Roland Stull


Relevant Degree Programs


Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Mar 2019)
Improving hub-height wind forecasts in complex terrain (2017)

Wind-speed forecasts from numerical-weather-prediction (NWP) models are important for daily wind-resource generation planning. However, NWP models are imperfect. The ability of energy planners to efficiently manage resources is a function of the accuracy of deterministic wind forecasts and of the associated probability estimates of forecast uncertainty. As the amount of energy generated from wind increases to significant levels, improving forecast accuracy and representation of forecast uncertainty is a key area of active research. This dissertation advances wind forecasting over regions of complex topography using the Weather Research and Forecasting (WRF) model. The optimal WRF-model configuration is a function of planetary-boundary-layer (PBL) physics, grid length, and initial-condition choice. The first component of this work determines which of these three factors most influences forecast accuracy over complex terrain. The two largest factors influencing forecast accuracy are the PBL-physics scheme and the grid length, with the dominant factor being a function of location, season, and time of day.The second component of the research addresses the need for probability-based forecast information, which is only recently being used within the industry. Wind forecasts from an ensemble using eight PBL schemes, three grid lengths, and two initial-conditions sources are converted into probability models that are then evaluated. Using the full, empirical ensemble distribution produces uncalibrated probabilistic forecasts. Prescribing a Gaussian probability distribution based on statistical moments of a past training dataset results in calibrated and sharp probabilistic forecasts. Such a method is also computationally cheap. The final aspect of this study evaluates the role of boundary-layer static stability on forecast performance. Traditionally, empirical surface-layer similarity theory has been used to relate surface fluxes of heat, momentum, and moisture to vertical profiles of temperature and wind. To evaluate and improve surface-layer similarity theories over mountain ridges, a year-long field campaign of temperature and wind measurements was conducted at wind farms in British Columbia. New empirical equations for complex terrain are proposed based on the field data, and found to perform well at an independent test location.

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A fully-compressible nonhydrostatic cell-integrated semi-Lagrangian atmospheric solver with conservative and consistent transport (2014)

Traditional semi-Lagrangian dynamical solvers are widely used in current global numerical weather prediction (NWP) and climate models, but are known to lack inherent mass-conserving properties. Some newer approaches utilize a cell-integrated (conservative) semi-Lagrangian (CISL) semi-implicit solver, which is inherently mass-conserving. However, existing CISL semi-implicit solvers lack consistent formulation among the discrete continuity equation and other discrete conservation equations for scalar tracers such as water vapour and air pollutants. Such inconsistency can lead to spurious generation or removal of scalar mass. In this dissertation, a new cell-integrated semi-Lagrangian (CISL) semi-implicit nonhydrostatic solver is presented with consistent discrete mass conservation equations for air and all tracers, and which preserves the shape of tracer-mass distribution. The discretization does not depend on a mean reference state, but maintains the same framework as typical semi-implicit CISL solvers, where a linear Helmholtz equation is constructed and a single application of the cell-integrated transport scheme is needed for scalar transport. Tests of this new solver are made for a series of increasingly complex flow scenarios. The initial testbed utilizes the hydrostatic, incompressible, shallow-water equations, for which the new solver is shown to be numerically stable. It maintains accuracy comparable to other existing solvers even for a highly nonlinear unstable jet. The second suite of tests are for nonhydrostatic two-dimensional (x-z) fully compressible flows in the atmosphere as governed by the moist Euler equations, which compare well to several idealized benchmark test cases from the literature. The third flow scenario is complex orography, where the nonhydrostatic equations are transformed to use a terrain-following height coordinate. Results from test cases of dry and moist flows over idealized mountain shapes are presented. In summary, the prototype development work presented in this dissertation shows that the proposed CISL nonhydrostatic solver with conservative and consistent transport may be a desirable candidate for a dynamical core in comprehensive global NWP and climate models.

