Using GLAS to Characterize Errors in Passive Satellite Cloud

Climatologies

Michael J Pavolonis* and Andrew K Heidinger#*CIMSS/SSEC/UW-Madison

#NOAA/NESDIS

Motivation• Passive sensors on operational satellites are limited in their

ability to detect thin cloud and multi-layered cloud systems, but they provide a 30-year data record, which makes them a critical component of the climate observing system.

Each passive climatology has its own error characteristics

and sensitivities, resulting in large differences. Can satellite-based active

measurements help resolve these differences?

GLAS Overview• The Geoscience Laser Altimeter System (GLAS) is a

space-based two channel (532 nm and 1064 nm) laser altimeter located on board ICESat. It’s data is primarily used for altimetry and cloud/aerosol studies.

• The GLAS near surface footprint is ~70 m and the vertical resolution is 76.8 m.

GLAS measurements are more sensitive to thin cloud/aerosol layers than passive measurements.

GLAS Overview

• Due to hardware failure, the GLAS dataset only covers a few short measurement periods.

• The October 16, 2003 – November 18, 2003 time period is used in this study since the 532 nm laser, which is more sensitive to cloud/aerosol detection, was in operation during this time.

• Only data from the 532 nm laser is included.• ICESat is in a near sun-synchronous orbit and had

a 0600 – 0800 LST descending node equator crossing time during this period.

GLAS Data Analysis

• Coincident GLAS/passive observations are relatively rare.

• In this study, globally mapped GLAS observations from the descending node (morning) of ICESat are compared to various passive observations that occur within 4 hours of the ICESat overpass.

• All data are mapped to a 2.5o equal area grid.

Spatial resolution may cause some of these differences, especially in the subtropics.

~7% of clouds have an optical depth < 0.1

~23% of clouds have an optical depth < 0.5

Most thin clouds are high clouds.

~14% of clouds have an optical depth < 2.0

Difference between passive and GLAS ~40%, so many thicker clouds are also being missed. So co-located active and passive data is needed to better characterize differences.

High Cloud Detection Limitations

The GLAS cloud amounts were calculated as a function of optical depth.

Only daytime, open ocean grid cells were considered, so this represents the best case scenario for passive measurements.

Consistency with Aircraft Measurements

From: B. Holtz and S. Ackerman Histogram of cloud optical depth from the Cloud Physics Lidar (CPL) for MODIS Airborne Simulator (MAS) “clear” FOVS (as determined by the automated cloud mask.

Most undetected clouds have an optical depth of less than ~0.3.

Concluding Thoughts

• We are taking steps towards quantifying limitations and differences in global satellite-derived cloud climatologies, but much more work remains.

• A more complete error analysis can be performed when CloudSat/CALIPSO are launched. Aqua MODIS data can be used to simulate various cloud masks and cloud properties. This is the best way to tie together past and future data records (e.g. AVHRR/VIIRS) and to understand the differences between climatologies and help in the development of an “optimal” multi-sensor long-term cloud climatology (e.g. AVHRR/HIRS).

• The effects of spatial resolution on cloud detection also need to be addressed.

• Can active measurements from satellite (e.g. GLAS or CALIPSO/CloudSat) be used in conjunction with passive observations to improve estimates of cloud vertical structure and thereby effectively extend the active measurements in time?