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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

How to Interactively Operate the Global Hawk UAS NOAA/NASA ENSO Payload, from Your Armchair, Five Thousand Kilometers Away

This paper will describe the information technologies developed by NASA and NOAA for the February 2016 Sensing Hazards with Operational Unmanned Technology (SHOUT) El Niño Southern Oscillation (ENSO) Campaign. The air vehicle is a NASA Global Hawk UAS, with a primary payload of four instruments, two developed by NASA, two developed by NOAA. The aircraft is based at the NASA Armstrong Flight Research Center, Edwards Air Force Base, California. The payload components are remotely operated by scientists at various facilities, and the data collected downloaded over satellite links in real time for analysis and collaboration. NOAA: Advanced Vertical Atmospheric Profiling System (AVAPS), developed by NCAR, which deploys dozens of dropsondes at altitudes up to 65,000 ft to collect high vertical resolution measurements of the temperature, pressure, relative humidity, and wind speed and direction. NASA: High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), a radar designed to examine the factors of storm intensity: formation, structure and intensification. NOAA: O3 Photometer (UAS-O3), designed specifically for autonomous, precise, and accurate O3 measurements in the upper troposphere and lower stratosphere (UT/LS). NASA JPL: High Altitude MMIC Sounding Radiometer (HAMSR), an atmospheric microwave temperature and humidity sounder instrument that looks at the microwave spectrum.

Southern Oscilliation↗
NASA NTRS2017

FOREWORD: Satellite Remote Sensing Beyond 2015

Satellite remote sensing has progressed tremendously since the first Landsat was launched on June 23, 1972. Since the 1970s, satellite remote sensing and associated airborne and in situ measurements have resulted in vital and indispensable observations for understanding our planet through time. These observations have also led to dramatic improvements in numerical simulation models of the coupled atmosphere-land-ocean systems at increasing accuracies and predictive capability. The same observations document the Earth's climate and are driving the consensus that Homo sapiens is changing our climate through greenhouse gas emissions. These accomplishments are the combined work of many scientists from many countries and a dedicated cadre of engineers who build the instruments and satellites that collect Earth observation data from satellites, all working toward the goal of improving our understanding of the Earth. This edition of the Remote Sensing Handbook (Vol. I, II, and III) is a compendium of information for many research areas of our Planet that have contributed to our substantial progress since the 1970s. Remote sensing community is now using multiple sources of satellite and in situ data to advance our studies, what ever they might be. In the following paragraphs, I will illustrate how valuable and pivotal role satellite remote sensing has played in climate system study over last five decades, The Chapters in the Remote Sensing Handbook (Vol. I, II, and III) provides many other specific studies on land, water, and other applications using EO data of last five decades, The Landsat system of Earth-observing satellites has led the way in pioneering sustained observations of our planet. From 1972 to the present, at least one and sometimes two Landsat satellites have been in operation. Starting with the launch of the first NOAA-NASA Polar Orbiting Environmental Satellites NOAA-6 in 1978, improved imaging of land, clouds, and oceans and atmospheric soundings of temperature were accomplished. The NOAA system of polar-orbiting meteorological satellites has continued uninterrupted since that time, providing vital observations for numerical weather prediction. These same satellites are also responsible for the remarkable records of sea surface temperature and land vegetation index from the Advanced Very High Resolution Radiometers (AVHRR) that now span more than 33 years, although no one anticipated these valuable climate records from this instrument before the launch of NOAA-7 in 1981. The success of data from the AVHRR led to the design of the MODIS instruments on NASA's Earth Observing System of satellite platforms that improved substantially upon the AVHRR. The first of the EOS platforms, Terra, was launched in 2000 and the second of these platforms, Aqua, was launched in 2002.

