Remote Sensing of Cloud Parameters

Why Cloud Observations? There are a number of fundamental reasons:

– Establishing climate quality data records

– Radiation budget studies (e.g., CERES/MODIS/GEO)

– Water budget/cycle studies (e.g., role of ice clouds and convection in upper troposphere humidity)

– Establishing data sets for climate and weather forecast validation, and model parameterization development

– Data assimilation

– Cloud process studies, including aerosol-cloud interactions

– Atmospheric chemistry (effect on photochemistry, Liu et al.,

2006)

Earth Radiation Budget Sensitivity to Cloud ChangesEarth Radiation Budget Sensitivity to Cloud Changes

Cloud radiative forcing 15 Wm-2 (cooling effect) (Ramanathan et al. 1989). Forcing by doubling atmospheric CO2 concentration 4 Wm -2 (warming effect) (IPCC, 1994).

Slingo (1990): Reducing stratocumulus re from 10 m to 8 m would balance the warming by CO2 doubling.

Coakley (1994):

ISCCP (International Satellite Cloud Climatology Project)– Routine operation since 1983– Primary data source is worldwide geosynchronous satellites having two bands

(visible and 11 µm thermal band)– Clouds are classified by optical thickness and cloud top pressure– Cloud optical thickness is higher in NH than SH, and is higher over land than

ocean– Effective radius is larger over ocean than land, and larger in SH than NH

HIRS (High Resolution Infrared Radiation Sounder)– Routine operation since 1979– Clouds found to be most prevalent in the Intertropical Convergence Zone

(ITCZ) of the deep tropics and the middle to high latitude storm belts– CO2 slicing estimates of cloud fraction and cloud top pressure– Decadal average cloud cover has not changed appreciably from the 1980s

• High altitude cirrus clouds increased 10% in the 1980s and 1990s over the tropics

International Satellite Sensors for Cloud Detection and Optical Properties from Operational Sensors

MODIS and beyond– Routine determination of cloud top pressure, optical

thickness, effective radius, and thermodynamic phase

– Diurnal sampling accomplished by AM and PM polar orbiting satellites (especially Terra and Aqua)

– Multilayer cloud structure estimated from both passive and active sensors

Long term trends require merging data from various sources

EOS Sensors for Cloud Detection and Optical Properties

Cloud detection/masking– Multispectral and/or multiview imagers with appropriate spatial resolution, lidar, radar

Cloud thermodynamic phase– Multispectral imagers with SWIR and/or IR (8.5 µm) bands– Polarimeters with multiangular views and good spatial resolution– Lidars with depolarization capability

Cloud top properties: pressure, temperature, effective emissivity

– Multispectral and/or multiview imagers (thermal window, CO2 bands, other gas absorbing bands)– UV imagers– Polarimeters

Cloud optical & microphysical properties: optical thickness(c), effective particle size (re), water path

– Solar reflectance imagers (re from 1.6, 2.1, 3.7 µm bands)

– IR imager and sounder retrievals of c, re for thin clouds

– Polarimeter with multiangular views (re)– Microwave radiometers (water path)

Cloud Products and Techniques

Cloud vertical structure: geometric information & optical/microphysical properties

– Radar (water content profile)

– Lidar (extinction profile)

Drizzle detection and precipitation– Radar

– Microwave imagers

Cloud Products and Techniques (continued)

MODIS Operational Cloud Products

Pixel level products (Level-2)– Cloud mask (S. A. Ackerman, R. A. Frey, U. Wisconsin/CIMSS)

• 1 km, 48-bit mask/12 spectral tests, clear sky confidence in bits 1,2– Cloud top properties – W. P. Menzel, R. A. Frey, U. Wisconsin/CIMSS

• Cloud top pressure, temperature, effective emissivity • 5 km, CO2 slicing for high clouds, 11 µm for low clouds

– Cloud optical & microphysical properties – M. D. King, S. Platnick, GSFC• optical thickness, c, effective particle size, re, water path, thermodynamic

phase• Primary re from 2.1 µm band

– IR-derived thermodynamic phase – B. A. Baum, U. Wisconsin/SSEC• SDS name Cloud_Phase_Infrared (day, night, and combined)

– Cirrus reflectance (via 1.38 µm band) – B. C. Gao, Naval Research Lab• SDS name Cirrus_Reflectance

Gridded & time-averaged products (Level-3)– Scalar statistics, 1-D and 2-D histograms– Contains all atmosphere products (clouds, aerosol, atmospheric profiles)

MO

D0

6,

MYD

06

MO

D3

5,

MYD

35

MO

D0

8,

MYD

08

Critical issues (especially for global processing):

– Cloud mask: To retrieve or not to retrieve?

