Extreme urban heat is a leading cause of weather-related human mortality in the United States and in many countries world-wide. Vulnerability to extreme heat is amplified in large cities due to the urban heat island and socioeconomic diversity.
System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)
SIMMER quantified the importance of characterizing urban properties in urban meteorological simulations and highlighted the role of adaptive capacity in understanding vulnerability to extreme heat.
Public health officials are now using this framework to reduce vulnerabilities.
To deliver sound climate science to decision-makers at regional and local scales to promote sustainability and reduce human system vulnerabilities to anticipated climate change impacts.
Climate Risk Management engine (CRMe)
CRMe is a standardized and modular climate data framework for evaluating and translating high-resolution climate data, information and knowledge for diverse applications. For this effort, over 50 downscaled and GCM-level datasets were prepared and evaluated using the standardized framework. More than 150,000 statistically and dynamically downscaled products and the results are available to application users through the Earth System Grid.
CRMe is being used within the Climate Science Applications Program’s Regional Climate Science for Adaptation group to link into other national laboratory-level projects, such as the Department of Interior North Central Climate Science Center at Colorado State University, projects for the USDA, World Bank Climate Knowledge Portal and the World Bank International Finance Corp.
Developing an advanced high-resolution numerical weather prediction (NWP) system that analyzes current weather and makes detailed predictions of weather over the next several days, specifically for the U.S. Army Test and Evaluation Command (ATEC).
Four-Dimensional (4DWX) System – 4DWX is the product of more than one and a half decades of research and development sponsored largely by the U.S. Army Test and Evaluation Command. This technology is accredited for operational use at seven Army Test Ranges. The system utilizes advanced numerical weather prediction, data assimilation, probabilistic methods, nowcasting, and plume dispersion technologies.
Knowledge of detailed predicted and actual weather conditions saves the Army millions of dollars annually. NCAR has since adapted the system and created derivative technologies for use by other organizations.
Protecting the nation from deliberate acts of terrorism (such as anthrax) or from a toxic release caused by a natural disaster or an industrial or transportation accident.
Homeland Security Technologies – NCAR has joined with other federal agencies to develop new technologies that make our communities less vulnerable to biological and chemical attack. Our Source-Term Estimation method research applies a variety of algorithms to the latest weather prediction and transport-and-diffusion models to help emergency managers reconstruct a release, and to develop a better situational awareness of how a population was exposed.
This technology was successfully integrated into the US Department of Defense (DoD) emergency response modeling systems—HPAC (Hazard Prediction and Assessment Capability) and JEM (Joint Effects Model) in 2012.
Creating weather and climate products that convey uncertainty is difficult and it often requires running dozens of weather or climate models with small variations in the initial conditions to understand the predictability of the atmosphere. This process requires very large computing resources. Less expensive methods are highly desired.
Analog Ensemble (AnEn). NCAR has introduced a new approach to generate accurate predictions and reliable uncertainty quantification, the analog ensemble (AnEn). The AnEn estimates a future observation of the quantity to be predicted with a probability density function (PDF) formed by a set of n past verifying observations corresponding to the n best analogs (past model predictions) to a current deterministic model forecasts.
The AnEn outperform a power prediction based on the European Center for Medium range Weather Forecasting (ECMWF) ensemble wind predictions, a leading in operational forecasting at a fraction of the cost. AnEn provides forecasters, decision makers, and emergency managers with accurate information to save lives and property.
High resolution weather prediction can be improved by assimilating local observations within the forecast region. Special handling of these observations is required by the modeling system to ensure that they have the appropriate effect on the forecast.
The Real-Time Four-Dimensional Data Assimilation system (RTFDDA)
The RTFDDA system analyzes and predicts whether through integration of computer scripting code, a numerical weather prediction (NWP) core, and a method of assimilating observations into that core.
Ensemble RTFDDA E-RTFDDA is identical to RTFDDA with one important difference: it produces multiple versions (forecasts) of weather at any given time in the near future.
CFDDA (Climate Four-Dimensional Data Assimilation) create analyses of current and forecasts of future weather.
This data assimilation system was designed to take advantage of local weather observations and has been used to improve weather forecasts and historical climate analyses for the US Army Test Ranges, wind energy prediction systems, and geospatial intelligence applications.
To generate fine scale climatologies of atmospheric information and make it easily available to decision makers that utilize geographical information systems (GIS).
