# Presentation: 312417

## Impact of Missing Absorption Channels on Infrared-based Cloud-Pressure Retrievals on VIIRS Relative to MODIS (& GOES-R)

**Andrew Heidinger, Michael Pavolonis**
*NOAA/NESDIS/Center for Satellite Applications and Research*
*Madison, WI*
**Sébastien Berthier**
*Cooperative Institute for Meteorological Satellite Studies (CIMSS)*
*Madison, WI*

*First MODIS/VIIRS Science Team Meeting, Baltimore, MD May 13-16, 2008*

**Notes:**
Note that is one of the IR cloud remote sensing talks

## Goal

-  Answer the question. What are the consequences on the cloud-top pressure estimation uncertainty on the IR channels used on VIIRS relative to MODIS and GOES-R?** **
-  Conduct this analysis in a way that is insensitive to any particular algorithm.
**Motivation**
-  Cloud vertical extent (Height/Pressure/Temperature) is a often studied parameter in various cloud climatologies.
-  It’s important in predicting the IR radiative budget of clouds
-  Cloud-top pressure from MODIS and GOES is being assimilated in multiple NWP models.
- CrIS is available for half of the VIIRS data but at a lower spatial resolution.  VIIRS 1km cloud height products remain important.

**Notes:**
Emphasize that we are not attacking the VIIRS algorithm.  We are looking at the consequences on the channel selection

## Outline

-  Review of the VIIRS IR spectral information for cloud remote sensing relative to that from MODIS.
-  Methodology for computing the solution space for IR cloud height algorithms
-  Demonstrate impact of absorption channels on the cloud pressure solution space for one scene.
-  Conclusions

## Spectral Differences in IR bands used for Cloud Remote Sensing

- MODIS 06 cloud top pressure was derived using the 15 m CO2 channels 33-36 and channel 31 (11 m)
- VIIRS was designed without any channels situated in CO2 or H2O IR absorption bands. VIIRS specs for cloud-pressure are 40-65 hPa.
-  GOES-R ABI will provide one CO2 channel similar to Channel 33 on MODIS and three H2O IR bands.

**Spectral Differences in IR bands used for Cloud Remote Sensing**

- MODIS

- VIIRS

- Nadir clear-sky transmission

- h2o

- co2

**Notes:**
Mention that VIIRS was designed as algorithm + sensor package.  VIIRS design team was free to pick channels necessary to meet the requirements.

## Data 

- To illustrate the solution space offered by the VIIRS and other infrared cloud height approaches, we focus our attention on one arbitrary nighttime granule from AQUA/MODIS during the CALIPSO era. (*August 10, 2006 20:35  over the Indian Ocean*)
-  False color image using 3.75, 11 and 12 m observations (*cirrus are whitish*)
-  532 nm total backscattering image
- cross-section of CALIOP cloud temperature, observed 11 m BT, clear-sky 11 m BT and derived 11 m cloud emissivity using CALIOP cloud boudaries.
- We focused on ice clouds here only.  We used the MYD06 IR phase product to accomplish this.
- CALIPSO co-locations and data provided by the Atmospheric PEATE (Bob Holz and Fred Nagle)

*CALIPSO TRACK*

- Example pixel

## Methodology Part 1

**Methodology Part 1**

-  The following slides demonstrate a methodology to define the solution space (*region of the atmosphere*) where a cloud can be placed and match all of the observations used in the particular retrieval.
-  These results are for one pixel in the previous granule along the CALIPSO track where CALIPSO detected a cloud between 160 and 290 hPa and derived 11 m emissivity was about 0.6.
- For an individual channel, the cloud pressure solution space is defined as any pressure where the cloud emissivity profile is between 0 and 1.

**Notes:**
Thee profiles represent a blending of the observations with the clear-sky RTM modelling.

## Methodology Part 2

**Methodology Part 2**

-   is solely a function of single scattering properties and is therefore directly related to particle size given an assumption of the crystal habit.
-  We assume aggregates and use the IR scattering properties from Professor Ping Yang of TAMU.
-  Once a scattering model is assumed (i.e. a habit or mix of habits),  values from different channel combinations are constrained to follow a predetermined relationship.

