Remote Sensing of Cloud Parameters

1. Remote Sensing of Cloud Parameters

2. 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)

3. Earth 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 stratocumulusre from 10 m to 8 m would balance the warming by CO2 doubling. • Coakley (1994):

4. International Satellite Sensors for Cloud Detection and Optical Properties from Operational Sensors • 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

5. EOS Sensors for Cloud Detection and Optical Properties • 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

6. Cloud Products and Techniques • 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)

7. Cloud Products and Techniques (continued) • Cloud vertical structure: geometric information & optical/microphysical properties • Radar (water content profile) • Lidar (extinction profile) • Drizzle detection and precipitation • Radar • Microwave imagers

8. MODIS Operational Cloud Products MOD35, MYD35 • 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) MOD06, MYD06 MOD08, MYD08

9. Optical & Microphysical Retrieval Issues 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

10. MODIS Instrument • 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 (0.41-14.38µm) • -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.

11. Cloud Masking or Cloud Identification

12. Shortwave Properties of CloudsCloud Mask Bands

13. Infrared Properties of Clouds

14. Overcast Cloud Mask Partly Cloudy Clear Sky Mask What Do We Mean by a Cloud Mask? Cloud Clear

15. Cloud Mask Tests

16. MODIS Cloud Mask(S. A. Ackerman, W. P. Menzel – Univ. Wisconsin) True Color Composite (0.65, 0.56, 0.47) Cloud Mask Confident Clear Probably Clear Probably Cloudy Cloudy June 4, 2001

17. Determination of Cloud Top Height

18. 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.

19. Infrared Properties of Clear Skies & CirrusCO2 Slicing Bands

20. CO2 Slicing for Cloud Top Pressure 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

21. Weighting Functions for CO2 Slicing • 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

22. Remote Sensing of Cloud Microphysics

23. 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.

24. Role of the 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.

25. Input Data and Procedures for R/S of Cloud Cloud mask Cloud thermodynamic phase Cloud top properties Atmospheric correction Surface albedo Ancillary data: atmo T(p), w(p); surface temperature, etc.

26. Cloud thermodynamic phase 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)

27. 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)

28. 0.86, 1.24 µm 1.64 µm 0.67 µm 2.13 µm 3.74 µm (1-way µ path) Absorption transmittance 3.74 µm cosine of viewing zenith angle (µ) Technique uncertainty 2-way atmospheric path transmittance (1/µ + 1/µ0) pc = 900 hPa, 2.0 g-cm-2 above-cloud precipitable water cosine of solar zenith angle (µ0) = 0.8 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, O2on band edges respectively 1.64 µm: primarily CO2 2.13 µm: some H2O throughout band 3.74 µm: H2O, some N2Oon long-wave band edge

29. Reflection Function of Clouds as a Function of Cloud Optical Thickness at 0.65 µm The effective radius re is defined by re = where • r = particle radius • n(r) = particle size distribution

30. Retrieval of tcand re • 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 tc and re using a visible and near-infrared band

31. Monthly Mean Cloud Fraction(S. A. Ackerman, R. A. Frey et al. – Univ. Wisconsin) April 2005 (Collection 5) Aqua Cloud_Fraction_Day_Mean_Mean Cloud_Fraction_Night_Mean_Mean

32. Zonal Mean Cloud Fraction(S. A. Ackerman, R. A. Frey et al. – Univ. Wisconsin) April 2005 (Collection 5) Aqua

33. Time Series of Cloud Fraction during the Daytime

34. Monthly Mean Cloud Top Properties(W. P. Menzel, R. A. Frey et al. – Univ. Wisconsin) April 2005 (Collection 5) Aqua Cloud_Top_Pressure_Mean_Mean Cloud_Top_Temperature_Mean_Mean

35. Zonal Mean Cloud Top Pressure(W. P. Menzel, R. A. Frey et al. – NOAA, Univ. Wisconsin) April 2005 (Collection 5) Aqua

36. Monthly Mean Cloud Fraction by Phase(M. D. King, S. Platnick et al. – NASA GSFC) July 2006 (Collection 5) Terra Cloud_Fraction_Liquid_FMean Cloud_Fraction_Ice_FMean

37. Monthly Mean Cloud Optical Thickness(M. D. King, S. Platnick et al. – NASA GSFC) April 2005 (Collection 5) Aqua (QA Mean) Cloud_Optical_Thickness_Liquid_QA_Mean_Mean Cloud_Optical_Thickness_Ice_QA_Mean_Mean

38. Monthly Mean Cloud Effective Radius(M. D. King, S. Platnick et al. – NASA GSFC) April 2005 (Collection 5) Aqua (QA Mean) Cloud_Effective_Radius_Liquid_QA_Mean_Mean Cloud_Effective_Radius_Ice_QA_Mean_Mean

39. An AVHRR Cloud Microphysics Retrieval Scheme Han et al. 1994 • 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.

40. AVHRR 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.

41. 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.

42. Limitation of the 3.7-m 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).

43. 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.

44. Schematic Illustration of a Bispectral Retrieval Procedure Conventional re retrievals by assuming dre/d = 0. The linear-re retrievals with dre/d = re/total, where re = 13.111.8 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.

45. DER Vertical Profile from MODIS and Radar Retrievals

46. Estimating the Cloud Liquid Water Path (LWP) • In convention, re is assumed to be independent of height (z). Thus, •  • 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 •  where c0 is determined based on the retrieved values of  and re.

47. Cloud Profiles

48. Status of GCM-derived High, Mid and Low Clouds Satellite cloud products  GCM validations High cloud Mid cloud Low cloud Courtesy of M.H. Zhang Stony Brook, New York. (Zhang et al. 2005, JGR)

49. Satellite Cloud Top Pressure vs. Cloud Optical Depth • Results are obtained for April 2001 between 60S-60N.

50. 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.