Jared A. Lee

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 (...

Improving SCIPUFF Dispersion Forecasts with NWP Ensembles

Abstract The relationships between atmospheric transport and dispersion (AT&D) plume uncertainty and uncertainties in the transporting wind fields are investigated using the Second-Order Closure, Integrated Puff (SCIPUFF) AT&D model driven by numerical weather prediction (NWP) meteorological fields. Modeled contaminant concentrations for episode 1 of the 1983 Cross-Appalachian Tracer Experiment (CAPTEX-83) are compared with recorded ground-level concentrations of the inert tracer gas C7F14. This study evaluates a Taylor-diffusion-based parameterization of dispersion uncertainty for SCIPUFF that uses Eulerian meteorological ensemble velocity statistics and a Lagrangian integral time scale as input. These values are diagnosed from NWP ensemble data. Individual simulations of the tracer release fail to reproduce some of the monitored surface concentrations of the tracer. The plumes that are predicted using the uncertainty model in SCIPUFF are broader, improving the overlap between the predicted and observed ...

Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling

Abstract A parameterization of numerical weather prediction uncertainty is presented for use by atmospheric transport and dispersion models. The theoretical development applies Taylor dispersion concepts to diagnose dispersion metrics from numerical wind field ensembles, where the ensemble variability approximates the wind field uncertainty. This analysis identifies persistent wind direction differences in the wind field ensemble as a leading source of enhanced “virtual” dispersion, and thus enhanced uncertainty for the ensemble-mean contaminant plume. This dispersion is characterized by the Lagrangian integral time scale for the grid-resolved, large-scale, “outer” flow that is imposed through the initial and boundary conditions and by the ensemble deviation-velocity variance. Excellent agreement is demonstrated between an explicit ensemble-mean contaminant plume generated from a Gaussian plume model applied to the individual wind field ensemble members and the modeled ensemble-mean plume formed from the ...

The Role of Unresolved Clouds on Short-Range Global Horizontal Irradiance Predictability

AbstractThe shortwave radiative impacts of unresolved cumulus clouds are investigated using 6-h ensemble simulations performed with the WRF-Solar Model and high-quality observations over the contiguous United States for a 1-yr period. The ensembles use the stochastic kinetic energy backscatter scheme (SKEBS) to account for implicit model uncertainty. Results indicate that parameterizing the radiative effects of both deep and shallow cumulus clouds is necessary to largely reduce (55%) a systematic overprediction of the global horizontal irradiance. Accounting for the model’s effective resolution is necessary to mitigate the underdispersive nature of the ensemble and provide meaningful quantification of the short-range prediction uncertainties.

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...

Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

AbstractThe relationship between atmospheric boundary layer (ABL) depth uncertainty and uncertainty in atmospheric transport and dispersion (ATD) simulations is investigated by examining profiles of predicted concentrations of a contaminant. Because ensembles are an important method for quantifying uncertainty in ATD simulations, this work focuses on the utilization and analysis of ensemble members’ ABL structures for ATD simulations. A 12-member physics ensemble of meteorological model simulations drives a 12-member explicit ensemble of ATD simulations. The relationship between ABL depth and plume depth is investigated using ensemble members, which vary both the relevant model physics and the numerical methods used to diagnose ABL depth. New analysis methods are used to analyze ensemble output within an ABL-depth relative framework. Uncertainty due to ABL depth calculation methodology is investigated via a four-member mini-ensemble. When subjected to a continuous tracer release, concentration variability...

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...

Principles of meteorology and numerical weather prediction

Numerical weather prediction (NWP) models are important tools in the process of generating forecasts of wind and solar power output from a farm. Before running an NWP model or being able to interpret its output, however, modelers and forecasters ought to develop an understanding of several foundational principles that undergird a successful NWP forecast. These foundational principles include atmospheric motion, observation sources and quality, data assimilation, the need for postprocessing model output, the value of probabilistic predictions, and how to perform validation and verification of the forecast. Additionally, knowledge about how the NWP model is discretized in space and time, the conditions under which the physical parameterizations have been tested and work well, and the quality of the initial and boundary conditions are all essential to producing a useful forecast. All of these principles are discussed, as is an example of tailoring an NWP model (WRF-Solar) specifically for solar power forecasting.

Improving Wind Predictions in the Marine Atmospheric Boundary Layer through Parameter Estimation in a Single-Column Model

AbstractA current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly because of a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL.In this study the WRF single-column model (SCM) is coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART) to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for...