Tucker, Compton J.↗
NASA NTRS2018

NASA SPoRT JPSS PG Activities in Alaska

SPoRT (NASA's Short-term Prediction Research and Transition Center) has collaboratively worked with Alaska WFOs (Weather Forecast Offices) to introduce RGB (Red/Green/Blue false color image) imagery to prepare for NOAA-20 (National Oceanic and Atmospheric Administration, JPSS (Joint Polar Satellite System) series-20 satellite) VIIRS (Visible Infrared Imaging Radiometer Suite) and improve forecasting aviation-related hazards. Last R2O/O2R (Research-to-Operations/Operations-to-Research) steps include incorporating NOAA-20 VIIRS in RGB suite and fully transitioning client-side RGB processing to GINA (Geographic Information Network of Alaska) and Alaska Region. Alaska Region WFOs have been part of the successful R2O/O2R story to assess the use of NESDIS (National Environmental Satellite, Data, and Information Service) Snowfall Rate product in operations. SPoRT introduced passive microwave rain rate and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) (IMERG) to Alaska WFOs for use in radar-void areas and assessing flooding potential. SPoRT has been part of the multi-organization collaborative effort to introduce Gridded NUCAPS (NOAA Unique CrIS/ATMS (Crosstrack Infrared Sounder/Advanced Technology Microwave Sounder) Processing System) to the Anchorage CWSU (Center Weather Service Unit) to assess Cold Air Aloft events, [and as part of NOAA's PG (Product Generation) effort].

Berndt, Emily↗
NASA NTRS2020

N20 VIIRS RSB Calibration Algorithms and Results: Collection 2.0

The NOAA-20 (N20) satellite, previously the Joint Polar Satellite System-1 satellite, was launched on November 18, 2017. One of the five major scientific instruments aboard the satellite is the Visible Infrared Imaging Radiometer Suite (VIIRS). VIIRS scans the Earth’s surface in 22 spectral bands, 14 of which are the reflective solar bands (RSBs) with band center wavelengths from 0.412 to 2.25μm. VIIRS regularly performs on-orbit radiometric calibration of its RSBs, primarily through the observations of the onboard solar diffuser (SD). The on-orbit change of the SD’s bidirectional reflectance distribution function, known as the H-factor, is determined by the onboard SD stability monitor (SDSM). Since the H-factor exhibits angular dependence, obtaining the H-factor along the SD to the telescope direction is a challenge for the NOAA-20 VIIRS. Recently, Collection 2.0 of the NASA Land Science Investigator-led Processing Systems (SIPS) products were released. As a part of this reprocessing effort, we made two major improvements in the N20 VIIRS RSB radiometric calibration. One is the improved SD and SDSM attenuation screen transmittance functions, obtained by using calibration data collected during both the yaw maneuver and a small portion of regular orbits, resulting in a higher quality H-factor for the SDSM view. Another is the use of the H-factor for the telescope view, derived from the H-factor for the SDSM view, by using the results for the SNPP VIIRS. In June 2019, we delivered a set of mission-long N20 VIIRS Collection 2.0 RSB radiometric calibration look-up-tables. These tables have been employed by the NASA Land SIPS group to reprocess the entire time series of the NOAA-20 VIIRS products. In this paper, we discuss the Collection 2.0 NOAA-20 VIIRS RSB calibration algorithms and results.

N20 VIIRS↗
NASA NTRS2021

Assessing Sensitivity of MERRA-2 to AMSU-A in the Upper Stratosphere

Microwavetemperaturesounders provide key observationsin data assimilation, both in the 9current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2),microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A)are assimilated beginning with NOAA-15and continuing through the current period. The time series of observation minus background statistics forAMSU-A channels sensitive to upper stratosphere and lower mesosphere show variabilities due to changes in AMSU-A constellation in the early AMSU-A period.Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites.This effort characterizesthesensitivity, both in terms of the observations and the MERRA-2 analysis. Furthermore, it exploresthe use of reprocessed and inter-calibrated datasets to evaluatewhether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this inter-platform sensitivitywhich can be mitigated by using reprocessed data.The results explore the importance of the reprocessing of the AMSU-A radiances as well as their inter-calibration.

