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The division comprises three branches:

Advanced Display Systems Branch – Designs and develops interactive display systems for operational use and prototype systems for operational demonstration.

Data Acquisition and Dissemination Systems Branch – Utilizes object-oriented technology to develop local data acquisition and advanced weather dissemination for modernized Weather Forecast Offices. Dissemination decision support systems offer easy-to-interpret information of rapidly evolving weather events for emergency preparedness agencies.

Scientific Applications Branch – Develops and implements scientific software systems designed to improve weather forecasting by taking advantage of opportunities offered by recent advances in meteorological observations and information systems.

Significant features provided in build 5.1.1 include more flexibility in recalling saved screen states (procedures), which improves productivity by allowing a single procedure to be used for any radar or multiple models. More options allow users easier setup and storage of procedures. Improved inventory manipulation allows users to look at different time periods or subsample data to more effectively review long animation sequences. To improve diagnosis of severe weather, data such as noncollocated radars can be viewed on their native scale. In addition, the efficiency of generating radar mosaics was improved for more usefulness in operations. Users now have a flexible process to set up a spotter identification map, which eases the process of gathering real-time severe weather reports. Additional datasets are now available, including forecast guidance from NWS River Forecast Centers and the National Operational Hydrologic Remote Sensing Center.

Build 5.1.2 was undergoing alpha testing at the end of 2001, with general deployment scheduled for early 2002. New features include more infrastructure improvements that reduce the load on the processors and result in better performance and more reliable data processing. Cloud-top height can now be estimated from infrared satellite images using model or sounding data. A proximity alarm feature allows users to be alerted to the arrival of text products that affect their area of responsibility. For example, a forecaster in Indianapolis can be notified when the satellite precipitation estimate product notes heavy rain in southern Indiana, or the severe weather forecasters in Norman can set an alarm on any tornado watch product issued that affects their county warning area.

Initial field testing of the Linux version of D2D was completed at the Boulder Weather Forecast Office. By the end of 2001, the National Weather Service had installed a Linux workstation at each of its field offices. The improved performance has been eagerly received by field users.

FX-Collaborate – The Advanced Display Systems Branch continued work on the FX-Collaborate (FXC) project, an exploratory development to gain practical experience with new technologies and system designs for new forecasting workstation developments. FXC is a Java application that supports collaboration among users, distributed processing, and distributed databases. The ready availability of of higher speed communication now makes interactive collaboration practical. Considerable time was invested last year in solidifying existing capabilities and adding new features, such as a slide briefing capability, a vastly extended drawing tool, many new meteorological products, and the creation and execution of procedures. Another added feature is the ability to access data on Web servers and include images from Websites or local storage.

FXC provides the capability for multiple users to view real-time meteorological data, manipulate the information and displays, share local data, and create manual graphical products in a collaborative session. It can be used as an independent workstation or in a collaborative mode, in which all requests and display interactions are shared with every user participating in a session.

The first extensive field use of FXC began last year. The NWS River Forecast Center (RFC) in Atlanta uses FXC to coordinate with Emergency Operations Centers (EOCs) in Florida, Georgia, North Carolina, and South Carolina. During potential flooding situations, RFC forecasters brief EOC personnel in collaborative mode. In addition, EOC staff use FXC stand-alone to access current weather information. Two servers were installed at FSL to support this experiment, and all RFC/EOC access to data comes from these two servers.

Support was also provided to the Aviation Division staff in their FXC work with projects at the Ft. Worth Center Weather Service Unit (at the FAA Air Route Traffic Control Center) and the NWS Aviation Weather Center in Kansas City.

D3D – After successfully completing testing at FSL with visiting National Weather Service forecasters, the 3-D system moved into a limited field evaluation. D3D was installed on PC/Linux machines in five NWS forecast offices and two regional headquarters. Users were asked for feedback, and enhancements were made based on these suggestions. The major modifications involved making the D3D user interface more like the AWIPS user interface, familiar to the forecasters, and redesigning the sounding tool.

Systems administration and testing and configuration management support were provided for the division. As in past years, the branch coordinated collecting, transporting, and setting up FSL and NOAA exhbits at the annual American Meteorological Society Meeting.

FX-Collaborate – A briefing tool for the RSA project will require further development of the slide show technique. Space launch operations require adherence to a number of launch criteria. The branch will provide a set of templates to allow Launch Weather Officers to create and edit presentations of observed and forecast weather conditions and their relation to these criteria.

