# Presentation: 687871

## MODIS Retrieval of Cloud Optical & Microphysical Properties

- Optical thickness, particle size (effective radius), and water path
- 1 km spatial resolution, daytime only, liquid water & ice clouds
- Solar reflectance technique, VIS through MWIR
- Water nonabsorbing bands: 0.65, 0.86, 1.24 μm
- Water absorbing bands: 1.6, 2.1, 3.7 μm
- Land, ocean, and snow/sea ice surfaces
- Land surface: 0.65 μm
- Ocean surface: 0.86 μm
- Snow/ice surfaces: 1.24 μm
- MODIS 1st satellite sensor with all useful SWIR, MWIR bands

- Michael D. King
- NASA Goddard Space Flight Center

## Terra

- ASTER

- Launched December 18, 1999

- MODIS

- CERES

- MISR

- MOPITT

## MODIS Scan Swath

## MODerate-resolution Imaging Spectroradiometer (MODIS)

- NASA, Terra & Aqua
- launches 1999, 2002
- 705 km polar orbits, descending (10:30 a.m.) & ascending (1:30 p.m.)
- Sensor Characteristics
- 36 spectral bands ranging from 0.41 to 14.385 μm
- cross-track scan mirror with 2330 km swath width
- Spatial resolutions:
- 250 m (bands 1 - 2)
- 500 m (bands 3 - 7)
- 1000 m (bands 8 - 36)
- 2% reflectance calibration accuracy
- onboard solar diffuser & solar diffuser stability monitor

## Shortwave Properties of Clouds

- c(0.75 μm) = 16

- re =	 4 μm

- re = 	8 μm

- re =	 12 μm

- re =	 20 μm

- re =	 16 μm

- re =	 20 μm
- 	       + vapor

- 0.5

- 1.5

- 1.0

- 2.0

- 2.5

- 3.0

- 3.5

- 4.0

- Wavelength (μm)

- 1.0

- 0.8

- 0.6

- 0.4

- 0.2

- 0.0

- Spherical Albedo

- MODIS Atmosphere Bands

## Infrared Properties of Clouds

- 1500

- 1000

- 2000

- 2500

- 3000

- Wavenumber (cm-1)

- 340

- 320

- 300

- 280

- 260

- 240

- Brightness Temperature (K)

- 12

- 16

- 10

- 5

- 4

- Wavelength (μm)

- 3

- 6

- 8

## Reflection Function of Clouds as a Function of Cloud Optical Thickness at 0.65 μm

## Definition of Effective Radius

*	**	**	*re =
- where
- r	=	particle radius
- n(r)	=	particle size distribution

## Infrared Properties of Clear Skies & Cirrus Clouds

## MODIS Reveals Atmospheric Moisture Details As Never Seen Before 

- MODIS Water Vapor (1 km)

- MODIS Reveals Atmospheric Moisture Details As Never Seen Before

- GOES-8 Water Vapor (4 x 8 km)

## Principal Channels

- MODIS Channels

## Global Level-1B Composite Image

- G	=	0.56 μm
- B	=	0.47 μm

- May 28, 2001

## Global Level-1B Composite Image

- G	=	0.56 μm
- B	=	0.47 μm

- May 28, 2001

- example data granule coverage (5 min)

## MODIS Cloud Products

- Pixel-level (level-2) products
- Cloud mask for distinguishing clear sky from clouds (288 @ 47.4 MB)
- Cloud radiative and microphysical properties (144 @ 69.6 MB; 144 @ 14.1 MB)
- Cloud top pressure, temperature, and effective emissivity
- Cloud optical thickness, thermodynamic phase, and effective radius
- Thin cirrus reflectance in the visible
- New algorithms from greater spectral coverage, heritage algorithms at higher spatial resolution, products include QA (processing, assessment info, weighted statistics)
- Gridded time-averaged (level-3) atmosphere product
- Daily, 8-day, and monthly products (474.8, 883.2, 883.2 MB)
- 1° 1° equal angle grid
- Mean, standard deviation, marginal probability density function, joint probability density functions
- Quicklook imagery available at MODIS atmosphere web site:
- modis-atmos.gsfc.nasa.gov

## The reflection function of a nonabsorbing band (e.g., 0.86 μm) is primarily a function of optical thickness
The reflection function of a near-infrared absorbing band (e.g., 2.14 μm) is primarily a function of effective radius
clouds with small drops (or ice crystals) reflect more than those with large particles
For optically thick clouds, there is a near orthogonality in the retrieval of c and re using a visible and near-infrared band

