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  • Author or Editor: Melinda Marquis x
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Theodore M. McHardy
,
James R. Campbell
,
David A. Peterson
,
Simone Lolli
,
Richard L. Bankert
,
Anne Garnier
,
Arunas P. Kuciauskas
,
Melinda L. Surratt
,
Jared W. Marquis
,
Steven D. Miller
,
Erica K. Dolinar
, and
Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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James M. Wilczak
,
Mark Stoelinga
,
Larry K. Berg
,
Justin Sharp
,
Caroline Draxl
,
Katherine McCaffrey
,
Robert M. Banta
,
Laura Bianco
,
Irina Djalalova
,
Julie K. Lundquist
,
Paytsar Muradyan
,
Aditya Choukulkar
,
Laura Leo
,
Timothy Bonin
,
Yelena Pichugina
,
Richard Eckman
,
Charles N. Long
,
Kathleen Lantz
,
Rochelle P. Worsnop
,
Jim Bickford
,
Nicola Bodini
,
Duli Chand
,
Andrew Clifton
,
Joel Cline
,
David R. Cook
,
Harindra J. S. Fernando
,
Katja Friedrich
,
Raghavendra Krishnamurthy
,
Melinda Marquis
,
Jim McCaa
,
Joseph B. Olson
,
Sebastian Otarola-Bustos
,
George Scott
,
William J. Shaw
,
Sonia Wharton
, and
Allen B. White

Abstract

The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

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Joseph B. Olson
,
Jaymes S. Kenyon
,
Irina Djalalova
,
Laura Bianco
,
David D. Turner
,
Yelena Pichugina
,
Aditya Choukulkar
,
Michael D. Toy
,
John M. Brown
,
Wayne M. Angevine
,
Elena Akish
,
Jian-Wen Bao
,
Pedro Jimenez
,
Branko Kosovic
,
Katherine A. Lundquist
,
Caroline Draxl
,
Julie K. Lundquist
,
Jim McCaa
,
Katherine McCaffrey
,
Kathy Lantz
,
Chuck Long
,
Jim Wilczak
,
Robert Banta
,
Melinda Marquis
,
Stephanie Redfern
,
Larry K. Berg
,
Will Shaw
, and
Joel Cline

Abstract

The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.

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