Initial Analysis of the Pixel-Level Uncertainties in Global MODIS Cloud Optical Thickness and Effective Particle Size Retrievals Steven Platnick 1, Robert Pincus 2, Brad Wind 1,3, Michael D. King 1, Mark Gray 1,3, and Paul Hubanks 1,3 1 NASA GSFC, 2 NOAA-CDC/CIRES, 3 L-3 Communications SPIE 4th International Asia-Pacific Symposium Honolulu, HI 9 November 2004 paper:

S. Platnick et al., SPIE, 9 Nov 2004 Topics An overview of MODIS reflectance-based cloud optical and microphysical retrievals Uncertainty analysis –Methodology –Examples from MODIS Summary and future efforts

Pixel-level cloud product for daytime observations at 1 km Cloud optical thickness (  ), effective particle radius ( r e ), water path, thermodynamic phase for liquid water and ice clouds, retrieved globally (i.e., land, water, snow/ice surfaces) Algorithm overview (current processing version - collection 4) –Cloud reflectance look-up table method using single water non- absorbing band (0.65, 0.86, 1.2 µm) w/three absorbing bands (1.6, 2.1, 3.7 µm) => 1 , 3 r e (2.1 µm derived r e is primary) –Shorter-wavelength band choice depends on surface –Retrieval gives homogeneous-equivalent cloud properties –Surface spectral albedo from MODIS ecosystem and albedo products Cloud reflectance look-up table calculated in the absence of an atmosphere and surface (atmospheric correction and surface albedo incorporated independently). Retrieval uncertainty can be couched in terms of the ability to accurately infer and model cloud-top reflectance. Cloud Optical & Microphysical Properties (MOD06) S. Platnick et al., SPIE, 9 Nov 2004

Retrieval Space Example 2.1 µm reflectance 0.86 µm reflectance Increasing r e (4-30 µm) Increasing  (1-100) retrievals sensitive to cloud-top reflectance retrievals less sensitive to cloud-top reflectance S. Platnick et al., SPIE, 9 Nov 2004

Makes use of sensitivity partial derivatives calculated at cloud-top: ex. derivative: Currently incorporating the effect of the following sources on inferred cloud-top reflectance: 1. Instrument calibration 2. Atmospheric correction uncertainty 3. Spectral surface albedo uncertainty Note: A likely minimum uncertainty, i.e., other missing components (ice cloud models, vertical cloud structure, etc.) Random L2 uncertainties may be reduced/eliminated during L3 aggregations Pixel-level Retrieval Uncertainty Analysis calculated directly from reflectance library S. Platnick et al., SPIE, 9 Nov 2004

Sensitivity Derivatives, Water Cloud Example Effective radius (µm) Cloud Optical Thickness large sensitivity

As an example, the bias and standard deviation associated with the overall  uncertainty due to N independent (uncorrelated) error sources can be written as: The bias and standard deviation for each source i can be expressed as: Pixel-level Retrieval Uncertainty Analysis (2) S. Platnick et al., SPIE, 9 Nov 2004

Retrieval Example Terra granule, coastal Chile/Peru, 18 July 2001, 1530 UTC [Platnick et al., IEEE Trans. Geosci. Remote Sens., 41, 2003] uncertainiceliquid water no retrieval phase retrieval RGB true-color composite S. Platnick et al., SPIE, 9 Nov 2004

Optical Thickness S. Platnick et al., SPIE, 9 Nov 2004

ice cloud Effective Radius (µm) (particle size derived with 2.1 µm band) S. Platnick et al., SPIE, 9 Nov 2004

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) mean relative RMS uncertainty for  vs.  -r e, water clouds over ocean effective radius (µm) cloud optical thickness 10 % 100 %

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) PDF of  uncertainty vs.  for water clouds over ocean & land cloud optical thickness relative optical thickness uncertainty (%)

Pixel-level Uncertainty Analysis Peru granule (18 July 2001)  : liquid water clouds S. Platnick et al., SPIE, 9 Nov 2004 water clouds over oceanwater clouds over land

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) r e : liquid water clouds S. Platnick et al., SPIE, 9 Nov 2004 water clouds over oceanwater clouds over land

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) LWP = 2/3  r e : water clouds over ocean S. Platnick et al., SPIE, 9 Nov 2004

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) IWP: ice clouds S. Platnick et al., SPIE, 9 Nov 2004 ice clouds over oceanice clouds over land

 /  (%) 10 1 Pixel-level Uncertainty Analysis - Terra MODIS orbit (20 Nov 2002) 

r e (µm) Pixel-level Uncertainty Analysis - Terra MODIS orbit (20 Nov 2002)  r e /r e (%)

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] RGB true-color composite

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] optical thickness

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] optical thickness relative uncertainty (%)

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] effective radius (µm)

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] effective radius relative uncertainty (%)

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] water path

Retrieval Example - Collection 5 Terra granule, coastal Antarctica, 12 Feb 2001, 0350 UTC [1 st science test, partial implementation of collect 5 algorithm] water path relative uncertainty (%)

A general methodology for assessing cloud , r e retrieval uncertainty has been developed for algorithms based on reflectance look-up tables The method has been applied to MODIS operational retrievals Pixel-level uncertainties are included in the upcoming MODIS Atmosphere Team collection 5 processing/re-processing stream, and to-date includes the following error sources: calibration, atmospheric corrections, and surface albedo –expected schedule (Terra algorithm processing/re-processing to begin in February ‘05; Aqua forward processing to begin in late spring ‘05) Extension to Level-3 aggregation statistics not clear (random pixel-level errors will be reduced/eliminated) Future efforts will include for ice clouds, the effect of size distribution assumptions (particle habits) on retrieval uncertainty Summary S. Platnick et al., SPIE, 9 Nov 2004

Backup Slides

Pixel level products (Level-2) overview  Cloud mask - S. Ackerman et al., U. Wisconsin/CIMSS 1km, 48-bit mask/11 spectral tests, clear sky confidence in bits 1,2  Cloud top properties (pressure, temperature, effective emissivity) - P. Menzel, NOAA-NESDIS/CIMSS 5 km, CO 2 slicing (HIRS heritage) for high clouds, 11 µm for low  Cloud optical & microphysical properties (optical thickness,  effective particle size, r e, water path) - M. D. King, S. Platnick, GSFC  Thermodynamic phase - B. Baum, NASA LaRC/CIMSS; GSFC)  Cirrus reflectance (1.38 µm band) - B. C. Gao, Naval Res. Lab Gridded & time-averaged products (Level-3): contains all atmosphere products (clouds, aerosol, clear sky aggregations) MODIS Operational Cloud Products Overview MOD06 MOD35 S. Platnick et al., SPIE, 9 Nov 2004

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) r e : ice clouds over ocean S. Platnick et al., SPIE, 9 Nov 2004

Pixel-level Uncertainty Analysis Peru granule (18 July 2001) r e : ice clouds over land S. Platnick et al., SPIE, 9 Nov 2004

Pixel-level Uncertainty Analysis Cyclone granule (20 Nov. 2002) IWP  : ice clouds 3 dB 0.4 dB 1 dB S. Platnick et al., SPIE, 9 Nov 2004