Dissertations / Theses on the topic 'Precipitation forecasting Africa, Southern'

Includes bibliographical references.
This study contributes to a broader effort of institutions toward improving seasonal forecasts over southern Africa. The primary objective is to understand where global models show shortcomings in their simulations, and how this impacts on their seasonal forecast skill. It is proposed that the skill of a model in simulating natural climate variability is an appropriate metric for a model's potential skill in seasonal forecasting. Thus the study investigates the performance of two global models in simulating the regional processes in relation to the processes variability, and how this is related to their forecast skill.

The predictive skill of seasonal forecast arises from the slowly evolving climate processes where the signature, that noticeably influence the mean state of weather conditions, mainly resides in the ocean. The interaction of the ocean and atmosphere is therefore the minimum level of complexity required for seasonal timescale. The practice of contemporary seasonal prediction is presumably achievable with the use of two distinct GCM (Global Climate Model) configurations commonly referred to as one- and two-tiered forecasting systems based on the manner in which the atmosphere and ocean exchange information. One-tiered forecasting systems (Coupled climate models) are placed at the highest hierarchy in the science of numerical modelling in terms of complexity. They are hypothesized to represent the state of the art of seasonal forecasting which inherently renders them to be convenient for seasonal climate prediction purposes. Notwithstanding, it may be important to appraise whether or not two-tier forecasting systems (uncoupled models) offer comparable levels of skill that are currently attainable by state-of-the-art coupled climate models under a constrained computational resources environment. Such a restrictive environment is commonly found in developing countries such as South Africa. With this in mind, the study attempts to test the notion under a perfect model framework where the atmospheric global climate model is forced with the best estimate of predicted sea-surface temperature (SST), while the two systems are kept similar in all other aspects. The framework eliminates differences between the two forecasting systems due to model biases and in fact enables the discrimination of the role of coupling on seasonal forecast skill. Due to the enormous computational resources required to develop and run an operational forecast system based on coupled models, their engagement for real-time forecasts has been negligible in South Africa. However, motivated by the recent advances in computing infrastructures in South Africa due to the establishment and maintenance of the Centre for High Performance Computing (CHPC) as well as international collaboration, the study pioneered in Africa the emergence of the South African Weather Service Coupled Model (SCM) also referred to as the ECHAM4.5-MOM3-SA. The model couples the ECHAM4.5 atmospheric general circulation model (AGCM) and Modular Ocean Model version 3 (MOM3) using the multiple program multiple data (MPMD) coupler paradigm. The model employs an atmospheric initialization strategy that is different from other models that couple the same atmosphere and ocean models. The study reveals that the South African coupled model has skill levels for ENSO (El Niño Southern Oscillation) forecasts comparable with other coupled models currently administered by international centres. Furthermore the model is also found to be skilful in predicting upper air dynamics, surface air temperature, rainfall and equatorial Indian Ocean Dipole (IOD). In the two-tiered experiment, the AGCM is constrained by the lower boundary conditions derived from predicted SST anomalies of two ocean-atmosphere coupled general circulation models (CGCMs) combined through equal weighting. In addition, the SST uncertainty amplitude (lower and upper bounds) defined from this combination is also considered as separate forcing fields. As with the CGCM, the AGCM is initialized with the realistic state of the atmosphere and soil moisture. Results from hindcasts show that this optimized forecasting system demonstrates large-scale consistent skill improvements for surface temperature and rainfall totals relative to forcing the AGCM with persisted SST anomalies and the AMIP-2 (Atmospheric Model Intercomparison Project) type simulations. Model evaluation further reveals that the AGCM is able to forecast anomalous upper air atmospheric dynamics (circulation) over the tropics up to several months ahead. In addition, the contribution of the predicted SST, which is based on a multi-model approach, is shown to be of significant importance for optimized AGCM results. However, the AGCM appears to be weakly sensitive to soil moisture initialization which may suggest an internal weakness of the model. The study has addressed some optimization issues for atmospheric models and proposed an optimal AGCM configuration that can serve as baseline against which more advanced models can be tested. Finally, the comparative experiments reveal that the GCM configurations widely differ in their performances and the superiority of one model over the other is mostly dependent on the ability to a priori determine an optimal global SST field for forcing the AGCM. In fact, the AGCM offers comparable predictive capabilities with the CGCM when the CGCMs skilful predicted SST evolution can in turn be used to force the AGCM. This finding supports the notion that the further enhancement of seasonal forecasting practices favours the use and further improvement of CGCMs (should computational resources be permissible) since the potential for further improvement of AGCM-based forecasts heavily depends on the improvement of CGCMs.
Thesis (PhD)--University of Pretoria, 2015.
Geography, Geoinformatics and Meteorology
PhD
Unrestricted

