Incorporating Meteosat Second Generation Products in Season Monitoring

1. Incorporating Meteosat Second Generation Products in Season Monitoring Blessing Siwela SADC Regional Remote Sensing Unit November 15 2005

2. Outline • METEOSAT introduction • METEOSAT data • Access to data • Data format(s) • METEOSAT-7 vs METEOSAT-8 • METEOSAT-8 products (current and potential) for season monitoring

3. METEOSAT • Geo-stationary • 36 000 km altitude • Spatial Resolution 3km x 3km • visible, thermal infra-red and water vapour channels • 42% of Earth covered • 7 year design lifetime

4. Access to METEOSAT-8 data • Data available in XPIF format on PUMA receiver(s) • All historic MSG data (older than 24 hours) available in the EUMETSAT archive via an online ordering interface on the EUMETSAT Web site;

5. METEOSAT-8 data format • 8-bit XPIF data, [0-255] • Visible data [0 - 255] grey levels • Temperature [-128 – 127] degrees C • Information in XPIF header indicates data format, projection, geo-referencing parameters

6. METEOSAT-8 data format • Original data 10-bit [0..1024] but rescaled to 8-bit by 2-met software on PUMA receiver 0[ ……..]255 Transformation Visible 0[ ……..]255 Temperature -128[ ……..]127 1, -128

7. METEOSAT-8 data 00 East, 6.340 North Projection: Platt-Carree (Latitude / Longitude) Grid Size (X) 0.0206825708, (Y) 0.0212385622 (degrees) 610 east, 44.50 south

8. METEOSAT-7 vs METEOSAT-8 • 3 channels • 5km IR, WV; 2.5km VIS • 30 minutes temporal resolution • 12 channels • 1km HRV; 3km other • 15 minutes temporal resolution

9. METEOSAT-7 vs METEOSAT-8

10. MSG Receiver Data Conversion Tools Other [ERDAS, ESRI BIL, etc] XPIF IDA (WinDisp) CHIPS Windows BMP

11. Cold Cloud Duration from TIR data

12. Cold Cloud Duration from TIR data hh00 hh30 hh15 hh45 ccd1 ccd2 ccd = (ccd1 + ccd2) / 2

13. Cold Cloud Duration from TIR data October 11-20 2005

14. METEOSAT-7 METEOSAT-8 Cold Cloud Duration from TIR data October 11-20 2005 CCD from METEOSAT-8 and METEOSAT-7 compare well; Historic M-7 data can be used for comparison of current CCD with average

15. Rainfall Estimation from TIR data • CCD -> RFE • Refine using rain gauge or other data sources • WMO-GTS • Radar data

16. Rainfall Estimate (RFE) images. Monitoring Rainfall Activity Percentage cumulative rainfall received

17. Rainfall Estimates Applications • Water Balance Models • Water Requirements Satisfaction Index (WRSI) • Standardized Precipitation Index • Statistical method for measuring drought • Hydrological modelling • Stream flow model

18. METEOSAT-8 RGB Composites Channel Z Channel X Channel Y

19. METEOSAT-8 RGB Composites Channel Z Channel X Channel Y VIS0.6 and VIS0.8 All (“thick”) clouds are white NIR1.6 ice clouds are dark, water clouds are bright RGB composite can separate ice clouds from water clouds

20. RGB Composites and interpretation of clouds Red: Cloud depth and amount of cloud water and ice. Day: Visible reflectance at 0.6 mm. Night: Optical depth, approximated by 12.0-10.8 mm channels. Green: Cloud particle size and phase. Day: Approximated by 1.6 mm or 3.9 mm solar reflectance component. Night: Approximated by 10.8 –3.9 mm brightness temperature. Day & Night: Water clouds have larger 10.8-8.7 mm temperature difference than ice clouds. No skill for drop size discrimination. Blue: Temperature is provided by 10.8 mm day and night.

21. 1 1. Multilayer mature cloud. Low cirrus above Low Cu+Sc. Little or no rain. Dark red above yellow-white. 2. Thunderstorms. Orange tint on red. 3. Mature rain cloud, moderate rain. Dark red + magenta. 4. Sc+Cu. no-precip. Yellow-white. 5. Local heavy rain shower. Bright Red. 6. Light warm rain under multi-layer clouds. Bright Magenta. 7. High level shield, raining on the east side. Orange riding over red. 8. Mid-level orographic clouds. No rain. Intense yellos. 9. Ciro-cumulus. No rain. Dirty yellow. 1 2 3 4 3 7 5 4 6 9 8 RGB - 149

22. NIR NIR Vegetation Monitoring

23. Vegetation Monitoring • Normalized Difference Vegetation Index: • (NIR – Red) / (NIR + Red) • Possible values -1 to 1 • dense vegetation has higher values (0.4 - 0.8), lightly vegetated regions have low values (0.1 - 0.2)

24. Vegetation Monitoring • METEOSAT-8 channels: • VIS008, VIS006 • IR108 , IR120 for cloud masking • NDVI = (VIS008 – VIS006) / (VIS008 + VIS006) • Values [ 0.0 … 0.5] • Difference between VIS008 and VIS006 gives good indication of vegetation density

25. Vegetation Monitoring METEOSAT-8 NDVI

26. Vegetation Monitoring Cloud interference sometimes limits use of NDVI

27. Vegetation Monitoring Time series NDVI for selected zones

28. Vegetation Monitoring NDVI image comparison with normal / average or other

29. Vegetation Monitoring • Thermal infrared radiation to monitor surface temperature of the crops can also be used to get information on crop health. • The more transpiration from crops, the cooler the leaves; warmer leaf temperature may suggest water stress.

30. Vegetation Monitoring • Vegetation Condition Index • 100*(NDVI – NDVIMin)/(NDVImax – NDVImin) • Temperature Condition Index • 100*(BTemp– BTempMin)/(BTempmax – BTempmin) • Combination used for monitoring drought and vegetation stress due to excessive wetness • Requires a long term dataset • MSG provides a number of temperature channels, notably IR039

31. Vegetation Monitoring • Vegetation Productivity Index • A measure of the difference between the current season vegetation response and the local norms as the statistical probability of having a worse case - this characterizes the severity of the deviation from the local normal • Current NDVI referenced against the NDVI percentile-images of the historical year, and classified in different frequency groups Requires a long term NDVI dataset

32. Weather hazard monitoring • 15 minute updates • More channels used as RGB composites

33. Summary • MSG provides more than just a continuation of the service from the MFG • MSG data can be used for applications other than meteorological eg monitoring land surface parameters