Wind energy forecasting can facilitate wind energy integration into a power system. In particular, the management of power system security would benefit from forecast information on plausible large, rapid change in wind power generation. Numerical Weather Prediction (NWP) systems are presently the best available tools for wind energy forecasting for projection times between 3 and 48 hours. In this thesis, the types of weather phenomena that cause large, rapid changes in wind power in southeast Australia are classified using observations from three wind farms. The results show that the majority of events are due to horizontal propagation of spatial weather features. A study of NWP systems reveals that they are generally good at forecasting the broad large-scale weather phenomena but may misplace their location relative to the physical world. Errors may result from developing single time-series forecasts from a single NWP grid point, or from a single interpolation of proximate grid points. This thesis presents a new approach that displays NWP wind forecast information from a field of multiple grid points around the wind farm location. Displaying the NWP wind speeds at the multiple grid points directly would potentially be misleading as they each reflect the estimated local surface roughness and terrain at a particular grid point. Thus, a methodology was developed to convert the NWP wind speeds at the multiple grid points to values that reflect surface conditions at the wind farm site. The conversion method is evaluated with encouraging results by visual inspection and by comparing with an NWP ensemble. The multiple grid point information can also be used to improve downscaling results by filtering out data where there is a large chance of a discrepancy between an NWP time-series forecast and observations. The converted wind speeds at multiple grid points can be downscaled to site-equivalent wind speeds and transformed to wind farm power assuming unconstrained wind farm operation at one or more wind farm sites. This provides a visual decision support tool that can help a forecast user assess the possibility of large, rapid changes in wind power from one or more wind farms.

Wind energy forecasting can facilitate wind energy integration into a power system. In particular, the management of power system security would benefit from forecast information on plausible large, rapid change in wind power generation. Numerical Weather Prediction (NWP) systems are presently the best available tools for wind energy forecasting for projection times between 3 and 48 hours. In this thesis, the types of weather phenomena that cause large, rapid changes in wind power in southeast Australia are classified using observations from three wind farms. The results show that the majority of events are due to horizontal propagation of spatial weather features. A study of NWP systems reveals that they are generally good at forecasting the broad large-scale weather phenomena but may misplace their location relative to the physical world. Errors may result from developing single time-series forecasts from a single NWP grid point, or from a single interpolation of proximate grid points. This thesis presents a new approach that displays NWP wind forecast information from a field of multiple grid points around the wind farm location. Displaying the NWP wind speeds at the multiple grid points directly would potentially be misleading as they each reflect the estimated local surface roughness and terrain at a particular grid point. Thus, a methodology was developed to convert the NWP wind speeds at the multiple grid points to values that reflect surface conditions at the wind farm site. The conversion method is evaluated with encouraging results by visual inspection and by comparing with an NWP ensemble. The multiple grid point information can also be used to improve downscaling results by filtering out data where there is a large chance of a discrepancy between an NWP time-series forecast and observations. The converted wind speeds at multiple grid points can be downscaled to site-equivalent wind speeds and transformed to wind farm power assuming unconstrained wind farm operation at one or more wind farm sites. This provides a visual decision support tool that can help a forecast user assess the possibility of large, rapid changes in wind power from one or more wind farms.

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The Weather Research and Forecast model (WRF) was utilized to study the effects of warmer lake surface temperatures on the lake effect snow (LES) environments of Lake Erie and Lake Ontario. Composites of recorded LES cases were created for WRF input to represent average LES conditions which revealed three distinct large-scale patterns. WRF runs consisted of altering lake temperatures up to 4.3°C for three future time frames. Lake Erie projections exhibited more sensitivity to alterations as more WRF runs revealed significant (p-value ≤ 0.05) changes to the environment. Lake Erie solely showed any distinctive changes with early and mid-century WRF runs with increased surface CAPE around 80 J/kg and total precipitation around 1.5 mm. Late century alterations for both lakes revealed significant (p-value ≤ 0.05) changes including up to 2.1 g/kg increased specific humidity and a 9K surface-850mb temperature difference indicating both lakes were most sensitive to late century alterations.

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This thesis considers one-dimensional moving mesh (MM) methods coupled with semi-Lagrangian (SL) discretisations of partial differential equations (PDEs) for meteorological applications. We analyse a semi-Lagrangian numerical solution to the viscous Burgers’ equation when using linear interpolation. This gives expressions for the phase and shape errors of travelling wave solutions which decay slowly with increasing spatial and temporal resolution. These results are verified numerically and demonstrate qualitative agreement for high order interpolants. The semi-Lagrangian discretisation is coupled with a 1D moving mesh, resulting in a moving mesh semi-Lagrangian (MMSL) method. This is compared against two moving mesh Eulerian methods, a two-step remeshing approach, solved with the theta-method, and a coupled moving mesh PDE approach, which is solved using the MATLAB solver ODE45. At each time step of the SL method, the mesh is updated using a curvature based monitor function in order to reduce the interpolation error, and hence numerical viscosity. This MMSL method exhibits good stability properties, and captures the shape and speed of the travelling wave well. A meteorologically based 1D vertical column model is described with its SL solution procedure. Some potential benefits of adaptivity are demonstrated, with static meshes adapted to initial conditions. A moisture species is introduced into the model, although the effects are limited.

