Satellite precipitation validation methodology with ground truth observation over Turkey

1. Satellite precipitation validation methodology with ground truth observation over Turkey İbrahim SÖNMEZ1, Ali Ümran Kömüşçü1 Ahmet ÖZTOPAL2, 1Turkish State Meteorological Service, Turkey 2Istanbul Technical University, Turkey

2. Content • Ground observation Network • Validation metedology • Scatter & pdf plots • Validation statistics • Results • Discussion

3. Ground Observation Netwok Sites : AWOS (Automated Weather ObservationStation)

4. Ground Observation Netwok Sites : AWOS (Automated Weather ObservationStation) • Observed at every 1 minute: • Precipitation • Temperature • Relative humidity • Wind speed & direction • Observed at every 10 minute: • Pressure • Soil temperature at different depths • Evaporation

5. Flag Value Status Brief Description 0 Good Datum has passed all QA Test 1 Suspect There is concern about accuracy of datum 2 Warning Datum is very questionable 3 Failure Datum is unstable RG Quality Control Procedures • Tests applied to the precipitation data: • Range Test (climatological comparison) • Step Test (sequential observation comparison) • Persistance Test (group of observation comparison)

6. Validation Methodology FOV for AMSU sensor AMSU(H02) product FOV centers 20090201 @ 00:28

7. Validation Methodology AMSU(H02) product FOV centers [20090201 @ 00:28] * : awos + : product

8. Validation Methodology AMSU(H02) product FOV centers [20090201 @ 00:28] * : awos + : FOV center

9. Either, Or, Validation Methodology • Observation Product * : awos + : FOV center point area How to compare these two?

10. Validation Methodology * : awos + : FOV center

11. ? Validation Methodology * : awos + : FOV center

12. S2 S1 W2 W1 SE SN WN W3 S3 . . W. W. Observation value at an unobserved location • W(r) is assumed to be: • Function of distance • Closer sites indicating higher weight • Farther sites indicating less weight!

13. Weighting functions • Weighting functions: • Theorocially defined • does not depend on observation itself Cressman (1951) Barnes (1964) W

14. Weightingfunctionalternative: Variogram Definition: • Variogram (V)counts on the relative change between two observations for the spatial variability and defined by the function of distance as, • Semivariogram(SV)is proposed by Matheron (1963) to provide the spatial dependence. Clark(1979) defined the mathematical formulation as,

15. AlternativeWeightingfunction: • Cumulative Semivariogram(CSV)concept has been proposed to the earth sciences by Sen(1989) for depicting spatial correlation structure of regionalized variables.

16. CSV study by Şen(1997) Şen, Z., 1997: Objective Analysis by Cumulative Semivariogram Technique and Its Application in Turkey, J. Of Applied Meteorology, 36, 1712-1724. • Monthly rainfall amountsin Marmara region(north-west part of Turkey) are used • Sites with at least 30 years of data are selected

17. CSV study by Şen(1997)

18. CSV study by Şen(1997)

19. AlternativeWeightingfunction: Point cumulative semivariogram (PCSV)was proposed by Sen (1995) where it measures the spatial variability around a site to provide the regional effect of all other sites within the area on the site of concern. * Specific SWF for every station can be defined

20. W(r) determined by PCSV (Sen,1996): • The observations are used rather than theorotical functions, such as Cressman or Barnes. Why PCSV is needed? r r * : awos + : FOV center

21. Summary • Before validation study is performed, there are couple of things to consider: • Sampling feature of the sensor OR geographical representation of the product. • What to use in validation (ground truth, or some other product) • The strategy to perform the validation(point to area OR area to point) • Use of the descriptive statistics(e.g., correlation, std, FAR, POD, etc)

22. Validation Results – Scatter Plots(H01)

23. Validation Results – Scatter Plots(H01)

24. Validation Results – Scatter Plots(H02)

25. Validation Results – Scatter Plots(H03)

26. Validation Results – pdf Plots

27. Validation Results – Correlation variation

28. Validation Results – Std variation

29. Summary & Conclusions • H01 :overestimation is dominant • H01 : Relatively lower RR are observed over cost than land • H02 :overestimation is dominant • H02 : RR over cost ~= RR over land • H02 :increasing RR amounts through summer is observed • RR amount of H02 < RR amount of H01 for a particular month

30. Summary & Conclusions • H03 :underestimation is dominant • H03 : no significant difference over land & cost • HO3 : relatively lower RR are observed respect to H01 & H02 product

31. Summary & Conclusions • JFM period : highest correlation @ H02 among three products • AMJ period :highest correlation @ H01 among three products • H02& H03: Highercorrelation occured in winter. • Decreasingstd trend through summer months is observed for H01&H02 • Increasingstd trend through summer months is observed for H03 • Higheststd in winter months is observed in H01 while H03 has the highest amount in summer time.

32. Questions & Comments