A key determinant of atmospheric circulation patterns and regional climatic conditions is sea surface temperature (SST). This has been the motivation for the development of various teleconnection methods aiming to forecast hydro-climatic variables. Among such methods are linear projections based on teleconnection gross indices (such as the ENSO, IOD, and NAO) or leading empirical orthogonal functions (EOFs). However, these methods deteriorate drastically if the predefined indices or EOFs cannot account for climatic variability in the region of interest. This study introduces a new hydro-climatic forecasting method that identifies SST predictors in the form of dipole structures. An SST dipole that mimics major teleconnection patterns is defined as a function of average SST anomalies over two oceanic areas of appropriate sizes and geographic locations. The screening process of SST-dipole predictors is based on an optimization algorithm that sifts through all possible dipole configurations (with progressively refined data resolutions) and identifies dipoles with the strongest teleconnection to the external hydro-climatic series. The strength of the teleconnection is measured by the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate ensemble hydro-climatic forecasts. The dipole teleconnection method is applied to the forecasting of seasonal precipitation over the southeastern US and East Africa, and the forecasting of streamflow-related variables in the Yangtze and Congo Rivers. These studies show that the new method is indeed able to identify dipoles related to well-known patterns (e.g., ENSO and IOD) as well as to quantify more prominent predictor-predictand relationships at different lead times. Furthermore, the dipole method compares favorably with existing statistical forecasting schemes. An operational forecasting framework to support better water resources management through coupling with detailed hydrologic and water resources models is also demonstrated.

This thesis develops an innovative process-based analysis of contemporary model performance of precipitation over southern Africa. This region is typically understudied and not fully understood due to the complexity of various influences and drivers of precipitation. Historical simulations of precipitation are assessed including principal drivers, sources of biases and dominant modes of interannual variability. The South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the south-west Indian Ocean, is evaluated as the feature of interest in historical simulations. Most CMIP5 models simulate an SIOCZ feature, but are typically too zonally oriented and discontinued between land and the adjacent Indian Ocean. Excessive precipitation over the continent is likely associated with excessively high low-level moisture flux around the Angola Low, which is almost entirely due to model circulation biases. Drivers of precipitation over southern Africa include three dominant moisture flux transport pathways which originate from flow around the SIOHP and SAOHP and monsoon winds. Interannual variability in the SIOCZ is shown by a clear dipole pattern, indicative of a northeast-southwest movement of the SIOCZ. Drivers of this shift are significantly related to the El Niño Southern Oscillation and the subtropical Indian Ocean dipole in observations. However models do not capture these teleconnections well, limiting confidence in model representation of variability. A large majority of the population rely heavily on precipitation over southern Africa for agricultural purposes. Therefore spatial and temporal changes in precipitation are crucial to identify and understand with intentions to ultimately provide useful climate information regarding water security over the region. Key climate change signals over southern Africa are established in this thesis (OND and DJF), in which the dominant regional mechanisms of precipitation change over southern Africa are quantified. Robustness and credibility of these changes are additionally quantified. The most notable projected change in precipitation over southern Africa is the distinct drying signal evident in the pre-summer season (OND). This has the implication of delaying the onset of the rainy season affecting planting and harvesting times. Future projections of the SIOCZ are determined, which indicate a northward shift of approximately 200km. A dipole pattern of precipitation wetting/drying is evident, where wetting occurs to the north of the climatological axis of maximum rainfall, hence implying a northward shift of the ITCZ, consistent with the SIOCZ shift. Using a decomposition method it is established that ΔP's dipole pattern emerges largely from the dynamic component, which holds most uncertainty, particularly over the south-west Indian Ocean. Changes in precipitation over land are not solely driven by dynamical changes but additionally driven by thermodynamic contributions, implying projected changes over land and ocean regions require different approaches. SST patterns of warming over the Indian Ocean corroborate the warmest-get-wetter mechanism driving wetting over the south-west Indian Ocean, which is robust in both key seasons. Coherent model behaviour is understood via across model correlation plots of principal components, whereby patterns of coherent warming patterns are identified. Composite analyses of diagnostic variables across models illustrate patterns driving projected precipitation changes. Drying is more robust over land than over the south-west Indian Ocean. Clear robust drying signal in OND, however magnitude is uncertain. Drivers of uncertainty include SST pattern changes, which modulate atmospheric circulation patterns. Therefore reductions in uncertainty rely on the accurate representation of these processes within climate models to become more robust. There is a desire from both climate scientists and policy-makers to reduce uncertainty in future projections. No one particular methodology is unanimously agreed upon, however one approach is analysed in this thesis. Uncertainties of future precipitation projections are addressed using a process-based model ranking framework. Several metrics most applicable to southern African climate are selected and ranked, which include aspects of both mean state and variability. A sensitivity test via a Monte Carlo approach is performed for various sub-samples of “top” performing models within the CMIP5 model dataset. Uncertainty is significantly reduced when particular sub-sets of “top” performing models are selected, however only for austral summer over the continent. The result has the implication that potential value is established in performing a process-based model ranking over southern Africa. However additional investigation is required before such an approach may become viable and sufficiently credible and robust. Reductions in model spread are additionally established in SIOCZ projections, whereby model processes of change exhibit agreement, despite differing initial SIOCZ conditions. Therefore model process convergence and coherence is established with respect to projected changes in the SIOCZ, irrespective of initial climatology biases.

