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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 State University
Ft Collins, CO USA
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
29 Feb/1 March 2000 CloudSat Data Processing Center Preliminary Design and Implementation Review Don Reinke 5
Colorado State UniversityAtmospheric Science Bldg
CIRA
Approx. location of newCloudSat DPC lab
(by fall 2001)
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
500m
~1.4 km
•Nadir pointing, 94 GHz radar• 3.3s 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 designTwo main components of design
demonstrated post launch
Hardware continues to operate with nominal performance
A brief overview
5
Core (Standard) & Enhanced Products
6
Core (Standard) & Enhanced Products
7
Radar2B-Geoprof
Radar+lidar2B geoprof-lidar
Difference
The Geoprof products
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 FARCloudSat
9
80N-S global averag
2B-FLXHR
10
Enhanced product - precip incidence & amount
Coads
11
Precipitation incidence and accumulation as a function of cloud top (min) height
Cs minCs max
Oceanic precipitation results
12
Incidence by highest cloud top height
13
Incidence by lowest cloud top height
14
Lowest cth
Highest cth
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
Case study example : 26 February 2007
• Analysis chart valid at 12 UTC
• CloudSat overpass at ~14:15 UTC
A
BBodas-Salcedo et al, 2008
17BA
Spuriousdrizzle
Less IWC
Deep evaporation zone
18
Global histograms: 2006/12 – 2007/02
CloudSat MetUM N320L50
Two regimes. Drizzling or not drizzling cloud?
Strong dependence of N0 with T
Reflectivity / dBZ
Hei
ght
/ km
Occurrence of Z > -27.5 dBZ
Frequency of occurrence
Hei
ght
/ km
Latitude
Reflectivity / dBZH
eigh
t /
km
Frequency of occurrence
Occurrence of Z > -27.5 dBZ
Hei
ght
/ km
Latitude
Lack of mid-level cloud
19
North Atlantic histograms: 2006/12 – 2007/02
CloudSat MetUM N320L50
Two regimes – drizzle / no drizzle?
Less hydrometeors?
Lack of congestus
Cloud top height very well captured
Reasonable ice microphysics?
20
Tropical west Pacific histograms: 2006/12 – 2007/02
CloudSat MetUM 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
• 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
New Record Minimum - Sept. 2007
Minimum Extent Time Series
The radiation balance of the Arctic
Kay et al., 2008
23
The A-train provides a
unique view of Arctic clouds.
2B-Geoprof-lidarISCCP D2(infrared)
Warren(surface obs.)
DJF Low Cloud Maps
24
A-train data reveal dramatic cloudiness reductions, T increases, and RH decreases associated with the 2007 circulation anomalies.
Kay et al., 2008
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
Arctic Low Cloud Fraction Comparisons:
IPCC AR4 Climate Model(NCAR’s CCSM3 climatology)
CloudSat/CALIOP(2006)
JJA
DJF
27
Evaluation of Climate Model Clouds using Radar Reflectivity
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)
29Ice and Snow layers
Dehydration-Greenhouse Feedback (DGF)
Less H2O vapour
Acid Aerosols **
* ** ** **
**
* **
**
**
*
* ***
*
* *** *
* *** * *
**Low Acid AerosolsHydrophilic
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 1Thin Ice Clouds type 2
Cold Ice and Snow Surface
30
(km-1 sr-1)
(mg m-3)Arctic case : January 19th, 2007
0.0005
0.0010
0.0015
0.0045
0.0090
8.00
12.00
20.00
2.00
1.00
0.01
31
.
Types Characteristics
TIC-1 Visible by lidar onlyNo saturation of the lidar signal
TIC-2aVisible by lidar and radar
Saturation of the lidar belowCovered by TIC-1 above
TIC-2bVisible by lidar and radar
No saturation of the lidar signal
32
Thin Ice Cloud type 2bForms slowly over many
days in cold high [aerosols] (acidic),
large ice crystalsand fast sedimentation
Thin Ice Cloud type 1low [aerosol] (pristine),
small crystalsslow sedimentation
Polluted PBL
A-Train observations reveal much about the cloud systems of the Arctic winter
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
Suzuki and Stephens, 2008
€
Re =3
2
1
ρw
LWP AMSR - E( )τ c MODIS( )
(Masunaga et al., 2002a,b; Matsui et al., 2004)
Ze: layer-mean radar reflectivityThe observables
€
Ze ≈ 64NRe6
€
Ze ≈48
πρww( )Re
3
The relationships
Fixed NRe6
Fixed w, Re3
Honing in on the coalescence Process in warm, oceanic clouds
35
N=const:
condensation
w=const:
coalescence
Suzuki and Stephens, 2008
36
‘Heavy’ Rain region (R > 0.1 mm/hour)
Light Rain region (R < 0.1 mm/hour)
37
CloudSat tropical cyclone data base
Examples of MODIS and CloudSat data corresponding to three eye/near eye radar intersections
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
The data base consists of 2,423 TC overpasses through The data base consists of 2,423 TC overpasses through
February 2008.February 2008.
