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1 Recent (selected) results from CloudSat and the A-Train Graeme L Stephens Co-op Institute for Res. Atmosphere (CIRA) and Dept Atmospheric Sciences Colorado.

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Presentation on theme: "1 Recent (selected) results from CloudSat and the A-Train Graeme L Stephens Co-op Institute for Res. Atmosphere (CIRA) and Dept Atmospheric Sciences Colorado."— Presentation transcript:

1 1 Recent (selected) results from CloudSat and the A-Train Graeme L Stephens Co-op Institute for Res. Atmosphere (CIRA) and Dept Atmospheric Sciences Colorado State University Ft Collins, CO USA

2 2 Outline Brief introduction to the A-Train and CloudSat Example of a few core products Highlights from the emerging ‘enhanced’ precipitation products Arctic Clouds (summer and winter) Model evaluation Warm rain processes Tropical storm data base

3 3 USAF conducts operations out of the RSC at Kirtland AFB in Albuquerque, NM USAF conducts operations out of the RSC at Kirtland AFB in Albuquerque, NM. First $5M was STP contribution to ground system development costs. Colorado State University, Fort Collins, processes science data. Near-realtime (3-8 hour) Level 1 data latency CloudSat launched April 2006; Completed prime mission in Feb 08. Approved for extended mission through FY11. ROSES-sci team June 2007 Flies in on-orbit formation with the A-train satellites First release to community Jan07 - All products of prime mission now available Maneuvers are planned and executed by the orbit analysts at the KAFB RSC Mission Overview

4 4 500m ~1.4 km Nadir pointing, 94 GHz radar 3.3  s pulse  480m vertical res, over- sampled at ~240m 1.4 km horizontal res. Calibration better than 2 dBZ Sensitivity ~ -28 dBZ (-30 dBZ) Dynamic Range: 80 dB 2. The Cloud Profiling Radar (CPR) 1. Formation with the A-Train Two main components of design demonstrated post launch Hardware continues to operate with nominal performance A brief overview

5 5 Core (Standard) & Enhanced Products

6 6

7 7 Radar 2B-Geoprof Radar+lidar 2B geoprof-lidar Difference The Geoprof products

8 8 The 2B-CWC Ice product Major ‘greenhouse’ contribution Important in precipitation processes Important to storm development and convection The first real ‘authoritative’ measure of total ice IPCC FAR CloudSat

9 9 80N-S global averag 2B-FLXHR

10 10 Enhanced product - precip incidence & amount Coads

11 11 Precipitation incidence and accumulation as a function of cloud top (min) height Cs min Cs max Oceanic precipitation results

12 12 Incidence by highest cloud top height

13 13 Incidence by lowest cloud top height

14 14 Lowest cth Highest cth

15 15 Instrument simulators in forecast, climate & CRM models- Key development for CFMIP II New diagnostic tools for analyzing the joint statistical properties of clouds and precipitation Model-evaluation studies

16 16 Case study example : 26 February 2007 Analysis chart valid at 12 UTC CloudSat overpass at ~14:15 UTC A B Bodas-Salcedo et al, 2008

17 17 BA Spurious drizzle Less IWC Deep evaporation zone

18 18 Global histograms: 2006/12 – 2007/02 CloudSatMetUM N320L50 Two regimes. Drizzling or not drizzling cloud? Strong dependence of N0 with T Reflectivity / dBZ Height / km Occurrence of Z > -27.5 dBZ Frequency of occurrence Height / km Latitude Reflectivity / dBZ Height / km Frequency of occurrence Occurrence of Z > -27.5 dBZ Height / km Latitude Lack of mid- level cloud

19 19 North Atlantic histograms: 2006/12 – 2007/02 CloudSatMetUM N320L50 Two regimes – drizzle / no drizzle? Less hydrometeors? Lack of congestus Cloud top height very well captured Reasonable ice microphysics?

20 20 Tropical west Pacific histograms: 2006/12 – 2007/02 CloudSatMetUM N320L50 Evaporating ice – or T dependence in convective cloud ice fraction? Reasonable ice microphysics? Lack of mid-level cloud Lack of non- drizzling low cloud

21 21 summer cloudiness and the sea ice loss (collab with Kay and Gettelman (NCAR) winter cooling and an aerosol-precipitation dehydration? (Collab with U Montreal - Blanchet and colleagues) Arctic Cloudiness

22 22 New Record Minimum - Sept. 2007 Minimum Extent Time Series The radiation balance of the Arctic Kay et al., 2008

23 23 The A-train provides a unique view of Arctic clouds. 2B-Geoprof-lidar ISCCP D2 (infrared) Warren (surface obs.) DJF Low Cloud Maps

24 24 A-train data reveal dramatic cloudiness reductions, T increases, and RH decreases associated with the 2007 circulation anomalies. Kay et al., 2008

25 25 The 2007-2006 radiation differences could melt ~0.3 m of sea ice or increase ocean mixed layer temperatures by ~2.4 K.

