of hydrologic variables and can develop the entire distribution of forecast around the initial single-value prediction. To establish the probability density function (PDF) of the forecast, a group of multivariate distribution functions, the so-called copula functions, are incorporated in the post-processing procedure. The performance of the new post-processing technique is tested on 2500 hypothetical case studies and the streamflow forecast of Sprague River Basin in southern Oregon. Verified by some deterministic and probabilistic verification measures, the method of Quantile Mapping as a traditional post-processing technique cannot generate the qualified forecasts as comparing with the copula-based method.
The post-processing technique is then expanded to exclusively study the drought forecasts across the different spatial and temporal scales. In the proposed drought forecasting model, the drought status in the future is evaluated based on the drought status of the past seasons while the correlations between the drought variables of consecutive seasons are preserved by copula functions. The main benefit of the new forecast model is its probabilistic features in analyzing future droughts. It develops conditional probability of drought status in the forecast season and generates the PDF and cumulative distribution function (CDF) of future droughts given the past status. The conditional PDF can return the highest probable drought in the future along with an assessment of the uncertainty around that value. Using the conditional CDF for forecast season, the model can generate the maps of drought status across the basin with particular chance of occurrence in the future. In a different analysis of the conditional CDF developed for the forecast season, the chance of a particular drought in the forecast period can be approximated given the drought status of earlier seasons.
The forecast methodology developed in this study shows promising results in hydrologic forecasts and its particular probabilistic features are inspiring for future studies.
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Tennant, Warren James. "A monthly forecast strategy for Southern Africa." Thesis, 1998. https://hdl.handle.net/10539/26794.
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Dissertation submitted to the Faculty of Science,
University of the Witwatersrand, Johannesburg
for the Degree of Master of ScienceVarious 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
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Kgakatsi, Ikalafeng Ben. "The contribution of seasonal climate forecasts to the management of agricultural disaster-risk in South Africa." Thesis, 2015. http://hdl.handle.net/10539/16916.
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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.
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Landman, Stephanie. "A multi-model ensemble system for short-range weather prediction in South Africa." Diss., 2012. http://hdl.handle.net/2263/27018.
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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. CopyrightDissertation (MSc)--University of Pretoria, 2012.
Geography, Geoinformatics and Meteorology
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Peck, Lara. "Impacts of weather on aviation delays at O.R. Tambo International Airport, South Africa." Diss., 2015. http://hdl.handle.net/10500/22201.
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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)
Books on the topic "Long-range weather forecasting Africa"
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Gusztáv, Götz. A havi és évszakos éghajlati előrejelzések elméleti alapjai és gyakorlati módszerei. Budapest: Országos Meteorológiai Szolgálat, 1986.
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Alberto, Troccoli, ed. Seasonal climate: Forecasting and managing risk. Dordrecht: Springer, 2008.
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Harnack, Robert P. Principles and methods of extended period forecasting in the United States, 1986. Temple Hills, MD: National Weather Association, 1986.
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Chichasov, G. N. Tekhnologii͡a︡ dolgostochnykh prognozov pogody. S.-Peterburg: Gidrometeoizdat, 1991.
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Zhong qi tian qi yu bao. Beijing: Ke xue chu ban she, 1985.
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Kenkyūjo, Mitsubishi Sōgō. Chikyū ondanka eikyō no rikai no tame no kikō hendō yosoku tō jisshi itaku gyōmu gyōmu hōkokusho: Heisei 25-nendo. [Tōkyō-to Chiyoda-ku]: Mitsubishi Sōgō Kenkyūjo, 2014.
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Medardova kápě, aneb, Pranostiky očima meteorologa. Praha: Horizont, 1985.
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World Meteorological Organization. Regional Association VI (Europe). Task Team on the Provision of Seasonal to Inter-annual Forecasts and Regional Climate Centre Services. Proceedings of the RA VI Task Team on the Provision of Seasonal to Inter-annual Forecasts and Regional Climate Centre Services (RA-VI-TT/SIRCC): Reading, United Kingdom, 14-16 April 2003. [Geneva, Switzerland]: World Meteorological Organization, 2003.
