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 CO 2 concentration  4 Wm -2 (warming effect) (IPCC, 1994).  Slingo (1990): Reducing stratocumulus r e from 10  m to 8  m would balance the warming by CO 2 doubling.  Coakley (1994):

4.  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 –CO 2 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

5.  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

6.  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, CO 2 bands, other gas absorbing bands) –UV imagers –Polarimeters  Cloud optical & microphysical properties: optical thickness(  c ), effective particle size (r e ), water path –Solar reflectance imagers (r e from 1.6, 2.1, 3.7 µm bands) –IR imager and sounder retrievals of  c, r e for thin clouds –Polarimeter with multiangular views (r e ) –Microwave radiometers (water path) Cloud Products and Techniques

7.  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)

8. 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, CO 2 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, r e, water path, thermodynamic phase Primary r e 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 MOD35, MYD35 MOD08, MYD08

9. 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

10.  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

11. Cloud Masking or Cloud Identification

12. Shortwave Properties of Clouds Cloud Mask Bands

13. Infrared Properties of Clouds

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

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) June 4, 2001 Cloud Mask Confident Clear Probably Clear CloudyProbably Cloudy

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.  CO 2 -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 & Cirrus CO 2 Slicing Bands

20. 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 CO 2 Slicing for Cloud Top Pressure

21.  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 CO 2 slicing bands at high spatial resolution (1 km) –technique has been applied to HIRS data for ~25 years Weighting Functions for CO 2 Slicing

22. Remote Sensing of Cloud Microphysics

23.  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?

24.  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

25. 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.

26. IR bi-spectral test (BT 8.5 -BT 11, BT 11 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., R 1.64 /R 0.65 & R 2.13 /R 0.65 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

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 p c (though both a weak function of temperature)  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 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. Technique uncertainty 2-way atmospheric path transmittance (1/µ + 1/µ 0 ) p c = 900 hPa, 2.0 g-cm -2 above-cloud precipitable water cosine of solar zenith angle (µ 0 ) = 0.8 Absorption transmittance cosine of viewing zenith angle (µ) 1.64 µm 0.67 µm 2.13 µm 3.74 µm (1-way µ path) 3.74 µm 0.86, 1.24 µm 0.67 µm: some H 2 O, O 3, O 2 on long-wavelength band edge 0.86 µm: some H 2 O on band edges 1.24 µm: some H 2 O, O 2 on band edges respectively 1.64 µm: primarily CO 2 2.13 µm: some H 2 O throughout band 3.74 µm: H 2 O, some N 2 O on long-wave band edge

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

30.  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 r e using a visible and near-infrared band Retrieval of  c and r e

31. 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

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_Temperature_Mean_Mean Cloud_Top_Pressure_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_Ice_FMean Cloud_Fraction_Liquid_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_Ice_QA_ Mean_Mean Cloud_Optical_Thickness_Liquid_QA_M ean_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_Ice_QA_ Mean_Mean Cloud_Effective_Radius_Liquid_QA_Me an_Mean

39.  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 Scheme Han et al. 1994

40. AVHRR Remote Sensing Retrieval of Cloud Droplet Effective Radius  AVHRR satellite measurements at 3.7-  m channel have been widely used for retrieving r e 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 r e 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 (r e ) 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 r e 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 r e 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 (a)Conventional r e retrievals by assuming dr e /d  = 0. (b)The linear-r e retrievals with dr e /d  =  r e /  total, where  r e = 13.1  11.8  m as obtained from the 3.7- (red) and 1.6-  m (green) retrieved r e values shown in Figure (a). (c)The optimal linear-r e retrieval for the two channels.

45. DER Vertical Profile from MODIS and Radar Retrievals

46.  In convention, r e is assumed to be independent of height (z). Thus,  Estimating the Cloud Liquid Water Path (LWP)  In this study, an empirical relationship between LWC (w) and r e is adopted (Bower et al. 1994; Gultepe et al. 1996; Liu and Hallet 1997) by  where c 0 is determined based on the retrieved values of  and r e.

47. Cloud Profiles

48. 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) High cloud Mid cloud Low cloud

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.

51. Inference of Cirrus Overlapping Low Clouds

52. Our Dual-layer Radiation Model  Simulated visible (0.65/0.86  m) and IR-window (11  m) radiance lookup tables were generated for various combinations of an ice-overlapping-water cloud.  Different layer cloud top pressure (P) and cloud optical depth (  ) were assigned: For the ice-cloud layer: P hc = 100, 300, and 500 hPa  hc = 0.01, 0.25, 0.5, 1, 2, 3, and 5 For the water-cloud layer: P lc = 500, 700, 900, and 1000 hPa,  lc = 0.05, 1, 2, …, and 100.

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

54. 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).

55. Apr.-Nov. 2001 at SGPApr.-Nov. 1999 at NAU A Bimodal Frequency Distribution of Cloud Top Height

56. Did you know that… Single-layer and two-layer cloud systems dominate the Earth’s atmosphere!

57. A Bimodal Frequency Distribution of Cloud Top Height Chang and Li (2005, J. Climate)

59. Zonal-mean Cloud Properties Cloud Top PressureCloud Top TemperatureCloud Optical Depth  Obtained for April 2001 Terra/MODIS.

60. Total High Cloud Amount (High1 + High2 + High3) January 2001April 2001 July 2001October 2001

61. Overlapped Cloud Amount (High2/Low2) January 2001April 2001 July 2001October 2001

62. Mid-level (500-600 mb) Cloud Amount January 2001April 2001 July 2001October 2001

63. Our New Low Cloud Amount (Low1 + Low2) January 2001April 2001 July 2001October 2001

64. Original MODIS Low Cloud Amount (Low1) January 2001April 2001 July 2001October 2001

65. 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 CO 2 - 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.

66. 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.Estimating the vertical variation of cloud droplet effective radius using multispectral near-infrared satellite measurements  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.A new method for detection of cirrus overlapping water clouds and determination of their optical properties  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.A new global climatology of single-layer and overlapped clouds and their optical properties retrieved from TERRA/MODIS data using a new algorithm

67. Homework due Mar 23 Write 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.