1. Validation of Satellite-Retrieved Cloud Properties Using SEAC 4 RS Data P. Minnis, W. L. Smith, Jr., K. M. Bedka NASA Langley Research Center, Hampton, VA C. Yost, S. T. Bedka, R. Palikonda, D. A. Spangenberg, S. Sun-Mack SSAI, Hampton, VA Thanks to M. McGill (CPL) and S. Woods & P. Lawson (SPEC) SEAC4RS Science Team Meeting, Pasadena, CA, 28-30 Apr 2015

2. Motivation Cloud retrievals becoming more valuable in weather and climate applications - assimilation in weather forecast models e.g., NOAA Rapid Refresh, WRF, & GMAO short-term forecasts Jones et al. MWR, 2014; Chen et al. JAMC, 2015 - model validation e.g., Stanfield et al. JC 2014; Painemal et al. JC, 2014 - aircraft icing, convective initiation nowcasts e.g., Smith et al. JAMC 2012; Mecikalski et al. Atm. Res. 2013 - process studies - aerosol effects on precip/albedo e.g., Cremean et al. Sci. 2013; Fan et al. ACP 2014 - aerosol inference in cloudy skies e.g., Saide et al. PNAS, 2012 Validations from CALIPSO/CloudSat, AMSR confined to nadir view (MODIS) and one time per day; surface sites cover only one small area - in situ data provide a unique way to understand the retrievals - vertical structure of clouds is key for many applications - how well are we doing? - what needs to be done?

3. Assess cloud property retrievals for variety of satellite imagers - focus on vertical structure: What can we infer about the vertical structure from 2-D retrievals? In situ droplet or ice crystal effective radius Airborne lidar and radar In situ water content profiles Provide satellite complement to in situ & modeling studies Provide high temporal resolution imagery Provide consistent retrievals of cloud properties Collaborate with ST members Objectives

4. GOES-E 4-km resolution (15 min, near real time) - 0.65, 3.9, 6.7, 10.8, 13.3 µm GOES-W 4-km resolution (15 min, near real time) - 0.65, 3.9, 6.7, 10.8, 13.3 µm MODIS, twice daily (Terra & Aqua [A-train]), 1-km (CERES) - multispectral NPP VIIRS, 0.75-km 1330 LT (CERES) - multispectral NOAA-16/18/19 AVHRR (climate CDR) - 0.65, 0.87, 3.9, 10.8, 12.0 µm ER-2 CPL (±30 min matching with LEO) DC-8 2D-S (±30 min matching with LEO) Data

5. Langley SEAC4RS Web Page http://cloudsgate2.larc.nasa.go v http://cloudsgate2.larc.nasa.go v Click on “SEAC4RS” on sidebar or from main SEAC4RS page Gives access to all of our data, digital & gif images Flight Day gifs and data have been updated

6. Cloud Mask, Phase Optical Depth, IR emissivity Cloud effective particle size Liquid/Ice Water Path Effective Temp, height, pressure Top/ Bottom Pressure Top/ Bottom Height Overshooting tops 0.65, 1.6 µm Reflectances 3.7, 6.7, 10.8 µm Temp 12 or 13.3 µm Temp Broadband TOA Albedo Broadband OLR Clear-sky Skin Temperature Icing Potential** Pixel Lat, Lon Pixel SZA, VZA, RAZ Multilayer ID (single or 2-layer) effective temperatureoptical depth, thickness effective particle size ice or liquid water path height, top/base height pressure Upper & lower cloud Standard, Single-Layer VISST/SIST Multi-Layer, CIRT, CO 2 channel only (GOES-12 & later) NASA Langley Cloud Products

7. Cloud Thickness Cloud thickness based on simple parameterizations For ice clouds, parameterization based on CC measurements as function temperature, IWP, & ln(COD) - 3 latitude ranges, land & ocean Cloud top height is related to cloud effective height (radiating height) - for thin cirrus (COD < 5), parameterization based on FIRE lidar data (1980s) - for thick ice clouds based on CALIPSO/MODIS measurements for one month - can be fooled by multilayer clouds, simple parameterization Comparison of ice cloud thickness to that from CC, tropical ocean, April 2007

8. Normalized TWC profiles, S*, from CloudSat RVOD product with a normalized vertical coordinate scaled from cloud top to cloud base as function of cloud type (defined by CTT, TWP) Vertical Structure - TWC Profiles CloudSat CWC-RVOD Jan-Mar, 2007 Vertical structure depends on cloud type CloudSat mass peaks at or above the mid- cloud level for optically thicker clouds – not realistic? Passive TWC(z) retrieval scheme: 1.Given CTH, CTZ, COD, IWP from satellite imager, estimate TWP and CBH 2.Apply TWP, CTH, CBH to appropriate S* curve and retrieve TWC(z) CONUS CloudSat-CALIPSO Perspective - W. L. Smith, Jr., PhD diss., 2014 z* = [z(i)-CBH] / [CTH-CTB] CWC = S(z*) IWP/([CTH-CTB]

9. Thompson microphysics scheme – SLW friendly TWC/TWP = liq + ice + rain + snow + graupel Vertical Structure - TWC Profiles RUC/Thompson Microphysics Jan-Mar, 2010 Normalized TWC profiles, S*, from NWP cloud model (NOAA RUC): Mass peaks are also higher for optically thicker clouds but not as high as CloudSat Combining CloudSat and NWP curves provides best results in profiling application CONUS Modeling Perspective Knowing melting layer temperature will help - W. L. Smith, Jr., PhD diss., 2014

