# Presentation: 216434

## JCSDA Workshop on

- Project Title:  Detection and Correction of Aerosol Contamination in 	         Infrared Satellite Sea Surface Temperature Retrievals
- Principal Investigators:  James Cummings, Doug Westphal           				Naval Research Laboratory, Monterey, CA
- Co-Investigators:  Jeff Hawkins, Doug May, Andy Harris
- Budget:  	   $110 FY03       $115 FY04        $150 FY05
- Talk Outline:    	Project Objectives and Tasks
- 			Progress to Date
- Future Plans

**JCSDA Workshop on**
**Satellite Data Assimilation **** **

## JCSDA Workshop on

**Project ****Objectives**

** **Detection of aerosol contamination in infrared satellite sea surface temperature (SST) retrievals using Navy Aerosol Analysis Prediction System (NAAPS) aerosol distributions.
-  Correction of satellite SSTs for aerosol contamination using NAAPS aerosol products.

**JCSDA Workshop on**
**Satellite Data Assimilation**** **

## JCSDA Workshop on

**Project Tasks**

** **Collocate NAAPS optical depth forecast fields valid for the time SST retrievals are generated (Doug May, NAVOCEANO).
-  Estimate SST retrieval reliability relationship to AOD content         (Doug May, NAVOCEANO - Jim Cummings, NRL)
-  Develop SST quality control schemes to recognize aerosol contamination (Jim Cummings, NRL).
-  Correct satellite SSTs for aerosol contamination                           (Andy Harris, NESDIS).
-  Validate NAAPS aerosol products using using independent data -  improve NAAPS model (Jeff Hawkins, Doug Westphal, NRL).

**JCSDA Workshop on**
**Satellite Data Assimilation**** **

## SST Retrievals and NAAPS Collocations at NAVOCEANO

On going since February 2004
NAAPS AOD forecast fields obtained via ftp from NRL 4 times daily
Append AOD value closest in time and location to each MCSST retrieval
total AOD used (sum of dust, smoke, sulfate components)
globally for N-16 and N-17 (26 Jan 2004)
Global SST observation data file with NAAPS AOD values provided daily at 1000 UT to US GODAE server in Monterey
New capabilities added May 2004
NAAPS AOD components plus total AOD collocated with MCSST
Cloud cleared radiances for AVHRR channels 3,4,5 saved with AOD values

- On going since February 2004
- NAAPS AOD forecast fields obtained via ftp from NRL 4 times daily
- Append AOD value closest in time and location to each MCSST retrieval
- total AOD used (sum of dust, smoke, sulfate components)
- globally for N-16 and N-17 (26 Jan 2004)
- Global SST observation data file with NAAPS AOD values provided daily at 1000 UT to US GODAE server in Monterey
- New capabilities added May 2004
- NAAPS AOD components plus total AOD collocated with MCSST
- Cloud cleared radiances for AVHRR channels 3,4,5 saved with AOD values

**JCSDA Workshop on**
**Satellite Data Assimilation**** **

## QC of SST Retrievals with NAAPS Collocations at NRL

Develop discriminant analysis functions to distinguish aerosol contaminated vs. uncontaminated SST retrievals
SST retrievals from verified Saharan dust events are used as training data sets
Discriminant functions computed using NAAPS AOD components (dust, sulfate, smoke), AVHRR channels 3,4,5 brightness temperatures, and SST innovation from 6 hourly global 9 km SST analysis
Provides probabilistic framework for QC 
outcome is probability SST retrieval is contaminated
allows simple query capability when gathering data for assimilation

- Develop discriminant analysis functions to distinguish aerosol contaminated vs. uncontaminated SST retrievals
- SST retrievals from verified Saharan dust events are used as training data sets
- Discriminant functions computed using NAAPS AOD components (dust, sulfate, smoke), AVHRR channels 3,4,5 brightness temperatures, and SST innovation from 6 hourly global 9 km SST analysis
- Provides probabilistic framework for QC
- outcome is probability SST retrieval is contaminated
- allows simple query capability when gathering data for assimilation

**JCSDA 2****nd**** Workshop on**
**Satellite Data Assimilation**** **

## QC Discriminant Analysis Training Data Sets 

**Case 20050212**

**Case 20040725**

- _QC Discriminant Analysis Training Data Sets_

-  Jun 2-6, 2004
-  Jul 15-17 and 20-25, 2004
-  Sep 12-15, 2004
-  Oct 10-13, 2004

-  Nov 2-4 and 6-8, 2004
-  Dec 13-15 and 28-29, 2004
-  Jan 5-8, 2005
-  Feb 10-13, 2005