– Cloud thermodynamic phase: liquid water or ice libraries?

– Ice cloud models

– Multilayer/multiphase scenes: detectable?

– Surface spectral albedo: including ancillary information regarding snow/ice extent

– Atmospheric correction: requires cloud top pressure, ancillary information regarding atmospheric moisture & temperature profiles

– Cloud-top temperature, ancillary surface temperature: needed for 3.7 µm emission (band contains solar and emissive radiance)

– 3D cloud effects

Optical & Microphysical Retrieval Issues

MODIS on board NASA Earth Observing System (EOS) Terra and Aqua satellites:

- 705 km polar orbit- Terra launched 18 Dec 1999 (descending 1030 local time)- Aqua launched 18 Apr 2002 (ascending 1330 local time)- Filter radiometer, 4 detector arrays, 36 spectral bands (0.41-14.38 µm)- Cross-track scan, 2330 km swath- Spatial resolution: 250m (bands 1-2), 500m (3-7), 1km (8-36).

MODIS provides 3.7-, 2.1-, and 1.6-m measurements useful for cloud Droplet Effective Radius retrievals.

MODIS Level-1B products of calibrated radiances at 0.63, 1.6, 2.1, 3.7, 11, and 12 m.

MODIS Instrument

Cloud Masking or Cloud Identification

Shortwave Properties of Clouds Cloud Mask Bands

Infrared Properties of Clouds

What Do We Mean by a Cloud Mask?

CloudCloud

ClearClear

Overcast Cloud Mask

Clear Sky Mask

Partly Cloudy

Cloud Mask TestsDaytime Ocean

Nighttime Ocean

Daytime Land

Nighttime Land

Daytime Snow/ice

Nighttime Snow/ice

Daytime Coastline

Daytime Desert

Polar Day

Polar Night

BT11 (Bit 13)

BT13.9 (Bit 14)

BT6.7 (Bit 15) 3 3 3 3 3 3 3 3 3 3

R1.38 (Bit 16) 3 3 3 3 3

BT3.9 – BT12 (Bit 17) 3 3 3

BT8.6 – BT11 and/or

BT11 – BT12 (Bit 18)3 3 3 3 3 3 3 3 3 3

BT11 – BT3.9 (Bit 19) 3 3 3 3 3 3 3 3 3 3

R0.66 or R0.87 (Bit 20) 3 3 3 3

R0.87/R0.66 (Bit 21) 3 3

BT7.3 – BT11 (Bit 23) 3 3 3

Surface Temperature Test (Bit 27) 3 3 3

BT8.6 – BT7.3 (Bit 29) 3

BT11 Variability Test (Bit 30)

3

MODIS Cloud Mask(S. A. Ackerman, W. P. Menzel – Univ. Wisconsin)

True Color Composite (0.65, 0.56, 0.47)

June 4, 2001

Cloud Mask

Confident Clear

Probably Clear

Cloudy

Probably Cloudy

Determination of Cloud Top Height

Satellite Determination of Cloud Top Height

Conventional IR-window method uses the 11-m channel (e.g., ISCCP, AVHRR, GOES).

- Most effective for opaque clouds.

CO2-slicing method uses the multiple sounding channels at nominally 13.3, 13.6, 13.9, 14.2 m (e.g., MODIS, HIRS).

- Most effective for non-opaque cirrus clouds.

Infrared Properties of Clear Skies & CirrusCO2 Slicing Bands

Wavenumber (cm-1)

200

220

240

260

Bri

gh

tne

ss T

em

pe

ratu

re (

K)

280

300

600 700 800 900 1000 1100

10111213141516

Wavelength (µm)

Cirrus Infrared Spectra 2 November 1986

MODIS Bands

Clear

Thin

Moderate

Thick

The ratio of the cloud effect in two neighboring channels can be written as

which is independent of the fractional cloud cover within the pixel

This function can also be evaluated from the infrared radiative transfer equation which can be written as

CO2 Slicing for Cloud Top Pressure

1000

100

10

0.0 0.2 0.4 0.6 0.8 1.0

Pre

ssu

re (

mb

)

Weighting Function dt(,p)/d ln p

Channel 32 33 34 35 36

Central Wavelength (µm)

12.020 13.335 13.635 13.935 14.235

36

1.2

35

34

33

32

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 ~25 years

Weighting Functions for CO2 Slicing

Remote Sensing of Cloud Microphysics

What cloud microphysics?

