AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: 10.1175/BAMS-D-13-00108.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. © 2013 American Meteorological Society revised Manuscript 1 Meteorology For Coastal/Offshore Wind Energy In The United States: 2 Recommendations And Research Needs For The Next 10 Years 3 Cristina L. Archer (corresponding author) 4 University of Delaware 5 College of Earth, Ocean, and Environment 6 Newark, DE 19716 7 [email protected] 8 9 Brian A. Colle 10 Stony Brook University/SUNY, Stony Brook, New York 11 12 Luca Delle Monache 13 National Center for Atmospheric Research, Boulder, Colorado 14 15 Michael J. Dvorak 16 Sailor’s Energy, Berkeley, California 17 18 Julie Lundquist 19 University of Colorado at Boulder, and 20 National Renewable Energy Laboratory, Golden, Colorado 21 22 Bruce H. Bailey and Philippe Beaucage 23 AWS Truepower, LLC, Albany, New York 24 25 Matthew J. Churchfield 26 National Renewable Energy Laboratory, Golden, Colorado 27 28 Anna C. Fitch and Branko Kosovic 29 National Center for Atmospheric Research, Boulder, Colorado 30 31 Sang Lee and Patrick J. Moriarty 32 National Renewable Energy Laboratory, Golden, Colorado 33 34 Hugo Simao 35 Princeton University, Princeton, New Jersey 36 37 Richard J. A. M. Stevens 38 Johns Hopkins University, Baltimore, Maryland, and 39 University of Twente, Enschede (The Netherlands) 40 41 Dana Veron 42 University of Delaware, Newark, Delaware 43 44 John Zack 45 AWS Truepower, LLC, Albany, New York 46 Offshore wind energy is just starting in the United States, with imminent offshore wind 47 farms in Massachusetts, Maryland, and Rhode Island waters and with an ambitious goal 48 of 10 GW of installed offshore capacity by 2020 set by the U.S. Department of Energy 49 (DOE), which has recently funded seven “Advanced Technology Demonstration” 50 offshore wind projects to help achieve that goal. Although new in the U.S., offshore wind 51 energy began over 20 years ago in Europe and has now reached over 5.5 GW of installed 52 capacity worldwide, predominantly in Denmark and the United Kingdom. Given the 53 unfortunate coincidence of introducing a new industry during challenging economic 54 times, it is essential that public and private financial resources be effectively and 55 optimally directed towards those meteorological research needs that are emerging today 56 and that will be critical in the next decade. Identifying these research needs for wind 57 energy along the U.S. East Coast, both coastal and offshore, was the goal of a two-day 58 symposium held at the University of Delaware on 2728 February 2013. Over 40 59 participants gathered from academia, national laboratories, wind industry, and funding 60 agencies. 61 62 During the symposium, three main topics were explored: 1) wind resource assessment, 2) 63 wind power forecasting, and 3) turbulent wake losses. Overviews of the latest findings in 64 the three topics were given on the first day in the form of presentations, which were open 65 to students and the general public. On the second day, the experts gathered in a workshop 66 to identify research needs and provide recommendations for urgent action items. Whereas 67 specific research needs were identified for each of the three main topics, two emerged as 68 cross-cutting and urgent: 1) continuous, publicly available, multilevel measurements of 69 winds and temperature over U.S. offshore waters, and 2) quantification and reduction of 70 uncertainty. These two research needs and relevant recommendations (in italics) are 71 described first. 72 73 Research need #1: More offshore observations 74 75 Offshore meteorological measurements are challenging and expensive. Ideal 76 measurements would quantify the wind resource at several vertical levels spanning the 77 height of the turbine rotor disk to understand the rotor equivalent wind speed and possible 78 impacts on turbine power production. In European waters, designated research platforms 79 (e.g., FINO1 in Germany) have been established for characterization of offshore flow as 80 well as validation of new measurement technologies such as light detection and ranging 81 (lidar) and modeling approaches. The few long-term meteorological observations off the 82 East Coast are typically buoy-based, thereby restricting the altitude of wind 83 measurements to a few meters above the surface. A sparse network of nine towers, with 84 an elevation of ≤50 m, extends along the coast from Florida to Maine, but fails to provide 85 multilevel information and measurements at turbine hub-height or above. 86 Periodically, detailed measurements of wind and temperature have been conducted 87 offshore in short-term field campaigns, but the consistent long-term measurements 88 required for resource assessment are generally not available off the East Coast (with the 89 only exception being the Cape Wind tower in Nantucket Sound, Massachusetts). The 90 standard approach considered buoy measurements and then extrapolated them to higher 91 altitudes with assumptions of the shape of the wind profile (log-law or power-law). By 92 extrapolating surface or near-surface measurements with such smooth profiles, important 93 wind structures such as low-level jets are ignored. 