10. IWC Validation: CloudSat/CALIPSO Comparison for all ice clouds ( 10 < COD <= 150), April 2010 MODIS IWC PDF CALIPSO + CloudSat IWC PDF Monthly Mean IWC (g/m 3 ) IWP (g/m 2 ) MODIS0.143583 CC0.141551 CloudSat+ CALIPSO MODIS CoudSat+ CALIPSO High sensitivity of CALIPSO responsible for large differences at high altitude Excellent agreement in monthly means as a function of altitude and overall MODIS PDF narrower (climatological approach does not capture extremes) Normalized profiles applied to MODIS, compared to independent CC datasets

11. SEAC4RS IWC Comparison DC-8 2D-S probe GOES-13 DC-8 Altitude DC-8 2D-S probe GOES-13 Comparison with in situ data provide independent check on CC-based profiles 13 & 21 September DC-8 flights

12. Vertical Structure - TWC Profiles - Better agreement on 13 September, more points below the 1:1 line - best agreement around IWC = 0.1 gm -2, similar to CC comparisons - suggests CC profiles accurate - disagreement at low end (< 0.05 gm -2 ) likely due to current limitations on cloud boundary accuracy (top height determination & thickness estimation)

13. DC-8 Ice Particle Effective Diameter Comparisons UTC [hr] September 2, 20 – 22 UTC Aqua and VIIRS overpasses at ~1930 UTC Best agreement for 3.7 µm near cloud top 2DS GOES Aqua (3.7) VIIRS (3.7) GOES cld boundariesDC-8 altitude 2DS GOES Aqua 1.24 Aqua 2.13 Aqua 3.7 2DS GOES VIIRS 1.24 VIIRS 2.13 VIIRS 3.7 De (µm) Z (km) 1.24 µm best deeper in cloud, not much difference between 2.13 & 3.7 Comparison Example

14. Ice Particle Size Comparisons: All Days De(DC8) increases with depth from cloud top - decreases to bottom from 0.7 level De(sat) smallest bias in top layer of cloud - VIIRS may have some problems - all other imagers have similar bias structure De(sat) greatest bias at 0.7 level De(sat) based on 3.7 µm, bias expected

15. Ice Particle Size Comparisons:  dependence  < 3 agreement in the mean - crossover near 0.3 level scatter quite high, expected 3.75 µm should be good for determining thin cirrus De - seen in previous studies  > 3 negative biases - VIIRS & MODIS = -10.4 µm crossover near 0 level expect better agreement with longer wavelength retrievals  < 3 -1.0 µm N=92 AVHRR GOES 2.2 µm N=67  > 3 -2.3 µm N=897 -18 µm N=1225 When quantified for more  intervals, should be possible to infer integrated De for all 

16. Particle Size & Height Comparisons GOES misses diffuse cloud section, underestimates physical height, more  < 8 corresponds to  < 3 for CPL, so  is likely underestimated for ice clouds De crossover point varies as scatter indicates, here De matches 5-7 km below cloud top 2D-S GOES 13 September, Coordinated DC-8 & ER-2  > 8

17. UTC [hr] GOES cld boundaries DC-8 altitude Aqua MODIS, N18, N19, VIIRS Ztop All 5 satellites produce nearly identical heights - N18 less consistent because of VZA & resolution effects Thick cloud correction accounts for much of diffuse cloud top (CPL at bottom) but pileus and some very low extinction cirrus still apparent above corrected cloud heights Cloud Heights Along DC-8 & ER-2 Flight Tracks September 13, 18 – 20 UTC

18. Cloud Heights: GOES vs CPL,  ( CPL ) < 0.5 Bias=-1.2 km SD = 3.9 km N = 46 Bias=-4.1 km SD= 3.9 km N = 324 Most  < 0.5 cases are likely multilayered Typically, algorithm selects water phase when upper layer  < 0.5 Aqua vs CALIPSO

19. Cloud Heights: Imagers vs CPL,  ( CPL ) > 0.5 AVHRR: -1.2 ± 2.9 km N=191 MODIS: -0.5 ± 2.3 km N=121 VIIRS: -0.5 ± 2.2 km N=61 GOES: -0.9 ± 1.8 km N=1028 differences for GOES, MODIS, & VIIRS similar to that found with CC AVHRR a bit worse, uses MERRA surface temperatures, affects low clouds most scattered cumulus clouds likely responsible for low cloud biases some low & high cloud bias due to ML clouds

20. Cloud Top Heights: CPL vs GOES-13 4 August 2013 Gross underestimates typically due to multilayer clouds, cirrus over cumulus - viewing perspective can produce some ML situations Smaller under estimates for deep convective tops - current correction from effective to physical top height inadequate?

21. Detecting Stratus Height With Overlying Smoke ER-2 CPL measured cloud & aerosol heights off California, 6 Aug 2013 - compared with two swaths from Terra MODIS (high angle views) - Terra typically within 100 m except for 19.75 & 20.3 UTC - 19.75, CPL calls aerosol cloud; 20.3, dense smoke increasing T11?

22. Summary & Future From the results, we can conclude that the following needs to be done Update Zeff-to-Ztop parameterizations Refine cloud thickness models Study Re-Z relationships on a case-by-case basis - profile normalization needs more contextual information - probably include more field program data (e.g., MACPEX) Determine impact of sfc temperature and pixel filling on low-cloud heights Examine multilayer retrievals Test new 2-habit microphysical model for ice clouds Liu et al. ACP, 2014 - asymmetry parameter 0.75-0.77 for full range of De - should yield improved tau and possibly better De