Gerry Wiener

TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology

Abstract A methodology is presented for the real-time automated identification, tracking, and short-term forecasting of thunderstorms based on volume-scan weather radar data. The emphasis is on the concepts upon which the methodology is based. A “storm” is defined as a contiguous region exceeding thresholds for reflectivity and size. Storms defined in this way are identified at discrete time intervals. An optimization scheme is employed to match the storms at one time with those at the following time, with some geometric logic to deal with mergers and splits. The short-term forecast of both position and size is based on a weighted linear fit to the storm track history data. The performance of the detection and forecast were evaluated for the summer 1991 season, and the results are presented.

An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting

Abstract An automated procedure for forecasting mid- and upper-level turbulence that affects aircraft is described. This procedure, termed the Graphical Turbulence Guidance system, uses output from numerical weather prediction model forecasts to derive many turbulence diagnostics that are combined as a weighted sum with the relative weights computed to give best agreement with the most recent available turbulence observations (i.e., pilot reports of turbulence or PIREPs). This procedure minimizes forecast errors due to uncertainties in individual turbulence diagnostics and their thresholds. Thorough statistical verification studies have been performed that focused on the probabilities of correct detections of yes and no PIREPs by the forecast algorithm. Using these statistics as a guide, the authors have been able to intercompare individual diagnostic performance, and test various diagnostic threshold and weighting strategies. The overall performance of the turbulence forecast and the effect of these stra...

A Wind Power Forecasting System to Optimize Grid Integration

Wind power forecasting can enhance the value of wind energy by improving the reliability of integrating this variable resource and improving the economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted wind power prediction system. Both the day-ahead forecast that is used in trading and the short-term forecast are critical to economic decision making. This wind power forecasting system includes high resolution and ensemble modeling capabilities, data assimilation, now-casting, and statistical postprocessing technologies. The system utilizes publicly available model data and observations as well as wind forecasts produced from an NCAR-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression. The model forecast data are combined using NCARs Dynamic Integrated Forecast System (DICast). This system has substantially improved Xcels overall ability to incorporate wind energy into their power mix.

Matrices, digraphs, and determinants

A detailed account of various determinantal formulas is presented in a graph-theoretic form involving paths and cycles in the digraph of the matrix. For cases in which the digraph has special local properties, for example, a cutpoint or a bridge, particular formulas are given that are more efficient for computing the determinant than simply using the matrix representation. Applications are also given to characteristic deter- minants, general minors, and cofactors.

Two-Dimensional Dealiasing of Doppler Velocities

Abstract The Doppler velocity dealiasing problem has been discussed for many years. Because aliasing is easily identified by detecting abrupt changes in the data field, most existing algorithms use this technique to correct aliased data. Such algorithms are typically based on local expansion methods. Such methods make a dealiasing decision for each gate based on the information of its dealiased neighbors and thus can be sensitive to scattered incorrect data. This paper introduces a new approach that attempts to find all dealiased values for a given dataset by solving a linear system involving the entire dataset and thus avoiding local expansion. Because the solution is global, the new technique is conceptually simple and displays good performance on a number of test cases. The new technique described here was implemented to support real-time dealiasing in an operational setting.

Improving Road Weather Hazard Products with Vehicle Probe Data: Vehicle Data Translator Quality-Checking Procedures

One of the goals of RITAs IntelliDrive initiative is utilization by the public and private organizations that collect, process, and generate weather products of vehicle sensor data to improve weather and road condition hazard products. Some users may not be able to, or not want to, contend with the complexities associated with vehicle data, such as data quality, representativeness, and format. With funding and support from the U.S. Department of Transportations RITA IntelliDrive initiative and direction from FHWAs Road Weather Management Program, the National Center for Atmospheric Research is conducting research to develop a vehicle data translator (VDT) to address these vehicle-based data challenges. This paper first describes the VDT quality check (QCh) concept and then examines QCh pass rates for temperature and pressure data collected from 11 specially equipped vehicles operating in the Detroit test bed in April 2009. Results show that temperature pass rates are higher than pressure pass rates. Additionally, pass rates are somewhat affected by vehicle type, vehicle speed, ambient temperature, and precipitation occurrence for both temperature and pressure.

Quality of Mobile Air Temperature and Atmospheric Pressure Observations from the 2010 Development Test Environment Experiment

The 2010 Development Test Environment Experiment (DTE10) took place from 28 January to 29 March 2010 in the Detroit, Michigan, metropolitan area for the purposes of collecting and evaluating mobile data from vehicles. To examine the quality of these data, over 239 000 air temperature and atmospheric pressure observations were obtained from nine vehicles and were compared with a weather station set up at the testing site.TheobservationsfromthevehicleswerefirstrunthroughtheNCARVehicleDataTranslator (VDT). As part of the VDT, quality-checking (QCh) tests were applied; pass rates from these tests were examined and were stratified by meteorological and nonmeteorological factors. Statistics were then calculated for air temperature and atmospheric pressure in comparison with the weather station, and the effects of different meteorological and nonmeteorological factors on the statistics were examined. Overall, temperature measurements showed consistent agreement with the weather station, and there was little impact from the QCh process or stratifications—a result that demonstrated the feasibility of collecting mobile temperature observations from vehicles. Atmospheric pressure observations were less well matched with surface validation, the degree of which varied with the make and model of vehicle. Therefore, more work must be done to improve the quality of these observations if atmospheric pressure from vehicles is to be useful.

A Wind Power Forecasting System to Optimize Power Integration

The National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States. The wind power forecasting system includes advanced modeling capabilities, data assimilation, nowcasting, and statistical post-processing technologies. The system ingests both external model data and observations. NCAR produces a deterministic mesoscale wind forecast of hub height winds on a very fine resolution grid using the Weather Research and Forecasting (WRF) model, run using the Real Time Four Dimensional Data Assimilation (RTFDDA) system. In addition, a 30 member ensemble system is run to both improve forecast accuracy and provide an indication of forecast uncertainty. The deterministic and ensemble model output plus data from various global and regional models are ingested by NCAR’s Dynamic, Integrated, Forecast System (DICast® ), a statistical learning algorithm. DICast® produces forecasts of wind speed for each wind turbine. These wind forecasts are then fed into a power conversion algorithm that has been empirically derived for each Xcel power connection node. In addition, a ramp forecasting technology fine-tunes the capability to accurately predict the time, magnitude, and duration of a ramping event. This basic system has consistently improved Xcel’s ability to optimize the economics of incorporating wind energy into their power system.Copyright

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...

Designing a road map for geoscience workflows

Advances in geoscience research and discovery are fundamentally tied to data and computation, but formal strategies for managing the diversity of models and data resources in the Earth sciences have not yet been resolved or fully appreciated. The U.S. National Science Foundation (NSF) EarthCube initiative (http://earthcube.ning.com), which aims to support community-guided cyberinfrastructure to integrate data and information across the geosciences, recently funded four community development activities: Geoscience Workflows; Semantics and Ontologies; Data Discovery, Mining, and Integration; and Governance. The Geoscience Workflows working group, with broad participation from the geosciences, cyberinfrastructure, and other relevant communities, is formulating a workflows road map (http://sites.google.com/site/earthcubeworkflow/). The Geoscience Workflows team coordinates with each of the other community development groups given their direct relevance to workflows. Semantics and ontologies are mechanisms for describing workflows and the data they process.