William P. Mahoney

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.

Gust Front Characteristics and the Kinematics Associated with Interacting Thunderstorm Outflows

Abstract The morphology, kinematic and thermodynamic characteristics of 30 gust fronts were examined with single and dual-Doppler radar and surface mesonet data collected in eastern Colorado during the summers of 1982 and 1984. The majority of gust fronts examined exhibited the general shape of laboratory-produced gravity currents, including the elevated head, body and turbulent wake region. The average head depth was 1.3 km, only 0.1 km above the average body depth. Small-scale features in the vertical and horizontal vorticity fields were also observed. The passage of the fronts was marked, in order of event, by a pressure rise, wind direction and velocity change, and temperature drop at the surface. The average propagation speed and maximum surface wind within the outflows were 8.6 and 14.5 m s−1, respectively. The average maximum temperature drop at the surface was 3.5°C and the average hydrostatic pressure rise was 0.06 kPa. Dual-Doppler analyses of colliding gust fronts revealed strong circulations a...

Realizing the Potential of Vehicle-Based Observations

The potential availability of millions of surface observations from passenger vehicles and fleets represents a potentially significant opportunity for the weather community. The success of this opportunity rests with the weather communitys technical understanding and eventual adoption of these unique datasets and their level of participation in connected vehicle initiatives within the transportation community. All sectors of the weather enterprise (e.g., public, private, and academic) must become involved to help define, shape, and support the effort to realize a distinct and positive outcome on the weather and transportation communities. For this reason, the American Meteorological Society (AMS) Board on Enterprise Planning (BEP), under the Commission on the Weather and Climate Enterprise (CWCE), established an Annual Partnership Topic (APT) Committee in 2009 focused on mobile observations and their potential for use by the weather and transportation communities. The primary finding of the committee is ...

Wind Power Forecasting

The National Center for Atmospheric Research (NCAR) has configured a Wind Power Forecasting System for Xcel Energy that integrates high resolution and ensemble modeling with artificial intelligence methods. This state-of-the-science forecasting system includes specific technologies for short-term detection of wind power ramps, including a Variational Doppler Radar Analysis System and an expert system. This chapter describes this forecasting system and how wind power forecasting can significantly improve grid integration by improving reliability in a manner that can minimize costs. Errors in forecasts become opportunity costs in the energy market; thus, more accurate forecasts have the potential to save substantial amounts of money for the utilities and their ratepayers. As renewable energy expands, it becomes more important to provide high-quality forecasts so that renewable energy can carve out its place in the energy mix.

Predicting weather and road conditions - Integrated decision-support tool for winter road-maintenance operations

Winter road-maintenance practitioners have expressed a strong interest in obtaining weather and road-condition forecasts and treatment recommendations specific to winter road-maintenance routes. These user needs led the FHWA Office of Transportation Operations Road Weather Management Program to support the development of a prototype winter road-maintenance decision-support system (MDSS). The MDSS is a unique data-fusion system designed to provide real-time treatment guidance (e.g., treatment times, types, rates, and locations) specifically regarding winter road-maintenance routes to winter maintenance decision makers. The system integrates weather and road data, weather and road-condition model output, chemical concentration algorithms, and anti-icing and deicing rules of practice. FHWA began the multiyear project in 2001 by engaging several national laboratories that had expertise in weather prediction and winter road engineering. A user-needs assessment for surface transportation weather information, performed by FHWA in 2000, formed the basis for the development effort. FHWA required that the system be developed in an open environment with significant input from the stakeholders (state transportation personnel and private-sector meteorological services). The resulting technologies have been released (in an initial version) on a nonexclusive basis to the surface transportation community. It is anticipated that the prototype MDSS will provide a springboard for the development and rapid deployment of operational systems by the private sector.

Enhancing Road Weather Information Through Vehicle Infrastructure Integration

Vehicle infrastructure integration (VII) represents a concept with the potential to aid in the reduction of weather-related accidents on U.S. roadways while increasing surface transportation mobility and efficiency. Technological advancements in the automotive and telecommunications industries have resulted in the ability of vehicles to acquire and use high temporal- and spatial-resolution information associated with environmental and roadway conditions. VII would enable vehicle-to-vehicle and vehicle-to-infrastructure communications through dedicated short-range communications (wireless radio communication at 5.9 GHz). This capability could potentially serve as a means of gathering and distributing vehicle data in support of applications and products designed to diagnose and predict road weather conditions. It is believed that the inclusion of VII-enabled data in road weather applications will improve weather and road condition analyses and forecasts. A summary is given of vehicle data elements that are likely to contribute to development and improvement of road weather products. A synopsis of probable VII product enhancements is provided, along with examples of how vehicle data can be used in the application development process. Developing a broad understanding of how to use vehicle data properly will require a significant amount of research. Research needs aimed at addressing the technical issues and barriers associated with the use of VII-enabled data are discussed.

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.

Aircraft measurements on microburst development from hydrometeor evaporation

Abstract During the Joint Airport Weather Studies (JAWS) project in 1982, the University of Wyomings King Air research aircraft made observations of raindrop size distributions, vertical and horizontal air motions, and the temperature and moisture variables in and near precipitation shafts. This research examines the kinematic, thermodynamic, and microphysical characteristics of microburst-producing showers. Four precipitation showers with radar reflectivities of <35 dBZ were selected for study, three of which produced microbursts. An equivalent potential temperature (θe) analysis, as well as vertical velocity measurements at cloud base, showed no strong evidence that the downdrafts were originating well above cloud base. A simple evaporation and downdraft model was used to examine the role of hydrometeor evaporation below cloud base as a microburst forcing mechanism. The one-dimensional model without entrainment provided the conceptual basis for microburst development by means of microphysical forcing a...

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

Diagnosing Road Weather Conditions with Vehicle Probe Data: Results from Detroit IntelliDrive Field Study

Over the past 2 years, the U.S. Department of Transportation RITA funded an IntelliDrive vehicle probe data collection test bed in the northwest Detroit, Michigan, area. The purpose of the test bed was to provide the infrastructure for both public and private organizations to collect, process, and generate a robust observation data set for multiple purposes (e.g., crash avoidance, automated toll services, weather diagnostics). During April 2009, a weather-specific field study was performed over an 11-day period. The resulting data set was processed by a vehicle data translator (VDT), which parsed, quality controlled, and combined these data (with ancillary weather data) in the generation of road weather-specific algorithms. This paper briefly describes the VDT concept and then examines the accuracy of the quality-controlled temperature and pressure data (for several different stratifications) collected from 11 specially equipped vehicles operated during the study time period. Results show that the vehicles accurately measure the temperature (compared with a nearby fixed weather station, KDTW), but are not as accurate at measuring the barometric pressure. In addition, stratification by speed, vehicle type, time of day, and occurrence of precipitation do not affect the accuracy of the temperature and barometric pressure measurements.