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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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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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Eric P. James
,
Curtis R. Alexander
,
David C. Dowell
,
Stephen S. Weygandt
,
Stanley G. Benjamin
,
Geoffrey S. Manikin
,
John M. Brown
,
Joseph B. Olson
,
Ming Hu
,
Tatiana G. Smirnova
,
Terra Ladwig
,
Jaymes S. Kenyon
, and
David D. Turner

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

Significance Statement

NOAA’s operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access
James Edson
,
Timothy Crawford
,
Jerry Crescenti
,
Tom Farrar
,
Nelson Frew
,
Greg Gerbi
,
Costas Helmis
,
Tihomir Hristov
,
Djamal Khelif
,
Andrew Jessup
,
Haf Jonsson
,
Ming Li
,
Larry Mahrt
,
Wade McGillis
,
Albert Plueddemann
,
Lian Shen
,
Eric Skyllingstad
,
Tim Stanton
,
Peter Sullivan
,
Jielun Sun
,
John Trowbridge
,
Dean Vickers
,
Shouping Wang
,
Qing Wang
,
Robert Weller
,
John Wilkin
,
Albert J. Williams III
,
D. K. P. Yue
, and
Chris Zappa

The Office of Naval Research's Coupled Boundary Layers and Air–Sea Transfer (CBLAST) program is being conducted to investigate the processes that couple the marine boundary layers and govern the exchange of heat, mass, and momentum across the air–sea interface. CBLAST-LOW was designed to investigate these processes at the low-wind extreme where the processes are often driven or strongly modulated by buoyant forcing. The focus was on conditions ranging from negligible wind stress, where buoyant forcing dominates, up to wind speeds where wave breaking and Langmuir circulations play a significant role in the exchange processes. The field program provided observations from a suite of platforms deployed in the coastal ocean south of Martha's Vineyard. Highlights from the measurement campaigns include direct measurement of the momentum and heat fluxes on both sides of the air–sea interface using a specially constructed Air–Sea Interaction Tower (ASIT), and quantification of regional oceanic variability over scales of O(1–104 mm) using a mesoscale mooring array, aircraft-borne remote sensors, drifters, and ship surveys. To our knowledge, the former represents the first successful attempt to directly and simultaneously measure the heat and momentum exchange on both sides of the air–sea interface. The latter provided a 3D picture of the oceanic boundary layer during the month-long main experiment. These observations have been combined with numerical models and direct numerical and large-eddy simulations to investigate the processes that couple the atmosphere and ocean under these conditions. For example, the oceanic measurements have been used in the Regional Ocean Modeling System (ROMS) to investigate the 3D evolution of regional ocean thermal stratification. The ultimate goal of these investigations is to incorporate improved parameterizations of these processes in coupled models such as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to improve marine forecasts of wind, waves, and currents.

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James D. Doyle
,
Jonathan R. Moskaitis
,
Joel W. Feldmeier
,
Ronald J. Ferek
,
Mark Beaubien
,
Michael M. Bell
,
Daniel L. Cecil
,
Robert L. Creasey
,
Patrick Duran
,
Russell L. Elsberry
,
William A. Komaromi
,
John Molinari
,
David R. Ryglicki
,
Daniel P. Stern
,
Christopher S. Velden
,
Xuguang Wang
,
Todd Allen
,
Bradford S. Barrett
,
Peter G. Black
,
Jason P. Dunion
,
Kerry A. Emanuel
,
Patrick A. Harr
,
Lee Harrison
,
Eric A. Hendricks
,
Derrick Herndon
,
William Q. Jeffries
,
Sharanya J. Majumdar
,
James A. Moore
,
Zhaoxia Pu
,
Robert F. Rogers
,
Elizabeth R. Sanabia
,
Gregory J. Tripoli
, and
Da-Lin Zhang

Abstract

Tropical cyclone (TC) outflow and its relationship to TC intensity change and structure were investigated in the Office of Naval Research Tropical Cyclone Intensity (TCI) field program during 2015 using dropsondes deployed from the innovative new High-Definition Sounding System (HDSS) and remotely sensed observations from the Hurricane Imaging Radiometer (HIRAD), both on board the NASA WB-57 that flew in the lower stratosphere. Three noteworthy hurricanes were intensively observed with unprecedented horizontal resolution: Joaquin in the Atlantic and Marty and Patricia in the eastern North Pacific. Nearly 800 dropsondes were deployed from the WB-57 flight level of ∼60,000 ft (∼18 km), recording atmospheric conditions from the lower stratosphere to the surface, while HIRAD measured the surface winds in a 50-km-wide swath with a horizontal resolution of 2 km. Dropsonde transects with 4–10-km spacing through the inner cores of Hurricanes Patricia, Joaquin, and Marty depict the large horizontal and vertical gradients in winds and thermodynamic properties. An innovative technique utilizing GPS positions of the HDSS reveals the vortex tilt in detail not possible before. In four TCI flights over Joaquin, systematic measurements of a major hurricane’s outflow layer were made at high spatial resolution for the first time. Dropsondes deployed at 4-km intervals as the WB-57 flew over the center of Hurricane Patricia reveal in unprecedented detail the inner-core structure and upper-tropospheric outflow associated with this historic hurricane. Analyses and numerical modeling studies are in progress to understand and predict the complex factors that influenced Joaquin’s and Patricia’s unusual intensity changes.