94 The first recommendation is the deployment of a more dense network of 95 meteorological towers, which will enable traditional resource assessment measurements 96 such as wind speed, wind direction, and turbulence at several levels from the surface to 97 the rotor disk top, and temperature profiles for quantifying atmospheric stratification and 98 stability. Ideally, such towers could also provide a platform for validating remote sensing 99 measurements. The U.S. DOE has proposed the Reference Facility for Offshore 100 Renewable Energy (RFORE) to be located at the Chesapeake Light Tower, 101 approximately 13 miles off the Virginia Coast. The facility provides a first step towards 102 addressing the shortage of offshore wind data. 103 Beyond meteorological towers, remote sensing technology mounted either on fixed 104 towers or on floating platforms could provide data over broader regions. Scanning 105 Doppler lidar, wind-profiling lidar, and sodar can provide valuable wind speed and 106 direction measurements throughout the turbine rotor disk and beyond. Radiometers can 107 quantify temperature and humidity profiles to determine atmospheric stability. 108 In addition to long-term measurements of winds, temperature, and moisture profiles, 109 short-term intensive measurement campaigns with a broader deployment of instruments 110 would also be of value, especially for model validation. 111 These recommendations for more intensive observations extend a prior call for more 112 onshore meteorological observations and focused field campaigns made by DOE in 2008. 113 Since then, new types of remote sensing instruments have become more widely available 114 and more accepted in the wind energy industry for wind resource characterization. 115 116 Research need #2: Uncertainty characterization 117 118 Deterministic wind power forecasts based on numerical weather prediction (NWP) can 119 provide useful information for decision-making. However, by design, a single plausible 120 future state of the atmosphere starting from a single initial state is generated. Imperfect 121 initial and boundary conditions and model deficiencies inevitably lead to nonlinear error 122 growth during model integration. Accurate knowledge of the continuum of plausible 123 future states, the forecast probability density function (PDF), is considerably more useful 124 for decision-making because it allows for a quantification of the uncertainty associated 125 with a forecast. 126 “Ensembles” are used today to generate a set of plausible future atmospheric states 127 and to estimate the forecast PDF of atmospheric variables relevant to wind power. 128 Ensembles are created from the outputs of NWP models using any of the following: 129 various initial conditions, different parameterizations within a single model, stochastic 130 approaches with diverse numerical schemes, different models, and coupled ocean- 131 atmosphere schemes. For wind energy, one important additional source of uncertainty 132 comes from the challenging step of wind-to-power conversion. 133 Ensembles are affected by biases in the ensemble mean and by lack of diversity 134 among the ensemble members, particularly in the planetary boundary layer (PBL). 135 Therefore, post-processing is an important component of the wind forecasting process 136 and should be explored further, preferably including methods and techniques developed 137 by the wind industry. Since the wind industry benefits from the findings published by the 138 research community and the public sector, it is recommended that a regular two-way 139 exchange of know-how between academia, public sector, and industry be established to 140 help advance the science and prevent the duplication of efforts. A promising post- 141 processing technique is the analog approach, in which past observations that correspond 142 to past predictions that best match selected features of the current forecast, such as time 143 series of wind speed and direction, are used to correct the current forecast. Other 144 promising techniques are advanced model output statistics (e.g., neural networks, support 145 vector machines, and random forests). 146 Recently, operational centers have generated multiyear reforecast datasets to support 147 successful calibration of both deterministic and probabilistic forecasts. It is expected that 148 in the next few years new calibration techniques, possibly combining statistical and 149 dynamical approaches, will lead to large improvements in the accuracy of wind power 150 predictions and in the reliable characterization of their uncertainty. 151 152 Next, the three main topics and their specific research needs are described. 153 154 Topic #1: Resource assessment 155 156 Initial maps of the U.S. offshore wind resource from the National Renewable Energy 157 Laboratory (NREL) and others by Stanford University have identified gross 158 characteristics of the hub-height offshore wind resource, which have been generally 159 useful to policy makers and researchers and for early-stage project development. Using 160 mesoscale modeling techniques, these maps provide estimates of wind speed and 161 direction, diurnal and seasonal patterns, wind shear, and air density at horizontal grid 162 scales of approximately 1-5 km. This information, although essentially unverified due to 163 the lack of hub-height measurements described in Research Need #1, has enabled 164 numerous project siting studies, wind farm layout and energy production simulations, and 165 estimates of development potential as a factor of water depth, distance from shore, wind 166 resource, and other factors. However, there is a need to accurately capture dynamic 167 coastal processes, such as sea breezes, low-level jets, and other land-air-ocean 168 interactions, as they represent a significant source of variability in