A Tropical Temperate Trough (TTT) is a type of weather system that links the tropics and the extra-tropics across southern Africa. TTT events have been studied statistically in detail, but very little research has been done to study this phenomenon dynamically and especially on a seasonal scale. This study therefore focuses on the predictability of the characteristics of TTTs across southern Africa on a seasonal scale, by using a state-of-the-art seasonal forecasting model, namely the GloSea5 developed by the UK Met Office. Gridded hindcast data for the months of November, December, January and February from 1996/1997 to 2009/2010 are compared to observed data. The different ensemble members of the GloSea5 model (with lead-times of 1 week up to 2 months) are first compared separately to the observed data, after which the model average, with a 0-month, a 1-month and a 2-month lead-time, is calculated and also compared to the observed dataset. TTT events have distinctive characteristics during the formation and the development phases. Most prominent of these characteristics are the cloud bands associated with these weather systems, which have a north-west to south-east orientation and move from west to east across southern Africa. To identify the TTTs, daily outgoing long-wave radiation values are processed by a Meteorological Robot (MetBot), with a strict criterion to identify the cloud bands that characterise these systems. The MetBot’s algorithm produces the information needed to further investigate the different characteristics of TTTs, such as the frequency, the location and the intensity of these systems. Analysis of the MetBot output includes calculating the Root Mean Square Error, the percentage error and in some cases the percentage deviation of the number of cloud bands, as well as the anchor point, the centroid position, the area, the tilt and the minimum and maximum OLR values of the cloud bands. This investigation revealed that the characteristics of TTT events can to some extent be predicted on a seasonal scale for the summer rainfall season of southern Africa. The model used in this study fared particularly well with a 1-month lead-time forecast (compared to a 0-month and a 2-month lead-time forecast). The intensity and the location of the cloud bands associated with TTT events are forecast with a smaller percentage error than the frequency of these systems, as the frequency of TTTs tend to be significantly under-predicted by the model. For some predicted quantities, such as the area of the cloud bands, a bias-adjustment is necessary which produces significantly better results with smaller percentage errors. In the conclusions, suggestions are made on possible future studies, and how to develop this study further to create seasonal forecasts with higher skill with special regards to TTT events.
Dissertation (MSc)--University of Pretoria, 2019.
Geography, Geoinformatics and Meteorology
MSc
Unrestricted

