A method for determining a quality controlled sensor set from a redundant sensor set comprising calculating a first-time correlation coefficient and a first autocorrelation coefficient based on a first-sensor time-series data calculating a second-time correlation coefficient and a second autocorrelation coefficient based on a second-sensor time-series data, calculating a first and a second-sensor correlation coefficient based on the first-sensor time series data and the second sensor time series data, and determining the quality controlled sensor set with a highest confidence level.
Method and System for Providing Quality Controlled Data from a Redundant Sensor System
Recipient(s)
Andrew Gaydos, Robert Kent Goodrich, Scott Landolt
Award Year
2021
Patent Issue Date
Award Type
patent
Awarding Organization or Entity
United States Patent Office
Detail Link
Interfacing Water, Climate, and Society: A Resource List
There is a timely and critical need for collaborative and integrative research on adaptation strategies between biophysical and social scientists in the climate and water community. In July of 2010, early career faculty and scientists convened for a Junior Faculty Forum at the National Center for Atmospheric Research in Boulder, Colorado to discuss the frontiers of integrated water-climate-society vulnerability and adaptation science.
Early career scientists are not just seeking, but demanding more opportunities for interdisciplinary research and education that bridge social and natural sciences. Universities and funding agencies are increasingly developing programs to try and meet those needs and there are such interdisciplinary investigations being undertaken; however information about these opportunities, resources, and investigations have not been collated in a central repository for interested parties to find. This interactive site is meant to serve that need.
Tropical Cyclone Data Project (TCDP)
This project is funded by the Risk Prediction Initiative (RPI2.0) to develop a new historical database of tropical cyclone wind and size parameters. Unlike other historical databases, such as the National Hurricane Center's Hurricane Database (HURDAT2), this new database will use objective methods to provide time-dependent error bounds on the estimated wind parameters. The goal is to provide the highest quality database possible for parametric wind modeling applications. Such models are used by the (re)insurance industry to simulate wind risk from tropical cyclones.
To accomplish this goal, the project is currently organized around four main objectives: (1) to provide an updated Vortex Data Message dataset for Atlantic tropical cyclones that occurred between 1989 and 2012, (2) to provide a new dataset of standardized high resolution flight level data for Atlantic tropical cyclones that occurred between 1999 and 2015, (3) to provide an updated dataset of QuikSCAT satellite-based wind parameters from 1999 to 2009, and (4) to use objective methods to combine the information from the above source datasets into a new historical database of tropical cyclone parameters.
Resources
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FLIGHT+
Description
The Extended Flight Level Dataset for Tropical Cyclones
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VDM+
Description
The Enhanced Vortex Data Message Dataset
- The Tropical Cyclone Observations-Based Structure (TC-OBS) Database
Tropical Cyclone Guidance Project (TCGP)
The aims of this project are: (a) to foster increased development of forecast aids for global basins by engaging the wider community of operational centers, academic researchers, and commercial interests; and (b) to go beyond track and intensity both by encouraging the development of forecast aids for structure change by providing structure data for use in track and intensity projection methods.
TCGP: Tropical Storm Laura early-cycle intensity guidance
To accomplish these aims, the project is organized around four main objectives: (1) to provide a global repository tropical cyclone forecast aids for track and intensity information, (2) to provide real-time plots these data for active tropical cyclones, and (3) to visualize structure and intensity parameters from observations taken by reconnaissance aircraft, (4) to provide retrospective plots of these data for past tropical cyclones.
Contact
Please direct questions/comments about this page to:
Jonathan Vigh
Project Scientist I
WRF-Solar®
Overview
WRF-Solar® is the first numerical weather prediction model specifically designed to meet the growing demand for specialized numerical forecast products for solar energy applications (Jimenez et al. 2016). WRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) model. The version 1 of the model was developed within the Sun4Cast® project funded by the U.S. Department of Energy that targeted to improve solar power forecasts at a wide range of temporal scales (Haupt et al. 2016).