## Case 20040725: Visible & AOD

**12:48 GMT**

**14:18 GMT**

**15:55 GMT**

**Case 20040725: Visible & AOD**

- s u n  g l I n t

- s u n  g l i n t

**Notes:**
This slide illustrates the dust event using visible (top row) and AOD (bottom row) imagery for 3 MODIS passes: Terra-12:48 GMT, Aqua-14:18 GMT, and Aqua-15:55 GMT.  A very thick dust plume coming off of NW Africa is illustrated in these images.  .  
Sun Glint filter:
There are regions of sun glint associated within the 12:48 GMT image (between yellow lines) and 14:18 GMT image (to the right of the yellow line), that are filtered out within the AOD images to prevent contamination within the AOD calculations.  These regions were filtered (blacked out) within the corresponding AOD images by applying the sun-glint flag obtained the MODIS Level 2 “MOD35” cloud product.  
 	Cloud filter: 
	There are 2 stages involved in the cloud filter process.  Most of the filter was obtained in the first stage, by applying a technique described by Martins, et al.  The technique consists of a 3x3 box of standard deviation (STD) of channel 1 reflectance over each pixel, where threshold STD values for cloud was determined to be > 0.0025.  The second filter stage consisted of a 3 x 3 box surrounding each pixel, where the surrounding 9 pixels (including the middle pixel) cannot have missing data.  This technique eliminated many isolated AOD pixels that were embedded within clouds. 

## Resultant Composite AOD Image

**Resultant Composite AOD Image**

**12:48 GMT**

**14:18 GMT**

**15:55 GMT**

**Composite**

**Notes:**
The bottom image represents a composite for the day from the 3 AOD products above. The composite product was developed by:
a)      including all good pixel values from 12:48 GMT.  
b)      Missing pixels within the 12:48 GMT image were replaced by:
		corresponding good pixels at 14:18 GMT
		if  corresponding pixels at 14:18 GMT were also missing, then good pixels at 15:55 GMT were used
The purpose for this technique is that the 12:48 GMT product is closest in time to the NAAPS AOD product at 12:00 GMT, and therefore carries more weight.  Within the resultant composite image, black areas within the AOD images are due either to no data, clouds, or sun glint, or land.  

## NPS AOD Image Reduction: Matching NAAPS Domain

**NPS AOD Image Reduction: Matching NAAPS Domain**

**Final: 20 X 20 pixels**

**Original: 2250x1200 pixels**

**Intermediate: 60 x 60 pixels**

**Notes:**
The original AOD COMPOSITE image (from previous slide) has line/sample (y-axis/x-axis) dimensions of 2250 km x 2100 km.  AOD values were calculated from a wavelength centered at 0.65 microns.  Since an AOD value is contained within each 1 x 1 km pixel (except for missing data), there are as many as 2250 x 2100 AOD values within this domain image.  To conform to the NAAPS model output of one value per degree within a 20o x 20o box (lower right image), the process required 2 reduction stages, Currently, a 2 step process is used:
step 1: aggregate the 2250 x 1220 pixels into 37 x 35 pixels of average AOD’s, resulting in a domain of 60 x 60 bins.  For each of these 37 x 35 pixel boxes, at least 25% are good pixels.
step 2: aggregate the 60 x 60 bins into 3 x 3 boxes of average AOD’s, resulting in a final domain of 20 x 20 bins.  For each of these 3 x 3 pixels, at least 75% are good pixels. 

The lower image displays the corresponding NAAPS AOD model product, valid at 12:00 GMT and calculated at a wavelength of 0.55 microns.  
Maximum dust concentrations are focused within a region between 22oN – 25oN for both NAAPS and NPS AOD products. The core of maximum dust concentrations is further west off the coast of Africa for the NPS-AOD product.  In addition, the NAAPS AOD displays significant dust concentrations throughout the JCSDA region, whereas the NPS AOD shows much lighter dust amounts outside of the dust core (yellow and red) region.

## MODIS (GSFC) AOD Image Reduction: Matching NAAPS Domain

**MODIS (GSFC) AOD Image Reduction: Matching NAAPS Domain**

**Final: 20 X 20 pixels**

**Original: 2250x1200 pixels**

**Intermediate: 60 x 60 pixels**

**Notes:**
This series of images present corresponding MODIS-AOD products developed at Goddard Space Flight Center.  The AOD values were calculated from a wavelength centered at 0.55 microns.  The reduction process is similar to the NPS images.  The actual MODIS dataset results in AOD’s within 10 x 10 km pixels.  
	Cloud and sun glint filters: 
	The cloud and sun-glint filter techniques have been applied internally to the MODIS level 2 AOD products.  Overall, the cloud filter applied to the MODIS original image is less restricitve than the corresponding NPS AOD original image in the previous slide.  

As in the previous slide, the lower image displays the same corresponding NAAPS AOD image.  Comparing maximum dust values between MODIS with NAAPS, there appears to be a closer matchup than with the NPS values. Within the MODIS product, the core of maximum dust amounts reach AOD values of 4.5.  The location of the maximum dust core is positioned further southwest for the MODIS than for the NAAPS.

## NAAPS vs NPS AOD (left) 

**202 observations**

**231 observations**

**NAAPS vs NPS AOD (left) **
**& **
**NAAPS vs MODIS (GSFC) AOD (right) **

**Notes:**
Out of 400 possible observations, the NAAPS vs NPS scatter plot contained 202 data pairs (observations). There were more data pairs within the NAAPS vs MODIS scatter plot (231) partly due to the less restrictive cloud filter within the MODIS AOD.
The overall correlation and R2 values (0.43 for both plots) are fairly poor in this case.  The patterns are similar in both comparisons.  Also, there appears to be 2 distinct patterns visible within these scatter plots: 
a)      AOD (x axis) values below 0.5 and 
b)      AOD (x axis) values above 0.5.  

