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NASA NTRS1985

Multiscale modeling to evaluate proposed space-based Doppler lidar sampling strategies

A proposal has been made to place a pulsed Doppler lidar on a space platform (Huffaker, et al., 1980; Emmitt, 1982) in a low earth orbit (200 to 800 km) to measure the atmospheric winds with a spatial resolution commensurate with the current continental rawinsonde network density - i.e., 300 to 500 km resolution. In the case of the space-based doppler lidar, the full range of space scales applies. Single shot pulses with dimensions of 10 x 1000 meters are used to sample areas 100,000 x 100,000 meters to resolved mass flow structure with wavelength of 1 million meters. Simulation studies, therefore, require an equally broad range of atmospheric models. A general circulation model is appropriate to answer questions regarding the impact of a global wind measuring system upon synoptic forecasts. Since the nominal resolution of the spacebased system is expected to be a few 100's of kilometers, then a numerical model with mesoscale dynamics is required. The meaning of an average Doppler shift within a laser pulse volume must be evaluated with models of turbulent/convective scale motions and aerosol gradients. Examples of how models on all these scales have been applied in an ongoing simulation study are presented. In particular, the uncertainties in a mesoscale wind estimate are separated into those arising from pulse scale variances and those due to sample distribution within a prescribed resolution volume. Trade-offs between accuracy and representativeness are discussed in terms of the model results.

Emmitt, G. D.↗
NASA NTRS1974

Short-range forecasts with the GISS model of the global atmosphere

Results of tests carried out during the period from December 1972 through January 1973 to determine the short-term forecasting accuracy of a multilevel numerical primitive-equation (PE) model of the global atmosphere developed at the Goddard Institute for Space Studies (GISS). Six 48-hr forecasts were carried out with the aid of this model, using nine vertical levels and a horizontal grid spacing of 4 deg in latitude and 5 deg in longitude for an effective grid point separation averaging slightly more than 400 km. Verification of forecast sea-level pressures, 1000-mb heights, and 500-mb heights, as well as 1000-mb and 500-mb vector geostrophic winds, shows that the model has forecast skill comparable to that of operational PE models. Based on the 36-hr evolution of 18 extratropical cyclones, the model forecasts exhibit a tendency toward underestimating their propagation speeds and overestimating their central pressures. Both deficiencies are attributed to inadequate horizontal grid resolution. Quantitative verification of forecast surface temperatures over the eastern United States shows a forecast skill equal to that achieved by combined dynamical-statistical procedures.

Druyan, L. M.↗
DOE OSTI2022

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗
DOE OSTI2025

Effects of Atmosphere and Ocean Horizontal Model Resolution on Tropical Cyclone and Upper-Ocean Response Forecasts in Four Major Hurricanes

A coupled atmosphere–ocean model is necessary for tropical cyclone (TC) prediction to accurately characterize ocean feedback on atmospheric processes within the TC environment. Here, the ECMWF coupled global model is run at horizontal resolutions from 9 to 1.4 km in the atmosphere, as well as 25 and 8 km in the ocean, to identify how resolution impacts forecast accuracy of four observed major TCs in the Atlantic: Irma, Florence, Teddy, and Ida. Most of the resolutions used here are unprecedented for global models. GOES-16 and synthetic aperture radar (SAR) satellite images and best track data are used for atmospheric validation. Salinity and temperature observations from Air-Launched Autonomous Micro-Observer (ALAMO) floats are used to validate modeled upper-ocean response, including mixed layer deepening, sea surface cooling, and near-inertial waves in the wakes of TCs. Increasing atmospheric resolution leads to more realistic TC structure and stronger winds, significantly improving TC intensity forecasts and modestly improving track errors. Ocean resolution impacts the upper-ocean response but does not influence atmospheric forecasts for the fast-moving TCs considered here. Stronger mixing, sea surface cooling, and near-inertial oscillations are found for both higher atmosphere and ocean resolutions, provided the initial upper-ocean state is the same for the two ocean resolutions. Whether this agrees better with the ALAMO observations also depends on the realism of the initial upper-ocean state in the model, emphasizing the importance of ocean initialization for the accurate upper-ocean response. Overall, the model at all resolutions correctly predicts stronger mixing, surface cooling, and near-inertial oscillation amplitudes to the right of a TC center, as observed by ALAMO floats.