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Kankyō, Kaiyōbu Kikō Jōhōka Japan Kishōchō Chikyū. 1-kagetsu yohō shisutemu no kōshin, JRA-55 no gaiyō: Heisei 26-nendo kisetsu yohō kenshū tekisuto. Tōkyō-to Chiyoda-ku: Kishōchō, 2015.
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Medium-range weather prediction: The European approach ; the story of the European Centre for Medium-Range Weather Forecasts. New York: Springer, 2006.
Find full textBook chapters on the topic "Long-range weather forecasting Africa"
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Alberiko Gil-Alana, Luis. "Time Trends and Persistence in the Snowpack Percentages by Watershed in Colorado." In Weather Forecasting [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.95911.
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In this paper we investigate the time trend coefficients in snowpack percentages by watershed in Colorado, US, allowing for the possibility of long range dependence or long memory processes. Nine series corresponding to the following watersheds are examined: Arkansas, Colorado, Gunnison, North Platte, Rio Grande, South Platte, San Juan-Animas-Dolores-San Miguel, Yampa & White and Colorado Statewide, based on annual data over the last eighty years. The longest series start in 1937 and all end in 2019. The results indicate that most of the series display a significant decline over time, showing negative time trend coefficients, and thus supporting the hypothesis of climate change and global warming. Nevertheless, there is no evidence of a long memory pattern in the data.
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Esther Babalola, Toju, Philip Gbenro Oguntunde, Ayodele Ebenezer Ajayi, and Francis Omowonuola Akinluyi. "Future Climate Change Impacts on River Discharge Seasonality for Selected West African River Basins." In Weather Forecasting [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99426.
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The changing climate is a concern to sustainable water resources. This study examined climate change impacts on river discharge seasonality in two West African river basins; the Niger river basin and the Hadejia-Jama’are Komadugu-Yobe Basin (HJKYB). The basins have their gauges located within Nigeria and cover the major climatic settings. Here, we set up and validated the hyper resolution global hydrological model PCR-GLOBWB for these rivers. Time series plots as well five performance evaluation metrics such as Kling–Gupta efficiency (KGE),); the ratio of RMSE-observations standard deviation (RSR); per cent bias (PBIAS); the Nash–Sutcliffe Efficiency criteria (NSE); and, the coefficient of determination (r2), were employed to verify the PCR-GLOBWB simulation capability. The validation results showed from satisfactory to very good on individual rivers as specified by PBIAS (−25 to 0.8), NSE (from 0.6 to 0.8), RSR (from 0.62 to 0.4), r2 (from 0.62 to 0.88), and KGE (from 0.69 to 0.88) respectively. The impact assessment was performed by driving the model with climate projections from five global climate models for the representative concentration pathways (RCPs) 4.5 and 8.5. We examined the median and range of expected changes in seasonal discharge in the far future (2070–2099). Our results show that the impacts of climate change cause a reduction in discharge volume at the beginning of the high flow period and an increase in discharge towards the ending of the high flow period relative to the historical period across the selected rivers. In the Niger river basin, at the Lokoja gauge, projected decreases added up to 512 m3/s under RCP 4.5 (June to July) and 3652 m3/s under RCP 8.5 (June to August). The three chosen gauges at the HJKYB also showed similar impacts. At the Gashua gauge, discharge volume increased by 371 m3/s (RCP8.5) and 191 m3/s (RCP4.5) from August to November. At the Bunga gauge, a reduction/increase of -91 m3/s/+84 m3/s (RCP 8.5) and -40 m3/s/+31 m3/s/(RCP 4.5) from June to July/August to October was simulated. While at the Wudil gauge, a reduction/increase in discharge volumes of −39/+133 m3/s (RCP8.5) and −40/133 m3/s (RCP 4.5) from June to August/September to December is projected. This decrease is explained by a delayed start of the rainy season. In all four rivers, projected river discharge seasonality is amplified under the high-end emission scenario (RCP8.5). This finding supports the potential advantages of reduced greenhouse gas emissions for the seasonal river discharge regime. Our study is anticipated to provide useful information to policymakers and river basin development authorities, leading to improved water management schemes within the context of changing climate and increasing need for agricultural expansion.