Water is recognised as a key driver for social and economic development in the Zambezi basin. The basin is riparian to eight southern African countries and the transboundary nature of the basin’s water resources can be viewed as an agent of cooperation between the basin countries. It is possible, however, that the same water resource can lead to conflicts between water users. The southern African Water Vision for ‘equitable and sustainable utilisation of water for social, environmental justice and economic benefits for the present and future generations’ calls for an integrated and efficient management of water resources within the basin. Ensuring water and food security in the Zambezi basin is, however, faced with challenges due to high variability in climate and the available water resources. Water resources are under continuous threat from pollution, increased population growth, development and urbanisation as well as global climate change. These factors increase the demand for freshwater resources and have resulted in water being one of the major driving forces for development. The basin is also vulnerable due to lack of adequate financial resources and appropriate water resources infrastructure to enable viable, equitable and sustainable distribution of the water resources. This is in addition to the fact that the basin’s economic mainstay and social well-being are largely dependent on rainfed agriculture. There is also competition among the different water users and this has the potential to generate conflicts, which further hinder the development of water resources in the basin. This thesis has focused on the Zambezi River basin emphasising climate variability and climate change. It is now considered common knowledge that the global climate is changing and that many of the impacts will be felt through water resources. If these predictions are correct then the Zambezi basin is most likely to suffer under such impacts since its economic mainstay is largely determined by the availability of rainfall. It is the belief of this study that in order to ascertain the impacts of climate change, there should be a basis against which this change is evaluated. If we do not know the historical patterns of variability it may be difficult to predict changes in the future climate and in the hydrological resources and it will certainly be difficult to develop appropriate management strategies. Reliable quantitative estimates of water availability are a prerequisite for successful water resource plans. However, such initiatives have been hindered by paucity in data especially in a basin where gauging networks are inadequate and some of them have deteriorated. This is further compounded by shortages in resources, both human and financial, to ensure adequate monitoring. To address the data problems, this study largely relied on global data sets and the CRU TS2.1 rainfall grids were used for a large part of this study. The study starts by assessing the historical variability of rainfall and streamflow in the Zambezi basin and the results are used to inform the prediction of change in the future. Various methods of assessing historical trends were employed and regional drought indices were generated and evaluated against the historical rainfall trends. The study clearly demonstrates that the basin has a high degree of temporal and spatial variability in rainfall and streamflow at inter-annual and multi-decadal scales. The Standardised Precipitation Index, a rainfall based drought index, is used to assess historical drought events in the basin and it is shown that most of the droughts that have occurred were influenced by climatic and hydrological variability. It is concluded, through the evaluation of agricultural maize yields, that the basin’s food security is mostly constrained by the availability of rainfall. Comparing the viability of using a rainfall based index to a soil moisture based index as an agricultural drought indicator, this study concluded that a soil moisture based index is a better indicator since all of the water balance components are considered in the generation of the index. This index presents the actual amount of water available for the plant unlike purely rainfall based indices, that do not account for other components of the water budget that cause water losses. A number of challenges were, however, faced in assessing the variability and historical drought conditions, mainly due to the fact that most parts of the Zambezi basin are ungauged and available data are sparse, short and not continuous (with missing gaps). Hydrological modelling is frequently used to bridge the data gap and to facilitate the quantification of a basin’s hydrology for both gauged and ungauged catchments. The trend has been to use various methods of regionalisation to transfer information from gauged basins, or from basins with adequate physical basin data, to ungauged basins. All this is done to ensure that water resources are accounted for and that the future can be well planned. A number of approaches leading to the evaluation of the basin’s hydrological response to future climate change scenarios are taken. The Pitman rainfall-runoff model has enjoyed wide use as a water resources estimation tool in southern Africa. The model has been calibrated for the Zambezi basin but it should be acknowledged that any hydrological modelling process is characterised by many uncertainties arising from limitations in input data and inherent model structural uncertainty. The calibration process is thus carried out in a manner that embraces some of the uncertainties. Initial ranges of parameter values (maximum and minimum) that incorporate the possible parameter uncertainties are assigned in relation to physical basin properties. These parameter sets are used as input to the uncertainty version of the model to generate behavioural parameter space which is then further modified through manual calibration. The use of parameter ranges initially guided by the basin physical properties generates streamflows that adequately represent the historically observed amounts. This study concludes that the uncertainty framework and the Pitman model perform quite well in the Zambezi basin. Based on assumptions of an intensifying hydrological cycle, climate changes are frequently expected to result in negative impacts on water resources. However, it is important that basin scale assessments are undertaken so that appropriate future management strategies can be developed. To assess the likely changes in the Zambezi basin, the calibrated Pitman model was forced with downscaled and bias corrected GCM data. Three GCMs were used for this study, namely; ECHAM, GFDL and IPSL. The general observation made in this study is that the near future (2046-2065) conditions of the Zambezi basin are expected to remain within the ranges of historically observed variability. The differences between the predictions for the three GCMs are an indication of the uncertainties in the future and it has not been possible to make any firm conclusions about directions of change. It is therefore recommended that future water resources management strategies account for historical patterns of variability, but also for increased uncertainty. Any management strategies that are able to satisfactorily deal with the large variability that is evident from the historical data should be robust enough to account for the near future patterns of water availability predicted by this study. However, the uncertainties in these predictions suggest that improved monitoring systems are required to provide additional data against which future model outputs can be assessed.

