Tyler McCandless

An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Prediction

AbstractEnsembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology.The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (...

Regime-Dependent Short-Range Solar Irradiance Forecasting

AbstractThis paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions of 15-min-average clearness index (global horizontal irradiance). This regime-dependent artificial neural network (RD-ANN) system classifies cloud regimes with a k-means algorithm on the basis of a combination of surface weather observations, irradiance observations, and GOES-East satellite data. The ANNs are then trained on each cloud regime to predict the clearness index. This RD-ANN system improves over the mean absolute error of the baseline clearness-index persistence predictions by 1.0%, 21.0%, 26.4%, and 27.4% at the 15-, 60-, 120-, and 180-min forecast lead times, respectively. In addition, a version of this method configured to predict the irradiance variability predicts irradiance variability more accurately than does a smart persistence technique.

The Effects of Imputing Missing Data on Ensemble Temperature Forecasts

A major issue for developing post-processing methods for NWP forecasting systems is the need to obtain complete training datasets. Without a complete dataset, it can become difficult, if not impossible, to train and verify statistical post-processing techniques, including ensemble consensus forecasting schemes. In addition, when ensemble forecast data are missing, the real-time use of the consensus forecast weighting scheme becomes difficult and the quality of uncertainty information derived from the ensemble is reduced. To ameliorate these problems, an analysis of the treatment of missing data in ensemble model temperature forecasts is performed to determine which method of replacing the missing data produces the lowest Mean Absolute Error (MAE) of consensus forecasts while preserving the ensemble calibration. This study explores several methods of replacing missing data, including ones based on persistence, a Fourier fit to capture seasonal variability, ensemble member mean substitution, three day mean deviation, and an Artificial Neural Network (ANN). The analysis is performed on 48-hour temperature forecasts for ten locations in the Pacific Northwest. The methods are evaluated according to their effect on the forecast performance of two ensemble post-processing forecasting methods, specifically an equal-weight consensus forecast and a ten day performance-weighted window. The methods are also assessed using rank histograms to determine if they preserve the calibration of the ensembles. For both post- processing techniques all imputation methods, with the exception of the ensemble mean substitution, produce mean absolute errors not significantly different from the cases when all ensemble members are available. However, the three day mean deviation and ANN have rank histograms similar to that for the baseline of the non-imputed cases (i.e. the ensembles are appropriately calibrated) for all locations, while persistence, ensemble mean, and Fourier substitution do not consistently produce appropriately calibrated ensembles. The three day mean deviation has the advantage of being computationally efficient in a real-time forecasting environment.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

AbstractAs integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Fo...

Solar Irradiance Nowcasting Case Studies near Sacramento

AbstractThe Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility Dis...