1. DCC analysis Outline 1. Comparisions between CERES model and Hu model.
Lin Chen from CMA. This work done in Eumetsat
Outline
1. Comparisions between CERES model and Hu model.
2. Comparisons between 3 kinds of statistics methods(mean, mode and median)
3. Comparisons between each GEO-satellites dominant areas
4. Seasonal cycle detection

2. Data and Method Instrument Aqua/MODIS Channels used
Channel 1 Channel 31
Data version
Collection 6 published by NASA
MYD02SSH, 5km
Data time period
Jan., 2004 to , Jun. 2014
Latitude Geographical extent
10°N to 10°S
Longitude Geographical extent
180°W~180°E(Global)
20°W~20°E(for MSG dominated area);
85°E~125°E(for FY2E dominated area);
125°E~165°E(for MTSAT dominated area);
Solar Zenith Angle
<40°
View Zenith Angle
DCC threshold
<205 K
Brightness Temperature Homogeneity
Standard deviation 5*5 BT11µm< 1°K
Visible Radiance Homogeneity
Standard deviation 5*5 R0.6µm<3%
DCC BRDF model
CERES ice cloud(τ>50) ADMs,
Hu model
DCC reflectance or radiance calculation
mode bin in reflectance and radiance
mean, mode, median
1% and 4
statistic interval
30 days interval with day by day rolling smooth (with homogeneous test )
or 30 days interval (without homogeneous test )

3. 1. Comparision between CERES model and Hu model.
the relationship between CERES model and Hu model is almost the linear
Hu model factor is bigger than the CERES model.

4. Global mean Ref Std degradation CERES 90.4 0.0156 -0.0327% Hu 88.68
Comparisons between CERES model and Hu model.
Global
mean
Ref
Std
degradation
CERES
90.4
0.0156 % Hu
88.68
0.0152 %
CERES higher by about 1.7%

5. Global
mode
Ref
Std
degradation
CERES
94.32
0.0113 % Hu
92.49
0.0084 %
CERES higher by about 1.8%
Hu model much better for MODE in STD

6. 2. Comparisons between 3 kinds of statistics methods(mean, mode and median)

7. Statistic for 3 kinds of methods
Rad w/m2/sr/um
Std
degradation/yr
Mean
453.9
0.0152 %
Mode
473.5
0.0083
-0.1%
Median
463.5
0.0099 %
The mode get the highest radiance but also the degradation rate

8. 3. Comparisons between each GEO-satellites dominant areas
Global

9. MSG dominant area

10. MTSAT dominant area

11. FY2E dominant area

12. Stastics for each dominate areas
Rad w/m2/sr/um
Std*
degradation/yr
Global
473.5
0.0083
-0.1%
MSG
472.0
0.0164 %
MTSAT
469.9
0.0243 %
FY2E
0.0198 %
*Std is normalized. It is defined as STDDEV(A)/Mean(A)
The 5*5 homogenous tests are used. It seems to be much strick so we can't get adequate DCC samplings. When we remove this homogenous test, the Std results are better.

13. 4. Seasonal cycle detection
Filtering the high frequence signal(noise) and passing the low frequence signals(signals)
Two filter: 1-D filter and Gauss filter:
1-D filter:The filter function filters a data sequence using a digital filter which works for both real and complex inputs. The filter is a direct form II transposed implementation of the standard difference equation
Gauss filter:returns a rotationally symmetric Gaussian lowpass filter of size hsize with standard deviation sigma (positive).
Filter function parameter set to: 31,61 and 91

14. How does the Gauss filter function-Exampleb c d e
1. Generating the cycle signals; (a)
2. adding some noise and make phase angle 90° behind the original signals; (b)
3. using Gauss filter to retrive the original signals and making it smoother; (c),(d),(e)

15. Global, 31

16. Global, 61

17. Global, 91

18. Global
3 kinds of filter parameters: 31;61,91

19. MSG, 31

20. MSG, 61

21. MSG, 91

22. MSG
3 kinds of filter parameters: 31;61,91

23. MTSAT,31

24. MTSAT,61

25. MTSAT,91

26. MTSAT
3 kinds of Gauss-filter parameters: 31;61;91

27. FY2, 31

28. FY2, 61

29. FY2, 91

30. FY2E
3 kinds of Gauss-filter parameters: 31;61;91

31. The possible seasonal cycle is related to which factors?

32. When we remove the homogenous test, we get the results as follows.

33. MTSAT
To compared with Masaya's work, here the figure shows the DCC without homogeneous test.
We can get sufficient DCC pixels

34. seasonal variation

35. MSG

36. seasonal variation

37. FY2

38. seasonal variation

39. Global

42. Statistic for each dominate areas with/without homogeneous test
Rad w/m2/sr/um
Std
degradation/yr
Global
473.5/
470.8
0.0083/
0.0053
-0.10%/
-0.11%
MSG
472.0/
471.8
0.0164/
0.0104
-0.021%/
-0.059%
MTSAT
469.9/
467.3
0.0243/
0.0087
-0.007%/
-0.113%
FY2E
468.6
0.0198/
0.0092
-0.136%/
-0.137%

43. It should be noticed that:
Radiance differences between the each GEO-satellites dominant areas;
Degradation: It seems to be more fast after 2010.
How to deal with the seasonal cycle? Especailly when we remove the homogeneous test then get the adequate DCC samples.