Among natural disasters affecting the United States, North Atlantic tropical cyclones (TCs) and hurricanes are responsible for the highest economic losses and are one of the main causes of fatalities. Although we cannot prevent these storms from occurring, skillful seasonal predictions of the North Atlantic TC activity and associated impacts can provide basic information critical to our improved preparedness. Unfortunately, it is not yet possible to predict heavy rainfall and flooding associated with these storms several months in advance, and the lead time is limited to few days at the most. On the other hand, overall North Atlantic TC activity can be potentially predicted with a six- to nine-month lead time. This thesis focuses on the evaluation of the skill in predicting basin-wide North Atlantic TC activity with a long lead time and rainfall with a short lead time. For the seasonal forecast of TC activity, we develop statistical-dynamical forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs using only tropical Atlantic and tropical mean sea surface temperatures (SSTs) as covariates. Our results show that skillful predictions of North Atlantic TC activity are possible starting from November for a TC season that peaks in the August-October months. The short term forecasting of rainfall associated with TC activity is based on five numerical weather prediction (NWP) models. Our analyses focused on 15 North Atlantic TCs that made landfall along the U.S. coast over the period of 2007-2012. The skill of the NWP models is quantified by visual examination of the distribution of the errors for the different lead-times, and numerical examination of the first three moments of the error distribution. Based on our results, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead times up to 48 hours, without a consistently best or worst NWP model.

This research has been conducted in order to develop a short-range ensemble numerical weather prediction system over southern Africa using the Conformal-Cubic Atmospheric Model (CCAM). An ensemble prediction system (EPS) combines several individual weather model setups into an average forecast system where each member contributes to the final weather forecast. Four different EPSs were configured and rainfall forecasts simulated for seven days ahead for the summer months of January and February, 2009 and 2010, for high (15 km) and low (50 km) resolution over the southern African domain. Statistical analysis was performed on the forecasts so as to determine which EPS was the most skilful at simulating rainfall. Measurements that were used to determine the skill of the EPSs were: reliability diagrams, relative operating characteristics, the Brier skill score and the root mean square error. The results show that the largest ensemble is consistently the most skilful for all forecasts for both the high and the low resolution cases. The higher resolution forecasts were also seen to be more skilful than the forecasts made at the low resolution. These findings conclude that the largest ensemble at high resolution is the best system to predict rainfall over southern Africa using the CCAM. / Dissertation (MSc)--University of Pretoria, 2014. / gm2014 / Geography, Geoinformatics and Meteorology / unrestricted

The Weather Research and Forecasting Model was used to study its accuracy and representation in modelling a study area within a complex wind system as well as the effects on the model when using different input data and physics schemes. The complex wind system consists of diurnal mesoscale effects from the nearby Pyrenees mountain range and diurnal effects from the planetary boundary layer. A total of six different simulations were performed. The model was able to represent the study area but the results could be improved as there were inaccuracies in wind speed and wind direction associated with the planetary boundary layer. The model was especially challenged at predicting the wind speed and wind direction in the layer from the top of the planetary boundary layer to few hundred meters above it. The comparisons based on planetary boundary layer height is however complicated by the fact that there are different definitions in effect. The choice of model physics schemes and input data led to some differences in the results and warrants consideration when conducting similar simulations. / Prognosmodellen WRF (Weather Research and Forecasting Model) användes för att undersöka hur väl den kunde representera ett område inom ett komplext vindsystem och även hur modellen påverkas av olika val vad gäller drivningsdata och fysikscheman. Det som utgör det komplexa vindsystemet är dygnsvarierande effekter från det atmosfäriska gränsskiktet och dygnsvarierande mesoskaliga effekter från den närliggande bergskedjan Pyrenéerna. Totalt genomfördes sex olika simuleringar. Prognosmodellen kunde representera området men med förbättringsbara resultat eftersom det fanns fel i vindhastighet och vindriktning relaterande till det atmosfäriska gränsskiktet. Modellen var speciellt utmanad i förutsägandet av vindhastighet och vindriktning i ett lager några hundra meter ovanför det atmosfäriska gränsskiktet. En tolkning baserad på atmosfärisk gränsskiktshöjd är dock svår eftersom det fanns flera definitioner var toppen på det atmosfäriska gränsskiktet låg. Val om prognosmodellens fysikscheman och drivningsdata orsakade en skillnad i resultat sinsemellan. Dessa val bör därför noggrannt uppmärksammas för simuleringar under liknande förutsättningar.