Climatology of Operational Parameters (CLIMOPS)
CLIMOPS is a regional climate analysis toolkit that aims at integrating the past 30 years of atmospheric and oceanic observations into fine scale gridded regional climate variables. The toolkit generates “on demand” large databases of gridded atmospheric parameters at high resolution, tailored to the specific needs for regional climate information of Defense agencies. The system takes advantage of the zooming and relocation capabilities of the embedded domains that is found in the community Weather Research and Forecast (WRF) model.
CLIMOPS has worldwide use. Users can relocate the model grids anywhere in the world from a laptop by simple point and click commands through a graphical interface.
The FAA NextGen program anticipates a significant growth in demand for air traffic services over the next couple decades. Since weather conditions can seriously restrict aircraft operations and levels of service available to system users, the manner by which weather is observed, forecast, disseminated, and used in decision-making is of critical importance.
Consolidated Storm Prediction for Aviation (CoSPA)
Researchers and engineers integrated a wealth of different forecasting datasets by focusing on the development of a single authoritative convective forecast system, covering both summer and winter storms. CoSPA integrates observation-based expert systems and numerical weather prediction model to provide seamless 0 – 8-hour forecasts of convective hazards.
This FAA funded project is a collaborative effort NCAR, MIT-LL, NOAA-ESRL. Annual benefits to the national aviation system are estimated to be $27M.
According to a review of NTSB data from 1992 to 2001, turbulence was a factor in at least 509 accidents in the United States that resulted in 251 deaths in general aviation. Between 2002 and 2013, there were 430 passenger and crew injuries due to turbulence.
NCAR Turbulence Detection Algorithm (NTDA)
Making use of wind variability data provided by Doppler weather radars, RAL scientists developed and tested the NCAR Turbulence Detection Algorithm (NTDA), designed for use on the nation's network of NEXRAD radars. NTDA utilizes NEXRAD reflectivity, radial velocity, and spectrum width to produce atmospheric turbulence intensity (eddy dissipation rate, EDR) measurements of "in–cloud" turbulence.
NTDA is operational on all NEXRAD systems; this technology has helped reduce turbulence-related incidents by diagnosing the location of turbulence near storms.
Accurate wind forecasts are crucial for power-grid integration and load balancing. Numerical weather prediction (NWP) models have historically only played a secondary role in providing 0–12 h wind-power forecasts.
This wind energy forecasting system has saved Xcel Energy ratepayers approximately $10M per year since 2009. The benefits are described in a report of the National Renewable Energy Laboratory (NREL).
Weather (clouds, rain, snow, fog, dust, etc.) severely impacts the energy produced from solar energy systems making the power difficult to integrate into the power grid.
Solar Energy Prediction
NCAR leads a partnership to advance the state-of-the-science of solar power forecasting. The project includes performing cutting edge research, testing the forecasts in several geographically- and climatologically-diverse high penetration solar utilities and ISOs, and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, industry partners, six utilities, and four balancing authorities.
The finished system will be made available to the solar power industry to lower costs and enable more solar power penetration. Significant savings to electrical utility rate payers is anticipated.
According to a review of NTSB data from 1992 to 2001, turbulence was a factor in at least 509 accidents in the United States that resulted in 251 deaths in general aviation. Between 2002 and 2013, there were 430 passenger and crew injuries due to turbulence.
Graphical Turbulence Guidance Product (GTG)
NCAR engineers designed the Graphical Turbulence Guidance Product that predicts clear-air and terrain-induced turbulence. The GTG product uses numerical weather prediction model forecasts to compute a number of turbulence diagnostics which are then weighted and combined. The weights are dynamically optimized for best agreement with the most recent available turbulence observations (in situ EDR data and pilot reports).
GTG is most useful for route planning, i.e., strategic avoidance of turbulence. This technology improves safety, airspace capacity and efficiency
Remote, oceanic regions have severely limited data availability and therefore, have few, if any, high resolution weather products that indicate locations of convection. Convective hazards impact the safety, efficiency and economic viability of oceanic aircraft operations by producing turbulence, icing and lightning and by necessitating aircraft rerouting while in-flight, leading to higher fuel costs and delays.
The NASA-sponsored Oceanic Convection Diagnosis and Nowcasting product, is an intelligent system that generates 0-2 hour nowcasts of oceanic convective hazard regions. Geostationary satellite imagery are used to define the locations of deep convective clouds in oceanic regions, through the weighted combination of three independent algorithms.