One aspect of the NWS' move toward gridded forecasting is the creation of a National Digital Forecast Database (see discussion of the Interactive Forecast Preparation System (IFPS) and GFESuite under the Modernization Division section). In support of this work, FXC will be used as a collaboration tool for intersite coordination of gridded forecast data.

Support will continue to be provided to the Aviation Division in their application of FXC to projects with the FAA and NWS. Work will continue on the RFC/Emergency Operations in the southeastern U.S., with hopes of its expansion to additional offices.

D3D – Field testing of D3D will continue, and display capabilities will be improved based on user feedback. The D3D Website will be enhanced to allow field offices to download the software for installation. (Figure 51 shows examples of how D3D can be used to enhance meteorological information from the Eta model.) A demonstration will be presented of how D3D can be used to process and display datasets from the three-dimensional lightning detection system (LDAR), used in the RSA project. These data will be prepared for evaluation by our RSA customer.

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NOAA has made a substantial investment in the NWS modernization over the last decade. New GOES and POES satellites provide very high-resolution cloud, water vapor, temperature sounding, and wind information that helps forecasters analyze short-term changes in the atmosphere. The NEXRAD radar network provides advanced reflectivity and velocity data that are invaluable for issuing severe storm watches and warnings. These datasets combined with new high-resolution observations and model guidance are fully integrated into the NWS AWIPS to provide forecasters with more flexibility in scrutinizing weather data.

The Data Acquisition and Dissemination Branch concentrates on deploying advanced weather dissemination technology to NWS field offices nationwide. The branch collaborates with emergency managers to conduct studies, develop prototype dissemination systems, and demonstrate the utility of using advanced weather information for state and local government users. Over several years of system development and iterative changes based on user feedback, the branch created the Emergency Manager Dissemination System (EMDS). It has been tested operationally at several WFOs, including Tulsa, Oklahoma; Des Moines, Iowa; and Boulder, Colorado (Figure 52).

Emergency managers run EMDS on a personal computer after they register with their local WFO Warning and Coordination Meteorologist (WCM), who works with local community emergency managers to tailor specific AWIPS products for their particular needs. The EMDS provides emergency managers with a mechanism to share situational awareness with many other area forecasters regarding local impacts of severe weather.

Branch staff continue to support the AWIPS Program Office for LDAD functionality, with particular attention given to the Web-based dissemination system. FSL continues to run a prototype dissemination system for experimental users to research weather dissemination technology.

Branch staff have prepared installation guides and training documents for EMDS users and NWS staff. After a year of use, hydrological observations have been added to the ingest of local data for AWIPS. FSL provides assistance when particular problems occur, and assists NWS with operational support and maintenance of EMDS data acquisition and operations.

The branch will continue building a prototype weather surveillance system that will allow users of EMDS to set criteria for their own emergency response during severe weather events. The surveillance system will watch AWIPS weather data coming in; when an event occurs that meets the criteria that have been set for a particular area, it will notify the emergency manager at this location that severe weather will likely occur.

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As surface analysis-only systems, MSAS and RSAS have the advantages of speed and closer fit to the observations. The systems produce a one-level, analysis-only grids and therefore require very few compute resources. Also, because the systems do not initialize a forecast model, their analyses are performed on the actual surface terrain and not along a model topography. Hence, no model surface-to-station elevation extrapolations are required, all surface observations may be used, and the fit to the observations is maximized. In addition, MSAS and RSAS incorporate elevation and potential temperature differences in the correlation functions used to model the spatial correlation of the surface observations. The resulting functions help to take into account physical blocking by mountainous terrain, and improve the representation of surface gradients.

Stations typically ingested by MSAS and RSAS include standard METARs, Coastal Marine Automated Network (C-MAN) observations, surface reports from fixed and drifting buoys, ships, and the NOAA Profiler Ground-based GPS Networks, as well as surface observations from any available local mesonets. Sophisticated quality control techniques are employed to help screen the surface observations. On AWIPS, the results of these techniques are passed to the AWIPS Quality Control and Monitoring System (QCMS).

To fill this need, MADIS was established to make value-added data available from FSL's Central Facility with the goal of improving weather forecasting, by providing support for data assimilation, numerical weather prediction, and other meteorological applications and uses.