26 26 Arctic Low Cloud Fraction Comparisons: IPCC AR4 Climate Model (NCAR’s CCSM3 climatology) CloudSat/CALIOP (2006) JJA DJF

27 27 Evaluation of Climate Model Clouds using Radar Reflectivity

28 28 Arctic Winter Cooling The artic is warming but the winter-time arctic has been cooling Arctic is heavily polluted by SO2, particularly in the winter The SO2 has been shown to inhibit ice particle nucleation Regions of strongest winter cooling coincide with regions of highest pollution The hypothesis is that aerosol affected precipitation serves to dehydrate the atmosphere Wang et al., 2003 AVHRR Arctic summer 20 yr temperature trends (C/year) AVHRR Arctic winter 20 yr temperature trends (C/year)

29 29 Ice and Snow layers Dehydration-Greenhouse Feedback (DGF) Less H 2 O vapour Acid Aerosols * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * Low Acid Aerosols Hydrophilic WarmerColder Reduced Greenhouse Increased Greenhouse Clouds forming on acidic ice nuclei precipitate more effectively, dehydrate the air, reduce greenhouse effect and cool the surface Slow Cooling Process adiabatic cooling and IR lost Thin Ice Clouds type 1 Thin Ice Clouds type 2 Cold Ice and Snow Surface

30 30 (km -1 sr -1 ) (mg m -3 ) Arctic case : January 19 th, 2007 0.0005 0.0010 0.0015 0.0045 0.0090 8.00 12.00 20.00 2.00 1.00 0.01

31 31. TypesCharacteristics TIC-1 Visible by lidar only No saturation of the lidar signal TIC-2a Visible by lidar and radar Saturation of the lidar below Covered by TIC-1 above TIC-2b Visible by lidar and radar No saturation of the lidar signal

32 32 Thin Ice Cloud type 2b Forms slowly over many days in cold high [aerosols] (acidic), large ice crystals and fast sedimentation Thin Ice Cloud type 1 low [aerosol] (pristine), small crystals slow sedimentation Polluted PBL A-Train observations reveal much about the cloud systems of the Arctic winter

33 33 When coalescence occurs, big drops grow by collecting little drops - that is the total droplet number concentration is reduced but the total mass of water doesn’t change When droplets grow by vapor deposition, the mass increases but not the number concentration Elementary growth processes

34 34 Suzuki and Stephens, 2008 (Masunaga et al., 2002a,b; Matsui et al., 2004) Z e : layer-mean radar reflectivity The observables The relationships Fixed N Re 6 Fixed w, Re 3 Honing in on the coalescence Process in warm, oceanic clouds

35 35 N=const: condensation w =const: coalescence Suzuki and Stephens, 2008

36 36 ‘Heavy’ Rain region (R > 0.1 mm/hour) Light Rain region (R < 0.1 mm/hour)

37 37 CloudSat tropical cyclone data base Examples of MODIS and CloudSat data corresponding to three eye/near eye radar intersections

38 38 Two main activities: 1)In partnership with Naval Research Labs, development of a new data base resource for studying tropical storms and the influence of the environment on storm structure 2)Use of new cloud radar observations with other A-Train data as a new opportunity to test theories of hurricane storm intensification. Hurricane intensity research Featured on the front cover of IEEE GSRL; Luo et al., 2008

39 39 The data base consists of 2,423 TC overpasses through February 2008. For each storm overpass: (A) Storm specific variables latitude, longitude, mslp, max winds, storm center SST, 850-200 mb wind shear latitude, longitude, mslp, max winds, storm center SST, 850-200 mb wind shear (B) Radial/Azimuthal Data Brightness Temperature (MODIS 11 um) Brightness Temperature (MODIS 11 um) MODIS Cloud top height, pressure and temperature MODIS Cloud top height, pressure and temperature AMSR-E SST, Wind Speed, LWP/IWP, Precipitation AMSR-E SST, Wind Speed, LWP/IWP, Precipitation (C) Numerical Weather Prediction Analyses (Naval Operational Global Atmospheric Prediction System (NOGAPS™) Temperature and Moisture Profiles Temperature and Moisture Profiles Surface Winds Surface Winds (D) CloudSat CPR Data GEOPROF Radar Reflectivity Profiles GEOPROF Radar Reflectivity Profiles TC Database Characteristics Located at http://www.nrlmry.navy.mil/archdat/tropical_cyclones/CPR_TC_Intercepts/