the available wind. 169 Data representing assessment periods of 2025 years (i.e., project lifetimes) are 170 typically required for bankable offshore projects; interannual speed variability of 4%6% 171 is not uncommon. The probability and magnitude of extreme events, particularly peak 172 winds and waves and hurricanes, and the effects of more common events, such as winter 173 storms, icing from sea spray, and salt corrosion, need to be better known to properly 174 design turbines and foundations and meet industry standards. In a changing climate, more 175 studies are needed to reduce the uncertainty of a changing wind resource as ocean, 176 offshore, and coastal temperatures change. Changes in the local wind environment over 177 time may also be caused by the increasing presence of other wind farms within a given 178 region, as described in Topic #3. 179 Recent studies have explored strategic temporal, climatological, and spatial aspects of 180 the offshore resource, including large-scale wind farm interconnection scenarios. U.S. 181 East Coast offshore wind has been found to be particularly coincident with peak- 182 electricity demand. Similar studies should be performed to identify resource attributes 183 that can add value to generally higher offshore costs and evaluate the sensitivity of 184 project location, including distance from the shore, to load coincidence. 185 Significant offshore resource assessment uncertainties exist. Most of the 186 aforementioned studies relied on mesoscale modeling that was validated with generally 187 sparse in-situ data. Perhaps the largest uncertainty is extrapolating surface observations, 188 generally 5-m buoy anemometer measurements to heights across the turbine rotor. As 189 such, there is an urgent need for multilevel wind and temperature observations at 190 platforms offshore (as in Research Need #1), equipped with either meteorological towers 191 that are as tall or taller than hub height, or lidars. In the coastal region, transport 192 processes (advection of either maritime air inland or continental air offshore) during sea 193 and land breeze events often cause the PBL to deviate from classic well-mixed, neutrally- 194 stable conditions. Existing PBL parameterizations struggle to perform well in these 195 conditions. Research effort is needed to improve such PBL parameterizations in coastal 196 regions. 197 Long-term wind climatologies require publicly available historic reanalysis data and 198 future climate data generated by models forced under different anthropogenic emission 199 scenarios. Most of the existing publicly available data are at a relatively coarse spatial 200 scale (>20 km) compared to the size of a typical wind farm. Dynamical downscaling 201 methods typically employ a regional climate model to generate higher spatio-temporal 202 wind climatologies but at a high computational expense for long climate records. 203 Stochastic downscaling methods are computationally cheaper and have been shown to 204 accurately downscale low-resolution reanalysis data with acceptable accuracy, as 205 compared to in-situ validation data. 206 207 Topic #2: Wind Power Forecasting 208 209 Wind power forecasting is challenging because the relationship between wind speed and 210 power production for a single wind turbine or a wind farm is nonlinear; for some wind 211 speed ranges, the sensitivity of power production forecasts to wind speed forecast error is 212 quite high. For example, a modest 1.5 m s-1 error in a wind speed forecast can, in some 213 cases, result in a power production forecast error of over 20% of a wind farm’s capacity. 214 A diverse set of prediction tools and input data have been applied to the wind power 215 forecast problem for a range of time scales. Intra-hour forecasts (060 minutes ahead) are 216 needed for regulation and real-time dispatch decisions. At this scale, the effects of small 217 eddies and turbulent mixing are important but cannot be resolved by operational models. 218 Therefore, mainly statistical methods are used, which are based on near real-time 219 observations. This has driven the deployment of meteorological sensors and lidars for 220 intra-hour forecasting. 221 The 1-6 hour-ahead forecast for load-following and next-operating hour commitment 222 has to account for various mesoscale weather phenomena (e.g., sea breezes, convective 223 systems, and local topography). The rapid-update NWP approach most likely offers the 224 best potential for improvement in this time frame. This is a tool with increasing 225 capability, largely because of improvements in data assimilation techniques (e.g., the 226 hybrid ensemble Kalman filter approach), the formulations of physics-based submodels, 227 and the amount and quality of data available for assimilation. The state of the art in rapid- 228 update systems is the High-Resolution Rapid Refresh (HRRR) model, currently 229 undergoing experimental operation at the National Oceanic and Atmospheric 230 Administration, which assimilates the latest data and generates a 15-hour forecast on a 3- 231 km grid every hour. 232 The day-ahead forecast is important for unit commitment, scheduling, and market 233 trading, which require knowledge of the evolving synoptic storm systems using NWP 234 models and ensembles. The seasonal predictions for resource planning and contingency 235 analysis require knowledge of global teleconnections (such as El Niño). These 236 predictions are based largely on the analysis of cyclical patterns and climate forecast 237 system models.