REAL-TIME APPLICATIONS & DISPLAY DEVELOPMENT and TECHNOLOGY IMPROVEMENT

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WDSS-II and NEXRAD

IDENTIFYING PROBLEMS AND FINDING SOLUTIONS:

Research to Operations

Problem: How can we make newer technology available to NWS operations?

Solution: WDSS-II vastly improves the pace of science and technology advancement by making it easy to test new products and concepts. WDSS-II was developed to be versatile by including an application programming interface (API) that allows algorithm and display developers to easily test and evaluate their products while WDSS-II is performing under operational conditions 24/7.

The unique capabilities of WDSS-II includes the ability to:

These unique capababilities allow applications to be developed quickly within WDSS-II and validated in an automated manner. Once an application has proven to be useful, it is often reimplemented on the operational systems of the National Weather Service (ORPG or AWIPS). However, the fact that it was developed and tested in a real-time system makes that transition to an operational system much easier.

Problem: Merging data from multiple radars is complicated. Issues include: varying beam geometry with range, vertical gaps between radar scans, lack of time synchronization between radars, storm movement, varying beam resolutions between different types of radars, beam blockage due to terrain, differing radar calibration and inaccurate time stamps on radar data.

Solution: NSSL developed and tested a technique based on an intelligent agent formulation for taking the base radar data (reflectivity and radial velocity), and derived products, from multiple radars and combining them in real-time into a rapidly updating 3D merged grid. This technique has been used to merge scalar fields, both reflectivity and derived shear products, from all the WSR-88D radars in the CONUS, and then transmitted in real-time to AWIPS/N-AWIPS workstations at weather forecast offices and other national centers for evaluation and verification. This same method may be used to compute 2D wind fields by merging radial velocity data from multiple radars and performing a real-time multi-Doppler analysis.

Improving Warnings

Problem: A warning forecaster must integrate all available information about a storm to improve the accuracy and reduce the uncertainty of a prediction.

Solution: The WDSS-II automatically integrates multiple sources of information to provide guidance and a "safety net" to forecasters through algorithms and displays for severe weather analysis, warnings, and forecasting. WDSS-II can ingest all types of radar data (WSR-88D, TDWR, SMART-R, DoW, NWRT, CASA, and Canadian radars), numerical model data (such as RUC in GRIB format) for near storm environment information, satellite (visible, infrared, and water vapor), Lightning Mapping Array (3D lightning sources) data, and METARS/OK Mesonet surface observations. Algorithms developed in WDSS-II are able to uniquely integrate these diverse data sources into knowledge datasets that may be used by forecasters. The display uses a 3-D earth-relative coordinate system, a configurable window layout, user configurable options, and several other unique features to help the forecaster evaluate the weather situation.

Problem: The ability to cross-section a storm with AWIPS is cumbersome and is often avoided during intense warning operations. As a result, NWS forecasters are forced to mentally build a cross-section of a storm using the different radar elevation angles.

Solution: NSSL and the NWS's Meteorological Development Laboratory (MDL) developed the Four-Dimensional Stormcell Indicator (FSI) to increase warning skill and warning consistency between forecasters. The FSI is a four-dimensional (animate in three dimensions) base radar data analysis tool that uses WDSS-II display concepts for NWS severe weather warning decision operations. The FSI displays a cross-section of a storm that changes location and redraws easily without a special request to the Radar Product Generator (RPG) by the NWS forecaster. The 4-panel vertical structure display will improve the forecasters’ conceptual model of the storm. Operational 3D visualization of radar data will also allow meteorologists to discover new 3D signatures useful in the diagnosis of severe storms. The first prototype of the FSI, limited to base radar data alone, is expected to be ready for the AWIPS Operational Build 8 in the Fall of 2007.

Improving Accuracy

Problem: How can these new tools improve severe weather warnings?

Solution: The WDSS-II's capability to ingest other data sources for use in algorithms and conceptual models gives the forecaster the ability to re-examine an area and diagnose a situation. Automated computer algorithms can ingest and perform routine data processing tasks faster than humans without suffering from fatigue or other lapses in judgment. For example, the NEXRAD hail algorithm currently uses temperature soundings taken every 12 hours. If RUC data is used, which is produced every hour on a 20km grid, the hourly and spatial resolution is much greater. The best solution is to allow the computer algorithms to perform the data ingest and processing tasks and to generate intermediate products, while the human forecaster is responsible for the interpretation/analysis of data and for making the final warning decision.

Problem: The NWS tracks warning performance statistics and desires to increase the probability of detection (POD) and lead time, while reducing the false alarm ratio (FAR). What techniques can be used to improve these metrics?

Solution: Observations may be assimilated into several storm-scale numerical weather models. These models may then be used to create forecasts of severe weather development. WDSS-II is involved as a visual research tool for modelers to view stormscale numerical model output data and to develop output that will provide forecasters the ability to "warn-on forecast" – issue probabilistic tornado warnings based on a forecast 30-60 minutes ahead of time rather than warnings based on detection or observation. WDSS-II may also be used to visualize and evaluate these probabilistic forecasts.

Sharing Technology

Problem: Scientists need a way to share experimental severe weather products with other researchers and operational meteorologists for evaluation and feedback.

Solution: A variety of multi-sensor severe weather products are generated by NSSL and shared to Google Earth and other Geographic Information Systems (GIS) users via the internet. Some of these products include spatially gridded fields of Vertically Integrated Liquid, Maximum Expected Hail Size, tracks of circulations derived from Doppler velocity data, composite reflectivity, and 30-to-60 minute forecast reflectivity fields, among others. These products have a spatial resolution of approximately 1km by 1km and are generated every one to five minutes by the WDSS-II on the scale of the CONUS. The WDSS-II system provides the images in GeoTIFF format which may be imported into most GIS software, including Google Earth.

What's Next: There are a lot of new meteorological observation sensors being developed and coming on-line.