MADIS also includes an Application Program Interface (API) that provides users with easy access to the observational information. The API allows each user to specify station and observation types, as well as quality control choices, and domain and time boundaries. Many of the implementation details that arise in data ingest programs are automatically performed. Users of the MADIS API, for example, can choose to have their wind data automatically rotated to a specified grid projection, and/or choose to have mandatory and significant levels from radiosonde data interleaved, sorted by descending pressure, and corrected for hydrostatic consistency.

MSAS – The major MSAS accomplishment in 2001 was the initial development of software upgrades necessary to increase grid resolution and vary domain boundaries for MSAS on the AWIPS system. These upgrades, scheduled for implementation with AWIPS build 5.2.2, include the incorporation of a customization script that allows each NWS WFO to specify the domain and resolution of their local MSAS systems, and also to specify the analysis grids desired by their forecasters. Although the domain and resolution parameters for MSAS are flexible, in previous AWIPS builds they have always been preset to cover the continental United States (CONUS) with a 60-km analysis grid. Starting in AWIPS build 5.2.1, however, each forecast office will be able to modify the location, size, and resolution of its local MSAS domain, and will also be able to specify the model background utilized in the MSAS analyses, the level of the MSAS pressure reduction (for example, 1500 m or sea level), and the time interval used in the MSAS pressure change analysis (for example, 1-hour or 3-hour pressure change). Changes in the domain size are linked to changes in the grid resolution in such a way as to minimize AWIPS impacts and guarantee that overall MSAS computational demands remain the same. For example, forecast offices can choose a 15-km, regional-scale domain, or a 60-km CONUS domain, but not a 15-km CONUS domain. Starting in build 5.2.2, MSAS will also, for the first time, support domains outside the Continental U.S., such as Alaska (Figure 53) and Puerto Rico.

RSAS – Work also continued on the next major RSAS upgrade at NCEP. Plans at NCEP call for increased horizontal resolution and extended domain boundaries for RSAS. The horizontal resolution will increase to 15 km, and the domain will stretch from Alaska in the north to Central America in the south, and also cover significantly more oceanic areas. Other upgrades include a new topography grid which has been improved to better match observation elevations, and provide better treatment of the model backgrounds.

RSAS will continue to provide hourly surface analyses, updated twice each hour (currently at 5 and 21 minutes past the hour), for RSAS sea-level pressure, NWS sea-level pressure, altimeter, potential temperature, dewpoint temperature, dewpoint depression, 3-hour pressure change, and surface winds. (Figure 54 shows an AWIPS D2D screen of the new RSAS 15-km North American domain. Sea-level pressure and 3-hour pressure change analyses are shown for 2200 UTC 10 May 2001.) In addition, temperature, specific humidity, and equivalent potential temperature will be provided as derived grids.

Development for the new RSAS version was completed in 2001, and the system was ported to NCEP computers for production testing. Objective and subjective evaluations are being conducted in preparation for operational implementation. Work was also completed on establishing an operational backup for RSAS in the FSL Central Facility.

For more information on the customization options offered in the AWIPS build 5.2.2, see http://www-sdd.fsl.noaa.gov/MSAS/localization.html. More information on the new RSAS version is available in the NWS Technical Procedures Bulletin at http://www-sdd.fsl.noaa.gov/MSAS/rsas_tpb.html. For general information on both MSAS and RSAS, see http://www-sdd.fsl.noaa.gov/MSAS/msas.html.

The datasets that make up the initial MADIS database include 1) upper-air observations: radiosonde, automated aircraft, NOAA Profiler Network (NPN) wind profiler, and 2) surface observations: maritime, meteorological aviation reports (METARs), surface aviation observations (SAOs), Local Data Acquisition and Dissemination (LDAD) mesonet, NPN surface, and GPS surface. The observations are acquired by the FSL Central Facility from a variety of sources, including NOAAPORT, Aeronautical Radio INCorporated (ARINC), and the NOAA Profiler Network (NPN) and Ground-Based GPS data Hubs within FSL's Demonstration Division. Mesonet data are decoded and stored with software originally developed for the NWS LDAD system, and includes over 2,000 stations from local, state, and federal agencies and private firms. Major contributors to the mesonet data stream are the NOAA Cooperative Institute for Regional Prediction (CIRP) at the University of Utah, which provides "MesoWest" data from the Cooperative Mesonets in the western United States, the Boulder NWS Forecast Office which provides mesonet data from the local Denver/Boulder area, and also the Remote Automated Weather System (RAWS) network which is run by the National Interagency Fire Center (NIFC). The mesonet dataset also includes observations from volunteer citizen weather observers. Wherever possible, FSL uses redundant sources to maximize data availability. Although most of the MADIS data is available without restrictions, aircraft and mesonet observations are proprietary to the data providers, and are subject to review and restriction by those providers. While some of the datasets are global, the bulk of the data currently available are contained in this domain extending from Alaska into Central America. Figure 55 shows MADIS observations available in the North American domain at 1200 UTC on 13 December 2001.

