Objective

To improve meteorological support through the development and application of new technologies.

Description

The Four-Dimensional Weather (4DWX) System is the product of twelve years of research and development sponsored largely by the U.S. Army Test and Evaluation Command (ATEC), and is accredited for operational use at seven test ranges. Through the use of new capabilities in high-resolution mesoscale modeling, short-term thunderstorm prediction, multi-dimensional integrated displays, and fine-scale climatological analysis tools, the ranges now provide test customers with more accurate test go/no-go guidance.

In its original conception, 4DWX was targeted solely for providing advanced capabilities to the ATEC range meteorology branches, but NCAR has since adapted the system and created derivative technologies for use by other organizations:

• Analysis of potential exposure of U.S. troops to nerve agents during the first Gulf War;
• Consequence assessment for counter proliferation mission planning in Afghanistan and Iraq;
• Anti-terrorism support for the 2002 Salt Lake City Olympics, the 2004 Summer Olympics in Athens and the 2006 Winter Olympics in Torino;
• Urban-scale modeling in Washington D.C., and OKC;
• Protection of the Pentagon and surrounding regions; and
• Support for firefighters in CO and AZ during summer wildfires in 2002.

Army Test-Ranges

4DWX was originally developed to provide advanced capabilities solely to the ATEC ranges. 

Case Studies

NCAR has adapted 4DWX and created derivative technologies for use by other organizations.

Demos

4DWX products stand alone or can be coupled with other specialized applications. 

Sponsor

ATEC | DTC
The U.S. Army Developmental Command (DTC) is the developmental testing arm of the U.S. Army Test and Evaluation Command (ATEC) and the Army's premier organization for conducting developmental testing of weapons and equipment. With the largest, most diverse array of testing capabilities in the Department of Defense , DTC tests military hardware of every description under precise conditions across the full spectrum of arctic, tropical, desert and other natural or controlled environments on highly instrumented ranges and test courses.

Collaborator

DTRA
The Defense Threat Reduction Agency safeguards America and its friends from weapons of mass destruction (chemical, biological, radiological, nuclear and high explosives) by reducing the present threat and preparing for the future threat.

NGIC
The National Ground Intelligence Center provides scientific and technical intelligence (S& TI) and general military intelligence (GMI) on foreign ground forces in support of the warfighting commanders, force and material developers, DA, DOD, and National- level decisionmakers. The NGIC also manages the Army's Foreign Material Exploitation Program and foreign material acquisition requirements and constitutes a single authoritative source for comprehensive ground forces threat to the Army and other services

EOL
The Earth Observing Laboratory develops, provides and operates state-of-the-art, reliable atmospheric observing systems and associated support services to the university-based research community for purposes of climate and weather research worldwide. Facilities include aircraft operation and instrumentation, the deployment of radars, in-situ instruments and arrays, sondes and other electronic sensors in the field, and the development and support of data assimilation and analysis software.

CISL
Computational and Information Systems Lab's goal is to enable the best atmospheric research in the world by providing and advancing high-performance computing technologies. CISL offers computing, research data sets, data storage, networking, and data analysis tools for NCAR users.

MMM
The Mesocsale and Microscale Meteorology Division's primary focus is on understanding atmospheric phenomena on spatial scales ranging from micrometers to megameters and time scales from seconds to a few days.

To support testing and evaluation of SDF, RAL is also developing a virtual testing and evaluation environment, known as Virtual THreat-Response Emulation and Analysis Testbed (VTHREAT). This will provide the capability of simulating a realistic CBRN release scenario, placement of CBRN and meteorological sensors, and extraction of the resulting synthetic sensor readings.  These synthetic observations can then be used by the algorithms to evaluate their ability to recreate the CBRN event.

Objective

Technology developed in this project utilizes Chemical, Biological, Radiological, Nuclear (CBRN), and meteorological sensor readings along with transport and dispersion models to characterize unknown CBRN source properties and refine CBRN downwind hazard assessments.