For each storm overpassFor each storm overpass::
(A) Storm specific variables (A) Storm specific variables
•latitude, longitude, mslp, max winds, storm center SST, 850-200 latitude, longitude, mslp, max winds, storm center SST, 850-200
mb wind shearmb wind shear
(B) Radial/Azimuthal Data(B) Radial/Azimuthal Data
•Brightness Temperature (MODIS 11 um)Brightness Temperature (MODIS 11 um)
•MODIS Cloud top height, pressure and temperatureMODIS Cloud top height, pressure and temperature
•AMSR-E SST, Wind Speed, LWP/IWP, PrecipitationAMSR-E SST, Wind Speed, LWP/IWP, Precipitation
(C) Numerical Weather Prediction Analyses (Naval Operational Global (C) Numerical Weather Prediction Analyses (Naval Operational Global
Atmospheric Prediction System (NOGAPS™)Atmospheric Prediction System (NOGAPS™)
•Temperature and Moisture ProfilesTemperature and Moisture Profiles
•Surface WindsSurface Winds
(D) CloudSat CPR Data(D) CloudSat CPR Data
•GEOPROF Radar Reflectivity ProfilesGEOPROF Radar Reflectivity Profiles
TC Database Characteristics
Located at http://www.nrlmry.navy.mil/archdat/tropical_cyclones/CPR_TC_Intercepts/
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
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
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
Suzuki and Stephens, 2008
€
Re =3
2
1
ρw
LWP AMSR - E( )τ c MODIS( )
(Masunaga et al., 2002a,b; Matsui et al., 2004)
Ze: layer-mean radar reflectivityThe observables
€
Ze ≈ 64NRe6
€
Ze ≈48
πρww( )Re
3
The relationships
Fixed NRe6
Fixed w, Re3
Honing in on the coalescence Process in warm, oceanic clouds
44
N=const:
condensation
w=const:
coalescence
Suzuki and Stephens, 2008
45
‘Heavy’ Rain region (R > 0.1 mm/hour)
Light Rain region (R < 0.1 mm/hour)
46IPCC, FAR,2007
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
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
aerosol effects?
Pristine: AI < 0.1
Polluted: AI > 0.1
50
Stephens and Wood, 2007
CloudSat 30S-30N
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 & stratiformCumulus CongestusShallow
Convection
CloudSat
MMF
Missing deep convective mode
RAMS
Luo et al., 2007
52
Low Cloud Fraction Comparisons:
IPCC AR4 Climate Model(NCAR’s CCSM3 climatology)
CloudSat/CALIOP(2006)
JJA
DJF
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 observationsGlobal -scale observations
IPCC models
54
Global Snowfall Occurrence
Sep. 2006-Aug. 2007
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
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:
Zsfc
60
40
20
0
-20
57
Zsfc
60
40
20
0
-20
Surface reflectivity can be ‘easily’ deduced over oceansSurface reflectivity can be ‘easily’ deduced over oceans
58
Zsfc
PIA
Rainfall / Intensity
Rain definite Rain probable Rain possible
Extremely sensitive detector of rain - ~0.02 mm/hr
59
TRMM comparison of precipitation amount
AN-PR product
60
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
AB
62
Cold front Warm frontWarm front
Revisiting historyThe Norwegian Cyclone ModelCirca, 1923
Posselt et al., 2008
Cold Front
63
Arctic Low Cloud Fraction Comparisons:
IPCC AR4 Climate Model(NCAR’s CCSM3 climatology)
CloudSat/CALIOP(2006)
JJA
DJF