Hydrometeor size and cloud column liquid/ice water content.

How critical is cloud microphysics, in its secondary status behind cloud cover, cloud albedo, and cloud top altitude, etc., to the earth’s climate?

Means for obtaining cloud microphysical properties fall short of capturing shifts that would be of comparable significance.

Cloud Microphysics?

Radiative processes –

Cloud radiative properties, like scattering-absorption ratio and angular scattering phase functions, are remarkably sensitive to changes in cloud Droplet Effective Radius (DER). Modification in cloud DER can promptly offset the radiative effect due to other cloud variations.

Hydrological processes –

The tendency of a cloud to produce precipitation depends upon the growth of droplet size distributions. The onset of rain droplet formation requires a certain range of growing droplet radius.

Role of the Cloud Microphysics

Input Data and Procedures for R/S of Cloud

Cloud thermodynamic phase

Cloud mask

Cloud top properties

Atmospheric correction

Surface albedo

Ancillary data: atmo T(p), w(p); surface temperature, etc.

IR bi-spectral test (BT8.5-BT11, BT11 thresholds) (Baum, Nasiri, Ackerman

et al., U. Wisc. CIMSS)Uses water/ice emissivity differences in 8.5 and 11 µm bands

5 km resolution (currently)

SWIR test (e.g., R1.64/R0.65 & R2.13/R0.65 ratio test) (Riédi et al.)

Cloud mask tests: ecosystem-dependent assessment of individual cloud

mask test results used as first guess for cloud optical/microphysical

retrievals

Tested/compared against MODIS Airborne Simulator instrument flown

on high altitude NASA ER-2 (can resolve water/ice spectral

signatures in 1.64, 2.13, 3.74 µm spectral bands)

Cloud thermodynamic phase

Atmospheric Correction

Cloud library calculations give cloud-top quantities (no atmosphere); atmosphere included during retrieval; need fast/efficient corrections w/ appropriate accuracy

Rayleigh scattering: iterative approach applied to 0.65 µm band only, important for thin clouds with large solar/view

zenith angle combinations

Atmospheric absorption: transmittance lookup table Water vapor assumptions: above-cloud column amount primary parameter,

profile of minor consequence; well-mixed gases a function of pc (though both a weak function of temperature)

Calculations: made at a variety of pc, above-cloud column water amounts (scaled from various water vapor and temperature profiles), geometries: using MODTRAN 4.0 w/scripts for 2-way transmittance calculations, MODIS band spectral response

Requirements: cloud top pressure and ancillary information regarding atmospheric moisture (currently using NCEP)

Technique uncertainty2-way atmospheric path transmittance (1/µ + 1/µ0)

pc = 900 hPa, 2.0 g-cm-2 above-cloud precipitable watercosine of solar zenith angle (µ0) = 0.8

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

0.400.500.600.700.800.901.00

Abs

orpt

ion

tran

smitt

ance

cosine of viewing zenith angle (µ)

1.64 µm

0.67 µm

2.13 µm3.74 µm(1-way µ path)

3.74 µm

0.86, 1.24 µm

0.67 µm: some H2O, O3, O2 on long-wavelength band edge

0.86 µm: some H2O on band edges

1.24 µm: some H2O, O2 on band edges respectively

1.64 µm: primarily CO2

2.13 µm: some H2O throughout band

3.74 µm: H2O, some N2O on long-wave band edge

The effective radius re is defined by

re =

wherer = particle

radiusn(r) = particle

size distribution

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

r3n(r)dr0

r2n(r)dr0

The reflection function of a nonabsorbing band (e.g., 0.66 µm) is primarily a function of cloud optical thickness

The reflection function of a near-infrared absorbing band (e.g., 2.13 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

Monthly Mean Cloud Fraction(S. A. Ackerman, R. A. Frey et al. – Univ. Wisconsin)

April 2005 (Collection 5)Aqua

Cloud_Fraction_Night_Mean_Mean

Cloud_Fraction_Day_Mean_Mean

Zonal Mean Cloud Fraction(S. A. Ackerman, R. A. Frey et al. – Univ. Wisconsin)

April 2005 (Collection 5)

Aqua

Time Series of Cloud Fraction during the Daytime

Monthly Mean Cloud Top Properties(W. P. Menzel, R. A. Frey et al. – Univ. Wisconsin)

April 2005 (Collection 5)Aqua

Cloud_Top_Temperature_Mean_Mean

Cloud_Top_Pressure_Mean_Mean

Zonal Mean Cloud Top Pressure(W. P. Menzel, R. A. Frey et al. – NOAA, Univ. Wisconsin)