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

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

Thesis (M.S.)--Florida State University, 2006.
Advisor: James J. O'Brien, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 19, 2006). Document formatted into pages; contains ix, 43 pages. Includes bibliographical references.

Heavy rainfall and flooding often occur over South Africa. A high percentage of the heavy rainfall events occur over the eastern interior of South Africa and generally during the late summer (January to March) when the influence of tropical weather systems becomes dominant. Research into forecasting techniques best suited for tropical weather systems over southern Africa has been neglected since the early 1970's. The aim of this research was to develop a Model for the Identification of Tropical Weather Systems (MITS) as well as a Tropical Heavy Rainfall Identification System (THERIS) for operational use in the weather forecasting offices of Southern Africa. This study explains the dynamical properties of tropical weather systems and identifies those variables, which favour the development of heavy rainfall. Three case studies are presented to illustrate the dynamical properties of tropical weather systems. THERIS is tested and verified for historical heavy rainfall events over South Africa. The heavy rainfall events of February 2000 over the northern Provinces of South Africa are discussed and both THERIS and MITS are tested for operational functionality. Results indicate that MITS can be used to identify tropical weather systems and that THERIS determines areas of heavy rainfall. It is recommended that the two products be tested and used operationally.
Dissertation (MSc)--University of Pretoria, 2000.
Geography, Geoinformatics and Meteorology
unrestricted

Thunderstorms, due to their high frequency of occurrence over southern Africa, and their major contribution to summer rainfall are the primary focus of very short range forecasting and nowcasting efforts in South Africa. With a limited number of surface and upper-air observations and the limited availability of numerical model output most southern African countries are heavily reliant on satellite technology. In developing tools for the first twelve forecast hours the South African Weather Service has to address both the national and regional needs. Thus, the blending of techniques in an optimal manner is essential. This study initially describes how the Global Instability Index product derived from the European Meteosat Second Generation Satellite was adapted for South African circumstances using a different numerical model to provide background information – creating the Regional Instability Indices (RII). The focus of the study is the development of a new convection indicator, called the Combined Instability Index (CII), which calculates the probability of convection from satellite derived instability indices and moisture, as well as height above sea level early in the morning when the sky is relatively cloud free. Early morning CII values were evaluated statistically against the occurrence of lightning over South Africa, where a lightning network is available, as well as against satellite derived precipitation over southern Africa, later in the same day. It is shown that the CII not only performs well, but also outperforms the individual RII when compared to the occurrence of lightning. The CII will be beneficial to operational forecasters to focus their attention on the area which is most favourable for the development of convection later in the day.
Environmental Sciences
Ph. D. (Environmental Sciences)

Thunderstorms, due to their high frequency of occurrence over southern Africa, and their major contribution to summer rainfall are the primary focus of very short range forecasting and nowcasting efforts in South Africa. With a limited number of surface and upper-air observations and the limited availability of numerical model output most southern African countries are heavily reliant on satellite technology. In developing tools for the first twelve forecast hours the South African Weather Service has to address both the national and regional needs. Thus, the blending of techniques in an optimal manner is essential. This study initially describes how the Global Instability Index product derived from the European Meteosat Second Generation Satellite was adapted for South African circumstances using a different numerical model to provide background information – creating the Regional Instability Indices (RII). The focus of the study is the development of a new convection indicator, called the Combined Instability Index (CII), which calculates the probability of convection from satellite derived instability indices and moisture, as well as height above sea level early in the morning when the sky is relatively cloud free. Early morning CII values were evaluated statistically against the occurrence of lightning over South Africa, where a lightning network is available, as well as against satellite derived precipitation over southern Africa, later in the same day. It is shown that the CII not only performs well, but also outperforms the individual RII when compared to the occurrence of lightning. The CII will be beneficial to operational forecasters to focus their attention on the area which is most favourable for the development of convection later in the day.
Environmental Sciences
Ph. D. (Environmental Sciences)

