NCAR’s Renewable Energy Forecasting for Kuwait project, a 3-year, $5.1M project sponsored by the Kuwait Institute for Scientific Research (KISR) (https://news.ucar.edu/126802/ncar-develop-advanced-wind-and-solar-energy-forecasting-system-kuwait), began in July 2017.

Figure 1. Gerry Wiener, Branko Kosovic, Sue Ellen Haupt, and Jared Lee at the 10-MW PV solar plant at the Shagaya Renewable Energy Park for its Grand Opening on 20 Feb 2019.

The ultimate goal of this project is to deliver to KISR an operational wind and solar power forecasting system, for both nowcasting and day-ahead time horizons (and beyond), with which they can provide forecasts to their national power grid operators and wind/solar power plant operators. The forecasting system is called the Kuwait Renewable Energy Prediction System (KREPS). 

Kuwait has a stated national goal of 15% renewable energy generation by 2030, and to that end has established the Shagaya Renewable Energy Park in the desert about 100 km west of Kuwait City. Phase 1 of Shagaya is now complete, with demonstration-scale 10-MW photovoltaic (PV) solar (Figure 1) and 10-MW wind plants (Figure 2) that were commissioned in May 2017, and a 50-MW concentrated solar power (CSP) plant (Figure 3) that was commissioned in December 2018. The official Grand Opening for the Shagaya Renewable Energy Park was held in February 2019.

Figure 2. Four of the five wind turbines of the 10-MW wind plant at the Shagaya Renewable Energy Park in western Kuwait.

Phase 2 of Shagaya will include a 1500-MW PV solar plant, which will be the second-largest PV plant in the world. Construction of this 1500-MW al-Dibdibah PV plant at Shagaya is hoped to begin late in 2019 or in 2020, with completion expected after the end of NCAR’s initial 3-year project. Additional wind, PV solar, and CSP solar capacity is planned beyond that in Phases 2 and 3 of Shagaya, with a goal of 3–5 GW of combined wind and solar power installed capacity at Shagaya by 2030. We hope to continue our partnership with KISR to grow and improve KREPS as more renewables come online in Kuwait.

Figure 3. Part of the 50-MW concentrated solar power (CSP) plant at the Shagaya Renewable Energy Park in western Kuwait, with the new KISR research facility and the wind turbines in the background.

The NCAR team, in collaboration with researchers from Penn State University and Solar Consulting Services, has been building various aspects of KREPS, leveraging advancements made on several past and current renewable energy forecasting projects in RAL, and developing new and improved techniques and products with pioneering research. Key components of the fully engineered system include high-resolution WRF-Solar® forecasts, a newly developed blend of WRF with MADCast (MAD-WRF) to improve cloud initialization for nowcasting, NCAR's DICast® system that produces dynamically blended and tuned forecast for the various sites, an analog ensemble (AnEn) implementation to quantify the uncertainty in the forecasts, and the StatCast-Solar and StatCast-Wind models that use machine learning techniques and recent real-time observations to generate nowcasts. A Display system to show operators and researchers the past and present performance of KREPS has also been developed. Assessment and development of the system and its components are ongoing, and KREPS will be transferred to KISR by the scheduled end of the project in summer 2020.

Several conference presentations and journal papers have already been submitted that stem from research accomplished on this project (click on the Resources tab to see a list of these references), and many more are planned through 2019 and 2020.

REFERENCES

Alessandrini, S., S. Sperati, and L. Delle Monache, 2019: Improving the analog ensemble wind speed forecasts for rare events. Mon. Wea. Rev., conditionally accepted and in revision.

Al-Rasheedi, M., C. A. Gueymard, A. Ismail, and T. Hussain, 2018: Comparison of two sensor technologies for solar irradiance measurement in a desert environment. Sol. Energy, 161, 194–206, https://doi.org/10.1016/j.solener.2017.12.058.

Brummet, T., J. A. Lee, and G. Wiener, 2019: The relationship between GHI and power in Kuwait. 10th Conf. on Weather, Climate, and the New Energy Economy/18th Conf. on Artificial and Computational Intelligence and its Applications to the Environmental Sciences. Phoenix, AZ, Amer. Meteor. Soc., J3.4, https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/350578.

Gueymard, C. A., and P. A. Jiménez, 2018: Validation of real-time solar irradiance simulations over Kuwait using WRF-Solar. 12^th Int. Conf. on Solar Energy for Buildings and Industry (EuroSun 2018). Rapperswill, Switzerland, Int. Solar Energy Soc., 2.A-1, https://doi.org/10.18086/eurosun2018.09.14.