Atmosphere-ocean interaction↗
DOE OSTI2021

Wave energy resources assessment for the multi-modal sea state of Hawaii

This paper describes development and validation of a 32-year wave hindcast for Hawaii as part of the wave energy resources assessment for the Exclusive Economic Zones of the US and affiliated territories. The nested model system comprises structured WAVEWATCH III models for the entire globe and the Central North Pacific as well as an unstructured SWAN model around the Hawaiian Islands with wind forcing from the Global Forecast System Reanalysis and its regional downscaling. The development effort follows the standards introduced by the International Electrotechnical Commission (IEC). The hindcast dataset was firstly validated with buoy measurements in terms of the six IEC wave energy resources parameters including the omnidirectional wave power ????, significant wave height ????????0, energy period ????????, spectral width ????0, direction of maximum directionally resolved wave power ?????????? ????????, and directionality coefficient ????. The comparison shows good overall agreement but with greater uncertainties for the spectral parameters ????0, ???????????????????? , and ???? formulated for narrow band spectra. The multimodal sea state in Hawaii always includes a mix of short-period wind seas, long-period swells from the North and South Pacific as well as waves with moderate periods from multiple sources. We partition the hindcast spectra based on the respective period ranges and directions and validate the partitioned spectral energy and parameters with measured values for implementation in wave energy resources assessment. The partitioned wave parameters from the hindcast allow separate seasonal descriptions of wind seas, north and south swells, and moderate-period waves for in-depth understanding of the wave climate and resources in Hawaii.

Li, Ning↗
DOE OSTI2022

Effect of data assimilation in the Polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard

Although the Polar Weather Research and Forecasting (PWRF) model has been developed for polar environments, simulations of atmospheric states in the polar region still have large uncertainties. Therefore, the effects of data assimilation (DA) to improve forecasts in the polar region were investigated for September 2017 using PWRF and the three-dimensional variational (3DVAR) DA method. The experiments without DA and those with DA assimilating only conventional observations and both conventional observations and satellite radiance data were performed. The forecasts from all experiments both without and with DA underestimated (overestimated) the downward longwave (shortwave) radiation flux due to the underestimation of the amount of Arctic clouds. When satellite radiance data (i.e., the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS)) were assimilated in addition to conventional observations in the PWRF, the distribution and amount of water vapor became closer to observations, which improves cloud liquid water forecasts. Therefore, when both conventional observations and radiance data were assimilated, the 25–30 h forecast errors of the downward longwave and shortwave radiation fluxes and sensible and latent heat fluxes decreased by 12.7%, 8.1%, 3.2%, and 7.8% with the WSM5 scheme and by 17.1%, 4.7%, 2.5%, and 3.1% with the 2-moment Morrison scheme, respectively, compared to those in the experiments without DA. The forecast errors of the 10 m wind and 2 m temperature with DA were smaller than those without DA at most observation stations. Therefore, the uncertainties of the Arctic forecasts in the PWRF decreased when using DA. To further improve Arctic forecasts, the assimilation of various additional satellite data is necessary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

2017 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. Highlights of this year’s report include: -Wind power capacity additions continued at a rapid pace in 2017: $11 billion was invested in new wind power plants in 2017. In 2017, wind energy contributed 6.3% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in four of those states—Iowa, Kansas, Oklahoma, and South Dakota. -Bigger turbines are enhancing wind project performance: Increased blade lengths have dramatically increased wind project capacity factors, one measure of project performance, and taller towers appear to be on the horizon. The average 2017 capacity factor among projects built from 2014 through 2016 was 42%, compared to an average of 31.5% among projects built from 2004 to 2011 and 23.5% among projects built from 1998 to 2001. -Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen from their highs in 2008 to $750–$950/kW. Overall, the average installed cost of wind projects in 2017 was $1,610/kW, down $795/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to around 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country (such as Oklahoma, Nebraska, Kansas). These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. -The domestic supply chain for wind equipment is diverse: Wind sector employment reached a new high of more than 105,000 full-time workers at the end of 2017. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (70-90%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the turbine. -Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, various forecasts for the domestic market show expected wind power capacity additions of 8,000 to 11,000 MW/year from 2018 to 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗
DOE OSTI2019