40 40 Storm structure analysis Shown are composite radar reflectivity profiles as a function of radial distance from storm center as a function of SST and wind shear Storm structure weakens with increased shear Storm structure strengthens with increased SST Work in progress

41 41 Summary The ability to observe clouds, aerosol and precipitation jointly and in placing these observations in the context of the environment is beginning to provide new insights on: Processes of cloud and precipitation formation Aerosol effects on these processes Effects of clouds on the radiation processes and energy balance These observations also provide important new evaluation of weather and climate prediction models

42 42 When coalescence occurs, big drops grow by collecting little drops - that is the total droplet number concentration is reduced but the total mass of water doesn’t change When droplets grow by vapor deposition, the mass increases but not the number concentration Elementary growth processes

43 43 Suzuki and Stephens, 2008 (Masunaga et al., 2002a,b; Matsui et al., 2004) Z e : layer-mean radar reflectivity The observables The relationships Fixed N Re 6 Fixed w, Re 3 Honing in on the coalescence Process in warm, oceanic clouds

44 44 N=const: condensation w =const: coalescence Suzuki and Stephens, 2008

45 45 ‘Heavy’ Rain region (R > 0.1 mm/hour) Light Rain region (R < 0.1 mm/hour)

46 46 IPCC, FAR,2007

47 47 Aerosol forcing of climate A state of much confusion - fundamental to all aspects is the water budget of clouds - including the state of precipitation

48 48 For the first time, we are able to observe all aspects of clouds that affect their albedo - as such we perhaps can say there appears to be a global Twomey effect and a correlation between precipitation probability and aerosol Twomey effect? Precipitation Aerosol indirect effects using atrain obs - Lebsock et al., 2008

49 49 aerosol effects? Pristine: AI < 0.1 Polluted: AI > 0.1

50 50 Stephens and Wood, 2007 CloudSat 30S-30N

51 51 Cloud echo top height (ETH) against the precipitation ETH => ETH of -30 dBZ versus ETH of 10 dBZ CP-ETH histograms Deep Convection Cirrus Anvil & stratiform Cumulus Congestus Shallow Convection CloudSat MMF Missing deep convective mode RAMS Luo et al., 2007

52 52 Low Cloud Fraction Comparisons: IPCC AR4 Climate Model (NCAR’s CCSM3 climatology) CloudSat/CALIOP (2006) JJA DJF

53 53 Launch, 4/2006 Operational, 6/2006 First global radar measure of precipitation First global estimate of snowfall First global portrayal of precipitation incidence ship Cloudsat -rain Cloudsat - rain + snow How much How often Global -scale observations IPCC models

54 54 Global Snowfall Occurrence Sep. 2006-Aug. 2007

55 55 CloudSat Mission science goals Measure vertical structure of clouds, quantify their ice and water contents as a step toward improved weather prediction and understanding of climatic processes What are the fundamental vertical structures of global clouds? How do structure & properties differ in the presence of precipitation? What fraction of clouds of Earth precipitate? What is the mass of ice suspended in the atmosphere? Quantify the relationship between cloud profiles and the radiative heating by clouds Do clouds heat or cool the atmosphere (relative to clear skies)? Do the radiative properties of precipitation and non-precipitating clouds differ? Evaluate cloud information derived from other research and operational satellites Improve our understanding of aerosol indirect effect on clouds and precipitation To what extent are the properties above (water, ice, precipitation, vertical structure) influenced by aerosol?

56 56 Oceanic precip incidence (and amount) The PIA within a raining column can be estimated by the decrease in surface reflectivity from the clear sky background value: Z sfc 60 40 20 0 -20

57 57 Z sfc 60 40 20 0 -20 Surface reflectivity can be ‘easily’ deduced over oceans

58 58 Z sfc PIA Rainfall / Intensity Rain definite Rain probable Rain possible Extremely sensitive detector of rain - ~0.02 mm/hr

59 59 TRMM comparison of precipitation amount AN-PR product

60 60

61 61 Historic First Images of CPR on May 20, 2006 Warm Front Storm Over the Norwegian Sea: 12:26-12:29 UTC MODIS Visible image A B

62 62 Cold front Warm front Revisiting history The Norwegian Cyclone Model Circa, 1923 Posselt et al., 2008 Cold Front

63 63 Arctic Low Cloud Fraction Comparisons: IPCC AR4 Climate Model (NCAR’s CCSM3 climatology) CloudSat/CALIOP (2006) JJA DJF


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