1. Radar composites Elena Saltikoff FMI 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus1

2. What is better than a radar ? Many radars ! Individual radars are useful for analysis and forecasting over a small region. Even more useful is a network of radars coordinated as a mesoscale observation network to observe mesoscale circulations such as the core of tropical cyclones. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus2

3. What is better than a radar ? Many radars ! 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus3

4. What is better than having a radar ? 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus4 Using your neighbour’s radar ?

5. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus5 A mosaic or composite does not replace individual radar images – it has different uses

6. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus6

7. The basic questions of composites How long shall we wait What to do at overlapping areas 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus7

8. What to do at overlapping areas Maximum: largest number wins. Average: Use the average of the available data. Bad with mountains. Priority: Use data from the best radar Nearest: Use data from the nearest radar Weighted: Use data from all the available radars with an averaging weight of 1/R where R is the range from each radar to the current pixel. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus8

9. Additional challenges Are all the radars calibrated the same way ? Does ”red” mean similar rain at all radars ? If we combine images, not data, there are not many options (not maximum, not average,…) Radars use different scanning schemes Elevations can be solved with matematics Time intervals more challenging 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus9

10. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus10 Elevations

11. Annoying blinking 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus11

12. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus12

13. …avoided with longer waiting times 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus13

14. In Finland we see differences in composites Colour scale is set in national products differently for snow and rain, in Scandinavian and European composites as standard Scandinavian composite is made (so far) from images, not data, each pixel 2 km, no mixing in overlap areas 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus14

15. Europeanwide data-composite in testing 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus15

16. In Finland composites are used in public a lot 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus16

17. Sometimes we combine radar + satellite 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus17

18. The Danger of Composites They look good and they are often easy to use … but they could make you forget the 3-dimensional nature of weather 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus18

19. Snow Density Case 26Feb 2009 What if we combined two radars here

20. Snow Density Case 26Feb 2009

22. Data from volume scan can still be combined several ways PPI Plan position CAPPI Constant altitude MAX maximum in column EchoTOP height of threshold 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus22

23. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus23 Black line = Data used in a PPI

24. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus24 Black line = Data used in a CAPPI

25. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus25 Black line = Data used in a CAPPI (with fill)

26. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus26 Black line = Data used in a MAX

27. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus27 Rings in TOPS product: example (-10 dBZ)

28. 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus28 Black line = Data used in a TOPS (20 dBZ)

29. Composite conclusions Use composites for synoptical analysis Be aware of the geometry Get details from single-radar products 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus29

30. Bonus:use of different products in same situation 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus30

31. CAPPI for general survey (this we show in television and in internet)

32. Cappi from different altitudes: 2 km and 800 m 31.5.2016Ilmatieteen laitos / PowerPoint ohjeistus32

33. MAX for overview. Robust product – popular in mountaineous countries.

34. TOP and XSECT for detailed meteorological analysis

35. Cross-section to estimate the age

36. Young and old cell