2018 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. Highlights of this year’s report include: ~Wind power capacity additions continued at a robust pace in 2018: $11 billion was invested in new wind power plants in 2018. In 2018, wind energy contributed 6.5% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in three of those states—Kansas, Iowa, and Oklahoma. 232 gigawatts of wind are seeking transmission interconnection, 2% of which represents proposed wind plants paired with storage. ~Bigger turbines are enhancing wind project performance: Increased blade lengths have dramatically increased wind project capacity factors, one measure of project performance, and taller towers are on the horizon. The average 2018 capacity factor among projects built from 2014 through 2017 was 42%, compared to an average of 31% among projects built from 2004 to 2011 and 24% among projects built from 1998 to 2001. Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen to $700–$900/kW. The average installed cost of wind projects in 2018 was $1,470/kW, down 40 percent since the peak in 2009 and 2010. ~Wind energy prices are at historical lows: After topping out at 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to below 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country. These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. Solar PPA prices have declined precipitously, pressuring wind’s competitive position. ~The grid-system value of wind has declined over the last decade, but rebounded over the last two years. The value of wind in wholesale power markets is impacted by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind in 2018 was the lowest in the Southwest Power Pool and Texas (average of $17/MWh and $18/MWh, respectively) whereas the highest-value market was New England (at $41/MWh). Wind energy prices are generally competitive with these value estimates. ~The domestic supply chain for wind equipment is diverse: For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (75-90%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the nacelle. Wind sector employment reached a new high of more than 114,000 full-time workers at the end of 2018. ~Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, various forecasts for the domestic market show expected wind power capacity additions of 9,000-12,000 MW in 2019 and 11,000-15,000 in 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗
NASA NTRSDate not supplied

Impacts of Non-Simultaneous Global Photospheric Magnetic Measurements on Coronal and Solar Wind Modeling

The intensity and arrival time of coronal mass ejections (CMEs) can be significantly influenced by the background solar wind encountered as they propagate outward from the Sun into the interplanetary medium. In addition, solar energetic particles (SEPs) race ahead of CME shock fronts and flare regions along magnetic field lines largely determined by the background solar wind. Predicting the solar wind accurately is therefore critical for improving forecasts of CMEs, SEPs, and high-speed streams. Modeling of the corona and solar wind is challenging in general, as it is highly dependent on global photospheric magnetic field maps, which serve as the boundary conditions to all coronal models that drive solar wind models. Unfortunately, less than half of the Sun’s photospheric magnetic field is reliably measured from any given vantage point and thus it is common for the maps to have highly dated and unreliable measurements in them. While Solar Obiter (SolO) now provides for the first time the opportunity to have simultaneous measurements of nearly the entire surface magnetic field of Sun (e.g., when SolO/PHI measurements are combined with those from SDO/HMI), the required alignment to accomplish this occurs only occasionally. Further, coronal models are extremely sensitive to the strengths of the polar magnetic fields of the Sun, which remain poorly observed. Recently, efforts to mitigate this problem include using flux transport models such as the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model, which evolves the field forward in time using well known transport processes occurring on the Sun. However, it cannot account for the emergence of new magnetic flux without direct observations. The ESA VIGIL mission will eventually provide continuous observations from the L5, and the proposed SunCHASER mission will likewise do this at the L4 vantage point should it be funded. Ultimately, what is needed is a constellation of spacecraft distributed around the Sun with magnetographs that continuously measure the global surface magnetic field. In this talk, we discuss how the lack of simultaneous global measurements of the photospheric magnetic field adversely impacts the predictive performance of coronal and solar wind models.