Description

SDF Algorithm demonstation using VTHREAT to produce a release scenario. SCIPUFF demonstrates source characterization and hazard refinement.

The Sensor Data Fusion (SDF) project is developing tailored meteorological decision-support applications for the military and domestic emergency-response communities.  In particular, these applications are used to enhance DoD's Chemical, Biological, Radiological, and Nuclear (CBRN) hazard prediction toolsets such as the Hazard Prediction and Assessment Capability (HPAC) and more recently the Joint Effects Model (JEM).

A main goal is developing an operational algorithm that can estimate an unknown CBRN source and predict a refined downwind hazard from that source while using available CBRN and meteorological sensor observations.  Integrating this algorithm into the HPAC/JEM hazard-prediction toolsets will also be addressed.

To support testing and evaluation of this product, RAL is also developing a virtual testing and evaluation environment, known as Virtual THreat-Response Emulation and Analysis Testbed (VTHREAT). This will provide the capability of simulating a realistic CBRN release scenario, placement of CBRN and meteorological sensors, and extraction of the resulting synthetic sensor readings.  These synthetic observations can then be used by the algorithms to evaluate their ability to recreate the CBRN event. 

While most of the work on this project is technology-development oriented, the ultimate objective is to implement a verified and validated SDF algorithm within the JEM framework for operational use in the battlefield and for non-wartime emergency response activities.

Technologies

SCIPUFF Dispersion Model from L3-Titan

The SCIPUFF (Second-order Closure Integrated PUFF) model is a Lagrangian puff dispersion model developed by Titan's ARAP Group that uses a collection of Gaussian puffs to represent an arbitrary, three-dimensional time-dependent concentration. The turbulent diffusion parameterization is based on turbulence closure theory, providing a direct relationship between the predicted dispersion rate and turbulent velocity statistics of the wind field. In addition to the average concentration value, the closure model also provides a prediction of the statistical variance in the concentration field resulting from the random fluctuations in the wind field. The closure approach also provides a direct representation for the effect of averaging time. SCIPUFF has been incorporated into the Defense Threat Reduction Agency's (DTRA) Hazard Prediction and Assessment Capability (HPAC) software. HPAC is utilized for planning and analysis as well as in the field by military personnel to rapidly determine consequences of dispersing chemical, nuclear and biological agents. SCIPUFF has been validated against a number of laboratory and field experiments, demonstrating its usefulness for non-military applications. It has been recommended as an alternative model by the EPA which can be used on a case-by-case basis for regulatory applications. The publicly available version of SCIPUFF is the same version incorporated in HPAC except that the proprietary and developmental features have been disabled. SCIPUFF runs on a PC with a user-friendly Graphical User Interface (GUI).

For more information http://www.titan.com/products-services/336/index.html?docID=336

MM5/WRF

MM5 is a numerical weather model developed by NCAR and the Pennsylvania State University. It is designed to simulate and predict mesoscale atmospheric circulations.

Variational Doppler Radar Assimilation System (VDRAS)

Developed at RAL, VDRAS provides detailed and frequently updated information on wind, rain, and other real-time weather variables. VDRAS is the first real-time system to diagnose low-level wind and temperature over a wide region using four-dimensional data assimilation of Doppler radar data.

Lagrangian Particle Dispersion Model (LPDM)

The emission, transportation, diffusion, and deposition of tracers can be computed in WRF-chem by turning off chemistry. Lagragian particle models that compute trajectories of a large number of particles (infinitesimally small air parcels) is another way to show the transport and diffusion of tracer in the atmosphere. The main advantage of Lagrangian models is that, there is no numerical diffusion. The Lagrangian system is independent of a computational grid and can resolve diffusion near point sources. Lagrangian models can also be used to determine source-receptor relationships and air mass trajectories.