## NAAPS vs NPS AOD 

**AOD ********.25  .30   .40     .50**
**R****2****:       .68 .53    38 .29  **

**AOD ******** .50**
**R****2****:         .37**

**202 observations**

**Scatter Plot: NPS  vs NAAPS AOD for Case 20040725**

**NAAPS vs NPS AOD **

**Notes:**
In taking just the NAAPS vs NPS AOD scatter plot from the previous slide, there are 2 distinct patterns that emerge.  As the NPS AOD values approach lower levels (below), the relationship between NAAPS and NPS AOD seems to strengthen.
a)      For all values: R2 ~ 0.43 (red regression line)
b)      For NPS-AOD > 0.5, R2 = .37 (brown line)
c)      For NPS-AOD <= 0.50, R2 = .29 (blue line)
d)      For NPS-AOD <= 0.40, R2 = .38 (green line)
e)      For NPS-AOD <= 0.30, R2 = .53 (pink line)
f)        For NPS-AOD <= 0.25, R2 = .68 (yellow line)

In a research study for the NPS AOD algorithm, the cutoff point between dust and no dust was determined to be ~0.25 for the NPS-AOD algorithm.  It is also at this level that R2 is at its highest.  

## Suggested Improvements

-   Cloud Filtering
-   Conversion of Image data to NAAPS grid
-   Include AERONET measurements

**Notes:**
Cloud filtering:
	Especially for dust cases, an effective cloud filter technique is required to filter out any questionable regions of cloud so that only the quality assured regions (non cloudy, non-sun glint) are used for comparisons between NAAPS vs NPS, or NAAPS vs MODIS AOD.  The cloud filter must be:
sensitive in eliminating all clouds (optically thin and thick)
able to clear clouds while preserving heavy dust regions
manage cloud edge detection regions properly 
eliminate noisy data (e.g., isolated pixels of AOD that occur within cloudy regions)

Converting NPS or MODIS AOD data to NAAPS grid:
	Attempts to match the pixel dimensions of NPS and MODIS AOD products with NAAPS model output are somewhat subjective with several levels of uncertainty.  Two issues need to be further developed in order to conduct proper validation studies.

1.      Maintain the integrity of the AOD image values during the reduction process from 2250 km lines x 1200 km samples to 20o latitude x 20o longitude bins.  Ideally, have only one reduction step; this will make uncertainties easier to manage and quantify.

2.      An objective technique to construct a daily composite image with representative AOD pixels from multiple satellite passes to the NAAPS valid time.  
a.    For example, if the NAAPS AOD product is valid at 12:00 GMT, then weight the AOD pixels from MODIS passes accordingly, i.e., satellite passes whose times are closest to 12:00 GMT should have greater weight than those with greater time differences

Finally, include AERONET AOD measurements within the domain 

## RT Modeling of aerosol effects

**RT Modeling of aerosol effects**

**Consider Merchant *****et al*****. notation...**

**SST = ***a***T***k******	******	******k*** is aerosol ‘mode vector’
- 			***a*** is vector of retrieval coefficients

**So, need to ascertain weights of mode vector for 3.7, 11 and 12 ****μm channels, *****i.e.***

## Effect on brightness temperatures

**Effect on brightness temperatures**

## Dependency on total transmittance

- Primary cause of scatter is** attenuation of near-surface aerosol effect by intervening atmosphere**

- Can be mitigated by** linear fit to total clear-sky transmittance**

**Different aerosol types have significantly different coefficients**

## Role of air-sea temperature difference

- Residual error in fit **depends on air-sea temperature difference**

- Magnitude **and range** of **ASTD-dependence is a function of total clear-sky transmittance**

- Could parameterize** **∂*T*/∂Χ** as a function of both **** and **ASTD...

## Suggested form of k-estimation

**Suggested form of *****k*****-estimation**

***k***-coefficients will be different for **different aerosol types**

## Conclusions from RTM studies

- Merchant *et al*. approach **requires modification** in the tropospheric case because **aerosols are not at the top of the atmosphere**

- Similar reason for greater success of Nalli & Stowe methodology in stratospheric aerosol case

- Addition of **total atmospheric transmittance** (from NWP or *e.g.* SSM/I water vapor) should assist in **correcting for much of the scatter**

**Air-sea temperature difference** (NWP) useful addition

- Still need **discrimination of aerosol type** (*e.g.* via NAAPS)

## Conclusions from RTM – part 2

- NAAPS data can permit full RT treatment of problem, but this is costly → reduced predictor approach proposed here

- More work required in order to develop and validate this approach

- May be desirable to adopt an **interim empirical approach** using satellite-derived AODs (analyses) and ancillary clear-sky transmittance, air-sea temperature differences (NCEP fields?) **Beware of cross-talk between AOD & WV, ASTD**

**Stratospheric aerosols have much greater impact for given AOD** – suggest using alternative sources (*e.g.* HIRS retrieval, or another analysis/product)