C. Nick Arge↗
NASA NTRS2001

Bias Correction and Random Error Characterization for the Assimilation of HRDI Line-of-Sight Wind Measurements

A new approach to the analysis of systematic and random observation errors is presented in which the error statistics are obtained using forecast data rather than observations from a different instrument type. The analysis is carried out at an intermediate retrieval level, instead of the more typical state variable space. This method is carried out on measurements made by the High Resolution Doppler Imager (HRDI) on board the Upper Atmosphere Research Satellite (UARS). HRDI, a limb sounder, is the only satellite instrument measuring winds in the stratosphere, and the only instrument of any kind making global wind measurements in the upper atmosphere. HRDI measures doppler shifts in the two different O2 absorption bands (alpha and B) and the retrieved products are tangent point Line-of-Sight wind component (level 2 retrieval) and UV winds (level 3 retrieval). This analysis is carried out on a level 1.9 retrieval, in which the contributions from different points along the line-of-sight have not been removed. Biases are calculated from O-F (observed minus forecast) LOS wind components and are separated into a measurement parameter space consisting of 16 different values. The bias dependence on these parameters (plus an altitude dependence) is used to create a bias correction scheme carried out on the level 1.9 retrieval. The random error component is analyzed by separating the gamma and B band observations and locating observation pairs where both bands are very nearly looking at the same location at the same time. It is shown that the two observation streams are uncorrelated and that this allows the forecast error variance to be estimated. The bias correction is found to cut the effective observation error variance in half.

Tangborn, Andrew↗
NASA NTRS1998

Design and Performance of a Miniature Lidar Wind Profiler (MLWP)

The directional velocity of the wind is one of the most critical components for understanding meteorological and other dynamic atmospheric processes. Altitude-resolved wind velocity measurements, also known as wind profiles or soundings, are especially necessary for providing data for meteorological forecasting and overall global circulation models (GCM's). Wind profiler data are also critical in identifying possible dangerous weather conditions for aviation. Furthermore, a system has yet to be developed for wind profiling from the surface of Mars which could also meet the stringent requirements on size, weight, and power of such a mission. Obviously, a novel wind profiling approach based on small and efficient technology is required to meet these needs. A lidar system based on small and highly efficient semiconductor lasers is now feasible due to recent developments in the laser and detector technologies. The recent development of high detection efficiency (50%), silicon-based photon-counting detectors when combined with high laser pulse repetition rates and long receiver integration times has allowed these transmitter energies to be reduced to the order of microjoules per pulse. Aerosol lidar systems using this technique have been demonstrated for both Q-switched, diode-pumped solid-state laser transmitters (lambda = 523 nm) and semiconductor diode lasers (lambda = 830 nm); however, a wind profiling lidar based on this technique has yet to be developed. We will present an investigation of a semiconductor-laser-based lidar system which uses the "edge-filter" direct detection technique to infer Doppler frequency shifts of signals backscattered from aerosols in the planetary boundary layer (PBL). Our investigation will incorporate a novel semiconductor laser design which mitigates the deleterious effects of frequency chirp in pulsed diode lasers, a problem which has limited their use in such systems in the past. Our miniature lidar could be used on a future Mars lander and perhaps find its own niche in terrestrial applications due to its potential low cost an small size.

Cornwell, Donald M., Jr.↗
NASA NTRS1981

The potential impact of scatterometry on oceanography - A wave forecasting case

A series of observing system simulation experiments have been performed in order to assess the potential impact of marine surface wind data on numerical weather prediction. In addition to conventional data, the experiments simulated the time-continuous assimilation of remotely sensed marine surface wind or temperature sounding data. The wind data were fabricated directly for model grid points intercepted by a Seasat-1 scatterometer swath and were assimilated into the lowest active level (945 mb) of the model using a localized successive correction method. It is shown that Seasat wind data can greatly improve numerical weather forecasts due to better definition of specific features. The case of the QE II storm is examined.