Sketch representing the physical processes that WRF-Solar® improves. The different components of the radiation are indicated.
The Community Version of WRF-Solar is in the public domain and can be downloaded from the official WRF Github repository. The WRF version 4.2 includes the enhancements of WRF-Solar Version 1 with upgrades in the physical parameterizations as well as other developments.Users are encouraged to use version 4.2.2 or upcoming versions.
This website provides a description of the model, the user’s guide, a reference configuration that should be used as a baseline for comparison by the WRF-Solar community, and ongoing developments.
Please visit the WRF-Solar forum if you are having troubles running the model.
Description
Version 1
The development of WRF-Solar® Version 1 provided the first numerical weather prediction model specifically designed to meet the needs of irradiance forecasting (Jimenez et al. 2016a). The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance (Xie et al. 2016). Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect, Ruiz-Arias et al. 2015). A fifth advance is that the aerosols now interact with the cloud microphysics (Thompson and Eidhammer 2014), altering the cloud evolution and radiative properties (aerosol indirect effects), an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization (Deng et al. 2014).
Several works highlighted the benefits of the solar augmentations for solar irradiance forecasting. WRF-Solar largely reduced errors in the simulation of clear sky irradiances wherein is important to properly account for the impacts of atmospheric aerosols (Jimenez et al., 2016a). WRF-Solar have also been shown to reduce biases in the surface irradiance over the contiguous U.S. in all sky conditions (e.g. Jimenez et al. 2016b). In a formal comparison to the NAM baseline, WRF-Solar showed improvements in the Day-Ahead forecast of 22-42% (Haupt et al. 2016). Another work has pointed out the potential of WRF-Solar for nowcasting applications (Lee et al. 2016). The study compared solar irradiance predictions using different nowcasting methodologies based on artificial intelligence or the utilization of satellite imagery to detect clouds. The comparison has shown that WRF-Solar was competitive, and in many times superior to these state-of-the-science methodologies of the short-term prediction (1-6 h).
Community Version of WRF-Solar
The augmentations introduced in WRF-Solar Version 1 have been progressively incorporated in the official WRF release. All are available to the community since the WRF release version 4.2. The parameterizations introduced in Version 1 have been revisited, and enhancements and bug fixes have been introduced. In addition, new functionality has been incorporated. The model can output the clear sky irradiances and includes a solar diagnostic package. This new package adds to the standard output a number of two-dimensional diagnostic variables (e.g., cloud fraction, vertically integrated hydrometeor content, clearness index, etc). The solar diagnostic package can output these variables and the surface irradiances every model time step at selected locations.
On-going efforts continue developing the Community Version of WRF-Solar to further increase its value for solar energy applications.
Ongoing developments
- WRF-Solar® EPS: Enhancing WRF-Solar® to provide probabilistic forecasts. The National Renewable Energies Laboratory (NREL) is leading a project and collaborates with NCAR to incorporate a probabilistic framework specifically tailored for solar energy applications.
- WRF-Solar® V2: Enhancing WRF-Solar physics for version 2. The Pacific Northwest National Laboratory (PNNL) is leading a project collaborating with NCAR to enhance the WRF-Solar physics and quantify uncertainties to model parameters.
- MAD-WRF: NCAR is leading a project to couple WRF-Solar with a modified version of MADCast to create MAD-WRF in order to improve the cloud initialization for nowcasting applications.
- PV modelling: Arizona State University is leading a project collaborating with NCAR to incorporate an online parameterization of PV panels production.
- Enhancing microphysics and DNI modelling: Brookhaven National Laboratory (BNL) is leading a project to enhance the WRF-Solar microphysics as well as to improve the representation of the cloud interactions with the DNI.
