A-Train formulation with the arrival of CloudSat and CALIPSO as
motivated to a large degree by a desire to better understand clouds
and aerosols and their impact on the radiation budget andhydrological cycle
1A-Train Sensor Diversity – when data are combined new
insights on important processes are revealed

An example of the diversity

A-Train science is having an profound
impact. There is a large & growing numberof A-Train papers in the peer reviewed
literature.
• Two CloudSat based papers were the #1(Suzuki et al) and #2 (Riely & Mapes)
downloaded papers of 2012 in AMS anda third AIRS paper was #7• One multi-sensor A-Train paper was the#6 most downloaded JGR papers of 2012
(Jiang et al., 2012)
A-Train data are also impacting model
development.
The value of A-Train data will only increasein time as the data record lengthens

Jiang eWhat are some new science achievements enabled by the A-Train?
Integration across different observations platforms & sensors leads to:(i) Richer validation of key products and expansion to fill in voids(ii) Extraction of new products from combinations of different matchedobservations,(iii) Combination products to yield new insights on processes(iv) With (i)-(iv), provide a more integrated view of Earth far beyond thatwhich had been possible.
What has made A-Train multi-disciplinary science possible?
(i) Public availability of Level 1 and 2 data,(ii) Public availability of key documents (instrument descriptions, ATBDs, ,open documentation of known problems, etc),(iii) Easy data access of data & sharing of data(iv) Open science team meetings(v) Careful management of constellation flying (e.g. MOWG)
What are some other advantages?
Ability to share calibration and validation efforts across missions (e.g. C3VP=
Cloudsat+CALIPSO+GPM, LpVex)

Selected highlights with a little
illustration of AMSR-E A-Train science
Science highlights - the A-Train constellation science has two
flavors– Science that results specifically from matching individual level 1‘footprint’ data & integrating to produce new products– Science that results from matching level 2 (and level 3) product data
more broadly to examine relations between variables• The iconic A-Train result due to formation flying creating avirtual observatory• Combining data for new insights on convection• Combining data for new insights on
aerosol/cloud/precipitation• Enhancing global precipitation products

Formation flying enabling scienceA-Train demonstrated how formation flying can create avirtual 2 satellite radar-lidar observatory. Matching
footprints yielded important new products.
CloudSat Radar Reflectivity(dBZ)
Precipitation

CALIPSO 532 nm Backscatter

Tropical Thin Cirrus
Mixed Phase
Cloud

Aerosol
MODIS 11 μm

10 -4

10 -3

10 -2

10 -1

Iconic A-Train formation flying result

CloudSat-CALIPSOAMSR-E

Cloud and precipitation frequency (Fig. 7.4, Chapter 7 of IPCC AR5) and ice andwater contents adding truly a new dimension this could not have been possible
without the careful matching of footprints that resulted from formation flying.

Example of combining data CloudSat,
MODIS & the convective process

A super-cell t’storm over Wyoming

The classic Riehl and Malkus(1958) paper introduced theconcept of “hot towers”.

heat
deficit
What kind of energy
transport mechanism?
heat
surplusThe classic Riehl and Malkus “hot towers”

How tall are these hot towers (CloudSat/CALIPSO)?
How cold are their tops (MODIS)?
How many hot towers?

(i)

Tparcel –Tenv= bouyancy

Cloud topheight =z(ii) h=CpT+gz+Lqh, henv = entrainment

30 km

Cloud top
temperature(MODIS 11 µm TB)= Tparcel
CPR profile =
identifies
convective
towerBuoyancy

ΔT = Tparcel – Tenv (K)Deep convection:
B <0 & λ < 10%/km

Luo et al. (2010)

Entrainment rate

Entrainment Rate (%/km)“Terminal” cumulus congestus:
B < 0 & λ up to 50%/km“Transient” cumulus congestus:
B > 0 & λ ~ 10%/km

The combination of entrainment and bouyancy provides a tool to identify hot towers

The first global map of hot towers

0.08% of tropics occupied by ‘hot towers’

A-Train reveals important insights
on aerosol effects on cloudreflection – an example of how weinfer processes by connecting dataAerosol indirect effects
The Twomey
Effect

