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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

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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

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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

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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

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Core (Standard) & Enhanced Products

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Core (Standard) & Enhanced Products

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Radar2B-Geoprof

Radar+lidar2B geoprof-lidar

Difference

The Geoprof products

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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

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80N-S global averag

2B-FLXHR

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Enhanced product - precip incidence & amount

Coads

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Precipitation incidence and accumulation as a function of cloud top (min) height

Cs minCs max

Oceanic precipitation results

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Incidence by highest cloud top height

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Incidence by lowest cloud top height

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Lowest cth

Highest cth

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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

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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

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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

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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?

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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

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• 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

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New Record Minimum - Sept. 2007

Minimum Extent Time Series

The radiation balance of the Arctic

Kay et al., 2008

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The A-train provides a

unique view of Arctic clouds.

2B-Geoprof-lidarISCCP D2(infrared)

Warren(surface obs.)

DJF Low Cloud Maps

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

Kay et al., 2008

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

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Arctic Low Cloud Fraction Comparisons:

IPCC AR4 Climate Model(NCAR’s CCSM3 climatology)

CloudSat/CALIOP(2006)

JJA

DJF

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Evaluation of Climate Model Clouds using Radar Reflectivity

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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

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(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

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.

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

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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

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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

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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

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N=const:

condensation

w=const:

coalescence

Suzuki and Stephens, 2008

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‘Heavy’ Rain region (R > 0.1 mm/hour)

Light Rain region (R < 0.1 mm/hour)

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CloudSat tropical cyclone data base

Examples of MODIS and CloudSat data corresponding to three eye/near eye radar intersections

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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

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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/

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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

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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

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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

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‘Heavy’ Rain region (R > 0.1 mm/hour)

Light Rain region (R < 0.1 mm/hour)

46IPCC, FAR,2007

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Aerosol forcing of climate

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

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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

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aerosol effects?

Pristine: AI < 0.1

Polluted: AI > 0.1

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Stephens and Wood, 2007

CloudSat 30S-30N

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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

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Low Cloud Fraction Comparisons:

IPCC AR4 Climate Model(NCAR’s CCSM3 climatology)

CloudSat/CALIOP(2006)

JJA

DJF

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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

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Global Snowfall Occurrence

Sep. 2006-Aug. 2007

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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?

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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

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Zsfc

60

40

20

0

-20

Surface reflectivity can be ‘easily’ deduced over oceansSurface reflectivity can be ‘easily’ deduced over oceans

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Zsfc

PIA

Rainfall / Intensity

Rain definite Rain probable Rain possible

Extremely sensitive detector of rain - ~0.02 mm/hr

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TRMM comparison of precipitation amount

AN-PR product

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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

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Cold front Warm frontWarm front

Revisiting historyThe Norwegian Cyclone ModelCirca, 1923

Posselt et al., 2008

Cold Front

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Arctic Low Cloud Fraction Comparisons:

IPCC AR4 Climate Model(NCAR’s CCSM3 climatology)

CloudSat/CALIOP(2006)

JJA

DJF