This product is uplinked to airline cockpits via electronic flight bag systems to support safe and efficient flight operations and re-routing decisions.
Snow and ice on a plane’s wings can prohibit it from gaining lift and taking off safely. Airlines and airport operations personnel need detailed information on the amount of ice and snow that can accumulate and dilute deicing fluid and how much time a plane can wait between deicing operations and takeoff.
Weather Support to Deicing Decision Making (WSDDM)
NCAR research found that the icing hazard for aircraft directly corresponds to the amount of water in the snow, rather than visibility. It is the latter that had traditionally been used to determine de–icing and take off decisions. Refocusing on the amount of water in the snow, this finding led to the development of WSDDM.
WSDMM provides airline and airport operations personnel critical information on the timing and effectiveness of aircraft deicing fluids. Use of the system during de–icing operations has been shown to reduce end of runway deicing; a significant cost savings. United Airlines saved $1M in one snow event at Chicago’s O’Hare Airport.
State Department of Transportations (DOTs) spend more than $1B annually on snow and ice control and need improved predictions of weather and road conditions and guidance on how to optimize wind maintenance operations.
Maintenance Decision Support System (MDSS) –Atmospheric science and civil engineering research led to the development of the winter MDSS that predicts pavement condition (ice, snow, wet, etc.) and provides detailed snow and ice control maintenance guidance to State DOT officials.
This technology has been adopted by private sector firms and State DOTs saving states millions of dollars annually in anti-icing materials and staff time. A USDOT benefits study indicated that the annual net benefit of using MDSS outweighed the costs, by significant amounts, ranging from $2.68 million to $488,000 in New Hampshire, Minnesota, and Colorado. Indiana DOT saved $12M in one winter using the MDSS.
Pilots have an overwhelming amount of weather information to wade through and a lot of it is not tailored to a specific flight plan. A system that tailors the weather information and presents it in an intuitive manner and can deliver data to airline dispatch operations was required.
Aviation Digital Data Service (ADDS)
ADDS is well known within the aviation community for its innovative, user-friendly methods of presentation. NCAR combined its advanced gridded aviation products (e.g., icing, turbulence, convective hazards) along with the traditionally used aviation weather products, such as AIRMETs, SIGMETs and METARs into a single application.
Since 1996, pilots, dispatchers, the military, airlines, and airports have benefited from increased weather awareness because of the comprehensive weather information available on ADDS. Today, Operational ADDS is hosted at the National Weather Service Aviation Weather Center. This site gets an average of 10 million hits per day with major users being commercial airline and general aviation pilots
In a typical year, there are 1.1 million weather-related vehicle crashes in the U.S., 385,000 injuries, over 4,700 fatalities, and 554 million vehicle-hours of delay during poor weather/road conditions. Drivers need significantly improved information on pavement and weather hazards.
Connected Vehicle Technology – Pikalert®. With funding and support from the U.S. Department of Transportation’s (USDOT), NCAR developed the Pikalert® Vehicle Data Translator that integrates vehicle-based measurements of the road and surrounding atmosphere with traditional weather data creating road and atmospheric hazard products for a variety of users.
It is anticipated that this system in the near future will be adopted by industry to push road hazard alerts to drivers in vehicles increasing safety.
In 2006, the FAA required a review of rescue helicopter crashes. The review revealed that a lack of detailed weather information was often a factor.
Helicopter Emergency Medical Services (HEMS)
The HEMS tool is an extension of NCAR’s Aviation Digital Data Service (ADDS) which provides pilots easy-to-use web access to a variety of critical aviation weather information, such as ceiling, visibility, flight category, winds, icing severity, relative humidity, temperature, radar, satellite, etc.
The HEMS tool became operational at the Aviation Weather Center in 2015. User reports showed ambulance pilots rely on the HEMS tool to make quick, life-saving decisions.
When multiple thunderstorms are occurring, it’s hard for forecasters to keep track of the characteristics of each one (severity, hail potential, etc.) and where the storms are moving.
Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) System. NCAR developed TITAN to support research on thunderstorm morphology and rain production, but it was expanded to support aviation hazard prediction, rain augmentation studies, and hydrology. TITAN has been installed at a number of sites around the world.
TITAN is used for meteorological and hydrological research, forecasting related to aviation, severe weather forecasting, precipitation analysis and conducting and evaluating weather modification projects. This technology is provided freely through a UCAR license.