- The reflection function of a near-infrared absorbing band (e.g., 2.14 μm) is primarily a function of effective radius
- clouds with small drops (or ice crystals) reflect more than those with large particles
- For optically thick clouds, there is a near orthogonality in the retrieval of c and re using a visible and near-infrared band

- Retrieval of *c** *and re

- Liquid Water Clouds - ocean surface

## Retrieval of c and re

- Retrieval of *c** *and re

- Ice Clouds - ocean surface

- The reflection function of a nonabsorbing band (e.g., 0.86 μm) is primarily a function of optical thickness
- The reflection function of a near-infrared absorbing band (e.g., 2.14 μm) is primarily a function of effective radius
- clouds with small drops (or ice crystals) reflect more than those with large particles
- For optically thick clouds, there is a near orthogonality in the retrieval of c and re using a visible and near-infrared band

## Cloud Optical & Microphysical Properties Retrieval Example

- Liquid Water Clouds - ocean surface

- Liquid Water Clouds - ice surface

## Cloud Optical & Microphysical Properties

- Critical input
- Cloud mask
- to retrieve or not to retrieve?
- Cloud thermodynamic phase
- use liquid water or ice libraries?
- Surface albedo
- for land, ancillary information regarding snow/ice extent (NISE data set)
- Atmospheric correction
- requires cloud top pressure, ancillary information regarding atmospheric moisture & temperature (e.g., NCEP, DAO, other MODIS products)
- 3.7 μm emission (band contains both solar and emissive signal)
- need cloud top temperature, ancillary for surface temperature (e.g., from NCEP, DAO, ...)

## MODIS Cloud Mask(W. P. Menzel, S. A. Ackerman, R. A. Frey)

- MODIS cloud mask uses multispectral imagery to indicate whether the scene is clear, cloudy, or affected by shadows
- Cloud mask is input to rest of atmosphere, land, and ocean algorithms
- Mask is generated at 250 m and 1 km resolutions
- Mask uses 17 spectral bands ranging from 0.55-13.93 μm (including new 1.38 μm band)
- 11 different spectral tests are performed, with different tests being conducted over each of 5 different domains (land, ocean, coast, snow, and desert)
- temporal consistency test is run over the ocean and at night over the desert
- spatial variability is run over the oceans
- Algorithm based on radiance thresholds in the infrared, and reflectance and reflectance ratio thresholds in the visible and near-infrared
- Cloud mask consists of 48 bits of information for each pixel, including results of individual tests and the processing path used
- bits 1 & 2 give combined results (confident clear, probably clear, probably cloudy, cloudy)

## Level-2 Cloud Mask Images

- April 25, 2001

## MODIS Cloud Thermodynamic Phase(M. D. King, S. Platnick, B. A. Baum, S. A. Ackerman, et al. – NASA GSFC, NASA LaRC, U. Wisconsin/CIMSS)

- Bispectral IR test (BT8.5-BT11, BT11 thresholds)
- Uses water/ice emissivity differences in 8.5 μm band (BT8.5-BT11 positive and large for ice clouds, small and negative for water clouds)
- 5 km resolution (currently)
- Solar test (e.g., R1.6/R0.86 ratio test, in development)
- Decision tree* *approach: ecosystem-dependent assessment of individual cloud mask test results, _current technique in production_
- Validation with MODIS Airborne Simulator instrument flown on high altitude NASA ER-2 (can resolve water/ice spectral signatures in 1.6, 2.1, 3.7 μm spectral bands)

## Decision Tree for Cloud Retrievals

- When to retrieve +
- Estimate of cloud phase

- Results of individual cloud mask tests +
- Cloud mask ecosystem map

- Decision Tree

## Cloud Phase Decision Tree – Processing Path

- cloud mask
- determined?

- daytime?

- No

- shadow?

- Yes

- Yes

- Yes

- No

- Determine Ecosystem Type

## Cloud Phase Decision Tree - Ocean Processing Example

- cloud mask
- probability

- Ocean

- heavy
- aerosol?

- Stop

- sunglint &
- prob. cloudy?