April 2005 (Collection 5)

Aqua

Monthly Mean Cloud Fraction by Phase(M. D. King, S. Platnick et al. – NASA GSFC)

July 2006 (Collection 5)Terra

Cloud_Fraction_Ice_FMean

Cloud_Fraction_Liquid_FMean

Monthly Mean Cloud Optical Thickness(M. D. King, S. Platnick et al. – NASA GSFC)

April 2005 (Collection 5)Aqua (QA Mean)

Cloud_Optical_Thickness_Ice_QA_Mean_Mean

Cloud_Optical_Thickness_Liquid_QA_Mean_Mean

Monthly Mean Cloud Effective Radius(M. D. King, S. Platnick et al. – NASA GSFC)

April 2005 (Collection 5)Aqua (QA Mean)

Cloud_Effective_Radius_Ice_QA_Mean_Mean

Cloud_Effective_Radius_Liquid_QA_Mean_Mean

AVHRR data have been the workhorse for measuring cloud DER since the work by Han et al. (1994), despite the AVHRR was not originally designed for the purpose of remote sensing of cloud DER.

Han et al.’s approach was based on ISCCP cloud retrievals…

1. Droplet Effective Radius (DER) initially assumed to be 10 m.2. 0.63-m visible reflectivity used to obtain cloud column liquid water amount

for assumed DER (initially 10 m).3. 11-m emission used with temperature profile to obtain cloud-top altitude

and thus computed emission at 3.7 m.4. 3.7-m radiance (measured) and 3.7-m emission (computed) used with

liquid water path to estimate 3.7-m reflectivity and NEW DER.

REPEAT 2-4 using NEW estimate of DER.

An AVHRR Cloud Microphysics Retrieval SchemeHan et al. 1994

AVHRR Remote Sensing Retrieval of Cloud Droplet Effective RadiusAVHRR Remote Sensing Retrieval of Cloud Droplet Effective Radius

AVHRR satellite measurements at 3.7-m channel have been widely used for retrieving re from space (Arking and Childs 1985; Coakley et al. 1987; Han et al. 1994; Platnick and Twomey 1994; Nakajima and Nakajima 1995).

Retrieval principle: The 3.7-m reflectance has a large dependence on re because larger droplets absorb more radiance than do smaller droplets and smaller droplets scatter more radiance than do larger droplets.

Limitation of Using Single-spectral (3.7-m) Retrieval Because cloud droplets absorb strongly at 3.7 m, photons rarely transport

far inside cloud top before being reflected. The DER (re) retrieval may only represent a shallow layer near cloud top.

The 3.7-m retrieved DER is biased if the cloud DER has an inhomogeneous vertical variation from cloud top to cloud base.

Limitation of the 3.7-Limitation of the 3.7-m Retrieval Methodm Retrieval Method

Due to the significant absorption at 3.7 m, it is rarely that a photon can transport far beneath cloud top without being absorbed by droplets. Hence, 3.7-m retrieved re can only represent a shallow layer at near the cloud top, which seldom represents the full cloud column.

In-situ observations of stratocumulus cloud often exhibit an increase in re with height (Nicholls 1984 at North Sea; Stephens and Platt 1987 at east coast of Australia; Duda et al. 1991 at San Nicholas Island; Martin et al. 1994 at coast of California; Albrecht et al. 1995 and Duynkerke et al. 1995, both at Azores/Madeira Islands).

Dependence of Different NIR reflectances on DER

Multispectral reflectances at distinct near-infrared wavelengths convey certain information on the cloud DER profile because of different photon penetration depths. But, the information alone is not sufficient for retrieving a DER profile.

Schematic Illustration of a Bispectral Retrieval Procedure

(a) Conventional re retrievals by assuming dre/d = 0.

(b) The linear-re retrievals with dre/d = re/total, where re = 13.111.8 m as obtained from the 3.7- (red) and 1.6-m (green) retrieved re values shown in Figure (a).

(c) The optimal linear-re retrieval for the two channels.

DER Vertical Profile from MODIS and Radar Retrievals

In convention, re is assumed to be independent of height (z). Thus,

Estimating the Cloud Liquid Water Path (LWP)

zdzrczdzr

zwz e

e

1

0

20

1

0

)(2

3

)(

)(

2

3)(

1

0 )(

)(

2

3)( zd

zr

zwz

e

erLWP 32

In this study, an empirical relationship between LWC (w) and re is adopted (Bower et al. 1994; Gultepe et al. 1996; Liu and Hallet 1997) by

)()( 30 zrczw e

zdzrcLWP e )(31

0

0

where c0 is determined based on the retrieved values of and re.

