remote sensing of cloud parameters
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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) - PowerPoint PPT PresentationTRANSCRIPT
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.