MERRA-2↗
NASA NTRS2021

Montana Water Resources II: Enhancing a Moisture Index for Drought and Flood Monitoring in the Missouri River Basin

The Missouri River Basin is a major global breadbasket, containing large amounts of agricultural land. Recent devastating weather events have motivated regional organizations to dedicate efforts to drought and flood monitoring and early warning systems. The DEVELOP team partnered with the following organizations: Montana Climate Office, NOAA National Weather Service (NWS) Missouri Basin River Forecast Center, NOAA Regional Climate Services of the Central Region, NOAA Physical Sciences Laboratory, and the US Army Corps of Engineers' Missouri River Basin Water Management Division. The team collaborated with the partners in their efforts to monitor flood and drought by enhancing a composite moisture index (CMI) for the Missouri River Basin. The CMI leverages NASA Earth observations to derive snow cover data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) mission and snow water equivalent and snowdepth datasets from the NOAA NWS National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System (SNODAS). Building upon this framework, the team added groundwater storage data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, soil moisture data from Soil Moisture Active Passive (SMAP), and United States Geological Survey in situ streamflow data to the CMI. To test the tool’s validity, the team compared the CMI results to historically extreme dry and extreme wet years, March 2017 and March 2019, respectively. The CMI accurately reflects both 2017 and 2019 climate conditions in the Missouri River Basin. The refined CMI enhances the understanding of antecedent soil moisture conditions and improves flood and drought forecasting in the Missouri River Basin before the growing season.

Chloe Schneider↗
NASA NTRSDate not supplied

Integrating Multi-agency Data Products in a Cloud-based Platform for Streamlined Discovery, Visualization, and Use

Earth science data users almost always have an interest in utilizing geospatial data from multiple agencies. As computing capability and cloud-based infrastructures accelerate the pace at which scientific research can be done, there is a growing need to enable search, discovery, and use of multi-agency geospatial observations relevant for a common use case - without undergoing the search and discovery process in a less efficient, disparate path with each agency. NASA’s Earth Observing System Data and Information System (EOSDIS) and NOAA’s National Environmental Satellite, Data and Information Service (NESDIS) both support a wide range of Earth science disciplines’ research, operations, and applications activities. Presently, however, there are few examples of data discovery frameworks supporting an inquiry of both NASA’s and NOAA’s extensive archives of Earth observations that are equally suitable for a particular science scenario, regardless of the agency that “owns” the data. NASA and NOAA are collaborating on a data expedition platform for exploring fire weather using data products from both agencies. Users will be able to search, discover, and visualize NASA and NOAA products in one interface. Each agency will curate metadata for its respective datasets, providing for a rich search experience. The collaboration will pilot a shared search interface into these metadata datastores. Data products will be stored in the cloud in cloud-optimized format(s). These formats will allow for optimized data access and visualization to support the “data expedition”. Avenues for further development and application of this cloud-based, multi-agency data provisioning platform will also be discussed.

cloud-based technology↗
NASA NTRSDate not supplied

Expanding the Time Dimension of Hyperspectral Infrared Sounding Observations: Designing the NUCAPS-Forecast System

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of enormous value to weather forecasters, they cannot provide the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment. A novel methodology was developed to extend snapshots of LEO soundings forward in time up to six hours into the future and has been made available within the NOAA National Weather Service Advanced Weather Interactive Processing System (AWIPS) for testing in a quasi-operational setting. This methodology is based on parcel forward-trajectory calculations from the satellite observing time to recreate future soundings of temperature and moisture at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms to allow for rapid near-real time product processing. Science code was transitioned to operational production in 2019 and the product, NUCAPS-Forecast, was tested at the NOAA Hazardous Weather Testbed Spring Experiment in 2019 and 2021. Forecaster feedback has led to improving the product and processing system to address product limitations and improve its applicability for pre-convective forecasting. This presentation highlights the design of the NUCAPS-Forecast system. Solutions were tested and implemented to remove excess moisture and conjoin forward-trajected parcels into vertical profiles for calculating grids of stability indices. Additionally, use cases that explore the tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) were investigated. When evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observational data set areas with elevated CAPE (reduced CIN) are shown to be associated with areas of precipitation and increase confidence the LEO soundings advected forward in time physically represent atmospheric processes. These results suggest that adiabatic parcel advection of LEO satellite sounding snapshots forward in time exhibits skill in discriminating locations and times that are more likely to lead to convective initiation. Leveraging trajectory modeling to increase the spatial and temporal resolution of LEO IR sounding observations today demonstrates the value of these observations from the future constellation of JPSS satellites and GeoXO mission.