Cloud Profiles

Status of GCM-derived High, Mid and Low Clouds

Satellite cloud products

GCM validations

Courtesy of M.H. Zhang Stony Brook, New York.

(Zhang et al. 2005, JGR)

Highcloud

Midcloud

Lowcloud

Satellite Cloud Top Pressure vs. Cloud Optical Depth

Results are obtained for April 2001 between 60S-60N.

Rationale of Our New Method

Case 1: A cirrus-overlapping-water cloud system observed on April 2, 2001 over the ARM Southern Great Plains (SGP) site in Oklahoma.

Case 2:A single-layer cirrus system observed on March 6, 2001 over the ARM SGP site.

Inference of Cirrus Overlapping Low Clouds

Our Algorithm Chang and Li (2005, J. Atmos. Sci.)

Validations at the ARM SGP Site Validation is based on

comparisons with the Active Remote Sensing Cloud Locations (ARSCL) data from DOE/ARM.

Overlapped cirrus clouds (open points) and low clouds (filled points) are validated during March-November 2001 by comparing the ARSCL and our cloud-top pressures (a) and cloud-top temperatures (b).

Apr.-Nov. 2001 at SGP Apr.-Nov. 1999 at NAU

A Bimodal Frequency Distribution of Cloud Top Height

Did you know that…

Single-layer and two-layer cloud systems dominate the Earth’s atmosphere!

A Bimodal Frequency Distribution of Cloud Top Height

Chang and Li (2005, J. Climate)

Zonal-mean Cloud Properties

Cloud Top Pressure Cloud Top Temperature Cloud Optical Depth

Obtained for April 2001 Terra/MODIS.

Total High Cloud Amount (High1 + High2 + High3)

January 2001 April 2001

July 2001 October 2001

Overlapped Cloud Amount (High2/Low2)

January 2001 April 2001

July 2001 October 2001

Mid-level (500-600 mb) Cloud Amount

January 2001 April 2001

July 2001 October 2001

Our New Low Cloud Amount (Low1 + Low2)

January 2001 April 2001

July 2001 October 2001

Original MODIS Low Cloud Amount (Low1)

January 2001 April 2001

July 2001 October 2001

Conclusions

Single-layer cloud assumption can result in systematic biases in satellite-derived cloud optical properties and cloud vertical distributions.

Dual-layer method recovered ~30% relatively more low-level clouds than the conventional product by differentiating overlapped clouds from single-layer clouds.

For cirrus-overlapping-low clouds, the conventional IR method tends to detect them as single-layer mid-level clouds; whereas the CO2-slicing method tends to detect them as single-layer high-thick clouds.

A distinct bimodal distribution was found of cloud top height, peaking at 250-300 hPa and 750-800 hPa. This finding differs from the ISCCP results, but is closer to the GCM results.

In general, ISCCP generates less high- and low-level clouds, but more mid-level clouds, whereas MODIS (collection 4) generates less mid- and low-level clouds.

Major References Rossow, W. B., and R. A. Schiffer, 1991: ISCCP cloud data products. Bull. Amer.

Meteor. Soc., 72, 2–20. ——, and ——, 1999: Advances in understanding clouds from ISCCP. Bull. Amer.

Meteor. Soc., 80, 2261–2287. King, M. D., Y. J. Kaufman, W. P. Menzel, and D. Tanre, Remote-sensing of cloud,

aerosol, and water-vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS), IEEE Trans. Geosci. Remote Sens., 30, 2 – 27, 1992.

Chang, F.-L., Z. Li, 2002, Estimating the vertical variation of cloud droplet effective radius using multispectral near-infrared satellite measurements, J. Geophy. Res., 107.

Chang, F.L., Z. Li, 2005, A new method for detection of cirrus overlapping water clouds and determination of their optical properties, J. Atmos. Sci., 62, 3993-4009.

Chang, F.L., Z. Li, 2005, A new global climatology of single-layer and overlapped clouds and their optical properties retrieved from TERRA/MODIS data using a new algorithm, J. Climate, 18, 4752-4771.

Homework due Mar 23Write an assay (5 pages single-space) to:

a. summarize the methods of cloud identification, estimating cloud optical depth, particle size and liquid water path.

b. describe global cloud climatology of cloud cover, optical thickness, particle size and vertical distribution in terms of their regional variation, zonal variation, land-ocean contrast, and seasonable variability

c. elaborate the importance of cloud observation data for climate studies.