Eulerian Lagrangian (EULAG) Large Eddy Simulation (LES)

EULAG is a numerical solver for all-scale geophysical flows. The underlying anelastic equations are either solved in an EULerian (flux form), or a LAGrangian (advective form) framework. EULAG model is an ideal tool to perform numerical experiments in a virtual laboratory with time-dependent adaptive meshes and within complex, and even time-dependent model geometries. These abilities are due to the unique model design that combines the nonoscillatory forward-in-time (NFT) numerical algorithms and a robust elliptic solver with generalized coordinates. The code is written as a research tool with numerous options controlling the numerical accuracy and to allow for a wide range of numerical sensitivity tests. These capabilities give the researcher confidence in the numerical solutions of his/her problem. The formulation of the model equations allow for various derivatives of the code including codes for stellar atmospheres, ocean currents, sand dune propagation or biomechanical flows. EULAG is a fully parallized code and is easily portable between different platforms.

Conference Publications and Presentations

Dr. Paul E. Bieringer. Chem/Bio Hazard Assessment and Refinement Through Sensor Data Fusion. September, 2007.

George Bieberbach. DTRA Weather Services Research and Development. December, 2005. 

The Climate Inspector is an interactive web application which expands Geographic Information Systems (GIS) mapping and graphing capabilities to visualize possible temperature and precipitation changes throughout the 21st century.

The Climate Inspector is an interactive web application which expands GIS mapping and graphing capabilities to visualize possible temperature and precipitation changes throughout the 21st century. The maps and graphs are generated from a large dataset of climate simulations by the NCAR Community Climate System Model (CCSM4). These simulations were prepared for the 5th Assessment Report of the Intergovernmental Panel on Climate Change.

With Climate Inspector you can explore how temperature and precipitation may change based on different emission trajectories (i.e., Representative Concentration Pathways), investigate climate changes around the globe and through time, inspect climate trends, variability and uncertainty, and download maps and data.  Here you can download temporal climate data for a single grid cell.

Climate-FDDA uses the RT-FDDA system (MM5/WRF mesoscale model + assimilation of observations) to build a database of gridded “weather” phenomena over a particular region of earth

“Weather database” extends over many years (usually 20-40) which are then “combined” to estimate the expected weather and its associated variability and/or uncertainty over a particular region

Climate-FDDA “extends” the climatography from the observation locations (SAMS, RAOB, etc.) to a fine-resolution grid using a dynamically consistent tool (takes into account fineresolution topography, land, coastline, etc.)

The Global Climatology Analysis Tool (GCAT) is capable of generating fine-scale (3.3km) climatological analyses anywhere around the globe. For example, in a given month, analyses for each of the past 40 years are generated. Uncertainty in the mean analysed meteorological fields is derived from the ensemble and, for risk assessment, can be input into plume models, such as the DOD HPAC application.

WRF ensemble settings runs anywhere in the world with the GCAT web interface.

By applying: 1) NCAR's MM5-based Real-Time Four-Dimensional Data Assimilation (RT-FDDA) system; 2) the NCAR-NCEP Reanalysis Project (NNRP) 2.5 degree, 40-year gridded model dataset for lateral boundary conditions; and 3) observations from the NCAR ADP historical repository, GCAT creates a set of probabilistic forecasts and plume products to support the National Ground Intelligence Center's (NGIC) mission for Chemical Biological and Radionucleide (CBRN) consequence analysis. GCAT uses the climatological information generated from RT-FDDA, and couples it to the Second order Closure Integrated PUFF (SCIPUFF) dispersion model, which is part of the Defense Threat Reduction Agency's (DTRA) Hazard Prediction and Assessment Capability (HPAC) toolset. This automated system takes advantage of the Linux cluster technology to perform the necessary climatological and plume-modeling computations. Outputs consist of data files and images that can be downloaded through a web interface.