Resources
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The Sun4Cast® Solar Power Forecasting System: The Result of the Public-Private-Academic Partnership to Advance Solar Power Forecasting (No. NCAR/TN-526+STR)
Description
Haupt, S. E., Kosovic, B., Jensen, T. L., Lee, J., Jimenez Munoz, P., Lazo, J. K., … Hinkleman, L. (2016). The Sun4Cast® Solar Power Forecasting System: The Result of the Public-Private-Academic Partnership to Advance Solar Power Forecasting (No. NCAR/TN-526+STR). doi:10.5065/D6N58JR2.
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WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction. Bull. Amer. Met. Soc.. 97, 1249-1264
Description
Jimenez, P.A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer and A. Deng, 2016a: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction. Bull. Amer. Met. Soc.. 97, 1249-1264. doi:10.1175/BAMS-D-14-00279.1.
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Implementation and evaluation of a new shallow convection scheme in WRF
Description
Deng, A., B. Gaudet, J. Dudhia and K. Alapaty, 2014: Implementation and evaluation of a new shallow convection scheme in WRF. 94th American Meteorological Society annual Metting, 26th Conference on Weather Analysis and Forecasting/ 22nd conference on Numerical Weather Prediction, 2-6 February, Atlanta, GA, 13 pp.
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The role of unresolved clouds on short-range global horizontal irradiance predictability
Description
Jimenez, P.A., S. Alessandrini, S. E. Haupt, A. Deng, B. Kosovic, J. A. Lee, L. Delle Monache, 2016b: The role of unresolved clouds on short-range global horizontal irradiance predictability. Mon. Wea. Rev., 144, 3099 - 3107. doi:10.1175/MWR-D-16-0104.1.
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Solar energy nowcasting case studies near Sacramento
Description
Lee, J. A., S. E. Haupt, P. A. Jimenez, M. A. Rogers, S. D. Miller and T. C. McCandless, 2016: Solar energy nowcasting case studies near Sacramento. J. Appl. Meterol. & Climatol. Doi: 10.1175/JAMC-D-16-0183.1, in press.
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A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model
Description
Ruiz-Arias, J.A., J. Dudhia and C. A. Gueymard, 2014: A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model. Geosci., Model Devel., 7, 1159-1174
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A study of aerosol impact on clouds and precipitation development in a large winter cyclone
Description
Thompson, G., and T. Eidhammer, 2014: A study of aerosol impact on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 3636-3658.
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A Fast-sky Radiation model for Solar applications (FARMS): Algorithm and performance evaluation
Description
Xie, Yu, M. Sengupta, J. Dudhia, 2016: A Fast-sky Radiation model for Solar applications (FARMS): Algorithm and performance evaluation. Solar Energy, 135, 435-445.
Release Notes
Contact
Please direct questions/comments about this page to:
Pedro Jimenez Munoz
Proj Scientist III
UCAR Outstanding Publication Award
Recipient(s)
Stefano Alessandrini, Luca Delle Monache, Simone Sperati, and Guido Cervone
Award Year
2020
Award Type
internal
Nominee or Winner
Nominee
Awarding Organization or Entity
UCAR
An analog ensemble for short-term probabilistic solar power forecast.
Journal - Applied Energy
Stefano Alessandrini, Luca Delle Monache, Simone Sperati, and Guido Cervone
UCAR Scientific and/or Technical Achievement Award
Recipient(s)
Cathy Kessinger, Bob Barron (ret.), Gary Blackburn (ret.), Jason Craig, Tenny Lindholm, and Dan Megenhardt
Award Year
2021
Award Type
internal
Nominee or Winner
Nominee
Awarding Organization or Entity
UCAR
Remote Oceanic Meteorology Information Operational (ROMIO). RAL proudly nominates Cathy Kessinger and her team for their outstanding efforts developing, demonstrating, and transferring the technology of unique weather guidance products for use by pilots in the cockpit that enable for a first time a shared situational weather awareness between the cockpit, airline dispatch, and air traffic control, which leads to more effective collaborative decision making regarding proactive avoidance of convective storm hazards through strategic rather than tactical rerouting maneuvers.