weather↗
NASA NTRSDate not supplied

CERES FLASHFlux: Low Latency Data Product for Education and Applied Science Uses

The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide a rapid release version of the CERES results, which are made available to the GLOBE (Global Learning and Observations to Benefit the Environment) program and the applied science research communities involving in the renewable energy and agricultural sectors within a week of the satellite observations by exchanging some accuracy for speed. FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra, Aqua, and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1-degree equal angle gridded data the combines Terra and Aqua observations. However, since both Terra and Aqua satellites are nearing end of life, the FLASHFlux data product will begin to transition to utilizing NOAA-20 observation in the TISA data product. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellites have different optics and spectral channels. We quantify the differences in the Level 2 SSF data products between the two satellites over a few sample months for cloud and radiative flux quantities for both the shortwave (solar wavelengths) and longwave (thermal infrared wavelengths) at TOA and surface. In addition, we also assess the impact of using NOAA-20 in place of Aqua in the Level 3 FLASHFlux TISA daily product on the TOA and surface radiative fluxes. Additionally, we highlight use case of FLASHFlux data product in combination with CERES EBAF to assess the Earth’s Radiation Budget, the accuracy of FLASHFlux data product compares to Surface sites measurement, and users of the FLASHFlux data through the POWER (Prediction of Worldwide Energy Resources) program.

PC Sawaengphokhai↗
NASA NTRSDate not supplied

Using DSCOVR EPIC as a Transfer Radiometer to Scale Multiple VIIRS Sensors Over Tropical Earth Views

The NASA CERES project provides Energy Balanced and Filled (EBAF) product climate-quality observed TOA and computed surface fluxes to the climate community. The CERES instruments are on board the Terra, Aqua, Suomi-NPP, and NOAA-20 satellites. The CERES project radiometrically scales the MODIS and VIIRS imager visible channel radiances to the Aqua- MODIS reference to retrieve consistent imager cloud properties between sensors, which are used to retrieve cloud properties required for converting CERES radiance observations into fluxes. The NPP, NOAA-20, and the future NOAA-21 VIIRS imagers will be in the same sun- synchronous orbit but spaced equally apart, which means that no simultaneous nadir overpasses (SNO) may be used to inter-calibrate the VIIRS imagers to each one another. Currently, the CERES project uses Aqua-MODIS to radiometrically scale between VIIRS imagers. Once the Terra and Aqua satellites start drifting toward the terminator, Aqua-MODIS can no longer be utilized as the transfer radiometer between VIIRS sensors and there will be no SNOs in common between either MODIS and or VIIRS imagers. The DSCOVR satellite orbits the Lagrange-1 (L1) point about 1.5 million kilometers from Earth. The Earth Polychromatic Imaging Camera (EPIC) instrument on the Earth-facing side of DSCOVR takes images ranging from the UV to the NIR of the sunlit side of the Earth. While the EPIC sensor has no onboard calibration systems, multiple inter-calibration studies have indicated that the EPIC instrument response is radiometrically stable. The stability of the EPIC instrument allows it to be used as a transfer radiometer between all MODIS and VIIRS imagers and is especially suited for drifting orbits because EPIC imager frequently samples the Earth diurnally. The study will demonstrate the use of EPIC as a transfer radiometer to radiometrically scale the NPP and NOAA-20 VIIRS imagers. The scaling factors will be compared with the scaling factors using Aqua-MODIS as the transfer radiometer.

