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2B-IWC CloudSat Product

2B-IWC CloudSat Product. Requirements Algorithm basis Evaluation and validation plans Limitations. Science Objectives. Evaluate the representation of clouds in NWP & GCMs ( where clouds occur and how much water & ice )

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2B-IWC CloudSat Product

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  1. 2B-IWC CloudSat Product Requirements Algorithm basis Evaluation and validation plans Limitations

  2. Science Objectives • Evaluate the representation of clouds in NWP & GCMs (where clouds occur • and how much water & ice) • Evaluate the relationship between cloud liquid water and ice content (Z) and the radiative properties of clouds • (optical depth, fluxes and radiative heating rates) • 3. Evaluate cloud properties retrieved using existing satellite measurements and promote development of new remote sensing methods for observing clouds (cloud profiles, optical properties, cloud physical properties) • Improve understanding of the indirect effect of aerosols on clouds (cloud • optical, physical (precip) properties and aerosol) ECMWF forecast Hurricane Bonnie

  3. Year 80-Contrl of transient 1% CO2 expts This confirms that on the global scale precip is correlated with changes to the atm radiation budget in the CMIP models Stephens, 2004

  4. If there were no cloud responses, (CT~0), then water vapor feedback would imply the relationship lies here LP Qatm- CT+ KT= LP • the spread in model estimates of precipitation is dictated by the different representation on cloud (atm) radiative heating in models • To the extent that the precip efficiency can be thought of as a ratio of cloudiness to precipitation, this ratio is fundamentally governed by cloud radiative processes

  5. Derivation of Science Requirements • Flows directly from science objectives • Measurement accuracies derived from GCM sensitivity simulations • Lead directly to key instrument observations • Some data products require combined instrument observations for best accuracy (notably radar/modis for LWC/IWC) • ATBDs published in refereed literature, evaluations are ongoing, piggybacking to various field activities and other prgram activities.

  6. Selected ‘standard’ Products

  7. Defining the CloudSat Requirements CSU GCM AIRS In cloud heating 1 K.day-1 T ~1-2 K P <20% TRMM ECMWF Forecast TOA, surface fluxes (~5-10 W.m-2) OSSE CERES

  8. Requirements Input: forecast clouds ‘verified’ for the 11 days of LITE 66 (night-time) orbits analyzed Consider different detection scenarios Include ‘modis’ in only a simple (worst case) mode Miller and Stephens, 2001

  9. Requirements

  10. Requirements Instrument Requirement Measurement Goal Measurement Requirement System Requirement Profiles 500m CPR @ 94 GHz Size, power Heating Fluxes 10(5) Wm-2 Lidar P-C sensitivity Radar/lidar overlap ~2km 0.1-0.3(H) 0.5-1.0(L) 25% CPR MDS~-28 Optical Depth Overlapping FOV Pointing ~ 0.7km MODIS, etc IWC< 50% LWC< 25% Ice and liquid water Calibration ~1-2 dBZ

  11. Active: Passive: y= F(x,b,…) measured Radiative Optical radiances Transfer Properties  Algorithm ‘Theoretical Basis’ This is great for single layered cloud systems which can be assigned an unambiguous tau Lidar/optical depth Radar The w-re dependency of lidar/ and radar back- scatter are ~ functionally orthogonal. ln w ln w ln re ln re Austin and Stephens, 2001; Benedetti et al., 2002

  12. Algorithm Original Formulation • Radar + Visible Optical Depth (RVOD): daytime only, when 2B-TAU available • Radar-only (RO): for all available radar data, including daytime • New 2B-TAU uses 5 channels producing day & night high cloud TAUs • The nightime TAUs have more severe dynamical range limitations (TAU<4) Earth Both modes Radar-only mode

  13. Tau errors IWP TAU

  14. Basic Algorithm Elements • Bayesian estimation framework • Extensive QC, including total error, error components, chi-squared, information content metrics, etc • Readily adapted to add • or subtract information • (other radiannce data, • lidar, etc) Kauai UAV example

  15. ‘Forward Model’ • Assumptions • form of microphysics • NT+ is constant • width is  constant • Includes a ‘Mie correction’ But input will need to include Cldclass, AN_ECMWF,.. Input vector Output vector

  16. The IWC Algorithm Status • Algorithm Formulation V1 complete, new ‘day/night’ tau • algorithm (v2) almost complete • Evaluations • Comparison to simulated cloud model data • Comparison to other retrieval methods based on different physics • Radar/lidar TWP Nauru • Sheba • Crystal-FACE (sub-mm/radar, radar/lidar, etc) • Comparison with direct aircraft measurements (not much available) • Kauai case study (sub tropics) • SGP case (aircraft comparisons) • Crystal-Face (ongoing) • The (daytime) method appears most robust for IWP, OK for IWC • approaching requirements (50%)and similarly OK for D0 but least • trustworthy for nu, NT.

  17. Evalauation:1 Clour model produced Microphysics Extinction and tau Z IWC Apply as input to various retrieval methods Sassen et al (2001?)

  18. T=-50C Ice Water Path g.m-2 T=-60C T=-70C

  19. IWC IWP

  20. Nauru TWP MMCR MPL

  21. Nauru example

  22. SHEBA Density correction brings the two estimates together

  23. IWC from theτ - Z method 28-29 April 1998 IWC from theETL radar radar-radiometermethod

  24. Val plans We have a very vigorous series of activities planned after launch – mostly from our international sci team members. Main activities (there are others): Aircraft • AMMA, 2006 African monsoon, tropical cirrus (IPSL/CETP) • CloudNet2 2006 (IPSL/CETP) • SPIDER (CRL) • TWPIce, Darwin, 2006 – Australian monsoon, cirrus • Cost Rica 2005 • CEASAR (UKMO) • Cold cloud IFOs (2X6 month expt, CSA) Other smaller activities, eg ARM UAV August 2004, MPACE 2004 (before launch) and alg intercomparison (ECAV, etc..) Surface – • Mirai cruise CRL/U Tok • Cabauw (KNMI), GKSS, IPSL, ARM, UMass

  25. Limitations Sensitivity and resolution radar sensitivity, vertical resolution produce biases, time and horizontal spatial resolution limitatioins on time-space mean properties, Optical depth limitations Independent ‘ground truth validation’ of IWC in situ measurement uncertainty Complex cloud systems mixed phase, deep convection where tau does little good, etc..

  26. Defining the Sampling Requirements and changes to sampling • CloudSat is a process study experiment seeking to provide observations of clouds and precipitation over the variety of conditions that occur on the global scale. • Measurements from CloudSat, as well as measurements from all polar orbiting satellites, suffer from intrinsic limitations that follow from sampling of the Earth from polar orbiting satellites. The under-sampling of measurements from polar-orbiters produces biases in cloud statistics • We have determined for the nominal CloudSat mission that these statistics are adequately represented when combining (compositing) data accumulated over a few (1-3 months). It is desirable that this averaging time be less than a season to avoid seasonal shifts aliasing the data. PDF of full minus sampled –sample errors are defined by lack of time resolution , not lack of swath This monthly average of (CloudSat) sampled data produces errors in total cloud cover that are ~ 5% on 2.5X2.5o resolution consistent with other science requirements GEWEX, 1993

  27. Cloudsat IWP retrieval bias = - Δ Δ= Δ= Radar–only (IWC-Z based) estimates Radar-radiometer (τ-Z) method

  28. Sensitivity high cloud low cloud CAVEX Aircraft data

  29. Benefit of retrieving slog

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