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  • Author or Editor: Michael M. Hardesty x
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Robert M. Banta
,
Yelena L. Pichugina
,
Neil D. Kelley
,
R. Michael Hardesty
, and
W. Alan Brewer

Addressing the need for high-quality wind information aloft in the layer occupied by turbine rotors (~30–150 m above ground level) is one of many significant challenges facing the wind energy industry. Without wind measurements at heights within the rotor sweep of the turbines, characteristics of the flow in this layer are unknown for wind energy and modeling purposes. Since flow in this layer is often decoupled from the surface, near-surface measurements are prone to errant extrapolation to these heights, and the behavior of the near-surface winds may not reflect that of the upper-level flow.

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Jeffry Rothermel
,
Dean R. Cutten
,
R. Michael Hardesty
,
Robert T. Menzies
,
James N. Howell
,
Steven C. Johnson
,
David M. Tratt
,
Lisa D. Olivier
, and
Robert M. Banta

In 1992 the atmospheric lidar remote sensing groups of the National Aeronautics and Space Administration Marshall Space Flight Center, the National Oceanic and Atmospheric Administration/Environmental Technology Laboratory (NOAA/ETL), and the Jet Propulsion Laboratory began a joint collaboration to develop an airborne high-energy Doppler laser radar (lidar) system for atmospheric research and satellite validation and simulation studies. The result is the Multicenter Airborne Coherent Atmospheric Wind Sensor (MACAWS), which has the capability to remotely sense the distribution of wind and absolute aerosol backscatter in three-dimensional volumes in the troposphere and lower stratosphere.

A factor critical to the programmatic feasibility and technical success of this collaboration has been the utilization of existing components and expertise that were developed for previous atmospheric research by the respective institutions. For example, the laser transmitter is that of the mobile ground-based Doppler lidar system developed and used in atmospheric research for more than a decade at NOAA/ETL.

The motivation for MACAWS is threefold: 1) to obtain fundamental measurements of subsynoptic-scale processes and features to improve subgrid-scale parameterizations in large-scale models, 2) to obtain datasets in order to improve the understanding of and predictive capabilities for meteorological systems on subsynoptic scales, and 3) to validate (simulate) the performance of existing (planned) satellite-borne sensors.

Initial flight tests were made in September 1995; subsequent flights were made in June 1996 following system improvements. This paper describes the MACAWS instrument, principles of operation, examples of measurements over the eastern Pacific Ocean and western United States, and future applications.

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Robert M. Banta
,
Yelena L. Pichugina
,
W. Alan Brewer
,
Eric P. James
,
Joseph B. Olson
,
Stanley G. Benjamin
,
Jacob R. Carley
,
Laura Bianco
,
Irina V. Djalalova
,
James M. Wilczak
,
R. Michael Hardesty
,
Joel Cline
, and
Melinda C. Marquis

Abstract

To advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.

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Wayman E. Baker
,
George D. Emmitt
,
Franklin Robertson
,
Robert M. Atlas
,
John E. Molinari
,
David A. Bowdle
,
Jan Paegle
,
R. Michael Hardesty
,
Robert T. Menzies
,
T. N. Krishnamurti
,
Robert A. Brown
,
Madison J. Post
,
John R. Anderson
,
Andrew C. Lorenc
, and
James McElroy

The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.

This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.

Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.

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Wayman E. Baker
,
Robert Atlas
,
Carla Cardinali
,
Amy Clement
,
George D. Emmitt
,
Bruce M. Gentry
,
R. Michael Hardesty
,
Erland Källén
,
Michael J. Kavaya
,
Rolf Langland
,
Zaizhong Ma
,
Michiko Masutani
,
Will McCarty
,
R. Bradley Pierce
,
Zhaoxia Pu
,
Lars Peter Riishojgaard
,
James Ryan
,
Sara Tucker
,
Martin Weissmann
, and
James G. Yoe

The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.

Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.

This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.

These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.

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