- Stop

- T11-T3.9 &
- R1.38 tests
- (<-8K, <0.035)*

- Yes

- No

- No

- Yes

- No

- Yes

- Water
- Cloud

- T14 test
- (< 240K)*

- No

- Yes

- No

- Yes

- Ice
- Cloud

- Ice
- Cloud

- Undetermined
- (water cloud)

- probably clear  (11)

- confident clear (10)

- probably cloudy (01)

- cloudy (00)

- R1.38 test
- (> 0.035)*

- branch removed

- * indicates MAS thresholds used in Arctic

- being evaluated

- No

- Ice
- Cloud

- Yes

- thin cirrus?
- R1.38 test

## MODIS Decision Tree Results

## MODIS Top Properties(W. P. Menzel, R. Frey, K. Strabala, L. Gumley, et al. – NOAA NESDIS, U. Wisc./CIMSS)

- Cloud top pressure, temperature, effective emissivity
- Retrieved for every 5 x 5 box of 1 km FOVs, when at least 5 FOVs are cloudy, day & night
- CO2 Slicing technique (5 bands, 12.0-14.2 μm)
- ratio of  cloud forcing in 2 nearby bands
- retrieve pc; Tc from temperature profile
- most accurate for high and mid-level clouds
- Previously applied to HIRS (NOAA POES, 20 km)
- MODIS 1st satellite sensor capable of CO2 slicing at high spatial resolution

## CO2 slicing method
ratio of cloud forcing at two near-by wavelengths
assumes the emissivity at each wavelength is same, and cancels out in ratio of two bands
The more absorbing the band, the more sensitive it is to high clouds
technique the most accurate for high and middle clouds
MODIS is the first sensor to have CO2 slicing bands at high spatial resolution (1 km)
technique has been applied to HIRS data for ~20 years
retrieved for every 5 x 5 box of 1 km FOVs, when at least 5 FOVs are cloudy, day & night

- ratio of cloud forcing at two near-by wavelengths
- assumes the emissivity at each wavelength is same, and cancels out in ratio of two bands
- The more absorbing the band, the more sensitive it is to high clouds
- technique the most accurate for high and middle clouds
- MODIS is the first sensor to have CO2 slicing bands at high spatial resolution (1 km)
- technique has been applied to HIRS data for ~20 years
- retrieved for every 5 x 5 box of 1 km FOVs, when at least 5 FOVs are cloudy, day & night

- Weighting Functions for CO2 Slicing

- 10

- 100

- 1000

- 0.0

- 0.2

- 0.4

- 0.6

- 0.8

- 1.2

- 1.0

- Weighting Function dt(,p)/d ln p

- Pressure (hPa)

- 32

- 35

- 33

- 34

- 36

- Channel
- 32
- 33
- 34
- 35
- 36

- Central Wavelength (μm)
- 12.020
- 13.335
- 13.635
- 13.935
- 14.235

## Cloud top pressure

- 700

- 550

- 400

- 250

- 850

- 1000

- 100

- Cloud Top Pressure (hPa)

## Cloud top temperature

- 273

- 250

- 225

- 250

- 295

- 320

- 180

- Cloud Top Temperature (K)

## Atmospheric Correction

- Cloud library calculations give cloud-top quantities (no atmosphere)
- atmosphere included during retrieval
- Rayleigh scattering
- iterative approach applied to 0.65 μm band only (used over land)
- important for thin clouds and for any clouds with large solar/view angle combinations
- Atmospheric absorption
- Well-mixed gases a function of *p*c, water vapor absorption a function of profile; both a weak function of temperature
- Assume above-cloud column water vapor amount the primary parameter, vapor profile of minor consequence
- Library calculations made at a variety of *p*c, above-cloud column water amounts (scaled from various water vapor and temperature profiles), geometries
- using *MODTRAN 4.0 *with scripts for 2-way transmittance calculations
- requires cloud top pressure, and ancillary information regarding atmospheric moisture (currently using NCEP)

## pc =	900 hPa
w =	2.0 g-cm-2 above-cloud precipitable water
μ0 =	0.8

- pc =	900 hPa
- w =	2.0 g-cm-2 above-cloud precipitable water
- μ0 =	0.8

- Two-way Atmospheric Path Transmittance (1/μ + 1/μ0)

**Absorption transmittance**

**cosine of viewing zenith angle (μ)**

**1.6 μm**

**0.67 μm**

**2.1 μm**

**3.7 μm**
**(1-way μ path)**

**3.7 μm**

**0.86, 1.24 μm**

**Notes:**
For the time being we’re using ecosystem as a surrogate for surface albedo.

## MOD12 (IGBP ecosystem classification) + USGS water + tundra

- Ecosystem Map(A. H. Strahler, C. B. Schaaf, et al. – Boston University)

**Notes:**
For the time being we’re using ecosystem as a surrogate for surface albedo.

## Surface Albedo Surface albedo = ecosystem + MOD43 (Strahler, Schaaf et al.) aggregation

**Notes:**
For the time being we’re using ecosystem as a surrogate for surface albedo.

## Albedo Movies

- _Albedo Movies_
- Loops through bands 0.65, 0.86,
-          1.24, 1.64, 2.1, and 3.7 μm
- Loops through seasonal equinox
-    and solstice, progressing from
-    Julian days 91, 173, 293, 356