Extensive testing and evaluation must be performed to ensure that modeling new techniques are ready for operational consideration.
Model Evaluation Tools (MET). The MET weather model verification package is designed to be a highly-configurable, state-of-the-art suite of verification tools to support the evaluation of weather prediction models.
This capability is used by hundreds of researchers and developers globally across the weather enterprise (public, private, and academic) and is helping to accelerate the adoption of weather prediction technology improvements.
Attention is increasingly focused on water scarcity as conflicts emerge and are likely to escalate over competing water demands for energy production, municipal use, agricultural irrigation, and ecosystem protection.
Water Evaluation and Planning Model (WEAP). One of the most powerful decision support technologies NCAR brings to water resource planning and management is the WEAP model, developed in collaboration with the Stockholm Environment Institute (SEI). WEAP is a sophisticated, yet user-friendly, tool that couples physical hydrology with relevant water management parameters, set by the user, to create scenarios to explore potential consequences of climate change on water management decisions.
WEAP is currently used by several thousand water resource managers in the U.S. and in 170 countries around the world.
Developing weather prediction models is time consuming and complex. Rapid progress requires a community of developers and a framework for integrating and testing the improvements to ensure skill and moving the improvements to operational forecast centers.
Weather Research and Forecasting Model (WRF). WRF is a next-generation mesoscale numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. The development effort in the late 1990s was a collaborative partnership principally among NCAR, National Oceanic and Atmospheric Administration (NOAA), the Air Force Weather Agency (AFWA), the Naval Research Laboratory (NRL), the University of Oklahoma, and the FAA.
The model serves a wide range of meteorological applications across scales from tens of meters to thousands of kilometers. WRF has thousands of users around the world.
Current flood and river flow prediction systems are based on algorithms that focus on statistical relationships between historical weather conditions and river flow levels measured at observations sites. This means that little information is known about the water flow and flood conditions where sensors do not exist. New capabilities are required that factor in all the effects of the water cycle and can provide information in between sensor sites.
Hydrometeorological research has led to the development of an advanced hydrologic prediction system called WRF-HYDRO. WRF-Hydro system was originally designed as a framework designed to facilitate easier coupling between the Weather Research and Forecasting model and components of terrestrial hydrological models. WRF-Hydro is both a stand-alone hydrological modeling architecture as well as a coupling architecture for coupling of hydrological models with atmospheric models. It includes a multi-scale functionality to permit modeling of atmospheric, land surface and hydrological processes on different spatial grids.
This framework is viewed as the future national water prediction system by the National Water Center and the WRF-Hydro system is being adopted by the National Weather Service and will become operational in 2016.
The impact of weather on the US economy is large, but difficult to measure.
U.S. Economic Sensitivity to Weather Variability – A 2011 economic study by Jeff Lazo et al. indicated that the inter-annual aggregate dollar variation in U.S. economic activity that is attributable to weather variability could be 3.4%, or $485 billion of the 2008 gross domestic product.
From very local short-term decisions about whether or not to pour concrete to broader decisions of when to plant or harvest a field, this report demonstrates how weather can have positive or negative effects on economic activity.
Between 1970 and 1994, a number of flights during takeoff and landing were forced to the ground by invisible, violent “downbursts.” Pilots were literally blind sighted by these deadly wind shear events. During that time, wind shear resulted in 570 fatalities in the U.S.
In the 1980s, scientists and engineers from NCAR and academia, with support from the federal government, developed and conducted a research project dedicated to understanding this deadly phenomena. Based on wind shear research studies such as the Joint Airport Weather Studies (JAWS), NCAR scientists and engineers developed two wind shear detection systems that have been deployed throughout the USA and world, coined The Low-Level Windshear Alert System (LLWAS) and in collaboration with MIT-LL, the Terminal Doppler Weather Radar (TDWR).
Savings in lives and property is estimated at $1B
To improve deficiencies identified in treating interactions among vegetation, soil, hydrology, snow, and long-term soil state evolution in the current Noah Land Surface Model.
NOAH Multi-Parameterization Land Surface Model.
NCAR is collaborating on the development of the advanced Noah-MP land surface model (LSM) which uses multiple physics options for key land-hydrology processe.
This technology is an important component of advanced weather and hydrological prediction.
To help pilots navigate through significant terrain-induced and convective wind shear and turbulence that occur along the flight paths due to the airport’s proximity to Lantau Island mountain peaks.
