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Kermit K. Keeter
and
Joel W. Cline

Abstract

The Local Objective Guidance for Predicting Precipitation Type (LOG/PT) consists of regression equations and nomograms. LOG/PT was designed to address problems inherent in forecasting wintry precipitation across North Carolina, where frozen and freezing precipitation are relatively infrequent, often occurring from mixed precipitation events, and where even small amounts can disrupt communities. Moreover, LOG/PT is an example of employing developmental strategies to maximize the yield from limited resources to produce an objective forecast tool for a critical local-forecast problem.

Stepwise linear regression, with modifications to approximate the sigmoid curve associated with logit regression, was used to derive relationships between precipitation type and 1000–700-, 850–700-, and 1000–850-mb thickness values from radiosonde observations (raobs). The soundings were concurrent with, or within 12 h prior to, the onset of the precipitation at the prediction sites.

The regression portion of LOG/PT discriminates frozen from liquid precipitation. LOG/PT demonstrated skill in detecting frozen events and in correctly specifying frozen-precipitation forecasts. When used in a perfect prog sense with the nested grid model (NGM) thickness forecasts, LOG/PT showed a tendency to overforecast the frequency of snow. LOG/PT's forecast success was limited by its dependence upon a one-raob prediction site with raobs taken 12 h part, and the characteristics of the NGM 1000–850-mb thickness forecasts. Operationally, the regression portion has been useful in predicting the location of the snow/rain boundary in storms with relatively narrow precipitation-type transition zones. In addition, nomograms were prepared to differentiate mixed-precipitation events that resulted in measurable amounts of frozen precipitation from those producing only a trace of frozen precipitation, and to identify icing events. Operationally, the nomograms are used to specify precipitation type in storms with broad bands of mixed precipitation.

In addition to statistical samples, the operational experience of local forecasters was used to gain insight concerning the forecast performance of LOG/PT and the Model Output Statistics (MOS) Probability of Precipitation Type (PoPT) guidance from the Limited-Area Fine Mesh (LFM) model. LOG/PT provides the forecaster with an additional source of objective precipitation-type guidance that can be helpful, especially when forecast errors in the LFM limit the accuracy of the resulting MOS guidance.

Future research efforts directed toward improving the LOG/PT guidance, and increasing the forecaster's knowledge of synoptic features and physical processes that determine precipitation type are also discussed.

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Irina V. Djalalova
,
Joseph Olson
,
Jacob R. Carley
,
Laura Bianco
,
James M. Wilczak
,
Yelena Pichugina
,
Robert Banta
,
Melinda Marquis
, and
Joel Cline

Abstract

During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown (RHB), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.

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Laura Bianco
,
Irina V. Djalalova
,
James M. Wilczak
,
Joel Cline
,
Stan Calvert
,
Elena Konopleva-Akish
,
Cathy Finley
, and
Jeffrey Freedman

Abstract

A wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power over short periods of time ) it is more difficult to balance the electric load with power production than during quiescent periods between ramp events. A ramp-specific metric is needed because standard metrics do not give special consideration to ramp events and hence may not provide an appropriate measure of model skill or skill improvement. This RT&M has three components. The first identifies ramp events in the power time series. The second matches in time forecast and observed ramps. The third determines a skill score of the forecast model. This is calculated from a utility operator’s perspective, incorporates phase and duration errors in time as well as power amplitude errors, and recognizes that up and down ramps have different impacts on grid operation. The RT&M integrates skill over a matrix of ramp events of varying amplitudes and durations.

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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.

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

Abstract

Evaluation of model skill in predicting winds over the ocean was performed by comparing retrospective runs of numerical weather prediction (NWP) forecast models to shipborne Doppler lidar measurements in the Gulf of Maine, a potential region for U.S. coastal wind farm development. Deployed on board the NOAA R/V Ronald H. Brown during a 2004 field campaign, the high-resolution Doppler lidar (HRDL) provided accurate motion-compensated wind measurements from the water surface up through several hundred meters of the marine atmospheric boundary layer (MABL). The quality and resolution of the HRDL data allow detailed analysis of wind flow at heights within the rotor layer of modern wind turbines and data on other critical variables to be obtained, such as wind speed and direction shear, turbulence, low-level jet properties, ramp events, and many other wind-energy-relevant aspects of the flow. This study will focus on the quantitative validation of NWP models’ wind forecasts within the lower MABL by comparison with HRDL measurements. Validation of two modeling systems rerun in special configurations for these 2004 cases—the hourly updated Rapid Refresh (RAP) system and a special hourly updated version of the North American Mesoscale Forecast System [NAM Rapid Refresh (NAMRR)]—are presented. These models were run at both normal-resolution (RAP, 13 km; NAMRR, 12 km) and high-resolution versions: the NAMRR-CONUS-nest (4 km) and the High-Resolution Rapid Refresh (HRRR, 3 km). Each model was run twice: with (experimental runs) and without (control runs) assimilation of data from 11 wind profiling radars located along the U.S. East Coast. The impact of the additional assimilation of the 11 profilers was estimated by comparing HRDL data to modeled winds from both runs. The results obtained demonstrate the importance of high-resolution lidar measurements to validate NWP models and to better understand what atmospheric conditions may impact the accuracy of wind forecasts in the marine atmospheric boundary layer. Results of this research will also provide a first guess as to the uncertainties of wind resource assessment using NWP models in one of the U.S. offshore areas projected for wind plant development.

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Sue Ellen Haupt
,
Branko Kosovic
,
William Shaw
,
Larry K. Berg
,
Matthew Churchfield
,
Joel Cline
,
Caroline Draxl
,
Brandon Ennis
,
Eunmo Koo
,
Rao Kotamarthi
,
Laura Mazzaro
,
Jeffrey Mirocha
,
Patrick Moriarty
,
Domingo Muñoz-Esparza
,
Eliot Quon
,
Raj K. Rai
,
Michael Robinson
, and
Gokhan Sever

Abstract

Accurately representing flow across the mesoscale to the microscale is a persistent roadblock for completing realistic microscale simulations. The science challenges that must be addressed to coupling at these scales include the following: 1) What is necessary to capture the variability of the mesoscale flow, and how do we avoid generating spurious rolls within the terra incognita between the scales? 2) Which methods effectively couple the mesoscale to the microscale and capture the correct nonstationary features at the microscale? 3) What are the best methods to initialize turbulence at the microscale? 4) What is the best way to handle the surface-layer parameterizations consistently at the mesoscale and the microscale? 5) How do we assess the impact of improvements in each of these aspects and quantify the uncertainty in the simulations? The U.S. Department of Energy Mesoscale-to-Microscale-Coupling project seeks to develop, verify, and validate physical models and modeling techniques that bridge the most important atmospheric scales determining wind plant performance and reliability, which impacts many meteorological applications. The approach begins with choosing case days that are interesting for wind energy for which there are observational data for validation. The team has focused on modeling nonstationary conditions for both flat and complex terrain. This paper describes the approaches taken to answer the science challenges, culminating in recommendations for best approaches for coupled modeling.

Full access
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.

Full access
James Wilczak
,
Cathy Finley
,
Jeff Freedman
,
Joel Cline
,
Laura Bianco
,
Joseph Olson
,
Irina Djalalova
,
Lindsay Sheridan
,
Mark Ahlstrom
,
John Manobianco
,
John Zack
,
Jacob R. Carley
,
Stan Benjamin
,
Richard Coulter
,
Larry K. Berg
,
Jeffrey Mirocha
,
Kirk Clawson
,
Edward Natenberg
, and
Melinda Marquis

Abstract

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

Full 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.

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