IPCC, AR4

Ship tracks are an example of
how aerosol, interact withclouds. They are analogs forboth the more global effects ofaerosol on clouds and for geoAMSR-E MODISCERES engineering concepts formodifying the albedo

Aerosol effects on clouds – largest uncertainty in climate
forcing and these too is shaped by the thermodynamic
properties of the boundary layer/free troposphere

The buffering of cloud albedo
More aerosol does not always make clouds brighter

We have developed an
A-Train ship track
inventory that consistsof Cloudsat, AMSR-E,
CALIPSO, CERES and
MODIS sensor data

Change in albedo (CERES)

Lifetime

A: Cloud albedo
LWP: Liquid water path

Entrainment

AMSR_E
Chen, Christensen, Seinfeld & Stephens, 2012•••

Differences in liquid water path primarily determine the sign andstrength of the cloud albedo response.
Humidity above cloud tops is responsible for the differences in LWP.
E-PEACE aircraft observations results agree with A-train observations.

The more global picture from the A-Train
AMSR-E and MODIS (GEMS)

Correlation between AMSR-E lwp and aerosol

RHft
•

•

Drier RHft imply a decrease in LWP (throughentrainment); yet higher LTS (more stable)
inhibits entrainment restricting.

LTS
AMSR-E and (MODIS (GEMS)

Globally, dry areas with low RHft
correspond to areas of high LTS (wherestratocumulus are prevalent). This
confounds our ability to infer how LWP
changesChen, Christensen,Seinfeld
and Stephens, 2013

More aerosol is
associated with lessliquid water implying a
positive AIE

More aerosol is
associated with more
liquid water implying
a negative AIE

A- Train Precipitation

Precipitation
2 Precipitation

January-December 2007

CloudSat and TRMM

Insight on the rain process - seeing rain form
using combinations of CloudSat, MODIS,AMSR-E

MODIS

A-Train observations

CloudSatThe AIE is mostly about cloud water budget changes which is determined by precipitaion

Three different 20th
century climate change
experiments by the
NOAA GFDL climate
model group- the only
difference is thestrength of the AIE –and the A-Train can tell
us which one is morecorrect

Global Precipitation – combining different sources of to gain a more representative global view

MCT*=TRMM+
CloudSat +AMSR-E+ diurnal cycle(Behrangi et al, 2012)

Global mean
MCT*-GPCP ~ 5%I MCT*-GPCP I ~10%

Behrangi et al., 2013

Snowfall observations:
CloudSat provides the 1st real
spaceborne global observations (IPWG)
further adding valuable information
about precipitation

CloudSat global snowfall product –the only real global product but how
good is it? It’s a challenge to retrieve
and a challenge to validate.

Summary(i) New science continues toemerge as the A-Train data
record continues to grow.
(ii) OCO is to join soon and it
offers quite unique information
about important components ofthe climate system IN ADDITION
TO CO2(iii) Steve Volz (HQ) has asked us
to plan a 3rd A-Train science
conference. Planning isunderwayConnecting CloudSat snowfall to GRACE ice mass changes
GRACE ice mass change 2004-2011

The mass increase(red) is equivalent toabout 10% of theannual sea level rise.
The mass increase isconsistent with
CloudSat snowfall
accumulation.

Boening et al., 2012; GRL

The ice mass gain
through snowfall is aconsequence of thechange in circulationand storm trajectory in
2009/2010

Comparison of level 2 products
Assessment of global models

Insights on storms and climate change

Key modes of variability include MJO (interseasonal), El Nino (internannual)

3/25/201332

CloudSat-CALIPSO-AMSR-E data document sensitivity of tropicalconvection to humidity during the Madden-Julian Oscillation
Observations

GISS GCM

AMSR-ESinglefootprints

Stronger entrainment

Cloudsat/calipso

Weaker entrainment

Large-scale
averages

Context: GCMs poorly
simulate the MJO because
their convection schemes areinsensitive to tropospherichumidity.
CloudSat/CALIPSO/AMSR-E
provides the first global direct
detection of convection depth,
while AMSR-E gives
simultaneous water vapor.
GEOPROF-LIDAR data wereaccumulated over thedeveloping phase of 10 MJOs.

