advanced

"Identify, explore, develop, and implement advanced weather decision support systems for new and emerging user sectors, and develop advanced user–oriented measures of performance and impact."

Motivation

Life and property could be spared and economic performance improved if weather information were utilized more effectively by decision makers across the nation. This is the motivation for engaging stakeholders in various economic sectors, many of which have not been historically well served by the meteorological community, to explore new solutions to specific problems.

During the next five years stakeholders in wireless vehicle technology programs, wildland fire management and mitigation, energy and particularly alternative energy, health, precision agriculture, and retail operations (just–in–time delivery concept) are ripe targets for the implementation of advanced weather–based decision systems.

Strategic approaches relative to this overarching goal are:

• To proactively engage end–user groups to understand and document their unmet needs for weather and climate information and address their needs through science and technology development.
• To focus on specific economic sectors including surface transportation, energy, fire science, agriculture, human health, and the retail industry.
• To develop advanced, user–focused weather detection and prediction verification techniques designed to accurately portray weather system performance advancements and provide baseline performance metrics to the atmospheric science and user communities.
• To improve the societal gains from weather information by infusing social science and economic research methods and capabilities into the planning, execution, and analysis of weather information, applications, and research directions.

RAL has actively engaged user sectors and has made significant progress in extending the reach and utilization of advanced weather technologies over the last decade. RAL will leverage the experience, expertise, and infrastructure that has been developed and employed to move this process forward. Research conducted across NCAR and the university community will also be leveraged in developed solutions. Improvements in weather detection and prediction are required to meet the growing demands of end–users.

Scientific challenges include, but are not limited to, the ability to predict near–surface processes (surface to 200 m), precipitation detection and prediction (type, amount, and rate), land surface modeling, turbulence, radiation, wind, and insolation. In addition, research advancements in the development of advanced user–focused verification methods and techniques must continue in order to measure performance improvements important for decision makers and product developers.

Near-Term Objectives

The following are critical existing projects, capabilities, and efforts that must be maintained to ensure continued success in RAL's ability to deliver advanced decision support technologies to the nation's decision makers.

Surface Transportation Weather (2009–2013)

Since the late 1990s, RAL has played a pivotal national role bringing the surface transportation and weather communities together to improve the performance of surface transportation weather services. RAL will continue this community building process, as the concept of advanced weather information for the surface transportation sector holds great promise. RAL will continue to work with the surface transportation stakeholders in a proactive manner to implement a research agenda that addresses national and international needs for improved surface transportation weather services for the surface transportation community and the traveling public. Surface transportation research efforts include the development of: the winter Maintenance Decision Support System (MDSS) that focuses on pavement condition modeling, precipitation prediction, data fusion techniques, and snow and ice control rules of practice; exploring the use vehicles as weather and road condition sensors; the Weather Data Translator that will ingest, process, and generate derived weather and road condition products for road segments; in–vehicle information systems for communicating weather and road hazards; and advanced data quality control techniques for fixed and mobile datasets.

Actions:

• 2009–2010 – Coordinate with the Federal Highway Administration (FHWA), State DoTs and private sector stakeholders to define specific R&D initiatives required to provide wireless in–vehicle operations with advanced weather and climate information in the following categories:
• Diagnosing pavement condition.
• Use and quality assurance of vehicles being used as weather probes.
• Precipitation detection and nowcasting.
• Processing of weather data from vehicle sensors.
• Formulation of informational products to communicate weather and road hazards to drivers, road maintenance, traffic management centers, and road safety stakeholders.
• 2009–2010 – Coordinate with the FHWA, Federal Railroad Administration, State DoTs and the private sector to define specific research and development initiatives required to provide these sponsors with decision support systems based on advanced weather and climate information to support their operations.
• 2010–2013 – Design, build and test prototype systems for sponsors identified below. Transfer software systems to the private sector for broad implementation.

Targeted Sponsors: FHWA, U.S. DoT Research and Innovative Technology Administration (RITA), State Departments of Transportation, private sector road weather service providers.

Anticipated Collaborators: Internal NCAR collaborations include WRF model developers and data assimilation researchers at ESSL (MMM) and RAL (JNT); land surface modelers (HRLDAS and Noah) at ESSL (MMM) and RAL (HAP). External collaborators include NOAA (GSD) who are transitioning MADIS to operations, Mixon/Hill, developer of the Clarus System, State DoTs, University of North Dakota, Surface Transportation Research Center, Iowa State University, Center for Transportation Research and Education.