**Notes:**
Beta is like an infrared angstrom exponent.  The VIIRS operational algorithm uses similar approach based on k-ratios that are identical to beta values.  They make a different microphysical assumption.

## Methodology Part 3

-  The VIIRS approach uses the 3.75, 8.5, 11 and 12 m channels on VIIRS which are similar to Channels 20, 29, 31 and 32 on MODIS
- The NGST approach uses a  value based on channels 31 and 20 and a  value based on channels 32 and 29.
-  The image on the left shows the  profiles computed from the emissivity profiles on the previous slide.
-  Using the relationships predicted for aggregates, we can used the (31,20) profile to predict what the (32,29) profile should be.
-  Where the predicted and observed (32,29) profiles agree defines the cloud pressure solution space.  This shown where the blue and red lines are close to each other.
-  Within this space, all of the derived channel emissivities are valid and the values are consistent with the chosen microphysical model.

**Notes:**
Discuss this also a way to look at the MODIS CO2 slicing algorithms expected they always assume beta = 1 and where the beta profile passes through the unity line, that is the co2 slicing solution for the channel pair.

## Methodology Part 4

-  In contrast to the VIIRS channel set which only uses IR window channels, when a absorption channel is used, the solution space shrinks (which is good).
-  In this example, the 11, 12, and 13.3 mm or MODIS channels 31,32 and 33 are used.
-  Here, the observed (red) and predicted (blue)  curves are close together over a smaller solution space.

##  A small solution space means that the channel set is very sensitive to variations in cloud pressure (good) 

-  A small solution space means that the channel set is very sensitive to variations in cloud pressure (good)
-  To objectively compute the cloud pressure solution space,
- we defined the solution space as the region where the predicted brightness temperature difference was within 0.5K of the level where it agreed most with the observations.
- For the example on the right, the solution space spanned by the GOES-R approach is much smaller than that spanned by the VIIRS approach.
-  The 0.5K is arbitrary

**Methodology Part 5**

## Depth of Solution Space Compared to CALIPSO Cloud Boundaries

-  The figures on the right show the variation in the pressure depth of solution space for ice cloud portion of the granule shown previously.
- The grey regions are those that are within the solution space spanned by the particular channel set.
-  The CALIPSO cloud boundaries of the highest cloud layer are plotted as the black symbols.
-  Based on this data, the depth of the solution space offered by the GOES-R ABI (Ch 31,32,33) channels is much smaller than offered by the VIIRS channels (Chs 20, 29, 31,32)
-  This analysis applied to the individual CO2 slicing pairs give similar results to the GOES-R channels.

*532 nm Image for Region of Interest*

** = myd06*

## Correlation of Depth of Solution Space with Cloud Emissivity

-  As expected, the pressure depth of the solution space is highly correlated with the cloud emissivity.
-  Cloud emissivity was derived using the MODIS Ch 31 radiance, clear-sky radiance estimates and the CALIPSO cloud boundaries.
-  This analysis points to lack of cloud height sensitivity for window-based solutions for optically thin clouds.

## Conclusions

- The lack of IR channels in absorption bands has a large impact on the sensitivity to cloud height provided by VIIRS.
- The inclusion of a single (*albeit weak*) 13.3 CO2 absorption channel on the GOES-R ABI greatly increases the sensitivity to cloud height.  MODIS with multiple CO2 channels is even more sensitive.
- Therefore, expect a large discontinuity in the cloud vertical extent climate record from MODIS to VIIRS.  VIIRS will look more like AVHRR than MODIS in this respect.
- The 3.75 m channel did not seem to help narrow the VIIRS solution space.  Therefore, an algorithm that can run with 8.5, 11 and 12 m channels in day/night consistent manner may be preferable.
- Note this analysis is purely looking at the information content from a single pixel.  ***Algorithms can do better than the performance shown*** here by using other information (channels from a sounder, spatial statistics etc).
- While cloud height sensitivity is small, the IR ***window channels do provide very good measures of emissivity and microphysics***.  We are developing ways to do this for the MODIS record from our support from NASA/ROSES which commences this summer.

## The benefits of solution space exploration go beyond cloud height...

**The benefits of solution space exploration go beyond cloud height...**

## End of Presentation

## Slide 16