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A probabilistic inflow forecasting system for operation of hydroelectric reservoirs in complex terrain (2013)

This dissertation presents a reliable probabilistic forecasting system designed to predict inflows to hydroelectric reservoirs. Forecasts are derived from a Member-to-Member (M2M) ensemble in which an ensemble of distributed hydrologic models is driven by the gridded output of an ensemble of numerical weather prediction (NWP) models. Multiple parameter sets for each hydrologic model are optimized using objective functions that favour different aspects of forecast performance. On each forecast day, initial conditions for each differently-optimized hydrologic model are updated using meteorological observations. Thus, the M2M ensemble explicitly samples inflow forecast uncertainty caused by errors in the hydrologic models, their parameterizations, and in the initial and boundary conditions (i.e., meteorological data) used to drive the model forecasts.Bias is removed from the individual ensemble members using a simple degree-of-mass-balance bias correction scheme. The M2M ensemble is then transformed into a probabilistic inflow forecast by applying appropriate uncertainty models during different seasons of the water year. The uncertainty models apply ensemble model output statistics to correct for deficiencies in M2M spread. Further improvement is found after applying a probability calibration scheme that amounts to a re-labelling of forecast probabilities based on past performance. Each component of the M2M ensemble has an associated cost in terms of time and/or money. The relative value of each ensemble component is assessed by removing it from the ensemble and comparing the economic gains associated with the reduced ensembles to those achieved using the full M2M system. Relative value is computed using a simple (static) cost-loss decision model in which the reservoir operator takes action (lowers the reservoir level) when significant inflows are predicted with probability exceeding some threshold. The probabilistic reservoir inflow forecasting system developed in this dissertation is applied to the Daisy Lake hydroelectric reservoir located in the complex terrain of southwestern British Columbia, Canada. The hydroclimatic regime of the case study watershed is such that flashy fall and winter inflows are driven by Pacific frontal systems, while spring and summer inflows are dominated by snow and glacier melt. Various aspects of ensemble and probabilistic forecast performance are evaluated over a period of three water years.

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Ski piste heat budget : a case study at the Whistler ski resort, British Columbia, Canada (2013)

Accurately calculating snow-surface temperature and liquid-water content for a groomed and compacted ski run, known as a ski piste, is crucial to the preparation of fast skis for alpine racing. This dissertation case study focuses on modelling the above variables for a clear-sky intensive observation period in February 2010. An automated weather station collected relevant meteorological data at a point on a ski piste in Whistler, BC, Canada, known as RC Whistler.The radiation budget is fundamental to this problem, and is affected by tall trees dominating the local horizon. Tree temperature was measured using an infrared camera to estimate thermal emissions. This data, along with calculations of sky downwelling longwave radiation by a radiative transfer model, was input to a new model created for this research. Longwave radiation contributions from trees and sky were weighted by their view factors, which had been calculated from a theodolite survey. Model output is total downwelling longwave radiation at the snow surface for RC Whistler, under clear skies.Downwelling solar radiation penetrates the snowpack, while the surface itself undergoes infrared cooling, resulting in a substantial temperature gradient just beneath the snow surface. A new one-dimensional numerical Lagrangian snowpack model has been written, solving the heat-, liquid-water-, and ice-budget equations to calculate the snow-surface temperature. Meteorological measurements from the clear-sky intensive observation period are prescribed as boundary conditions. Model components and parameters are validated and chosen with idealized model runs. In addition to natural atmospheric processes occurring at and just above the snow surface, human factors were considered. These are frequent skiers compacting the snowpack, and grooming snowcats that churn the top layer of the snowpack and work to increase the snow density and hardness, usually once daily. These effects are simulated in the numerical model.The model successfully simulates snow-surface temperature for the RC Whistler clear-sky intensive observation period. This exploratory investigation indicates that the model shows promise. It is a starting point for a more sophisticated version, incorporating complex boundary conditions such as precipitation and cloudiness, and later being driven by numerical weather prediction output.

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A component-based probabilistic weather forecasting system for operational usage (2012)

This dissertation presents a probabilistic weather prediction system for operational (real-time) usage. The proposed system provides complete probability distributions for both continuous weather variables, such as temperature, and mixed discrete-continuous variables like precipitation accumulations.The proposed system decomposes the process of generating probabilistic forecasts into a series of sequential steps, each of which is important in the overall goal of providing probabilistic forecasts of high quality. Starting with an ensemble of input predictors generated by numerical weather prediction models, the system uses the following four components: 1) correction; 2) uncertainty modeling; 3) calibration; and 4) updating. The correction component bias-corrects the input predictors. The uncertainty model converts these predictors into a suitable probability distribution. The calibration component improves this distribution by removing any distributional bias. The update component further improves the forecast by incorporating recently made observations of the true state.The system is designed to be modular. Namely, different implementations of each component can be used interchangeably with any combination of implementations for the other components. This allows future research into probabilistic forecasting to be focused on any one component and also allows new methods to be easily incorporated into the system.The system uses a number of existing correction and uncertainty models, but the dissertation also presents two new methods: Firstly, a new method for calibrating probabilistic forecasts is created. This method is shown to improve probabilistic forecasts that exhibit distributional bias. Secondly, a new method for incorporating recently made observations to existing probabilistic forecasts is developed.The system and its components are tested using meteorological data from daily operational runs of ensemble numerical weather prediction models and their verifying observations from surface weather stations in North America. Each component's contribution to overall forecast quality is analysed.