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

The El Niño – Southern Oscillation (ENSO) has far-reaching impacts across the globe and provides the most reliable source of seasonal to interannual climate prediction over North America. Though numerous studies have discussed the impacts of ENSO teleconnections on North America during boreal winter, it is becoming more and more apparent that the regional impacts of ENSO teleconnections are highly sensitive to the seasonal evolution of ENSO events. Also, the significant impacts of ENSO are not limited to the boreal winter seasons. To address these knowledge gaps, this thesis examines the seasonal dependence of ENSO teleconnections and impacts on North American surface climate, focusing on two examples. Chapter 1 examines the relationship between El Niño – California winter precipitation. Results show that the probability of the anomalous statewide-wetness increases as El Niño intensity increases. Also, the influences of El Niño on California winter precipitation are statistically significant in late winter (Feb-Apr), but not in early winter even though that is when El Niño usually reaches its peak intensity. Chapter 2 further investigates why the strong 2015/16 El Niño failed to bring above normal winter precipitation to California, focusing on the role of westward shifted equatorial Pacific sea surface temperature anomalies (SSTAs) based on two reasons: the maximum equatorial Pacific SSTAs was located westward during the 2015/16 winter compared to those during the 1982/83 and 1997/98 winters, both of which brought extremely wet late winters to California. Also, the North American Multi-Model Ensemble (NMME) forecasts overestimated the eastern tropical Pacific SSTAs and California precipitation in the 2015/16 late winter, compared to observations. The Atmospheric General Circulation Model (AGCM) experiments suggested that the SST forecast error in NMME contributed partially to the wet bias in California precipitation forecast in the 2015/16 late winter. However, the atmospheric internal variability could have also played a large role in the dry California winter during the event. ENSO also exerts significant impacts on agricultural production over the Midwest during boreal summer. Chapter 3 examines the physical processes of the ENSO summer teleconnection, focusing on the summer when a La Niña is either transitioning from an earlier El Niño winter or persisting from an existing La Niña winter. The results demonstrate that the impacts are most significant during the summer when El Niño is transitioning to La Niña compared to that when La Niña is persisting, even though both can loosely be defined as developing La Niña summer. During the transitioning summer, both the decaying El Niño and the developing La Niña induce suppressed deep convection over the tropical Pacific and thereby the corresponding Rossby wave propagations toward North America, resulting in a statistically significant anomalous anticyclone over northeastern North America and, therefore, a robust warming signal over the Midwest. These features are unique to the developing La Niña transitioning from El Niño, but not the persistent La Niña. In Chapter 4, we further evaluate the performance of NCAR CAM5 forced with historical SSTA in terms of the La Niña summer teleconnections. Though the model ensemble mean well reproduces the features in the preceding El Niño/La Niña winters, the model ensemble mean has very limited skill in simulating the tropical convection and extratropical teleconnections during both the transitioning and persisting summers. The weak responses in the model ensemble mean are attributed to large variability in both the tropical precipitation, especially over the western Pacific, and atmospheric circulation during summer season. This thesis synthesizes the physical processes and assessments of climate models in different seasons to establish the sensitivity of regional climate to the seasonal dependence of ENSO teleconnections. We demonstrate that the strongest impacts of ENSO on North American regional climate might not be necessarily simultaneous with maximum tropical Pacific SST anomalies. We also emphasize the importance of the multi-year ENSO evolutions when addressing the seasonal impacts on North American summertime climate. The findings in this thesis could benefit the improvement of seasonal hydroclimate forecasting skills in the future.

碩士
中華科技大學
土木防災工程研究所在職專班
107
The development of Taiwan economy and innovative technology triggers distinct and more severe impact by contemporary meteorological disasters; once if more accurate weather forecast segregating from invasion by radical weather can be offered, the meteorological personnel’s advanced goal to diminish the anti-disaster jobs and preventions as well as effective reduction of casualties shall be identified. From August 23 to August 26, 2018, the heavy casualties and agricultural losses by severe flooding in Taiwan was triggered by a tropical depression; this research monitors and analyzes the drastic rainfall case by WRF numerical model. The results show that the tissue convective rain belt accompanied by the tropical depression system is shrouded in the Southern region, resulting in severe flooding by persistent heavy rainfall; whose features also correspond with the characteristics of precipitation of this and past studies. In addition, although the tropical depression does not evolve to a typhoon, the low-pressure center accompanied by the tissue convective rain band in the above-mentioned areas causes short-term torrential rain; integrating with slow motion of tropical low-pressure, rapid rainfall accumulation emerges. Considering of the probability of similar rainfall pattern as frequent mode under the influence of climate change, various regions face severe challenge to the capabilities of existing anti-flood facilities; which shall be applied as reference for future drastic precipitation control.