- _Ecosystem Color Scheme_
- Pink = Crops
- Green = Trees
- Yellows = Barren/Deserts
- Blues = Savannas

## Cloud Optical Thickness in the Arctic:Provisional Production Code (edition 3)

**c

- 0

- 5

- 10

- 20

- 15

- June 2, 2001

## Cloud Optical Thickness in the Arctic:Provisional Production Code (new correction)

**c

- 0

- 5

- 10

- 20

- 15

- June 2, 2001

## Cloud Effective Radius in the Arctic:Provisional Production Code (edition 3)

- Cloud Effective Radius in the Arctic:Provisional Production Code (edition 3)

- re(μm)

- 4

- 10

- 22

- 40

- 34

- 16

- 28

- June 2, 2001

## Cloud Effective Radius in the Arctic:Provisional Production Code (new correction)

- re(μm)

- 4

- 10

- 22

- 40

- 34

- 16

- 28

- June 2, 2001

## Level-2 Global Cloud Images

- October 1, 2001

## SAFARI 2000 Core Sites

- Maun

- Sua Pan

- Skukuza

- Mongu

- Inhaca Island

- Tshane

- Swakopmund

- Namib Desert

- Drakensberg escarpment

- Okavango
- Delta

- Etosha Pan

## MODIS true color

- 11 Sept. 2000, 0940 UTC

- C-130
- (red = in-cloud portions)

- ER-2

- ER-2, C-130
- ground tracks

- Validation
- region

## UK C-130 in situ droplet radius, liquid water content

- 11 Sept. 2000, 0941-0953 UTC (S. Osborne, Met Office)

## Previous + SAFARI 2000 Namibian Sc studies

- 0

- 5

- 10

- 15

- 20

- 25

- cloud droplet effective radius (μm)

- Namibian
- Sc

- (Jan. & April 1989, 2 AM scenes, )

- (Sep. 1999, 3 PM scenes)

- (Oct. 1995, SATE-2 validation)

***AVHRR***

***ATSR-2***

- California
- Sc

- California central valley fog

- Arctic stratus

***MAS***

***AVHRR***

## Comparison of Visible Optical Thickness(G. G. Mace, S. Bensen, K. Sassen – University of Utah)

- MOD06 Optical Thickness

- Retrieved Optical Thickness

## Gridded Level-3 Joint Atmosphere Products(M. D. King, S. Platnick, P. A. Hubanks, et al. – NASA GSFC, UMBC)

- Daily, 8-day, and monthly products (474.8, 883.2, 883.2 MB)
- 1° 1° equal angle grid
- Mean, standard deviation, marginal probability density function, joint probability density functions

## Cloud Optical Thickness (M. D. King, S. Platnick, M. Gray, E. Moody, et al. – NASA GSFC, UMBC)

- Cloud Optical Thickness (M. D. King, S. Platnick, M. Gray, E. Moody, et al. – NASA GSFC, UMBC)

**c

- 0

- 4

- 8

- 12

- 16

- 20

- Level-3 Monthly
- April 2001

## Cloud Effective Particle Radius (M. D. King, S. Platnick, M. Gray, E. Moody, et al. – NASA GSFC, UMBC)

- Cloud Effective Particle Radius (M. D. King, S. Platnick, M. Gray, E. Moody, et al. – NASA GSFC, UMBC)

- 4

- 10

- 16

- 28

- 40

*r*e(μm)

- 34

- 22

- Level-3 Monthly
- April 2001

## MODIS L3 aggregation from 6°x 6° grid off Namibian coastliquid water clouds

- L3 product bin sizes (liquid water clouds)

- MODIS L3 aggregation from 6°x 6° grid off _Namibian_ coastliquid water clouds

## MODIS L3 aggregation from 6°x 10° grid off California coastliquid water clouds

- L3 product bin sizes (liquid water)

- MODIS L3 aggregation from 6°x 10° grid off _California_ coastliquid water clouds

- May 28, 2001

**Notes:**
Clearly shows the bin sizes categories

## Cloud Top Pressure(W. P. Menzel, R. Frey, K. Strabala, L. Gumley, et al. – NOAA NESDIS, U. Wisconsin/CIMSS)

- Cloud Top Pressure(W. P. Menzel, R. Frey, K. Strabala, L. Gumley, et al. – NOAA NESDIS, U. Wisconsin/CIMSS)