Conor Haney↗
NASA NTRSDate not supplied

Harnessing CPF Hyperspectral Radiances to Homogenize Earth Observing Systems

Homogenization across Earth observing systems is essential for ensuring consistency in geophysical retrievals over time and enabling the construction of long-term climate records. As the first SI Traceable Satellite (SITSat) for the reflected solar, the CLARREO Pathfinder (CPF) will provide high-accuracy spectrally resolved benchmark measurements of Earth-reflected solar radiation, serving as a standardized in-orbit reference for other reflected solar (RS) instruments. The CPF intercalibration team has developed a state-of-the-art method to intercalibrate the RS channels of the VIIRS instrument onboard the NOAA-20 satellite using CPF benchmark measurements, aiming for a methodology uncertainty of 0.3-0.6% (1-sigma). To achieve this stringent accuracy, the CPF team has devised strategies to mitigate the effects of spatial, spectral, polarization, and angular disparities between the intercalibration footprints of CPF and VIIRS. Despite nearly identical instrument designs, the three VIIRS instruments currently in operation onboard SNPP, NOAA-20, and NOAA-21 platforms exhibit systematic radiometric biases up to 8%. Although each VIIRS sensor’s calibration is independent and traceable to standards maintained by NIST, there is significant concern within the community about which VIIRS sensor most accurately represents the absolute radiometric scale. The precise intercalibration between CPF and NOAA-20 VIIRS will reveal the absolute radiometric accuracy of VIIRS. This accuracy can then be extended to other RS sensors, including MODIS and other VIIRS sensors, via vicarious methods. The 3-nm spectral sampling of CPF measurements will enable users to quantify scene-dependent spectral biases between sensors, thereby aiding in establishing both spectral and radiometric homogenization across these platforms. The CPF mission overview, along with details of its intercalibration capabilities and homogenization approaches, will be discussed at the meeting.

Raj Bhatt↗
DOE OSTI2026

BLOC Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Block Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗
DOE OSTI2026

NANT Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Nantucket Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗
DOE OSTI2017

ARM Radiosondes for National Polar-Orbiting Operational Environmental Satellite System Preparatory Project Validation Field Campaign Report

This IOP has been a coordinated effort involving the U.S. Department of Energy (DOE) Atmospheric Radiation (ARM) Climate Research Facility, the University of Wisconsin (UW)-Madison, and the JPSS project to validate SNPP NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and moisture sounding products from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). In this arrangement, funding for radiosondes was provided by the JPSS project to ARM. These radiosondes were launched coincident with the SNPP satellite overpasses (OP) at four of the ARM field sites beginning in July 2012 and running through September 2017. Combined with other ARM data, an assessment of the radiosonde data quality was performed and post-processing corrections applied producing an ARM site Best Estimate (BE) product. The SNPP targeted radiosondes were integrated into the NOAA Products Validation System (NPROVS+) system, which collocated the radiosondes with satellite products (NOAA, National Aeronautics and Space Administration [NASA], European Organisation for the Exploitation of Meteorological Satellites [EUMETSAT], Geostationary Operational Environmental Satellite [GOES], Constellation Observing System for Meteorology, Ionosphere, and Climate [COSMIC]) and Numerical Weather Prediction (NWP forecasts for use in product assessment and algorithm development. This work was a fundamental, integral, and cost-effective part of the SNPP validation effort and provided critical accuracy assessments of the SNPP temperature and water vapor soundings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

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.

17 WIND ENERGY↗
DOE OSTI2013

National K-12 Educator Conference; "Earth Then, Earth Now: Our Changing Climate"