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Pedgley, D. E., D. R. Reynolds, and G. M. Tatchell. "Long-range insect migration in relation to climate and weather: Africa and Europe." In Insect Migration, 3–30. Cambridge University Press, 1995. http://dx.doi.org/10.1017/cbo9780511470875.002.
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Ingersoll, Andrew P. "Jupiter Winds and Weather." In Planetary Climates. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691145044.003.0008.
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This chapter examines the effect of winds on Jupiter's weather. The Great Red Spot is an atmospheric structure—a storm—that is free to move about under the laws of fluid dynamics. On Earth, these laws lead to turbulence, chaos, and limited predictability. By comparison, the Red Spot is well behaved. It stays in one latitude band, rolling like a ball bearing between two conveyor belts—a westward current to the north and an eastward current to the south. All the large-scale features are remarkably constant. Atmospheric scientists during the Voyager encounter were surprised by the areas outside the Red Spot and the three white ovals—formerly featureless areas that had become turbulent convective regions. The chapter first provides an overview of long-range weather forecasting on Jupiter before discussing the dynamics of rotating fluids, momentum transfer by eddies, stability of zonal jets, geostrophic balance, vorticity, and abyssal weather.
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Lyell, Christopher Sean, Usha Nattala, Rakesh Chandra Joshi, Zaher Joukhadar, Jonathan Garber, Simon Mutch, Assaf Inbar, et al. "A forest fuel dryness forecasting system that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models." In Advances in Forest Fire Research 2022, 21–27. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_1.
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Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (
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Memon, Nimrabanu, Samir B. Patel, and Dhruvesh P. Patel. "Deep Learning Solutions for Analysis of Synthetic Aperture Radar Imageries." In Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis, 107–29. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3981-4.ch008.
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The potential of Synthetic Aperture Radar (SAR) to detect surface and subsurface characteristics of land, sea, and ice using polarimetric information has long piqued the interest of scientists and researchers. Traditional strategies include employing polarimetric information to simplify and classify SAR images for various earth observation applications. Deep learning (DL) uses advanced machine learning algorithms to increase information extraction from SAR datasets about the land surface, as well as segment and classify the dataset for applications. The chapter highlights several problems, as well as what and how DL can be utilized to solve them. Currently, improvements in SAR data analysis have focused on the use of DL in a range of current research areas, such as data fusion, transfer learning, picture classification, automatic target recognition, data augmentation, speckle reduction, change detection, and feature extraction. The study presents a small case study on CNN for land use land cover classification using SAR data.
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Chapin III, F. Stuart, and A. David McGuire. "Climate Feedbacks in the Alaskan Boreal Forest." In Alaska's Changing Boreal Forest. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195154313.003.0026.
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The boreal forest biome occupies an area of 18.5 million km2, which is approximately 14% of the vegetated cover of the earth’s surface (McGuire et al. 1995b). North of 50°N, terrestrial interactions with the climate system are dominated by the boreal forest biome because of its large aerial extent (Bonan et al. 1992, Chapin et al. 2000b; Fig. 19.1). There are three major pathways through which the function and structure of boreal forests may influence the climate system: (1) water/energy exchange with the atmosphere, (2) the exchange of radiatively active gases with the atmosphere, and (3) delivery of fresh water to the Arctic Ocean. The exchange of water and energy has implications for regional climate that may influence global climate, while the exchange of radiatively active gases and the delivery of fresh water to the Arctic Ocean are processes that could directly influence climate at the global scale. In this chapter, we first discuss the current understanding of the role that boreal forests play in each of these pathways and identify key issues that remain to be explored. We then discuss the implications for the earth’s climate system of likely responses of boreal forests to various dimensions of ongoing global change. Most of the energy that heats the earth’s atmosphere is first absorbed by the land surface and then transferred to the atmosphere. The energy exchange properties of the land surface therefore have a strong direct influence on climate. Boreal forest differs from more southerly biomes in having a long period of snow cover, when white surfaces might be expected to reflect incoming radiation (high albedo) and therefore absorb less energy for transfer to the atmosphere. Observed winter albedo in the boreal forest varies between 0.11 (conifer stands) and 0.21 (deciduous stands; Betts and Ball 1997). This is much closer to the summer albedo (0.08–0.15) than to the winter albedo of tundra (0.6–0.8), which weather models had previously assumed to be appropriate for boreal forests. The incorporation of true boreal albedo into climate models led to substantial improvements in medium-range weather forecasting (Viterbo and Betts 1999).