Technologies

WRF

NCEP/NCAR Reanalysis Project (NNRP)
Scientific Computing Division's Data Support Section

Automated Data Processing (ADP)
Scientific Computing Division's Data Support Section

Resources

Description

F. Vandenberghe, R. Weingruber, M. Casado, S. Swartz, R. Sheu, M. Ge, Al  Bourgeoi, T. Betancourt, S. Swerdlin, T. Warner The Global Climatology Analysis Tool . March 2006.
(pdf) (html)

Journal Publications

Warner, TT., Bowers, JF., Swerdlin, SP., Beitler, BA. A Rapidly Deployable Operational Mesoscale Modeling System for Emergency-Response Applications. Bull. Am. Meteorol. Soc. Vol. 85, no. 5, pp. 709-716. May 2004. abstract

Warner, TT., Sheu, R; Bowers, JF., Ian Sykes, R., Henn, DS. Ensemble Simulations with Coupled Atmospheric Dynamic and Dispersion Models: Illustrating Uncertainties in Dosage Simulations. J. Appl. Meteorol. Vol. 41, no. 5, pp. 488-504. May 2002. abstract

Conference Publications and Presentations

F. Vandenberghe. Estimates of Regional Climate using a Model-Based Analysis Tool. October 2005.
(pdf) (jpg)

F. Vandenberghe, T. Warner, S. Swerdlin, R. Babarsky. A Relocatable Regional Climatological Analysis Tool for CBR Hazards Assessment.July 2005.
(pdf) (html)

Animations

Simulation of an anthrax release in downtown Torino in February. The mean winds from an ensemble of 40 MM5 runs was used to drive the Defense Threat Reduction Agency SCIPUFF Transport and Diffusion model.

Simulation of gas release over Gaza, using GCAT downscaling capabilities.

Advanced Wind Prediction System

Fig. 1 Diagram of the WRF domains used in the wind energy prediction system. The grid spacings are as follows: D1=30km, D2=10km, and D3=3.3km.

In late December 2008, RAL began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their energy generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies will help operators make critical decisions about powering down traditional coal–and natural gas–powered plants when sufficient winds are predicted, enabling the increased reliance on alternative energy while still meeting the needs of its customers. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is also collaborating by developing algorithms to calculate the amount of energy that the turbines generate by winds blowing at various speeds for a broad spectrum of wind facilities. The wind prediction technologies have been designed to cover Xcel Energy wind farms in Colorado, Minnesota, New Mexico, Texas, and Wyoming. It is anticipated that wind energy forecasting companies in the United States and overseas may adopt the developed technologies to help utilities that need more accurate wind predictions to transition away from fossil fuels.

To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources, including satellites, aircraft, weather radars, ground–based weather stations, and even sensors on the wind turbines. The information is utilized by three powerful NCAR–based tools:

• The Weather Research and Forecasting (WRF) computer model, which generates finely detailed simulations of future atmospheric conditions
• The Real–Time Four–Dimensional Data Assimilation System (RTFDDA), which continuously updates the simulations with the most recent observations
• The Dynamic Integrated Forecast System (DICast^®), which statistically optimizes the output based on recent performance

Wind predictions are made for each wind turbine and a sophisticated post–processing algorithm converts the hub–height wind predictions into energy predictions. The energy generation values for each turbine, wind facility and connection node are provided to Xcel Energy.

Fig. 2 Conceptual diagram of the wind energy prediction technology components that will be incorporated into the final configuration.

In the first six months of the agreement, NCAR successfully developed the initial capabilities and began providing wind energy predictions. By late September 2009, all (40+) wind facilities were included. Real–time information from Xcel Energy's largest wind facilities is utilized by the wind energy system to refine the power curve calculations and tune the forecasts.

The Real–Time Four Dimensional Data Assimilation (RTFDDA) and forecasting system, that has been developed by RAL to satisfy the meteorological needs of Army test ranges, has been adapted for wind–energy prediction. RAL implemented an operational RTFDDA system over the western and central states for supporting wind–power forecasting. This system contains three modeling domains with grid sizes of 30, 10 and 3.3 km (Fig. 1). The 3.3 km domain covers the Rocky Mountains from New Mexico to Montana, the High Plains states, and most areas of the Central Plains. The system runs with a 3–hour cycle. In each cycle, it produces 27–hour forecasts for the innermost domain and 72–hour forecasts for the two coarser domains. The inner domain (3.3 km) generates output at 15–minute time steps.

