1. Fine-scale comparisons of satellite estimates Chris Kidd School of Geography, Earth and Environmental Sciences University of Birmingham

2. Rationale for finescale comparisons Daily and monthly estimates hide algorithm problems: Rain areas/occurrence Rain intensities - Temporal and spatial smoothing reduces irregularities Daily products also have sampling issues – which can cause strobe-like effects with rain movement 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

3. Which UK validation data set? Gauges 'Ideal' choice – representing 'true' 'at surface' rainfall, but: daily coverage good – hourly sparse (even in the UK) poor immediacy (~1-2 months delay) higher-temporal resolution available, but poor intensity resolution (tips/min logging = 6 mm/h min rain rate) Radar Temporally and spatially superior (down to 5min, 2km), available within an hour of collection: but, ground clutter & bright band (despite corrections applied) range dependency (ditto) 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

4. Daily vs hourly gauge data Daily gauge network 06-06Z Hourly gauge network 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

5. Radar: advantages/disadvantages Blue = radar rain / IR no-rain Red = IR rain / radar no-rain Daily total (mm) 14 Sept 2006 IR:radar matching 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

6. Time skill scores of rain retrievals Radar PMW IR Rainfall is temporally very fickle 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

7. Finescale Comparisons Instantaneous comparisons: Results at instantaneous / 5 km resolutions AMSR L2 rainfall product (GPROF) PCT (thresholds set – Kidd 1998 → dT×0.04+dT 2 ×0.005) data remapped and processed on European IPWG polar-stereographic projection Future comparisons… 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

8. SSMI PCT 06-09-02 06:36

9. SSMI PCT 06-09-02 07:12

10. SSMI PCT 06-09-02 09:18

11. AMSR PCT 06-09-02 03:31

12. AMSR-L2 06-09-02 13:30

13. Correlations : instantaneous cases AMSR PCT & GPROF 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

14. Ratio – accumulation : instan. cases 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

15. Ratio – occurrence : instan. cases 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

16. Need for case-classification - rather than the wholesale 'lumping' all data into large temporal results – need to look at the component meteorology associated with the estimates:

17. Statistics: blame it on the weather! Movement: Is the movement perpendicular or along the rain band?  Intensity What is the range of values within the rain area?  Size/variability What is the size and variability of the rain area(s)?  Statistical success has as much to do with meteorology as the algorithms ability…

18. So… what now? i) we must remember that PM instantaneous results are better than Vis/IR-based techniques – including merged techniques ii) high temporal and spatial data can produce very good statistics – if the data is of good quality iii) prescribed temporal and spatial sampling is not always ideal – are these applicable to applications? At present, comparisons at fixed regions and time scales Need for flexibility – to match user requirements Initial step at thinking about user-defined spatial and temporal time scales 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

19. Current 'interactive' comparison User dataUser text Radar data generate time slots; copy radar files; accumulate data Graphics 'Standard' IPWG EU region Statistics: bias, ratio, RMSE, CC, HSS etc Disk-store E-mail User QC checks file size; byte order; data range Info checks e-mail; date range; time range The User FTP Why FTP? Simple to use and set up batch jobs… Why e-mail? Puts the results on the User's desktop… 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006 Maybe a Java version too?

20. Conclusions Finescale – instantaneous / ~5km important: it allows us to disentangle algorithm performance to assess performance under different conditions address issues of rain occurrence and intensities But, issues over: data integrity (data reliability – flagging of bad pixels) instrument noise (e.g. AMSR – and RFI) Need for fine-resolution test cases: particularly with common input data sets. 3 rd IPWG workshop, Melbourne, Australia. 23-28 October 2006

22. Freezing levels “Only one thing we do know is that the freezing level is relatively stable” Tom Wilheit

23. Effects and contribution of surface variability to precipitation retrievals. V19 stddevV37 stddevV85 stddev Surface Variability 4 th International GPM Planning Meeting, DC : 15-17 June 2004

24. -0.5 Rain/no-rain induced biases Differences in rain/no-rain boundaries reveal regional variations that do not exist in reality Further complicated since rain/no-rain boundaries tend to differ over land/sea areas Trends in Global Water Cycle Variables, UNESCO, Paris. 3-5 November 2004

25. Conclusions PMW estimates are capable of retrieving light rainfall Statistics often confuse the issue: more light rain tends to produce poorer statistics Instrument noise can be problematic (e.g. AMSR) Surface screening - potential problems with 'false alarms' over cold/snow surfaces Lack of 'common' data sets – different algorithms use different source data – different Q.C.

26. Products Raw DataAlgorithms Radar Gauges Remapping to polar stereographic projection global quick-look images Statistical analysis & image generation Daily 00-24Z results User-defined periods (& resolutions)