With the support of the Department of Energy, the National Science Teachers Association and the National Oceanic and Atmospheric Administration, Imaginary Lines Inc. (dba Sally Ride Science) delivered a highly successful 2-day conference to 165 K-12 educators on climate change. The event took place o July 23rd and 24th, 2008 at the NOAA facility in Silver Spring, MD. The conference celebrated the 25th anniversary of Dr. Sally Ride’s first flight into space in 1983 and examined how our understanding of Earth has changed in those 25 years. One the first day of the conference, participants heard a keynote talk delivered by Dr. Sally Ride, followed by presentations by well-known climate change scientists: Dr. Richard Somerville, Dr. Inez Fung and Dr. Susan Solomon. These sessions were concurrently webcast and made available to educators who were unable to attend the conference. On the second day of the conference, participants attended breakout sessions where they performed climate change activities (e.g. “Neato Albedo!”, “Greenhouse in a Bottle”, “Shell-Shocked”) that they could take back to their classrooms. Additional break-out sessions on using remote sensing images to illustrate climate change effects on Earth’s surface and how to address the climate change debate, were also offered. During lunch, participants attended an Educator Street Fair and had the opportunity to interact with representatives from NOAA, NASA, the EPA, NEEF and the JASON project. A follow-up evaluation survey was administered to all conference attendees immediately following the conference to evaluate its effectiveness. The results of this survey were overwhelmingly positive. The conference materials: presentation Power Points, workshop handouts and activities were available for teachers to download after the conference from the Sally Ride Science website. In summary, the approximately $55K support for the Department of Energy was used to help plan, deliver and evaluate the “Earth Then, Earth Now: Our Changing Climate”, conference which took place on July 23rd and 24th, 2008 at the NOAA facility in Silver Spring, MD.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.

