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

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 )

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.

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 -0.0327% Hu 88.68 0.0152 -0.0411% CERES higher by about 1.7%

Global mode Ref Std degradation CERES 94.32 0.0113 -0.0583% Hu 92.49 0.0084 -0.0974% CERES higher by about 1.8% Hu model much better for MODE in STD

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

Statistic for 3 kinds of methods Rad w/m2/sr/um Std degradation/yr Mean 453.9 0.0152 -0.0411% Mode 473.5 0.0083 -0.1% Median 463.5 0.0099 -0.0662% The mode get the highest radiance but also the degradation rate

3. Comparisons between each GEO-satellites dominant areas Global

MSG dominant area

MTSAT dominant area

FY2E dominant area

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 -0.0211% MTSAT 469.9 0.0243 -0.0072% FY2E 0.0198 -0.1362% *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.

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

How does the Gauss filter function-Example b 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)

Global, 31

Global, 61

Global, 91

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

MSG, 31

MSG, 61

MSG, 91

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

MTSAT,31

MTSAT,61

MTSAT,91

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

FY2, 31

FY2, 61

FY2, 91

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

The possible seasonal cycle is related to which factors?

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

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

seasonal variation

MSG

seasonal variation

FY2

seasonal variation

Global

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%

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.