The POWER Experiment: Impact of Assimilation of a Network of Coastal Wind Profiling Radars on Simulating Offshore Winds in and above the Wind Turbine Layer
The POWER Experiment: Impact of Assimilation of a Network of Coastal Wind Profiling Radars on Simulating Offshore Winds in and above the Wind Turbine Layer
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.
Creamean, J. M.; White, A. B.; Minnis, P.; Palikonda, R.; Spangenberg, D. A.; Prather, K. A.;
Published Date:
2016
Source:
Atmospheric Environment, 140, 298-310.
Description:
Ice formation in orographic mixed -phase clouds can enhance precipitation and depends on the type of aerosols that serve as ice nucleating particles (INPs). The resulting precipitation from these clouds is a viable source of water, especially for reg...
Zawislak, J.; Jiang, H. Y.; Alvey, G. R.; Zipser, E. J.; Rogers, R. F.; Zhang, J. A.; Stevenson, S. N.;
Published Date:
2016
Source:
Monthly Weather Review, 144(9), 3333-3354.
Description:
The structural evolution of the inner core and near environment throughout the life cycle of Hurricane Edouard (2014) is examined using a synthesis of airborne and satellite measurements. This study specifically focuses on the precipitation evolution...
Turney, C. S. M.; Jones, R. T.; Lister, D.; Jones, P.; Williams, A. N.; Hogg, A.; Thomas, Z. A.; Compo, G. P.; Yin, X. G.; Fogwill, C. J.; Palmer, J.; Colwell, S.; Allan, R.; Visbeck, M.;
Published Date:
2016
Source:
Environmental Research Letters, 11(6), 064009.
Description:
Determining the timing and impact of anthropogenic climate change in data-sparse regions is a considerable challenge. Arguably, nowhere is this more difficult than the Antarctic Peninsula and the subantarctic South Atlantic where observational record...
The phrasing of the first of three questions motivating CMIP6 - "How does the Earth system respond to forcing?" - suggests that forcing is always well-known, yet the radiative forcing to which this question refers has historically been uncertain in c...
This study documents a very rapid increase in convective instability, vertical wind shear, and mesoscale forcing for ascent leading to the formation of a highly unusual tornado as detected by a ground-based microwave radiometer and wind profiler, and...
Journal of Geophysical Research-Atmospheres, 121(6), 2705-2718.
Description:
An intercomparison of landfalling atmospheric rivers (ARs) between four reanalysis data sets using one satellite-derived AR detection method as a metric to characterize landfalling atmospheric rivers (ARs) along the U.S. West Coast is performed over ...
Hobbins, M. T.; Wood, A.; McEvoy, D. J.; Huntington, J. L.; Morton, C.; Anderson, M.; Hain, C.;
Published Date:
2016
Source:
Journal of Hydrometeorology, 17(6), 1745-1761.
Description:
Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage bet...
Mahoney, K.; Jackson, D. L.; Neiman, P.; Hughes, M.; Darby, L.; Wick, G.; White, A.; Sukovich, E.; Cifelli, R.;
Published Date:
2016
Source:
Monthly Weather Review, 144(4), 1617-1632.
Description:
An analysis of atmospheric rivers (ARs) as defined by an automated AR detection tool based on integrated water vapor transport (IVT) and the connection to heavy precipitation in the southeast United States (SEUS) is performed. Climatological water va...
Cox, C. J.; Uttal, T.; Long, C. N.; Shupe, M. D.; Stone, R. S.; Starkweather, S.;
Published Date:
2016
Source:
Journal of Climate, 29(18), 6581-6596.
Description:
Recent studies suggest that the atmosphere conditions arctic sea ice properties in spring in a way that may be an important factor in predetermining autumn sea ice concentrations. Here, the role of clouds in this system is analyzed using surface-base...
The question of whether ocean coupling matters for the extratropical Northern Hemisphere atmospheric response to projected late 21st century Arctic sea ice loss is addressed using a series of experiments with Community Climate System Model version 4 ...