Specific Measurements of Success: Adoption of MDSS technologies by State DoTs performing snow and ice control and by private sector road weather service providers by 2013. National deployment of wireless vehicle technologies and the utilization of weather and road condition data from passenger vehicles including establishing a national demonstration of the wireless vehicle Weather Data Translator.

Wildland Fire Management and Mitigation (2009–2013)

The number and intensity of wildland fires continue to grow taking a huge toll on society. Wildland fire costs the U.S. approximately $10B annually. Climate change and the expansion of developed lands will likely increase the impact of wildland fires. Over the last decade, significant progress has been made developing and coupling numerical weather prediction models (e.g., WRF) with fire behavior models. In addition, significant scientific and technical interactions have occurred between the operational and research components of both the atmospheric and fire communities. These efforts have significantly improved our understanding of fire behavior and mitigation strategies. Researchers are at the threshold of being able to run near–real–time fire behavior models to support fire control operations. Continued sponsorship of fire research and development activities will ensure that wildland firefighters have the tools they need to safely perform their duties and advance research associated with coupled modeling systems.

Actions:

• 2009 – Sponsor a stakeholder workshop to discuss the concept of a fire modeling testbed, methods of testing, relevant operationally–oriented metrics that could be applied and define the current baseline of capability used in today's operations.
• 2009–2010 – Coordinate with potential sponsors to.
• Establish an inaugural community initiative to begin to test and evaluate coupled (air/fuel/fire) fire models. Acquire necessary funding for this beginning step.
• 2010–2013 – Expand testing program to a national and international program level that would be comparable to the existing NCAR Joint Numerical Testbed capabilities and resources.

Targeted Sponsors: NSF Cyber–Enabled Discovery and Innovation (CDI) Initiative, NOAA (USWRP, line item, etc), Department of Agriculture–Forest Service, and NIST.

Anticipated Collaborators: Internal NCAR collaborations include WRF model developers and data assimilation researchers at ESSL (MMM) and RAL (JNT); land surface modelers (HRLDAS and Noah) at ESSL (MMM) and RAL (HAP). External collaborators include University of Denver (Dr. Jan Mandel), NOAA, NIST, OFCM National Wildland Fire Weather Needs Assessment Joint Action Group, International Association of Wildland Fire.

Specific Measurements of Success:
Implementation of a numerical modeling testbed applicable to fire weather–centric issues based upon the Joint Numerical Testbed concept. This testbed, with the participation of a broad group within the wildland fire community would:

• Establish relevant metrics for model evaluation.
• Provide a baseline of current capabilities.
• Establish a neutral environment to test and evaluate various model configurations (including numerous meteorological and fire behavior models) against datasets using decision maker–defined metrics in various research and operational scenarios.

Precision Agriculture Decision Support (2010–2013)

"Using previous work with HRLDAS, DICAST® and Noah Land Surface Model as a baseline, develop decision support systems focused on precision agriculture concepts for various stakeholders (agricultural companies and weather provider companies) in the agricultural sector."




Weather, both directly and indirectly, is the critical factor in the success of a harvest and farmers' livelihoods. Severe weather events, such as hail, high winds, tornados, and flash floods can destroy an entire harvest in a very short period. However, many agricultural decisions simply require more accurate forecasts of the weather and the resultant soil conditions. Precise soil temperature and soil moisture forecasts are critical to the timely application of pesticides and to efficient irrigation practices. With NASA funding, RAL has been collaborating with industry to develop an agricultural decision support system that optimizes the timing of pesticide application and irrigation. The project utilizes advanced weather and land surface models and an intelligent data fusion technology that continuously optimizes the weather and soil predictions. This research has led to improvements in the High-Resolution Land Data Assimilation System (HRLDAS), Dynamic, Integrated Forecast System (DICAST®), and Noah Land Surface Model. A major objective of the research is to evaluate the impact of incorporating NASA MODIS data into the system. This research is instrumental in providing critical feedback to the weather and land surface modeling, and satellite communities and represents a cross disciplinary effort. Continued work in this area will lead to more precise prediction of weather and soil condition and more efficient and profitable agricultural operations.

