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Brian J. Gaudet
,
G. García Medina
,
R. Krishnamurthy
,
W. J. Shaw
,
L. M. Sheridan
,
Z. Yang
,
R. K. Newsom
, and
M. Pekour

Abstract

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.

Significance Statement

Using atmospheric models to forecast winds in the environments of offshore wind turbines will be critical in the new energy economy. The models used are imperfect, however, being sometimes too coarse, and may not properly represent the wind field at typical turbine hub heights of 90 m, for which we have limited observations in the marine environment. To help address this gap, two buoys equipped with lidars that measured hub-height winds continuously were deployed off the U.S. East Coast from 2014 to 2017. We used the lidar buoy data to show the benefits of a relatively high-resolution atmospheric model over existing reanalysis products, as well as including both the impacts of waves on winds and vice versa.

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Edward I. Tollerud
,
Fernando Caracena
,
Steven E. Koch
,
Brian D. Jamison
,
R. Michael Hardesty
,
Brandi J. McCarty
,
Christoph Kiemle
,
Randall S. Collander
,
Diana L. Bartels
,
Steven Albers
,
Brent Shaw
,
Daniel L. Birkenheuer
, and
W. Alan Brewer

Abstract

Previous studies of the low-level jet (LLJ) over the central Great Plains of the United States have been unable to determine the role that mesoscale and smaller circulations play in the transport of moisture. To address this issue, two aircraft missions during the International H2O Project (IHOP_2002) were designed to observe closely a well-developed LLJ over the Great Plains (primarily Oklahoma and Kansas) with multiple observation platforms. In addition to standard operational platforms (most important, radiosondes and profilers) to provide the large-scale setting, dropsondes released from the aircraft at 55-km intervals and a pair of onboard lidar instruments—High Resolution Doppler Lidar (HRDL) for wind and differential absorption lidar (DIAL) for moisture—observed the moisture transport in the LLJ at greater resolution. Using these observations, the authors describe the multiscalar structure of the LLJ and then focus attention on the bulk properties and effects of scales of motion by computing moisture fluxes through cross sections that bracket the LLJ. From these computations, the Reynolds averages within the cross sections can be computed. This allow an estimate to be made of the bulk effect of integrated estimates of the contribution of small-scale (mesoscale to convective scale) circulations to the overall transport. The performance of the Weather Research and Forecasting (WRF) Model in forecasting the intensity and evolution of the LLJ for this case is briefly examined.

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