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Exploration of the potential for gene expression programming to solve some problems in meteorology and renewable energy (2012)

This dissertation describes research to enhance hydrometeorological forecasts and their application towards clean energy. The secondary objective of this research is exploration of a new evolutionary algorithm as a possible statistical tool to describe some nonlinear aspects of the atmosphere. The products of this work are summarized in four chapters.Motivated by the difficulty in forecasting montane precipitation for hydroelectricity, a novel model output statistical method is introduced to improve numerical daily precipitation forecasts. The proposed method is gene expression programming (GEP). It is used to create a bias-corrected ensemble, called a deterministic ensemble forecast (DEF), which could serve as an alternative to the traditional ensemble average. Comparing the verification scores of GEP DEF vs. an equally- weighted (traditional) ensemble-average DEF, it is found that GEP DEFs were better for about half of the mountain weather stations tested.The need for an enhanced electric load forecasting model with better connections to weather variables is addressed next. GEP is used to forecast relative load minima during nighttime and mid- day, and relative load maxima in the morning and evening. A different method is introduced to use GEP to forecast electric load for the next hour. These methods are verified against independent data for a year of daily load forecasts, and are compared against the operational load forecasts archived by BC Hydro, British Columbia’s largest electric utility company.Also, GEP is used to parametrize two non-iterative approximations for saturated pseudoadiabats (also known as moist adiabats). One approximation determines which moist adiabat passes through a point of known pressure and temperature, such as through the lifting condensation level on a skew- T or tephigram. The other approximation determines the air temperature at any pressure along a known moist adiabat, such as the final temperature of a rising cloudy air parcel. This work can be used to better predict cloudy convection in the atmosphere, which can cause hazardous wind gusts at wind turbines, and can drop heavy precipitation in hydroelectric watersheds.

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Improving hydrometeorologic numerical weather prediction forecast value via bias correction and ensemble analysis (2008)

This dissertation describes research designed to enhance hydrometeorological forecasts. The objective of the research is to deliver an optimal methodology to produce reliable, skillful and economically valuable probabilistic temperature and precipitation forecasts.Weather plays a dominant role for energy companies relying on forecasts of watershed precipitation and temperature to drive reservoir models, and forecasts of temperatures to meet energy demand requirements. Extraordinary precipitation events and temperature extremes involve consequential water- and power-management decisions.This research compared weighted-average, recursive, and model output statistics bias-correction methods and determined optimal window-length to calibrate temperature and precipitation forecasts. The research evaluated seven different methods for daily maximum and minimum temperature forecasts, and three different methods for daily quantitative precipitation forecasts, within a region of complex terrain in southwestern British Columbia, Canada.This research then examined ensemble prediction system design by assessing a three-model suite of multi-resolution limited area mesoscale models. The research employed two different economic models to investigate the ensemble design that produced the highest-quality, most valuable forecasts. The best post-processing methods for temperature forecasts included moving-weighted average methods and a Kalman filter method. The optimal window-length proved to be 14 days. The best post-processing methods for achieving mass balance in quantitative precipitation forecasts were a moving-average method and the best easy systematic estimator method. The optimal window-length for moving-average quantitative precipitation forecasts was 40 days. The best ensemble configuration incorporated all resolution members from all three models. A cost/loss model adapted specifically for the hydro-electric energy sector indicated that operators managing rainfall-dominated, high-head reservoirs should lower their reservoir with relatively low probabilities of forecast precipitation. A reservoir-operation model based on decision theory and variable energy pricing showed that applying an ensemble-average or full-ensemble precipitation forecast provided a much greater profit than using only a single deterministic high-resolution forecast.Finally, a bias-corrected super-ensemble prediction system was designed to produce probabilistic temperature forecasts for ten cities in western North America. The system exhibited skill and value nine days into the future when using the ensemble average, and 12 days into the future when employing the full ensemble forecast.

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