1. Application of GI to weather forecasting
GI: a primer
Application of GI to
weather forecasting
11th February 2005

2. TOPICS Operational needs of weather forecasts Operational constraints
Old technology and GI
Remote sensing for weather
Modelling
The future

3. Operational Needs Rapidly changing (dynamic)
Regular instrumental updates (global)
Dense coverage of stations
Point to surface conversion (interpolate)
Rapid dissemination to public
Global, regional and local scales

5. ABOVE: moored buoy
LEFT: drifting buoy

6. LEFT:
radiosonde
LEFT: launch
of radiosonde
balloon
RIGHT:
sounding rocket

7. Operational Constraints
Locations of stations are often sparse
No regular updates from inhospitable places (data retrieved from tapes)
Large gaps in data – both spatial and temporal
Collection of meteorological data requires access to Global Telecommunication System (GTS)

8. Global Station Coverage

9. Old technology and GI
Historically, meteorological records have satisfied the basic requirements of geographical data
Each station has a specific latitude, longitude and height above mean sea-level
For each station, the synoptic hourly observations are the attribute data

10. Old technology and GI
All climate records possess an x,y,z coordinate reference
The problem has always been the estimation of gaps between existing station locations
Spatial analysis makes use of techniques such as interpolation and kriging to generate surfaces

11. Old Technology and GI X Y Z 1 30 4 27 2 3 22 19 Example:
4 stations with temperature readings (left)
Typically, we have to generate a continuous surface from these isolated points.

12. Individual Points

13. Interpolated
Nearest Neighbour

14. Kriging applied

15. Remote Sensing and Weather
Geostationary satellites such as Meteosat provide high frequency data updates for a target region (15-30mins)
Spectral channels on board the satellites yield useful information about position, direction and velocity of weather systems

17. Infrared
radiant energy

18. Visible
albedo

19. Water vapour
Tropos. Water
Cloud motion

20. AVHRR
29/11/01
13:39
< VIS
IR >

21. Meteosat: 29/11/2001 at 12:00z

22. TOPEX-POSEIDON
For much of our oceans, temperature is not measured directly – but by proxy
Warmer water expands – if surrounded by cooler water it rises. Its height is therefore an indication of its temperature

23. TOPEX-POSEIDON TOPEX is an altimetric satellite
Return time of pulses of energy sent by TOPEX to the ocean surface are measured
Distance between satellite and water surface can be accurately measured
TOPEX used to measure El Niño

25. Modelling
Because of serious gaps in station observations, satellite data supplements ground station, ship, buoy and ascent readings
ALL data, once collected, is used to initialise climate prediction models
Smooth gridded interpolated surfaces of observed data are called reanalysis

26. Modelling
Reanalysis fields are generated for different pressure levels…from surface to 31 or so levels up to the top of the atmosphere

27. Modelling
All spatially referenced meteorological data are processed at the Met. Office and fed into global climate models via the COSMOS system
The current Unified Model (HadAM3) performs weather (short-range) and climate (long-range) forecasts

28. Modelling
Weather and climate predictions generated by models are essentially thematic maps showing specific variables (rain, temperature, cloud etc.)
All forecast field data are spatially referenced and can be easily fed into additional models (flood defence, agriculture, hydrology etc.)

29. The future
Meteosat Second Generation is a new European weather satellite capable of observing Europe and Africa every 15 minutes
Has more channels than the older Meteosat
Can help resolve cloud physics parameters

30. The future
Jason-1 is a new altimetric satellite designed to follow on from the TOPEX POSEIDON mission