McCandless, T. C., and S. E. Haupt, 2019: The super-turbine wind power conversion paradox: Using machine learning to reduce errors caused by Jensen’s Inequality. Wind Energy Sci. Discuss., in review, https://doi.org/10.5194/wes-2018-74.

Naegele, S. M.,T.C. McCandless, S. E. Haupt, G. S. Young, and S. J. Greybush, 2019: Climatology of Wind Energy Variability for the Kuwait Region. 10th Conf. on Weather, Climate, and the New Energy Economy/18th Conf. on Artificial and Computational Intelligence and its Applications to the Environmental Sciences. Phoenix, AZ, Amer. Meteor. Soc., 10.2, https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/352390.

Partners

Kuwait Institute for Scientific Research (KISR)

It’s well known that adverse winter weather can significantly disrupt airport operations. Snow and ice buildup on the runways reduces the pavement friction and can cause airplanes to slide upon landing or takeoff. When the surface friction falls below certain levels, runways must be closed in order to keep planes safe. The safety and efficiency of airport and flight operations hinges on timely and accurate weather forecasts that can also give an indication as to when the runway friction is reduced to the point where runways must be closed. Therefore, having an accurate forecast of runway friction can help airport managers make better decisions on when and how long to close runways.

Example Output: Multi-Panel Plot

Example Runway Closure Matrix

Until recently, the airport community has relied on conventional methods for acquiring and applying weather-related runway friction information in the runway closure decision process usually from multiple sources. Minneapolis–Saint Paul International Airport (MSP) experiences several winter storms each season where the runways must be closed due to a loss of runway friction.  As a result, MSP contacted the National Center for Atmospheric Research (NCAR) for help in automating the procedure for recording and relating friction observations to runway closure times. NCAR entered into a contract with MSP in the fall of 2017 to develop an initial Runway Friction and Closure Prediction System (RFCPS). The system provides a forecast of runway friction values and runway closure alerts from 0 to 6 hours at 15-minute temporal resolution.

The initial RFCPS prototype system relies on data processing and machine learning techniques developed at NCAR. The RFCPS uses a backend forecast engine combined with machine learning modules and rules of practice to predict runway friction and runway closures alerts. The friction values are combined with rules of practice to predict runway closure alerts. The RFCPS technology can be applied at any airport that has runway frictions issues during adverse winter weather. For example, NCAR will also be working with Denver International Airport (DEN) over the next several years to develop a runway friction prediction system at the airport.

MSP Airport during winter

The initial RFCPS predicts runway friction values out to 6 hours at 15-minute lead-times. The system currently updates every hour but will be modified to update sub-hourly considering the latest runway friction measurements (observations). The system provides output in several formats: a pdf containing multi-panel forecast plots for each 1/3 segment along a runway; a combined csv file that contains the same forecast data that goes into the pdf multi-panel plots, but in comma separated text format; and lastly a runway closure guidance matrix, which shows friction values and closure alerts for each runway segment in a grid displaying forecast lead-times (a quick way to show when a runway closure is most likely and on what runway).

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.

Overview

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. 

[Full Playlist]

Snow Graupel Ice Pellets Rain Drizzle Mixed Mixed ACY

Snow

IMPACTS

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)

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Snow Graupel Ice Pellets Rain Drizzle Mixed Mixed ACY

Graupel

GRAUPEL BEGINS

GRAUPEL ENDS

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Snow Graupel Ice Pellets Rain Drizzle Mixed Mixed ACY

Ice Pellets

ICE PELLETS BEGIN

• Dry Target
  • Look for translucent pellets bouncing on the target (Day/Night)
  • Speed slide video to determine good start time (D/N)
• Wet Target
  • Look for translucent pellets sticking on target (D/N)
  • Look for drops and floating ice in puddles (D)
  • Look for drops in reservoir, if reservoir is filled (D/N)
  • Speed slide video to determine good start time (D/N)
• Look at pellets bouncing off of objects (Roaming Camera) (D/N)
• Note: Zoom into videos to see bouncing clearer (D/N)

ICE PELLETS INCREASING

• Wide view visibility drop (D/N)
• Pause video look for increase in number of translucent pellets per volume (D/N)
• Speed slide video to determine increase time (D/N) (Static Cameras)
• Increase of bouncing off objects/target (D/N)
• Look for increase of translucent pellets and splashing, if reservoir is filled (D/N)