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Polyak, Ilya. "Historical Records." In Computational Statistics in Climatology. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099997.003.0008.
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In this chapter, the historical records of annual surface air temperature, pressure, and precipitation with the longest observational time series will be studied. The analysis of the statistically significant systematic variations, as well as random fluctuations of such records, provides important empirical information for climate change studies or for statistical modeling and long-range climate forecasting. Of course, compared with the possible temporal scales of climatic variations, the interval of instrumental observations of meteorological elements proves to be very small. For this reason, in spite of the great value of such records, they basically characterize the climatic features of a particular interval of instrumental observations, and only some statistics, obtained with their aid, can have more general meaning. Because each annual or monthly value of such records is obtained by averaging a large number of daily observations, the corresponding central limit theorem of the probability theory can guarantee their approximate normality. In spite of this, we computed the sample distribution functions for each time series analyzed below and evaluated their closeness to the normal distribution by the Kolmogorov- Smirnov criterion. As expected, the probability of the hypothesis that each of the climatic time series (annual or monthly) has a normal distribution is equal to one with three or four zeros after the decimal point. As seen in this section, the straight line least squares approximation of the climatic time series enables us to obtain very simple and easy-to-interpret information about the power of the long period climate variability. Carrying out such an approximation, we assume that the fluctuation with a period several times greater than the observational interval will become apparent as a gradual increase or decrease of the observed values. Using only a small sample, it is impossible to determine accurately the amplitude and frequency of such long-period climate fluctuation. Consequently, the straight-line model is the simplest approach in this case. Let us begin with an analysis of the annual surface air temperature time series, the observations of which are published in Bider et al., (1959), Bider and Schiiepp, (1961), Lebrijn (1954), Manlcy (1974), and in the World Weather Records (1975).
Conference papers on the topic "Long-range weather forecasting Africa"
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Portilla-Yandún, Jesús. "Open Access Atlas of Global Spectral Wave Conditions Based on Partitioning." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77230.
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An open access atlas of wave spectral characteristics at global scale is presented (GLOSWAC). This atlas is based on a recently developed technique for deriving spectral statistics, following the concept of partitioning. This development has been possible due to the parallel release of the wave spectra variable from the ERA-Interim archive of the European Centre for Medium-Range Weather Forecast (ECMWF). Although wave spectra are commonly available nowadays for wave analysis and forecasting, standard integral wave parameters are still in dominant use, both in practical and scientific applications. Although integrated parameters can give a good account of the wave spectral distribution in unimodal cases, they are subject to serious shortcomings when the sea state is bimodal or multi-modal. This issue can be easily tackled with the use of partitioning approaches. Spectral partitioning allows identifying the different wave components with different meteorological origin present in the spectrum. These components can be represented by their integrated parameters, which are much more meaningful than the averaged ones for the whole spectrum. Apart from the increased consistency, this method allows to summarize spectral information, offering the possibility to develop wave spectral statistics. Based on a statistical descriptor, the Probability Distribution of Spectral Partitions (PDS), which is the main outcome from GLOSWAC, the local long-term wave systems can be identified and characterized. In addition, several other spectral parameters are computed and distributed in a web format. For illustration, an arbitrary reference location is used here to guide the interpretation and the use of the information derived.