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Justin Sharp
and
Clifford F. Mass

Abstract

This paper quantifies the impact of the Columbia Gorge on the weather and climate within and downstream of this mesoscale gap and examines the influence of synoptic-scale flow on gorge weather. Easterly winds occur more frequently and are stronger at stations such as Portland International Airport (KPDX) that are close to the western terminus of the gorge than at other lowland stations west of the Cascades. In the cool season, there is a strong correlation between east winds at KPDX and cooler temperatures in the Columbia Basin, within the gorge, and over the northern Willamette River valley. At least 56% of the annual snowfall, 70% of days with snowfall, and 90% of days with freezing rain at KPDX coincide with easterly gorge flow.

Synoptic composites were created to identify the large-scale patterns leading to strong winds, snowfall, and freezing rain in the gorge. These composites showed that all gorge gap flow events are associated with a high-amplitude 500-mb ridge upstream of the Pacific Northwest, colder than normal 850-mb temperatures over the study region, and a substantial offshore sea level pressure gradient force between the interior and the northwest coast. However, the synoptic evolution varies for different kinds of gorge weather events. For example, the composite of the 500-mb field for freezing rain events features a split developing in the upstream ridge with zonal flow at midlatitudes, while for easterly gap winds accompanied by snowfall, there is an amplification of the ridge.

Full access
Melinda Marquis
,
Jim Wilczak
,
Mark Ahlstrom
,
Justin Sharp
,
Andrew Stern
,
J. Charles Smith
, and
Stan Calvert

Advances in atmospheric science are critical to increased deployment of variable renewable energy (VRE) sources. For VRE sources, such as wind and solar, to reach high penetration levels in the nation's electric grid, electric system operators and VRE operators need better atmospheric observations, models, and forecasts. Improved meteorological observations through a deep layer of the atmosphere are needed for assimilation into numerical weather prediction (NWP) models. The need for improved operational NWP forecasts that can be used as inputs to power prediction models in the 0–36-h time frame is particularly urgent and more accurate predictions of rapid changes in VRE generation (ramp events) in the very short range (0–6 h) are crucial.

We describe several recent studies that investigate the feasibility of generating 20% or more of the nation's electricity from weather-dependent VRE. Next, we describe key advances in atmospheric science needed for effective development of wind energy and approaches to achieving these improvements. The financial benefit to the nation of improved wind forecasts is potentially in the billions of dollars per year. Obtaining the necessary meteorological and climatological observations and predictions is a major undertaking, requiring collaboration from the government, private, and academic sectors. We describe a field project that will begin in 2011 to improve short-term wind forecasts, which demonstrates such a collaboration, and which falls under a recent memorandum of understanding between the Office of Energy Efficiency and Renewable Energy at the Department of Energy and the Department of Commerce/National Oceanic and Atmospheric Administration.

Full access
William J. Shaw
,
Larry K. Berg
,
Joel Cline
,
Caroline Draxl
,
Irina Djalalova
,
Eric P. Grimit
,
Julie K. Lundquist
,
Melinda Marquis
,
Jim McCaa
,
Joseph B. Olson
,
Chitra Sivaraman
,
Justin Sharp
, and
James M. Wilczak

Abstract

In 2015 the U.S. Department of Energy (DOE) initiated a 4-yr study, the Second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part because of inherent assumptions underlying their boundary layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration (NOAA), and the DOE’s national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote sensing and in situ measurement systems. The study area comprised the Columbia basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

Full access
Katherine McCaffrey
,
James M. Wilczak
,
Laura Bianco
,
Eric Grimit
,
Justin Sharp
,
Robert Banta
,
Katja Friedrich
,
H. J. S. Fernando
,
Raghavendra Krishnamurthy
,
Laura S. Leo
, and
Paytsar Muradyan

Abstract

Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and especially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer profilers, six radar wind profilers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical profiles of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the field project. Characteristics of these events are described, including statistics of the wind speed distributions and profiles, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.

Free access
Robert M. Banta
,
Yelena L. Pichugina
,
W. Alan Brewer
,
Aditya Choukulkar
,
Kathleen O. Lantz
,
Joseph B. Olson
,
Jaymes Kenyon
,
Harindra J. S. Fernando
,
Raghu Krishnamurthy
,
Mark J. Stoelinga
,
Justin Sharp
,
Lisa S. Darby
,
David D. Turner
,
Sunil Baidar
, and
Scott P. Sandberg

Abstract

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s−1. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

Free access
Robert M. Banta
,
Yelena L. Pichugina
,
W. Alan Brewer
,
Kelly A. Balmes
,
Bianca Adler
,
Joseph Sedlar
,
Lisa S. Darby
,
David D. Turner
,
Jaymes S. Kenyon
,
Edward J. Strobach
,
Brian J. Carroll
,
Justin Sharp
,
Mark T. Stoelinga
,
Joel Cline
, and
Harindra J. S. Fernando

Abstract

Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.

Significance Statement

Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Open access
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.

Full access