Open access
Stanley G. Benjamin
,
Stephen S. Weygandt
,
John M. Brown
,
Ming Hu
,
Curtis R. Alexander
,
Tatiana G. Smirnova
,
Joseph B. Olson
,
Eric P. James
,
David C. Dowell
,
Georg A. Grell
,
Haidao Lin
,
Steven E. Peckham
,
Tracy Lorraine Smith
,
William R. Moninger
,
Jaymes S. Kenyon
, and
Geoffrey S. Manikin

Abstract

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

Full access
David C. Dowell
,
Curtis R. Alexander
,
Eric P. James
,
Stephen S. Weygandt
,
Stanley G. Benjamin
,
Geoffrey S. Manikin
,
Benjamin T. Blake
,
John M. Brown
,
Joseph B. Olson
,
Ming Hu
,
Tatiana G. Smirnova
,
Terra Ladwig
,
Jaymes S. Kenyon
,
Ravan Ahmadov
,
David D. Turner
,
Jeffrey D. Duda
, and
Trevor I. Alcott

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

Significance Statement

NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access
Brian J. Butterworth
,
Ankur R. Desai
,
Stefan Metzger
,
Philip A. Townsend
,
Mark D. Schwartz
,
Grant W. Petty
,
Matthias Mauder
,
Hannes Vogelmann
,
Christian G. Andresen
,
Travis J. Augustine
,
Timothy H. Bertram
,
William O.J. Brown
,
Michael Buban
,
Patricia Cleary
,
David J. Durden
,
Christopher R. Florian
,
Trevor J. Iglinski
,
Eric L. Kruger
,
Kathleen Lantz
,
Temple R. Lee
,
Tilden P. Meyers
,
James K. Mineau
,
Erik R. Olson
,
Steven P. Oncley
,
Sreenath Paleri
,
Rosalyn A. Pertzborn
,
Claire Pettersen
,
David M. Plummer
,
Laura D. Riihimaki
,
Eliceo Ruiz Guzman
,
Joseph Sedlar
,
Elizabeth N. Smith
,
Johannes Speidel
,
Paul C. Stoy
,
Matthias Sühring
,
Jonathan E. Thom
,
David D. Turner
,
Michael P. Vermeuel
,
Timothy J. Wagner
,
Zhien Wang
,
Luise Wanner
,
Loren D. White
,
James M. Wilczak
,
Daniel B. Wright
, and
Ting Zheng
Full access
Brian J. Butterworth
,
Ankur R. Desai
,
Philip A. Townsend
,
Grant W. Petty
,
Christian G. Andresen
,
Timothy H. Bertram
,
Eric L. Kruger
,
James K. Mineau
,
Erik R. Olson
,
Sreenath Paleri
,
Rosalyn A. Pertzborn
,
Claire Pettersen
,
Paul C. Stoy
,
Jonathan E. Thom
,
Michael P. Vermeuel
,
Timothy J. Wagner
,
Daniel B. Wright
,
Ting Zheng
,
Stefan Metzger
,
Mark D. Schwartz
,
Trevor J. Iglinski
,
Matthias Mauder
,
Johannes Speidel
,
Hannes Vogelmann
,
Luise Wanner
,
Travis J. Augustine
,
William O. J. Brown
,
Steven P. Oncley
,
Michael Buban
,
Temple R. Lee
,
Patricia Cleary
,
David J. Durden
,
Christopher R. Florian
,
Kathleen Lantz
,
Laura D. Riihimaki
,
Joseph Sedlar
,
Tilden P. Meyers
,
David M. Plummer
,
Eliceo Ruiz Guzman
,
Elizabeth N. Smith
,
Matthias Sühring
,
David D. Turner
,
Zhien Wang
,
Loren D. White
, and
James M. Wilczak

Abstract

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

Open access
Ben P. Kirtman
,
Dughong Min
,
Johnna M. Infanti
,
James L. Kinter III
,
Daniel A. Paolino
,
Qin Zhang
,
Huug van den Dool
,
Suranjana Saha
,
Malaquias Pena Mendez
,
Emily Becker
,
Peitao Peng
,
Patrick Tripp
,
Jin Huang
,
David G. DeWitt
,
Michael K. Tippett
,
Anthony G. Barnston
,
Shuhua Li
,
Anthony Rosati
,
Siegfried D. Schubert
,
Michele Rienecker
,
Max Suarez
,
Zhao E. Li
,
Jelena Marshak
,
Young-Kwon Lim
,
Joseph Tribbia
,
Kathleen Pegion
,
William J. Merryfield
,
Bertrand Denis
, and
Eric F. Wood

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.

Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

Full access