Quality Control (QC) of the observations in the MADIS database is also provided, since considerable evidence exists that the retention of erroneous data, or the rejection of too many good data, can substantially distort forecast products. Observations in the database are stored with a series of flags indicating the quality of the observation from a variety of perspectives (such as temporal consistency and spatial consistency), or more precisely, a series of flags indicating the results of various QC checks. Users of MADIS can then inspect the flags and decide whether or not to ingest the observation.

MADIS QC checks were implemented on three levels, with Level 1 QC checks considered the least sophisticated and level 3 the most sophisticated. Level 1 checks include validity checks which compare the observed values to specified tolerance limits, and position consistency checks which compare the current location and time report to previous reports to ensure a moving platform's position is consistent with its reported movement. Inconsistent positions are identified as unreal speeds or unlikely changes from the last reported position.

Level 2 checks consist of internal and temporal consistency checks for all observation types, as well as time-height consistency checks for wind profiler data, and hydrostatic, superadiabatic lapse rate, and wind shear checks for the radiosonde data. In general, temporal consistency checks restrict the temporal rate of change of each observation to a set of prespecified tolerance limits, and internal consistency checks enforce reasonable, meteorological relationships among observations measured at a single station. For surface data, temporal consistency checks are performed on all pressure, humidity, temperature, and wind data; for aircraft data, they are applied to temperature and altitude reports only. A common example of an internal consistency check is the comparison of dewpoint temperature to temperature: the dewpoint observation must not exceed the temperature observation made at the same station or both observations are flagged as failing. Internal consistency checks for wind profiler data include a bird contamination check which inspects and combines profiler measurements of wind direction, velocity variance, and vertical velocity to detect the presence of bird migration. If birds are determined to be present, the corresponding wind data are flagged as failing the internal consistency check. The time-height check for profiler data then ensures consistency in the time and height dimensions of the winds by using pattern recognition techniques to quality control the current hour's data with past data in a 6-hour sliding window. For radiosonde data, the hydrostatic, super adiabatic lapse rate, and wind shear checks ensure hydrostatic consistency between vertical layers, and also reasonable vertical consistency for the temperature and wind data.

MADIS level 3 QC checks use surrounding observations to check the spatial consistency of the observation being quality controlled. The only level 3 check supported in the initial version of MADIS is a spatial consistency check applied to surface observations by the RSAS system running at FSL. Results of the RSAS check are included with all MADIS surface observations in the FSL database.

The API released with the initial version of MADIS is a library of subroutines, callable from FORTRAN, which provides access to all of the MADIS observation and quality control information. In general, the API is very easy to use, and is designed so that the underlying format of the database is completely invisible to the user, a design that also allows it to be easily extended to other databases. In the initial verison of the API, support is provided for the FSL database, and also for the database used in the AWIPS deployed at all National Weather Service Forecast Offices.

The FSL MADIS database and API are freely available to interested parties in the meteorological community. MADIS data files are compatible with AWIPS and with the analysis software provided by the FSL Local Analysis and prediction System (LAPS). For more information on MADIS, or to apply for a MADIS datafeed, see http://www-sdd.fsl.noaa.gov/MADIS/.

Observations and capabilities will continue to be added to MADIS. Emphasis will be on increasing the number of observations in the mesonet database, working with the FSL Demonstration Division to add support for multiagency profiler data, and also providing a MADIS software interface for the data ingest system of the community-developed Weather Research and Forecasting (WRF) model. Access to MADIS will continue to be provided through the Web interface which provides the forms necessary to request real-time and archived data, and also allows users to download the MADIS API, a "README" installation guide, documentation, and sample programs and data.

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