Uttal, T.; Starkweather, S.; Drummond, J. R.; Vihma, T.; Makshtas, A. P.; Darby, L. S.; Burkhart, J. F.; Cox, C. J.; Schmeisser, L. N.; Haiden, T.; Maturilli, M.; Shupe, M. D.; De Boer, G.; Saha, A.; Grachev, A. A.; Crepinsek, S. M.; Bruhwiler, L.; Goodison, B.; McArthur, B.; Walden, V. P.; Dlugokencky, E. J.; Persson, P. O. G.; Lesins, G.; Laurila, T.; Ogren, J. A.; Stone, R.; Long, C. N.; Sharma, S.; Massling, A.; Turner, D. D.; Stanitski, D. M.; Asmi, E.; Aurela, M.; Skov, H.; Eleftheriadis, K.; Virkkula, A.; Platt, A.; Forland, E. J.; Iijima, Y.; Nielsen, I. E.; Bergin, M. H.; Candlish, L.; Zimov, N. S.; Zimov, S. A.; O'Neill, N. T.; Fogal, P. F.; Kivi, R.; Konopleva-Akish, E. A.; Verlinde, J.; Kustov, V. Y.; Vasel, B.; Ivakhov, V. M.; Viisanen, Y.; Intrieri, J. M.;
Published Date:
2016
Source:
Bulletin of the American Meteorological Society, 97(6), 1033-1056.
Description:
International Arctic Systems for Observing the Atmosphere (IASOA) activities and partnerships were initiated as a part of the 2007-09 International Polar Year (IPY) and are expected to continue for many decades as a legacy program. The IASOA focus is...
Hoerling, M.; Eischeid, J.; Perlwitz, J.; Quan, X. W.; Wolter, K.; Cheng, L. Y.;
Published Date:
2016
Source:
Journal of Climate, 29(7), 2313-2332.
Description:
Time series of U.S. daily heavy precipitation (95th percentile) are analyzed to determine factors responsible for regionality and seasonality in their 1979-2013 trends. For annual conditions, contiguous U.S. trends have been characterized by increase...
Hodyss, D.; Satterfield, E.; McLay, J.; Hamill, T. M.; Scheuerer, M.;
Published Date:
2016
Source:
Monthly Weather Review, 144(4), 1649-1668.
Description:
Ensemble postprocessing is frequently applied to correct biases and deficiencies in the spread of ensemble forecasts. Methods involving weighted, regression-corrected forecasts address the typical biases and under-dispersion of ensembles through a re...
The future rate of climate change in mountains has many potential human impacts, including those related to water resources, ecosystem services, and recreation. Analysis of the ensemble mean response of CMIP5 global climate models (GCMs) shows amplif...
Self-organizing maps (SOMs) were used to explore relationships between large-scale synoptic conditions, especially vertically integrated water vapor transport (IVT), and extreme precipitation events in the U.S. Intermountain West (IMW). By examining ...
Bakker, D. C. E.; Pfeil, B.; Landa, C. S.; Metzl, N.; O'Brien, K. M.; Olsen, A.; Smith, K.; Cosca, C.; Harasawa, S.; Jones, S. D.; Nakaoka, S.; Nojiri, Y.; Schuster, U.; Steinhoff, T.; Sweeney, C.; Takahashi, T.; Tilbrook, B.; Wada, C.; Wanninkhof, R.; Alin, S. R.; Balestrini, C. F.; Barbero, L.; Bates, N. R.; Bianchi, A. A.; Bonou, F.; Boutin, J.; Bozec, Y.; Burger, E. F.; Cai, W. J.; Castle, R. D.; Chen, L. Q.; Chierici, M.; Currie, K.; Evans, W.; Featherstone, C.; Feely, R. A.; Fransson, A.; Goyet, C.; Greenwood, N.; Gregor, L.; Hankin, S.; Hardman-Mountford, N. J.; Harlay, J.; Hauck, J.; Hoppema, M.; Humphreys, M. P.; Hunt, C.; Huss, B.; Ibanhez, J. S. P.; Johannessen, T.; Keeling, R.; Kitidis, V.; Kortzinger, A.; Kozyr, A.; Krasakopoulou, E.; Kuwata, A.; Landschutzer, P.; Lauvset, S. K.; Lefevre, N.; Lo Monaco, C.; Manke, A.; Mathis, J. T.; Merlivat, L.; Millero, F. J.; Monteiro, P. M. S.; Munro, D. R.; Murata, A.; Newberger, T.; Omar, A. M.; Ono, T.; Paterson, K.; Pearce, D.; Pierrot, D.; Robbins, L. L.; Saito, S.; Salisbury, J.; Schlitzer, R.; Schneider, B.; Schweitzer, R.; Sieger, R.; Skjelvan, I.; Sullivan, K. F.; Sutherland, S. C.; Sutton, A. J.; Tadokoro, K.; Telszewski, M.; Tuma, M.; van Heuven, S.; Vandemark, D.; Ward, B.; Watson, A. J.; Xu, S. Q.;
Published Date:
2016
Source:
Earth System Science Data, 8(2), 383-413.