- Level-3 Monthly
- April 2001

- 300

- 400

- 500

- 700

- 1000

- pc (hPa)

- 900

- 800

- 600

## Precipitable Water over Land & Sunglint(B. C. Gao, et al. – Naval Research Laboratory)

- Precipitable Water over Land & Sunglint(B. C. Gao, et al. – Naval Research Laboratory)

- Level-3 Monthly
- April 2001

- q (cm)

- 0.0

- 1.0

- 2.0

- 3.0

- 4.0

- 6.0

- 7.0

- 5.0

## Ship Tracks in NE Pacific

- California, Azores, Namibia, and Peru
- Conditions for formation
- High humidity
- Small air-sea temperature difference
- Low wind speed
- Boundary layer between 300 and 750 m deep
- Enhanced reflectance of clouds at 3.7 μm
- Larger number of small droplets arising from particulate emission from ships

## When water vapor condenses in the atmosphere, it always does so on some solid nucleus
The dust particles in the air form the nuclei on which it condenses
If there was no dust in the air there would be no fogs, no clouds, no mists, and probably no rain
			John Aitken (1880)				“On dust, fogs, and clouds” 

- The dust particles in the air form the nuclei on which it condenses
- If there was no dust in the air there would be no fogs, no clouds, no mists, and probably no rain
- 			*John Aitken (1880)**	*			*“On dust, fogs, and clouds”*

- Cloud Physics

## Ship Tracks in NE Pacific

- MODIS
- 2.1 μm

- October 2, 2000

## Ship Track Formation

*N*	~ 40 cm-3
*W*	~ 0.30 g m-3
*r**e*	~ 11.2 μm

*N*	~ 100 cm-3
*W*	~ 0.75 g m-3
*r**e*	~ 10.5 μm

## Distribution of Ship Tracks in NE Pacific during June 1994

- Ship Tracks identified from AVHRR imagery
- 1362 identified during June 1994 off the US west coast
- The head of each track is denoted by a dot
- The greatest concentration of tracks occurs along the great-circle shipping lanes
- Ship Track Characteristics
- 296 ± 233 km long
- 7.3 ± 6 hours old
- 9 ±5 km wide
- 16 ± 8 km from the head of the ship track and 25 ± 15 minutes after ship passed
- Boundary layer between 300 and 750 m deep
- No tracks formed in boundary layers > 800 m deep

## Ship Track off West Coast of Chile

- July 20, 1994

## Image of a Ship and Corresponding Ship Track in NE Pacific

**250 m**

**1400 m**

- June 13, 1994
- RC-10 Camera

## Ship Tracks in NE Pacific – June 30, 1994

- Wind from NNW at 13.5 ms-1
- Ship motion vectors

## Ship Track Characteristics

## Ship Track Occurrence

- Ship tracks form preferentially in boundary layers shallower than about 800 m depth

- Boundary Layer Depth (m)

- Number of Cases and Mean Tracks/Case

## Global Occurrence Statistics

**805 ****tracks**

**470 ****tracks**

- Analysis Pending

**809 ****tracks**

**572 ****tracks**

**5537 ****tracks**

**49 ****tracks**

## Particles emitted by ships increase concentration of cloud condensation nuclei (CCN) in the air
Increased CCN increase concentration of cloud droplets and reduce average size of the droplets
Increased concentration and smaller particles reduce production of drizzle (100 μm radius) droplets in clouds
Liquid water content increases because loss of drizzle particles is suppressed
Clouds are optically thicker and brighter along ship track

- Increased CCN increase concentration of cloud droplets and reduce average size of the droplets
- Increased concentration and smaller particles reduce production of drizzle (100 μm radius) droplets in clouds
- Liquid water content increases because loss of drizzle particles is suppressed
- Clouds are optically thicker and brighter along ship track

- Summary

## The increase in CCN of industrial origin might explain why the Northern Hemisphere has warmed less than the Southern Hemisphere over the last 50 years
Even if some compensation in surface warming occurs because of changes in sulfate and greenhouse gases, it is not clear whether that compensation will continue in the future
The sulfate effect would tend to act only regionally, whilst the greenhouse forcing is global
		IPCC Scientific Assessment of Climate Change (1990)

- Even if some compensation in surface warming occurs because of changes in sulfate and greenhouse gases, it is not clear whether that compensation will continue in the future
- The sulfate effect would tend to act only regionally, whilst the greenhouse forcing is global
- 		*IPCC Scientific Assessment of Climate Change (1990)*

- Indirect Aerosol Effects