Left panels: A-train data show transition from shallow to deep convection at intermediate
column water vapor amounts (50-68 mm), but with all depths possible at these values
depending on details of the humidity profile.
Right panels: GISS GCM shallow-deep convection transition occurs too soon when too
little dry air is entrained into clouds, but gets the correct transition with strong entrainment.
Del Genio et al. (2012), Journal of Climate

MODIS-Aerocom

GOSAT-Aerocom

Aerosol optical depth

MODIS-Aerocom

GOSAT-Aerocom
Aerosol single scatter albedo

McGarragh & Stephens, 2013

Multi-sensor fusion - new insights on planets major storms
Vertical Extent ofConvective Burst

Vertical Extent of Central
Dense Overcast Eye

Tropical Cyclone ChoiWen …
(Imagery Courtesy Naval
Research Laboratory,
Marine Meteorology
Division, Monterey CA)

Oct 6th, 2009

34th AMS Radar Conference Williamsburg
VA

35

Access to Information• Public availability of key documents





•
•
•

•

Mission and instrument descriptions
Algorithm Theoretical Basis Documents (ATBDs)
Data Catalogues and data set examples
Data Quality Summaries
Open and advertised A-Train Data Policy
Open and advertised Science Team meetings

Public availability of Level 1 and 2 data products soon after startof operations (beta or provisional data quality)
 Better to keep data formats simple
 Near real-time access
Creation of fused data sets (slow to develop for A-Train)
Example - initial data release for CALIPSO & CloudSat withinfirst 6 months

Access to Information• Public availability of key documents





•
•
•

•

Mission and instrument descriptions
Algorithm Theoretical Basis Documents (ATBDs)
Data Catalogues and data set examples
Data Quality Summaries
Open and advertised A-Train Data Policy
Open and advertised Science Team meetings

Public availability of Level 1 and 2 data products soon after startof operations (beta or provisional data quality)
 Better to keep data formats simple
 Near real-time access
Creation of fused data sets (slow to develop for A-Train)
Example - initial data release for CALIPSO & CloudSat withinfirst 6 months

NASA ESD Operating Missions(LDCM not shown, Launched 11 Feb 2013)

NASA ESD Operating Missions(LDCM not shown, Launched 11 Feb 2013)

NASA A-Trainmember missions

Anticipated A-Train highlights
Sensor data
usedWhat is provided

Why useful

Interesting tidbits

CloudSat &CALIPSO

Vertical profiles of cloud
occurrence, newdefinitions of high thincloud, cloud base, cloudlayering, etc

This vertical structure is required formany weather and climate related
processes

Multiple layering is prevalent
in tropics (60%), total cloud
cover ~76%

MLS,
CloudSat,AIRS

Ice water content and pathcomparison and relation to
UTH

Important climate feedbacks
revolve around high, thin ice clouds- agreement between these twodata sets confirms validity of
products

Connection to water vapor
implies processes.
Gross errors in the relation
between UT ice and vapor inclimate models -AIRS, MODIS
Cloudsat,AMS
RE & CALIPSO

Cloud & precipitation
information from different
sensors can be tested
including cloud liquidwater path of raining/nonraining clouds

Can calibrate longer time records ofother sensor data, like cloud top
heights, precipitation, – useful forother applications like cloud trackwinds

Major biases incloud/radiance climatologies
exposed, AMSR-E precip
occurrence is ~ 2X less than
CloudSat, exposes large
uncertainty in mid alt
precipitation

AMSR-E,CERES,
CloudSat,
MODIS &CALIPSO

More integrated view ofaerosol indirect effects on
observed cloud albedos

Large uncertainties in AIE is one ofthe principle tools that constrain
our ability to predict cliimate
warming.

AIE are inferred to be small
composed due tocombination of processes
that buffer one another.
Production of precipitation
grossly influences AIE

A-Train
Serendipity
Convective
Provides unique, global

Sensor data used
MODIS IR,
CloudSat,CALIPSO

What is provided

Why usefulbuoyancy,
entrainment

information that is
beginning to
revolutionize model
convection
parameterization

AMSR-E, CALIPSO

Surface wind fromlidar surface
reflection

CALIPSO surface windsees in between clouds
and is less contaminatedby cloud effects

CloudSat &CALIPSO

Aerosol opticaldepth via PIA –radar
surface reflectivity isused to define lidar
surface reflection

