The CO-FPS project is a five-year effort funded by the State of Colorado with the goal of designing, building, and transferring to the state a cutting-edge system for predicting a wildfire’s extent and rate of spread; the heat and smoke it generates; the wind, temperature, and humidity in the fire’s immediate environment; and aviation hazards around the fire.
The project began in 2015 with a bill signed into law by Governor John Hickenlooper. The state manages the project through its Center of Excellence for Advanced Technology Aerial Firefighting (CoE).
The core predictive technology in CO-FPS is based on NCAR’s coupled atmosphere wildland fire prediction system, which uses the Weather Research and Forecasting WRF-Fire model for simulating weather, and a fire-prediction module for simulating a fire’s behavior, fuels, local atmospheric conditions, and the effects those conditions have on fuel moisture.
The Federal Aviation Administration (FAA) Weather Observation Improvements (WOI) program manages the evolution of the existing aviation weather observation sensor network to one that provides the optimal quantity and quality of ground, air, and space-based sensors. Of primary focus is the surface weather sensor network in the airport terminal environment. Accurate knowledge of airport weather conditions is critically important for aircraft and airline operations (e.g., determining aircraft deicing holdover times as well as takeoff and landing performance calculations) and airport traffic management (e.g., selection of appropriate runways and estimating airport arrival and departure capacity). The nation’s current ground-based airport weather observation architecture has evolved over decades in different ways at different airports, leading to inconsistencies and shortfalls in airport weather sensing and data dissemination. The purpose of the information provided on this page is to provide visual examples of the different types of winter precipitation that can be encountered at airports around the country.
The Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) primarily serve as the source on-airport sensor and algorithmically determine a precipitation type and intensity. The precipitation type sensor on the ASOS has reached its end of life and the latest generation of present weather sensors must be tested to determine their capabilities for correctly identifying and reporting the types of precipitation that are occurring. To do this, high resolution video cameras have been collocated with the new sensors to record the various weather conditions for later comparison to the sensor data. Examples of the different types of precipitation recorded by the cameras are shown below, which would be used for comparison against the sensor data.
Any observations of snowfall require an aircraft undergo deicing operations. Light and moderate intensity snow (-SN, SN) have holdover times (HOTs) that tell pilots and ground deicing crews how long their anti-icing fluids will last under the given conditions. Heavy snow (+SN), which occurs in conditions where the visibility drops below 5/8 of a mile or the liquid water equivalent of the falling snow is greater than 0.1 inches per hour, has no holdover times and often results in an airport shutting down in more extreme conditions. Snowfall of any amount can also have impacts on runway plowing operations, which can reduce the number of takeoffs and landings due to runway closures while plowing and and chemical application operations are occurring.
SNOW BEGINS
Dry Target
Look for multiple opaque flakes/pellets on the target (Day/Night)
Speed slide video to determine good start time (D/N)
Wet Target
Look for opaque flakes/pellets on target (D/N)
Speed slide video to determine good start time (D/N)
Wide views reduction in visibility (D/N)
Note: Floating motion (D/N)
Pellets that are opaque slightly bounce and roll (D/N)
Note: Shows up much brighter than rain (D/N)
SNOW INCREASING
More opaque pellets/flakes accumulate on target/ground (D/N)
Wide view visibility drop (D/N)
Pause video look for increase in number of opaque pellets/flakes per volume (D/N)
Speed slide video to determine increase time (D/N) (Static Cameras)
Increase of snowflake size (D/N)
SNOW DECREASING
Slowing of opaque pellets/flakes accumulating on target/ground (D/N)
Wide view visibility increase (D/N)
Pause video look for decrease in number of opaque pellets/flakes per volume (D/N)
Speed slide video to determine decrease time (D/N) (Static Cameras)
Decrease of snowflake size (D/N)
SNOW ENDS
No more fall streaks (D/N)
No more accumulation (D/N)
Speed slide video to determine good end time (D/N)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