Cane, M. A.↗
DOE OSTI2018

A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current earth system models to capture and simulate and-atmosphere feedback. To facilitate its realistic simulation in next generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land-atmosphere (L-A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable. Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive data sets for characterizing L-A feedback independently of any model input. Several new applications are introduced such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain, the testing of Monin-Obukhov similarity theory and turbulence parameterizations, the direct measurement of entrainment fluxes, and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE data set are introduced. We envision the initiation of innovative L-A feedback studies in different climate regions to improve weather forecast, climate, and earth system models worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Sensitivity of Turbine-Height Wind Speeds to Parameters in the Planetary Boundary-Layer Parametrization Used in the Weather Research and Forecasting Model: Extension to Wintertime Conditions

This work extends the model sensitivity analysis of Yang et al. (Boundary-Layer Meteorology, 2017, Vol. 162, 117-142) to include results for February 2011, in addition to May of the same year. We investigated the sensitivity of simulated hub-height wind speeds to the selection of 26 parameters applied in the Mellor-Yamada-Nakanishi-Niino (MYNN) planetary boundary-layer parameterization in the Weather Research and Forecasting (WRF) model, including parameters used to represent the dissipation of turbulence kinetic energy (TKE), Prandtl number (Pr), and turbulence length scales. Differences in the sensitivity of the ensemble of simulated wind speed to the various parameters can largely be explained by changes in the static stability. The largest monthly differences were found during the day, while the sensitivity to many of the parameters during the night were similar regardless of the month. This finding is consistent with an increased frequency of day-time stable conditions in February compared to May. The spatial variability of the sensitivity to TKE dissipation, Pr, and k can also be attributed to variability in the static stability across the domain at any point in time.

parametrization schemes, Parametric sensitivity, P↗
NASA NTRS2001

The Impact of QuikScat on Weather Analysis and Forecasting

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse, mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et al. (2001) and Bloom et al. (1999). This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Atlas, Robert↗
NASA NTRS1996

Responses of the Tropical Pacific to Wind Forcing as Observed by Spaceborne Sensors and Simulated by an Ocean General Circulation Model

In this study, satellite observations, in situ measurements, and model simulations are combined to assess the oceanic response to surface wind forcing in the equatorial Pacific. The surface wind fields derived from observations by the spaceborne special sensor microwave imager (SSM/I) and from the operational products of the European Centre for Medium-Range Weather Forecasts (ECMWF) are compared. When SSM/I winds are used to force a primitive-equation ocean general circulation model (OGCM), they produce 3 C more surface cooling than ECMWF winds for the eastern equatorial Pacific during the cool phase of an El Nino-Southern Oscillation event. The stronger cooling by SSM/I winds is in good agreement with measurements at the moored buoys and observations by the advanced very high resolution radiometer, indicating that SSM/I winds are superior to ECMWF winds in forcing the tropical ocean. In comparison with measurements from buoys, tide gauges, and the Geosat altimeter, the OGCM simulates the temporal variations of temperature, steric, and sea level changes with reasonable realism when forced with the satellite winds. There are discrepancies between model simulations and observations that are common to both wind forcing fields, one of which is the simulation of zonal currents; they could be attributed to model deficiencies. By examining model simulations under two winds, vertical heat advection and uplifting of the thermocline are found to be the dominant factors in the anomalous cooling of the ocean mixed layer.

Liu, W. Timothy↗
DOE OSTI2022

Storm DEPART(Damage Estimate Prediction And Recovery Tool)

Damage prediction, materials needed, and resource allocation modeling capability in order to support pre-incident planning and preparation by predicting damage to the power generation capacity, transmission grids, distribution networks, and communications assets. The capabilities will be developed by multiple factors to include wind bands, storm surge, and flooding forecasts to participant’s assets which will result in a report of predicted damages. Additional factors to include available participants infrastructure data to include class, age, construction, location, wind rating, and additional information not in the public domain. Based on predicted damages, the output will be a bill of materials (BOM) to support short-term recovery operations. The extensiveness and level of detail for this BOM will depend on the replacement configuration specifications provided for each individual participant asset. Any limiting factors for the model will be applied related supply chain and resource constraints.

Klett, Mary↗