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Approved for Public Release; Distribution is Unlimited / The impact of non-conventional observations and increased horizontal resolution on the numerical weather prediction (NWP) system of the National Center for Aeronautic Meteorology and Climatology of the Italian Air Force (CNMCA) has been investigated. The present study is part of ongoing research activities whose goal is the improvement of CNMCA's operational numerical weather prediction capabilities through the assimilation of non-conventional observations. Additional data derived from satellite observations, such as 10 m wind retrieved from Quikscat polar-orbit satellite, atmospheric motion vectors (AMVs) from Meteosat geostationary satellites and manual and automated aircraft observations were used. The NWP system, which is in operational use, is based on an "observation space" version of the 3D-Var method for the objective analysis component (3D-PSAS), while the prognostic component is based on the High Resolution Regional Model (HRM) of the German Meteorological Service (DWD). The analysis and forecast fields derived from the NWP system were objectively evaluated through comparisons with radiosonde and conventional surface observations. Comparisons with parallel runs of the HRM model starting from the 3D-Var operational analysis have showed that each of those observations have a measurable positive impact on forecast skill. / Captain, Italian Air Force

We have developed, tested, and operationally implemented a web based system for collecting and analyzing in nearreal time weather forecast and observational data to assess: (a) the performance of forecasts; and (b) the operational impacts of forecasts. A major goal of the system is to quantify the impacts of weather forecasts on the planning, execution, and outcomes of military operations. Our tests and implementation were focused on the METOC support provided by Naval Pacific Meteorology and Oceanography Detachment (NPMOD) Fallon to Naval Strike and Air Warfare Center (NSAWC) operations at Naval Air Station Fallon. Data are collected by NPMOD Fallon and entered via a web interface into a database at the Naval Postgraduate School (NPS) where the data are analyzed and results are reported in near-real time. The results include quantitative assessments of: (1) forecasts used in planning NSAWC missions; (2) changes made during mission planning in response to forecasted weather; (3) deviations from mission plans that occurred in response to weather conditions actually encountered; (4) positive and negative impacts on missions due to forecasts; (5) METOC Tactical Decision Aid forecast accuracy and mission impacts; and (6) forecast performance and mission impacts with respect to specific weather factors.

Within the Pacific Air Forces (PACAF) area of responsibility, tropical deep convection that is not associated with tropical cyclones can cause significant impacts to operations. In this study, convective indices calculated from five sites in the central and western tropical North Pacific are examined with respect to their ability to predict the onset and intensity of deep convection. Two predictands are utilized: measures of convection derived from surface weather observations and the Naval Research Laboratory (NRL) Blended Rainrate estimates, which are derived from infrared and microwave satellite observations and interpolated to the five sites. Eighteen indices derived from rawinsondes are ranked by predictive skill for specific locations and seasons. Indices that exhibit significant skill are used in a discriminant analysis to define a multivariate experimental tropical convective index, which is then evaluated for each region and season. The multivariate index was not able to discriminate between convective and non-convective environments over the central North Pacific. Although the multivariate index exhibited skill for sites in the tropical western North Pacific during summer, it did not perform better than the highest-ranked single indices. For many of the locations and seasons evaluated, the Severe Weather Threat (SWEAT) Index exhibited the most skill.

This dissertation explores how decisions about the forecasting process can affect the evaluation of forecasting performance, in general and in the domain of residential electricity demand estimation. Decisions of interest include those around data sourcing, sampling, clustering, temporal magnification, algorithm selection, testing approach, evaluation metrics, and others. Models of the forecasting process and analysis methods are formulated in terms of a three-tier decision taxonomy, by which decision effects are exposed through systematic enumeration of the techniques resulting from those decisions. A computation platform based on the models is implemented to compute and visualize the effects. The methods and computation platform are first demonstrated by applying them to 3,003 benchmark datasets to investigate various decisions, including those that could impact the relationship between data entropy and forecastability. Then, they are used to study over 10,624 week-ahead and day-ahead residential electricity demand forecasting techniques, utilizing fine-resolution electricity usage data collected over 18 months on groups of 782 and 223 households by real smart electric grids in Ireland and Australia, respectively. The main finding from this research is that forecasting performance is highly sensitive to the interaction effects of many decisions. Sampling is found to be an especially effective data strategy, clustering not so, temporal magnification mixed. Other relationships between certain decisions and performance are surfaced, too. While these findings are empirical and specific to one practically scoped investigation, they are potentially generalizable, with implications for residential electricity demand estimation, smart electric grid design, and electricity policy.

M.Com. (Econometrics) / This dissertation investigates the ability of different models to predict a recession in South Africa (SA) by choosing a best performing model based on the smallest prediction errors made by the models. One of the purposes of using econometric models is to predict a recession, with the goal to uncover the probability of a recession or real GDP growth rate as accurately as possible. Although linear and non-linear models prediction strength is frequently compared, none of the studies within SA compare the prediction ability of the four models used in this dissertation. The intent of this research is to ascertain the best prediction model for SA so as to advise policy makers on the soundest model to use if there is suspicion that SA could enter a recession in the future due to global and domestic uncertainty. This is done by comparing the prediction ability of the linear ARIMA, VAR and ARMV models’ and non-linear dynamic probit model; thereby contributing toward the standing literature. It is verified which model outperforms the others in predicting future real GDP growth by comparing the Mean-Square-Error (MSE), Mean-Absolute-Error (MAE) and RMSE percentage. The importance of predicting real GDP growth is accentuated so that policy makers are in the position to develop or apply policies that can stimulate growth in the economy, should a recession occur. By adding dynamics to the system, predictions are improved. The linear VAR model outperforms the other linear and non-linear model based on the RMSE, MAE and RMSE percentage.