Description:
The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO(2) (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO(2) values from 3646 data ...
Pettersen, C.; Bennartz, R.; Kulie, M. S.; Merrelli, A. J.; Shupe, M. D.; Turner, D. D.;
Published Date:
2016
Source:
Atmospheric Chemistry and Physics, 16(7), 4743-4756.
Description:
Multi-instrument, ground-based measurements provide unique and comprehensive data sets of the atmosphere for a specific location over long periods of time and resulting data compliment past and existing global satellite observations. This paper explo...
Bianco, L.; Djalalova, I. V.; Wilczak, J. M.; Cline, J.; Calvert, S.; Konopleva-Akish, E.; Finley, C.; Freedman, J.;
Published Date:
2016
Source:
Weather and Forecasting, 31(4), 1137-1156.
Description:
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 Delta p over short periods of time Delta t) it is more difficult to balance the electric load with power ...
Matrosov, S. Y.; Cifelli, R.; Neiman, P. J.; White, A. B.;
Published Date:
2016
Source:
Journal of Applied Meteorology and Climatology, 55(6), 1345-1358.
Description:
S-band profiling (S-PROF) radar measurements from different southeastern U.S. Hydrometeorology Testbed sites indicated a frequent occurrence of rain that did not exhibit radar bright band (BB) and was observed outside the periods of deep-convective p...
Hoell, A.; Hoerling, M.; Eischeid, J.; Wolter, K.; Dole, R.; Perlwitz, J.; Xu, T. Y.; Cheng, L. Y.;
Published Date:
2016
Source:
Geophysical Research Letters, 43(2), 819-825.
Description:
The sensitivity of California precipitation to El Nino intensity is investigated by applying a multimodel ensemble of historical climate simulations to estimate how November-April precipitation probability distributions vary across three categorizati...
Pichugina, Y. L.; Banta, R. M.; Olson, J. B.; Carley, J. R.; Marquis, M. C.; Brewer, W. A.; Wilczak, J. M.; Djalalova, I.; Bianco, L.; James, E. P.; Benjamin, S. G.; Cline, J.;
Published Date:
2017
Source:
Monthly Weather Review, 145(10), 4277-4301.
Description:
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....
Wilczak, J. M.; Olson, J. B.; Djalalova, I.; Bianco, L.; Berg, L. K.; Shaw, W. J.; Coulter, R. L.; Eckman, R. M.; Freedman, J.; Finley, C.; Cline, J.;
Published Date:
2019
Source:
Wind Energy, 22(7), 932-944.
Description:
Abstract During the first Wind Forecast Improvement Project (WFIP), new meteorological observations were collected from a large suite of instruments, including wind velocities measured on networks of tall towers provided by wind industry partners, wi...
Banta, R. M.; Pichugina, Y. L.; Brewer, W. A.; James, E. P.; Olson, J. B.; Benjamin, S. G.; Carley, J. R.; Bianco, L.; Djalalova, I. V.; Wilczak, J. M.; Hardesty, R. M.; Cline, J.; Marquis, M. C.;
Published Date:
2018
Source:
Bulletin of the American Meteorological Society, 99(6), 1155-1176.
Description:
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...