Development of Weather Processing System by Integrating Weather Data into GIS
Display of meteorological data:
The first goal in GIS Meteorology is to convert meteorological data and information to "GIS negotiable" formats. The following table summarizes the relationships of weather data to GIS formats (or shape), but is not intended to be exhaustive. Several examples are provided (some from previous papers). The most notable is that images must be provided in standard projections, and sufficient registration information must be provided in order to construct a World File for each image. Many systems also assume that the satellite image is the "natural" projection, and maps are transformed to match the greater data volume. Another change relates to data formats and customary business practices within the weather services. There are many formats extant for weather data and products, and weather systems must develop a decoder/encoder pair for each. However, we have found that these products reduce to a few formats, usually defined by geographic coverage, and a message(s) or datum (data array). The data/product format field can be simplified, and the formats could also be simplified.
| Shape | Weather data type |
| point | Surface obs, rain gage, river gage, pilot reports, model grid point data, lightning, Tropical Cyclone position |
| line | Contours, fronts, rivers and river stage, rawinsonde profiles, roads and road conditions, air parcel trajectories |
| polygon | Radar, watch/warning boxes, area/zone forecasts, plumes (air parcels) |
| image | Satellite images, charts |
| grid object | Intermediate objects for all data on a surface. Surfaces include constant height (e.g. MSL), constant pressure (isobaric), and constant potential temperature (isentropic). |
Customize GIS functionality
The arrival of ESRI Spatial Analyst opened the way for serious meteorological analysis, substantially reducing our need to call external functions. Various conventional meteorological procedures were prototyped. Remote Sensing plays an important role in data acquisition and processing. Procedures demonstrated include:
Cloud Data - Infrared image raster data are converted to a grid, calibrated for radiance, and then transformed to cloud top temperature. Independent information on atmospheric temperature variation with height allows conversion of brightness temperature to geopotential height (meters above MSL). The resulting grid is essentially a "DTM" of cloud tops. This capability is relevant to the analysis of Tropical Cyclone character.
Surface Analysis - Atmospheric soundings from rawinsondes (plus satellite and aircraft observations) are used to estimate the Montgomery Stream Function (MSF) on a surface of constant potential temperature. Note that ArcView is well suited to calculating the actual geographic locations of the balloons (they move during ascent to 100 mb and above). Contours of MSF on this surface can be used to calculate isentropic air parcel trajectories.
Satellite Data - Selected multi-spectral images from Polar and Geostationary satellites can be arithmetically combined to estimate "soundings" of meteorological parameters (e.g. Temperature) and trace species (e.g. water vapor). The trick is knowing which coefficients are best in the (usually linear) combination of brightness temperatures.
Integrate other sources of information
It became obvious to us that the typical GIS-based systems architecture should be redesigned to account for the streamlining gained through our prototyping with external interfaces (DDEs and RPCs). This modification demonstrates the similarity of external algorithms, models, and other support functions. The closed loop from data ingest (1), to user interaction (2), to analysis and simulation/modeling (3), is commonly known as the "data assimilation cycle" (4).
User friendly interface
We have an informal list of terms which are commonly used by meteorolgists, but which are known by a different name to those more familiar with GIS. For example, an ArcView "theme" is known as an "overlay" to IMD field personnel familiar with WPS (s weather processing system). Classification of a theme in ArcView is close to "enhancement" of an image by meteorologists, but there is little corollary for older weather systems. Most of our GIS effort supporting the IMD Modernization concerns the development of map databases. Such databases include IMD forecast zones, marine zones, time zones, political boundaries, etc. We have to select ESRI Shapefile TM to provide map "backgrounds", which are considered to be "static" and fundamentally different from the "dynamic" hydromet data which are so important to all of us.
Dynamic linking and animation
Animation is a key function for weather display and analysis systems. It is clear that ESRI could support animation using frame sequencing, but such a capability would be unique to each hardware platform. Commercial frame animation software is also available, but there remains the issue of coupling the ArcView View object with the animation page or frame.
3.0 Results and Discussions:
3.1 Results:
A sophisticated WPS that will protect people, property and businesses, managers in our largest metro areas face challenges and threats more complex than ever. More often than not, weather is at the heart of any given emergency, and government officials need the best information possible to prepare for the impact of a severe weather event and to support future and long-term city planning issues
3.2 Summary:
By integrating real-time weather information into a GIS-based decision support system, and utilizing advanced software tools for storm tracking, hazard prediction and consequence assessment, today’s decision makers or managers are technologically prepared to make better and faster decisions which can reduce disaster costs, minimize loss of life, and improve preparedness against natural and technological disasters.
3.3.Future considerations:
In the future, it is possible to envisage a suite of tools that could support MEAs such as Kyoto. The Remote Sensing component would include a constellation of optical,LIDAR and radar instruments, flying roughly in formation that would collect data simultaneously over the same land areas. These would need to be operational, with a commitment to long-term data provision. These would be linked, in turn, with in situ observations (for ground-truthing), improved estimates of biomass stocks, and to models that would integrate the Remote Sensing data and provide some predictive capacity regarding future land-use changes and their relationship to emissions and concentrations of GHGs.
4.0 References
- Beddoe (1997) GIS Meets Weather Systems Head-On, GIS World, vol. 10, no. 2, pp 52-53.
- Brennan and Waddington, Utility of Spatially Related Data for Managing Agricultural Variability, ESRI 1997 User Conference.
- Kasraei and Van Zuyle, Near Real-Time Hydrologic Modeling and Forecasting Using GIS, ESRI 1997 User Conference.
- Knitis, Analysis of the Effect of Weather on FEDEX Ground Operations Using GIS, ESRI 1997 User Conference.
- Shipley, Graffman and Beddoe, GIS Does the Weather, ESRI 1996 International Users Conference.
- Shipley, Graffman, Beddoe and Smith (1997) Rapid Integration of COTS GIS for Interactive Weather Processing, AMS 13th IIPS Proceedings, paper 13.11, pp 420-421.