17 WIND ENERGY↗
DOE OSTI2015

A Public-Private-Academic Partnership to Advance Solar Power Forecasting

Executive Summary NOAA is making major contributions to the solar forecasting project in three areas. First, it is improving its forecasts of solar irradiance, clouds, and aerosols in its numerical weather prediction models. Second, it is providing advanced satellite products for DOE's FOA awardees to use in their forecast systems. Third, it is using high-quality ground-based measurements from SURFRAD and ISIS stations to verify and validate forecast model output. This reports covers results from all three areas for the period May 1, 2014 - April 30, 2015. Modeling In its modeling effort, NOAA continues work to improve the skill of solar forecasts from the Earth System Research Lab (ESRL) research versions of the 13-km Rapid Refresh (RAP) and the 3-km High-Resolution Rapid Refresh (HRRR) models, which are in turn transitioned into operations at the National Centers for Environmental Prediction (NCEP). A major milestone was achieved in September 2014 with the initial operational implementation of the HRRR at NCEP. In the ESRL research versions of the models, testing and development, in both real-time runs and retrospective experiments, is guided by an extensive in-house verification system. Early in the SFIP project, we developed the capability to verify our model forecasts against the high-quality surface radiation measurements from the SURFRAD and ISIS networks. This highlighted some shortcomings with the RAP and HRRR forecasts of incoming shortwave radiation. Most of our effort during Phase 1 of SFIP was focused on addressing these problems with a variety of model system improvements. The RAP and HRRR models during the warm season of 2014 had a noticeable warm and dry bias in near-surface conditions over most of the central and eastern United States, and our new SURFRAD/ISIS verification revealed that there was also a large excess of incoming global horizontal irradiance in the models. We hypothesized that a lack of cloud cover (particularly low-level cloud cover) in the models was resulting in too much heating of the land surface. This, in turn, caused unrealistically strong surface heat fluxes and turbulent mixing in the planetary boundary layer (PBL), which further reduced the already deficient cloud cover. We addressed these issues with a combination of data assimilation system modifications and model physics improvements. Many of our data assimilation changes were made with a view towards improving the near-term representation of clouds and precipitation. One of these changes involved better accounting for regions of weak reflectivity in the RAP cloud / hydrometeor assimilation system, in order to improve the representation of light precipitation in the RAP initial conditions and provide more realistic initial cloud cover. Additional modifications more accurately accounted for radar beam blockage and data gaps (particularly in the western United States), which improves shorter lead times forecasts of clouds and precipitation. We have also tested the assimilation of new data sources within the RAP and the HRRR, including radar radial velocity data and surface mesonet observations. Within the HRRR, we have tested the cycling of the 3-km land surface fields to allow a higher-resolution treatment of land surface processes. In terms of model physics development for SFIP, we have implemented a shallow cumulus scheme within the RAP, and have made numerous improvements to the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL scheme to address insufficient low-level cloud cover in the models. We have conducted tests incorporating the radiation effects of (parameterized) boundary-layer clouds within the modified MYNN PBL scheme (independent of the convective schemes). The Grell-Freitas-Olson shallow cumulus scheme has also been tested within the 3-km HRRR. Finally, we have also modified the RUC land surface model (LSM) treatment of the vegetation wilting point, reducing it to increase evapotranspiration and increase cloud cover in the boundary layer. All of these changes work in tandem to significantly improve the model forecasts of cloud cover, incoming shortwave radiation, and near-surface temperature and moisture. Satellite The role of NOAA/NESDIS in the Solar Forecasting Improvement Project is to provide Advanced Satellite Products (ASPs) for the two forecasting teams at NCAR and IBM. The ASPs are cloud, surface, and atmosphere products derived from geostationary satellite imagery at the highest possible spatial and temporal resolution - such quantities as cloud mask, cloud probability, cloud transmission, cloud top height, cloud top temperature, cloud effective particle size, etc. Ancillary data, such as elevation and numerical weather prediction fields are provided in the files at the same resolution as well. There are at this time 147 different variables in the ASP output, including quality flags and processing information. The main goals for Year 1 of the project were to implement an Advanced Satellite Products system for the use of the IBM and NCAR teams, begin validation, and make any needed changes based on feedback from the teams. ASP files are being produced every GOES Imager acquisition, which occur on a 15-30 minute schedule. Processing is done on a dedicated computer, with a turn-around time of 8-21 minutes from image acquisition to results available on ftp. Several helpful visualizations of the data are also created for users on web pages. Users have been provided with a document titled "User's Guide for 1km Cloud Products Derived from GOES Imager Data using CLAVR-x", which discusses the basics of the source imagery, the process by which it is turned into Advanced Satellite Products, and considerations users should make when using the data. Validation of selected variables from the older 4km version of the products was also included. Future work will concentrate on validation of the 1km products and improving the turn-around time, product variety, and product quality as needed. Ground Observations In the ground-based measurement effort, NOAA's main objectives are to provide high quality radiation products for validation and verification of short-term to day-ahead solar forecasts. More specifically for the three year project, our goals include (1) Maintaining and providing data from our 7 SURFRAD and 7 ISIS; (2) Update ISIS radiation measurements from 3 min to 1 min data: (3) Purchase and install new pyrheliometers for direct solar irradiance measurements at the 7 SURFRAD sites; (4) Building, testing, and deploying two mobile SURFRAD stations at two utility plants in collaboration with DOE sponsored partners, and includes ongoing maintenance and processing of the data at the mobile sites; (5) Upgrading the data acquisition and communications at 7 SURFRAD sites and 7 ISIS sites; (6) Providing radiation data at the 7 SURFRAD sites in near real-time; (7) Develop and provide aerosol optical depth and cloud images and cloud fraction at our two mobile sites; (8) Provide data recovery rates each year; (9) Provide temporally and spatially averaged radiation products for comparison to HRRR and RAP solar forecasts and advanced satellite products; (10) Provide a data-set for analysis of conversion of direct and diffuse to sloped surfaces; (11) and as time permits develop and provide spectral solar irradiance, cloud optical depth and spectral albedo from the mobile sites. Milestones this year include working with the DOE sponsored teams to find locations to deploy two mobile SURFRAD stations. One existing unit was deployed at a 30MW PV facility in the San Luis Valley in collaboration with Xcel and the NCAR team in August, 2014. The second unit was built and tested at our facilities in Boulder, CO and deployed near Green Mountain Power's Education Center in Rutland, VT in collaboration with Green Mountain Power and the IBM Team in October, 2014. Data processing was implemented and the radiation data from these two mobile sites have been made available on our ftp server in near real-time. We also are providing images and cloud fraction from the TSI cameras for these two mobile sites on our ftp site. Another milestone was upgrading our data acquisition and communication systems at 7 SURFRAD and 7 ISIS sites. We accelerated our schedule for these upgrades to provide timely radiation products. These upgrades allow more reliable and near-real time radiation data delivery to the DOE sponsored teams to meet their goals. Lastly, we changed the data rate at the ISIS sites from 3 min to 1 min.

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