# Presentation: 506910

## Synergistic use of MODIS and MISR to quantify the uncertainties in cloud microphysical properties over the globe

*Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign*
- Steve Platnick and Brad Wind
*NASA Goddard Space Flight Center*
- Third I3RC Workshop, Kiel, Germany
- October 11 – 14, 2005

## Question

- Using the standard satellite VIS-SWIR technique for retrieving optical depth of water clouds over ocean, what fraction of pixels with retrieved optical depths have uncertainties of better than 20%?
- 	a) > 90%
- 	b) 70 to 90%
- 	c) 50 to 70%
- 	d) 30 to 50%
- 	e) < 30%

## Motivation

- •	The retrieval of cloud microphysical properties (e.g., optical depth, , and effective radius, *r**e*) over the globe are now routine using visible and near-IR satellite imaging systems (e.g., MODIS, AVHRR).
- •	These datasets are used extensively in many scientific studies of global water and energy cycles.
- •	The conclusions of these studies are hampered by an incomplete assessment of the uncertainties in the retrieved microphysical properties, where the uncertainties are likely dependent on the cloud optical properties and their spatial distribution, the sun-view geometry, etc.

## How do we know the pixel-level uncertainty in  and re?

- 1)	Uncertainty range from 3-D radiative transfer modeling
- 2)	Compare with ground-based estimates (e.g., from ARM)
- 	*These approaches cannot give the global distribution of *
*	**the uncertainties.*
- 3)	Uncertainty estimates from retrieval sensitivity to changes in cloud-top reflectance, etc., as in MODIS Collection 5.
- 	*This is a great start, but only describes part of the uncertainty... not all contributing factors (e.g., cloud vertical and horizontal heterogeneity) are accounted for.*

## Optical Thickness Retrieval Relative Uncertainty (%) (collection 5 preliminary)

## How do we know the pixel-level uncertainty in  and re?

- 1)	Uncertainty range from 3-D radiative transfer modeling
- 2)	Compare with ground-based estimates (e.g., from ARM)
- 	*These approaches cannot give the global distribution of *
*	**the uncertainties.*
- 3)	Uncertainty estimates from retrieval sensitivity to changes in cloud-top reflectance, as in MODIS Collection 5.
- 	*This is a great start, but only describes part of the uncertainty... not all contributing factors (e.g., cloud vertical and horizontal heterogeneity) are accounted for.*

## Multi-angle Imaging SpectroRadiometer

- _Multi-angle Imaging SpectroRadiometer_
- _Attributes_
- • 400 km swath
- • 443, 550, 670, 865 nm channels
- • 275 m – 1.1 km sampling
- • 7 minutes to view the same scene
-    from all 9 cameras

## Slide 8

## Can also use re, LWP or albedo

**Can also use r****e****, LWP or albedo**

## Data Fusion Requires...

- 1)	reprojecting the MODIS data onto the MISR grid with a Nearest Neighbor technique;
- 2)	registering the same cloud element within the 9 MISR images using the MISR M2 and M3 stereo matching techniques;
- 3)	the reprojection is verified by applying the matching techniques between the MODIS and MISR-nadir images;
- 4)	other radiometric considerations not described here.

## Slide 11

## Trade Cu

- _Trade Cu_
- Path 233
- 12°N to 29°N
- 7 days

## Mixed clouds

- _Mixed clouds_
- Path 48
- 20°N to 40°N
- 6 days

## -metric

- -metric

- _White regions:_
- • Clear pixels
- • < 7 cameras registered
- • Ice clouds from MODIS flag
- • Poor MODIS-MISR Registration

## -metric:  Path 48 (mixed clouds)

- ___-metric:  Path 48 (mixed clouds)_

## -metric:  Path 233 (trade cumuli)

- ___-metric:  Path 233 (trade cumuli)_

## Path 48 Mixed Clouds

- Path 233 Trade Cumulus

## Summary to Date

- •	To my surprise and Steve’s relief, ~70 to 80% of MODIS retrieved optical depths for water clouds had relative errors of less than 20% (Answer b!). *The quality looks worse for the BRF-metric.*
- • Higher horizontal heterogeneity led to an increase in optical depth uncertainty, but horizontally homogeneous clouds had a large range of uncertainty in optical depth... *it is not just 3D effects that contribute uncertainty to the retrieval.*

## Path 48  BRF metric

## BRF-metric

## October 29, 2003

- 25.7°N, 60.4°W

**MISR**
**1-D RTM**

## Slide 22

## 0.00

- 0.25

- 0.50

- 1.00

- 0.75

**Nearest neighbor errors**

**distance (km)**

## October 29, 2003

- 25.7°N, 60.4°W

## BRF Metric

## T-metric

## Path 233  BRF metric

## Path 233 Trade Cumulus

- 1.1 km x 1.1 km

- 7.7 km x 7.7 km

## Slide 29

## Optical Thickness Retrieval(collection 5 preliminary)

**Notes:**
Now the retrievals ...