Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1982. / Microfiche copy available in Archives and Science. / Vita. / Bibliography: leaves 218-225. / by Randall M. Dole. / Ph.D.

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.

Thesis (M.Sc. (Geography and Environmental Studies)) -- University of Limpopo, 2022 / The purpose of this study was to assess and document the use of the Indigenous Knowledge System (IKS) in weather and seasonal climate forecasting in Moletjie, Limpopo Province. The objectives of this study were to document the biological, meteorological, and astrological indicators used to forecast local weather and climate conditions. In addition, the study sought to determine the effectiveness of indicators used in forecasting local weather and climate conditions and to represent the spatial distribution of indigenous weather knowledge using a Participatory Geographic Information System (P-GIS). Both qualitative and quantitative research approaches were used. Surveys and in-depth interviews were used to obtain primary data. The secondary data collected from formally published material (books and journals), served to compliment the primary data. Purposive sampling was used to obtain data from participants with knowledge about Indigenous Knowledge (IK) in seasonal and weather forecasting. From the study, the distribution of indigenous indicators used by Moletjie smallholder farmers was mapped using ArcGIS 10.6. Results show different types of human, biological, meteorological, and astronomical indicators utilized by communities to forecast weather and climate as such they were captured, characterized, and documented. Smallholder farmers considered indigenous knowledge indicators to be more accurate and reliable in their forecasting than scientific projections, which were described as inaccurate and received late. The research found that plants and birds used in this case are in danger of extinction at an alarming rate because of climate change and anthropogenic activities. The study derives key insights from how smallholder farmers in Moletjie village use the IK weather forecast to make farming decisions to ensure farm productivity. It also demonstrates that the smallholder farmers share their trust in modern technology and have confidence and the readiness for the use of scientific forecasting and climate projections which are expected to add value when integrated with IK / National Research Foundation (NRF)

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)