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this renewable resource. NCAR and a team of researchers from public, private, and academic sectors partnered to develop and assess a new, cutting-edge solar power forecasting system called Sun4Cast®. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.

Sun4Cast® integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is NCAR’s WRF-Solar®, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple numerical weather prediction (NWP) models are blended via the DICast® System.  For short-range (0-6 h) forecasts, Sun4Cast® leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast® systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making.

After testing Sun4Cast® at multiple sites, the research team has determined that it can be up to 50 percent more accurate than current solar power forecasts. This improved accuracy will enable utilities to deploy solar energy more reliably and inexpensively, reducing the need to purchase energy on the spot market. 

The  NCAR's GIS Program Climate Change Scenarios GIS data portal is intended to serve a community of GIS users interested in climate change. The free datasets of climate change projections can be downloaded as a shapefile, a text file, or as an image. Many 2D variables from modeled projected climate are available for the atmosphere and land surface. These climate change projections were generated by the NCAR Community Climate System Model, or CCSM, for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC).

Controlling snow and ice buildup on roadways during winter weather events presents several challenges for winter maintenance personnel. Among these challenges is the need to make effective winter maintenance decisions (treatment types, timing, rates, and locations), as these decisions have a considerable impact on roadway safety and efficiency. Additionally, poor decisions can have adverse economic and environmental consequences. In an effort to mitigate the challenges associated with winter maintenance decisions, the Federal Highway Administration (FHWA) Office of Transportation Operations (HOTO) initiated a program in 2001 aimed at developing a winter road Maintenance Decision Support System (MDSS®).

Maintenance Decision Support System (MDSS®)

The MDSS® project goal is to develop a prototype capability that:

• Capitalizes on existing road and weather data sources
• Augments data sources where they are weak or where improved accuracy could significantly improve the decision–making task
• Fuses data to make an open, integrated and understandable presentation of current environmental and road conditions
• Processes data to generate diagnostic and prognostic maps of road conditions along road corridors, with emphasis on the 1–to 48–hour horizon (historical information from the previous 48 hours will also be available)
• Provides a display capability on the state of the atmosphere and roadway
• Provides a decision support tool, which provides recommendations on road maintenance courses of action
• Provides all of the above on a single platform, with simple and intuitive operating requirements, and does so in a readily comprehensible display of results and recommended courses of action, together with anticipated consequences of action or inaction

If you're interested in developing an MDSS® project contact:

[staffdir:person:linden]

Funding

Technology

MDSS® Prototype Technical Overview

The MDSS® ingests weather forecast data at locations important to the user’s operations. These forecast locations are typically at surface observation stations such as RWIS and METAR sites, though they need not be. The weather forecasts at each forecast location serve as input to the pavement heat balance model (e.g., METRo) that predicts the road surface and subsurface temperatures and the snow depth at each forecast lead-time. These forecast road conditions are used to generate treatment plans at each site based on Rules of Practice guidelines. The prototype MDSS® also includes a graphical user interface display designed for easy interpretation by road maintenance managers. This display application is designed to allow the maintenance manager to generate “what-if” scenarios by setting up customized treatment plans and seeing the resulting predicted road conditions.

SYSTEM ARCHITECTURE USED IN CURRENT MDSS® WINTER DEMONSTRATIONS.

ILLUSTRATION OF ROAD CONDITION AND TREATMENT MODULE LOGIC.

ILLUSTRATION OF ROAD CONDITION AND TREATMENT MODULE LOGIC.

FACTORS AFFECTING CHEMICAL CONCENTRATION.

Operations

MDSS® Screen Views

MDSS® state view showing Colorado weather alerts, Automated Vehicle Location (AVL) data, and radar data.

 

View of E-470 maintenance district.

 

Event Summary Display

 

Forecast history plot

 

Treatment Selector

 

Weather Forecast Graph

 

Visible Satellite Plot

 

CONUS radar view