Actions:


• 2009–2010 – Coordinate with potential sponsors to identify needs within the precision agriculture sector that can benefit from decision support systems using advanced weather and climate information.
• 2010–2013 – In collaboration with sponsors and development team members, design, build and test prototype decision support systems.
• 2012–2013 – License all fully tested and documented software to the private sector for broad implementation.

Targeted Sponsors: NASA ROSES, private sector weather service providers (e.g., Telvent), and USDA.

Anticipated Collaborators: Internal NCAR collaborations include WRF model developers and data assimilation researchers at ESSL (MMM) and RAL (NSAP); land surface modelers (HRLDAS and Noah) at ESSL (MMM), RAL (JNT) and RAL (HAP).

Specific Measurements of Success: Improved soil temperature forecasts, higher resolution soil temperature and moisture datasets, and industry adoption of these products to support precision agriculture operations.

Intelligent Weather Systems and Probabilistic Prediction (2009–2013)

"Explore new statistical processing methods such as data mining and ensemble techniques to improve the overall forecast skill of RAL's DICast system and develop new capabilities to generate probabilistic weather prediction products (e.g., probability density functions) and calibrated ensembles. Develop new user–focused techniques for communicating uncertainty."




RAL has been a leader in the development of so–called intelligent weather prediction systems that blend data from numerical weather prediction models, statistics datasets, real–time observations, and human intelligence to optimize forecasts at user–defined locations. The Dynamic Integrated Forecast System (DICast®) is an example of this technology and it is currently being used by two of the nation's largest private sector weather service companies. There is a growing desire in industry to have fine–tuned forecasts for specific user–defined locations. This trend is clear in the energy, transportation, agriculture, and location-based service industries. RAL's expertise in meteorology, engineering, and applied mathematics and statistics, will be utilized to address society's growing need for accurate weather information.




Actions:


• 2009–2010 – Coordinate with potential sponsors to identify and prioritize operational needs for probabilistic prediction systems; pursue the top three priority applications as long as required to obtain funding.
• 2010–2011 – In collaboration with sponsors and development team members, design, build and test prototype decisions support systems.
• 2012–2013 – License fully tested and documented software to the private sector for broad implementation.

Targeted Sponsors: NSF, NASA, NOAA, FHWA, State DoTs, and private sector weather service providers.

Anticipated Collaborators: Environment Canada, ESSL (MMM), IMAGe, University of Colorado.

Specific Measurements of Success: Expand utilization of RAL's DICast technology by public and private sector organizations into new markets (e.g., location-based services) by 2013. Develop and implement a high–resolution gridded capability that includes probabilistic forecast variables.

Frontiers

Renewable Energy (2010–2013)

"Develop new methods to more accurately predict wind and solar radiation in support of renewable energy industries."




Risks associated with energy supply and demand revolve around market dynamics, financial conditions, politics, technology choice, fuels, environmental quality, and weather. The recent policy trend to require a larger fraction of the energy portfolio devoted to renewable energy sources, such as wind and solar, puts additional strain on the energy industry as these sources are less predictable than traditional generation sources. The influence of significant weather events on the energy industry has increased with diminishing reserve margins to meet peak loads. In the Western U.S., weather factors have not only included unusually hot summer and cold winter events, but also low precipitation that reduces hydropower capacity and increases electrical demand for irrigation pumping and groundwater withdrawal. Improved weather prediction and precise spatial analysis of mesoscale weather events are crucial to both short and long–term energy management.

Actions:


• 2009–2010 – NCAR and its research collaborators will actively engage the renewable energy industry and seek opportunities for improving the analysis and prediction of weather, particularly wind and solar radiation. Improving the prediction of wind and solar radiation will require research investments in weather modeling (global to local scales), data assimilation, boundary layer meteorology, cloud and precipitation processes, turbulence, and land surface characterization and prediction.
• 2010–2013 – NCAR will penetrate this new economic sector by the end of 2010 by forming partnerships in the research and business community, joining the American Wind Energy Association (AWEA) and other relevant alternative energy organizations, serve on committees focused on the utilization of climate and weather information in alternative energy operations, present relevant papers at alternative energy conference, and exhibit NCAR capabilities at relevant stakeholder conferences and trade shows.
• 2010–2013 – NCAR and its research collaborators will focus on weather and climate systems and science that are critical to infrastructure planning and management, prediction of energy demand, management of energy supply, energy pricing and markets, energy system operations and regulatory compliance, and economic risk minimization. The energy industry has expressed needs in the following areas which will be NCAR's specific foci:
• Improved weather forecast accuracy
• Improved resolution of forecast information and diagnostic data in time and space (e.g., hourly heating and cooling degree day calculations, hourly and shorter term wind and solar predictions)
• A better understanding of the uncertainty that is inherent in the forecasts
• Increased attention to understanding sponsor requirements
• Improved understanding of user capabilities to utilize information for specific applications
• Understanding of barriers in the use of improved information in decision making
• Additional weather observations at strategic locations as determined by quantitative methods.