The Wind Shear & Turbulence Warning System
Between 1993 and 1997, NCAR researchers conducted a field program focused on wind flow over the mountainous terrain around Hong Kong's airport. This study was followed by the development of a warning system concept, feasibility studies, system design and development, testing, implementation and training. RAL scientists and engineers designed and developed the Wind Shear and Turbulence Warning System (WTWS)– Hong Kong.
This system generates alerts for easy interpretation by pilots, controllers, traffic managers and aviation forecasters and allows aircraft to operate safely when operating at this very busy international airport.
Pilots landing and departing from Juneau, Alaska face some of the nation's most challenging conditions; the airport has a history of turbulence-related incidents involving passenger jets. In the aftermath of a 737 aircraft nearly being lost upon encountering severe turbulence, the Federal Aviation Administration (FAA) imposed restrictive rules of operation that were to be maintained until a new sophisticated warning system could be developed.
Juneau Airport Wind System (JAWS)
This project involved the development and implementation of sensing systems and turbulence alerting software. This role meant that the team would have to identify best placement of weather profiling stations in order to pinpoint areas of greatest turbulence, then design, build and maintain the sensor sites that provided information about wind speed, wind direction, air temperature, etc. In 1997, NCAR began the development of the Juneau Airport Wind System (JAWS) with the goals of improving flight operational safety and safe access to the airport. JAWS was fully tested and put into operational use by the FAA in 2012.
The JAWS allows aircraft to operate into and out of Juneau Airport in a safe and efficient manner.
JAWS-like systems could be used at airports around the United States experiencing similar terrain–influenced turbulence. Pilots flying into and out of airports located in Maui, Reno, and Las Vegas, and other sites in Alaska, for example, could benefit from JAWS technology, and new prototypes would benefit from lessons learned during the Juneau alert system's development and maintenance.
There is a growing desire to have accurate weather predictions in remote locations far away from weather observation locations (e.g., airports).
Location Optimized, Gridded & Integrated forCast (LOGICast™) System.
To generate forecasts where observations are not available, this software system starts with model data from a numerical weather prediction (NWP) model(s) and then downscales those data onto the target grid using a sophisticated climatological difference interpolation scheme. LOGICast™ typically use 4–km grid cell resolution. Higher resolution grids may be appropriate in regions of rapidly changing topography.
LOGICast™ is being used as a core forecasting technology by commercial weather companies. Leveraging this system, other applications now include road temperature forecasts along an entire roadway, and soil temperature forecasts for agriculture.
Increased accuracy of weather forecasts is required to satisfy the general public and decision makers and forecasts are desired for a growing number of global locations.
Dynamic, Integrated Forecast System (DICast®)
DICast® integrates meteorological data and generates forecasts at user-defined sites and lead times. A single consensus forecast from the set of individual forecasts is generated at each user-defined forecast site based on a processing method that takes into account the recent skill of each forecast module.
To better understand and model the timing and extent of vector-borne disease outbreaks. For example, viruses propagated by mosquitos to humans cause an estimated 400-million dengue infections annually.
Weather, Climate, & Health
NCAR, in collaboration with the Centers for Disease Control (CDC) is addressing diseases such as dengue, meningitis, plague, and West Nile.
Scientists are developing scalable, transferable methodologies that quantitatively and qualitatively link high-resolution socio-economic, health, and behavioral data with geophysical data from global climate and mesoscale weather models to better understand the complex interactions among climate processes, ecosystems, and health.
The ability to predict when and where outbreaks will occur would help allocate limited public health resources. This research is leading to better placement of care givers and medicines to reduce illness and fatalities.
Mosquitoes exploit a wide range of containers as sites for laying their eggs and development, yet approaches for modeling container water dynamics have been limited making it difficult to predict when mosquitos will mature and become a threat.
WHATCH'EM - Water Height And Temperature in Container Habitats Energy Model WHATCH’EM is a state-of-the-science, physically based energy balance model of water environment in containers that may serve as development sites for dengue vector mosquitos or other container-inhabiting arthropods. WHATCH’EM simulates the highly non-linear manner in which air temperature, humidity, rainfall and clouds interact with container characteristics (shape, size, and color) to determine water temperature and height, leading to results that are not always intuitive and likely not simulated by standard empirical models.
WHATCH’EM is now being leveraged for use in other model-based studies funded by NASA, NIH and DTRA to develop an early warning system for dengue risk.