Policy makers, water managers, farmers and many other sectors of the society in southern Africa are confronting increasingly complex decisions as a result of the marked day-to-day, intra-seasonal and inter-annual variability of climate. Hence, forecasts of hydro-climatic variables with lead times of days to seasons ahead are becoming increasingly important to them in making more informed risk-based management decisions. With improved representations of atmospheric processes and advances in computer technology, a major improvement has been made by institutions such as the South African Weather Service, the University of Pretoria and the University of Cape Town in forecasting southern Africa’s weather at short lead times and its various climatic statistics for longer time ranges. In spite of these improvements, the operational utility of weather and climate forecasts, especially in agricultural and water management decision making, is still limited. This is so mainly because of a lack of reliability in their accuracy and the fact that they are not suited directly to the requirements of agrohydrological models with respect to their spatial and temporal scales and formats. As a result, the need has arisen to develop a GIS based framework in which the “translation” of weather and climate forecasts into more tangible agrohydrological forecasts such as streamflows, reservoir levels or crop yields is facilitated for enhanced economic, environmental and societal decision making over southern Africa in general, and in selected catchments in particular. This study focuses on the development of such a framework. As a precursor to describing and evaluating this framework, however, one important objective was to review the potential impacts of climate variability on water resources and agriculture, as well as assessing current approaches to managing climate variability and minimising risks from a hydrological perspective. With the aim of understanding the broad range of forecasting systems, the review was extended to the current state of hydro-climatic forecasting techniques and their potential applications in order to reduce vulnerability in the management of water resources and agricultural systems. This was followed by a brief review of some challenges and approaches to maximising benefits from these hydro-climatic forecasts. A GIS based framework has been developed to serve as an aid to process all the computations required to translate near real time rainfall fields estimated by remotely sensed tools, as well as daily rainfall forecasts with a range of lead times provided by Numerical Weather Prediction (NWP) models into daily quantitative values which are suitable for application with hydrological or crop models. Another major component of the framework was the development of two methodologies, viz. the Historical Sequence Method and the Ensemble Re-ordering Based Method for the translation of a triplet of categorical monthly and seasonal rainfall forecasts (i.e. Above, Near and Below Normal) into daily quantitative values, as such a triplet of probabilities cannot be applied in its original published form into hydrological/crop models which operate on a daily time step. The outputs of various near real time observations, of weather and climate models, as well as of downscaling methodologies were evaluated against observations in the Mgeni catchment in KwaZulu-Natal, South Africa, both in terms of rainfall characteristics as well as of streamflows simulated with the daily time step ACRU model. A comparative study of rainfall derived from daily reporting raingauges, ground based radars, satellites and merged fields indicated that the raingauge and merged rainfall fields displayed relatively realistic results and they may be used to simulate the “now state” of a catchment at the beginning of a forecast period. The performance of three NWP models, viz. the C-CAM, UM and NCEP-MRF, were found to vary from one event to another. However, the C-CAM model showed a general tendency of under-estimation whereas the UM and NCEP-MRF models suffered from significant over-estimation of the summer rainfall over the Mgeni catchment. Ensembles of simulated streamflows with the ACRU model using ensembles of rainfalls derived from both the Historical Sequence Method and the Ensemble Re-ordering Based Method showed reasonably good results for most of the selected months and seasons for which they were tested, which indicates that the two methods of transforming categorical seasonal forecasts into ensembles of daily quantitative rainfall values are useful for various agrohydrological applications in South Africa and possibly elsewhere. The use of the Ensemble Re-ordering Based Method was also found to be quite effective in generating the transitional probabilities of rain days and dry days as well as the persistence of dry and wet spells within forecast cycles, all of which are important in the evaluation and forecasting of streamflows and crop yields, as well as droughts and floods. Finally, future areas of research which could facilitate the practical implementation of the framework were identified.
Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2007.