Targeted Sponsors: DOE (National Renewable Energy Laboratory), and the private sector energy industry.

Anticipated Collaborators: ESSL (MMM), RAL (JNT), University of Colorado, Colorado School of Mines, Colorado State University, and private sector energy and weather service provider companies.

Specific Measurements of Success: Reduce mean absolute error in wind energy prediction by 10% by 2013. Develop an international standard definition of a "wind ramp" event, and develop user–focused verification metrics for the wind energy prediction community. Seek adoption of advanced wind prediction technologies across the renewable energy industry.

Atmospheric Boundary Layer Research (2010–2013)

"Perform research to significantly advance our knowledge of the atmospheric boundary layer and develop new methods and techniques to more accurately characterize and model the boundary layer to improve the prediction of surface and near- surface weather conditions."



The lower atmospheric boundary layer (ABL) is characterized by strong thermodynamic and kinematic turbulent energy exchanges between the atmosphere and the land surface. As such, the ABL is sensitive to terrain characteristics, surface roughness, vegetation, albedo, and soil conditions. Synoptic and mesoscale dynamical processes also impact the ABL including, but not limited to, low–level jets, frontal processes, thermally driven circulations, gravity waves, and critical levels.

Our ability to accurately predict near–surface weather conditions is highly constrained by our lack of knowledge of ABL processes and by their excessively simple treatment in numerical weather prediction models. A better understanding of the ABL will lead to improved prediction of ABL structure and evolution and in turn improved near–surface weather forecasts. Major stakeholder communities that are sensitive to near–surface weather include agriculture, surface transportation, wind energy, aviation, construction, water resources (evaporation), air quality, homeland security (e.g., hazardous plume transport), and recreation.

In addition to this research leading to improved forecasts of ABL weather, the knowledge gained and new parameterizations developed will have an equally large impact on regional simulations of future climates. Virtually all of the stakeholders, for whom climate-downscaling simulations aim to provide information for critical decisions, care most about conditions at or near the surface. Modeling the ABL properly in regional climate models is essential to capitalizing on the large investment in the IPCC model runs used to drive the downscaling work.

It is becoming increasingly apparent that ensemble weather prediction on the mesoscale and synoptic scale should include the effects of uncertainties in the parameterizations. The proposed research on ABL processes and parameterization will benefit the further development of such stochastic methods for representing the ABL and its interaction with the land and water surface.

Required Actions:


• 2009–2010 – Seek opportunities for funding to improve our understanding of the ABL with an initial focus on turbulence, momentum mixing, and the low–level jet, because these phenomena play a critical role in the weather community's ability to predict surface and near–surface wind conditions, which is critical for both the wind energy industry and aviation. NCAR will seek opportunities to participate in field experiments that focus on boundary–layer processes and measurements. Large eddy simulation models will also be utilized to evaluate new and improved physics schemes and their impact on predictability.
• 2011–2013 – Expand the research to include the convective atmospheric boundary layer and its sensitivity to surface characteristics. During this period, knowledge obtained from the ABL research will be focused on improving the boundary layer physics schemes (explicit and parameterized) contained in advanced numerical weather prediction models such as WRF. Research will be performed to verify the performance of new schemes. The Joint Numerical Testbed will be used to test the new schemes. Successful improvements will become part of the WRF community modeling system.

Targeted Sponsors: NSF, DOE, FAA and the private sector energy industry.

Required Collaborations other than Sponsors: ESSL (MMM), Penn State, and the University of Colorado.

Specific Measurements of Success: Initial measurements of success will focus on improved near–surface wind and turbulence physics schemes that will result in a 10% improvement in NWP predicted surface wind speed and turbulence from 400 m to the surface. Longer–term goals will be to improve the characterization (location, depth, and strength) of low–level jets and improve storm forecasts through better characterization of the low–level wind field.