ICE PELLETS DECREASING

• Wide view visibility increase (D/N)
• Pause video look for decrease in number of translucent pellets per volume (D/N)
• Speed slide video to determine decrease time (D/N) (Static Cameras)
• Decrease of bouncing off objects/target (D/N)
• Look for decrease of translucent pellets and splashing, if reservoir is filled (D/N)
• Note: Zoom into videos to see bouncing clearer (D/N)
• Note: Different cores Liquid splat, solid bounce (D/N)

ICE PELLETS END

• No more fall streaks (D/N)
• No more accumulation (D/N)
• No more splashing (D/N)
• Speed slide video to determine good end time (D/N)

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Snow Graupel Ice Pellets Rain Drizzle Mixed Mixed ACY

Rain

RAIN BEGINS

• Dry Target
  • Look for multiple drops on the target (Day/Night)
  • Speed slide video to determine good start time (D/N)
• Wet Target
  • Look for drops on target (D/N)
  • Look for drops in puddles (D)
  • Look for drops in reservoir, if reservoir is filled (D/N)
  • Speed slide video to determine good start time (D/N)
• Note: Fall streak tends to be straight (D/N)

RAIN INCREASING

• Larger drops hitting target (D/N) 
• More splashing in puddles (D)
• More splashing in reservoir if filled (D/N)
• Pause video look for increase in number of drops per volume (D/N)
• Speed slide video to determine increase time (D/N) (Static Cameras)
• Target saturation increases (D/N)

RAIN DECREASING

• Larger drops diminish (D/N) 
• Less splashing in puddles (D)
• Less splashing in reservoir if filled (D/N)
• Pause video look for decrease in number of drops per volume (D/N)
• Speed slide video to determine decrease time (D/N) (Static Cameras)
• Target saturation decreases (D/N)

RAIN ENDS

•  
  • No more fall streaks (D/N)
  • No more splashing puddles (D)
  • No more splashing reservoir (D/N)
  • Speed slide video to determine good end time (D/N)

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Snow Graupel Ice Pellets Rain Drizzle Mixed Mixed ACY

Drizzle

DRIZZLE BEGINS

• Dry Target
  • Look for small/uniform particles accumulation on target (Day/Night)
  • Speed slide video to determine good start time (D/N)
• Wet Target
  • Look for small/uniform particles accumulation on target (D/N)
  • Look for no drops in reservoir, if reservoir is filled (D/N)
  • Speed slide video to determine good start time (D/N)
• Look for reduced visibility  (D/N)
• Note: Floating motion (D/N)

DRIZZLE INCREASING

• Look for more small/uniform particles on target (D/N)
• Look for more coalescence on target producing more frequent fall streaks (D/N)
• Look for reduction of visibility (D/N)
• Speed slide video to determine increase time (D/N) (Static Cameras)
• Volume in camera view increase (pause) (D/N)

DRIZZLE DECREASING

• Look for less small/uniform particles on target (D/N)
• Look for less coalescence on target producing less frequent fall streaks (D/N)
• Look for increase of visibility (D/N)
• Speed slide video to determine decrease time (D/N) (Static Cameras)
• Volume in camera view decrease (pause) (D/N)

DRIZZLE ENDS

• No more fall streaks (D/N)
• No more accumulation (D/N)
• Speed slide video to determine good end time (D/N)

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Snow Graupel Ice Pellets Rain Drizzle Mixed Mixed ACY

MIXED

RAIN/SNOW COMBINATION

• Noticeable drops hitting target
• Splashing in puddles/target reservoir
• Opaque snowflakes/pellets hitting and possibly sliding down target
• Snowflakes may show up considerably larger than falling rain drops

ICE PELLET/RAIN/SNOW COMBINATION

• Noticeable drops hitting target
• Splashing in puddles/target reservoir
• Opaque snowflakes/pellets hitting and sticking to target
• Snowflakes may show up considerably larger than falling rain drops
• Look for translucent pellets bouncing off the target/objects/ground

SNOW/ICE PELLET COMBINATION

• Opaque snowflakes/pellets hitting and sticking to target
• Look for translucent pellets bouncing off the target/objects/ground

MIXED IN ACY

Mixed Phase Precipitation in Atlantic City

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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. 

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.

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.