AOD much less sensitive
to aerosol model
assumptions that plague
all other methods

Interesting tidbits
Verified hot towerhypothesis – 0.02% oftropics contain
undilute convectivecores
1m/s rms, near zero
bias compared to
AMSR-EA-Train Serendipity
Sensordata used

What is provided

Why usefulMODIS
vis, nir,
CloudSat,

Correlation between
radar reflectivity and
MODIS particle size

Provides unique identification Time scale is muchof the transition from cloud to longer in nature than israin and time scale of rainassumed in models
formation

OMIi ,
CloudSat,

Inferred cloud top
heights fro UV
scattering matched tocloud profiles

Impacts ozone estimation
above clouds

Considerable UV
multiple scattering
makes OMI cloud tops
appear many kms low

CloudSat& MODISA confirmation of
MODIS particle size andits relation toprecipitation

Passive measures particle sizeof low clouds can be used tocharacterize drizzle/precio
occurrence.

Drizzle is so persistentin oceanic clouds that it
measurably affects themean particle size

ColudSat&CALIPSO

Identification of thin
winter time ice clods
and it precipitation

Explosive development ofprecipitation altered byaerosol affecting the rate ofdehydration of polar clouds

Interesting tidbits

A-Train Science Concepts
4 slides from:“ESA-NASA Constellation Management Workshop
May 17, 2011
Saint-Hubert, Quebec, Canada
Perspectives on Maximizing Science Return
Chip Trepte, CALIPSO Project Scientist, NASA/LARC”

Common Interests• A-Train formulation motivated to a large degree by a desire tobetter understand clouds and aerosols and their impact on theradiation budget and hydrological cycle• Prior to A-Train international science community was alreadyengaged in collaborative research efforts across multiple fronts, forexample: climate and weather forecast modeling (GEWEX)
 field measurement campaigns (Pinatubo eruption)
 remote sensing research: vis/ir sensors, polarimeters, lidar,
radar• Deep seeded desire for global observations – time was ripe
• Recognition that multiple measurement approaches are needed
Example – large volume of publications using A-Trainobservations (Aqua > 500, CloudSat >300, CALIPSO >350, Aura
>500, Parasol >150; may be some duplication between missions)Leadership• Mission leads (Principal Investigators, Project Scientists, ProjectManagers, Program Scientists, Agency Leads) recognize thevalue of collaborative efforts at an early stage
 collaborations between missions and discipline communities
evolved on their own
 no centralized approach; self organizing• Effective communication to the science community
 well articulated expectations
 routine meeting opportunities strive to provide clearer messages to the public• Effective Mission Operations Working Group tight connection between science needs and capabilities
 operates under clear and established procedures
 Routine communication across management, science, andengineering sectors• Funding for cross discipline/mission and multi-sensor research• Supportive of new and young scientists

Access to Information• Public availability of key documents





•
•
•

•

Mission and instrument descriptions
Algorithm Theoretical Basis Documents (ATBDs)
Data Catalogues and data set examples
Data Quality Summaries
Open and advertised A-Train Data Policy
Open and advertised Science Team meetings

Public availability of Level 1 and 2 data products soon after startof operations (beta or provisional data quality)
 Better to keep data formats simple
 Near real-time access
Creation of fused data sets (slow to develop for A-Train)
Example - initial data release for CALIPSO & CloudSat withinfirst 6 months

Open Validation•••

Sustained calibration and validation efforts
 Formulation of pre-launch plans Data processing effort includes iterative processing tocapitalize on improved calibration/validation approaches
 Committed funding from sponsoring agencies
Coordinated comparison field missions
 Optimizes resources (instruments, aircraft, ground systems)
 Brings more eyes to a set of issues
 Promotes additional interest
Independent assessments

Example – ozone trend studies in late 1980: TOMS, SBUV, SAGE,HALOE, NDSC
Example – Cirrus optical thickness: CALIPSO lidar and IIR, MODIS,
PARASOL, CloudSat, in situ measurements, CPL

Convective core cloud• Generally defined as moist buoyant

updrafts in LES studies used to develop
convection parametrizations (e.g.
Siebesma & Cuijpers, 1995).

• Area (and hence radiative effect) of coreis relatively small – probably less than 0.1
in typical global model grid
• Currently ignored in UM but test show it
does have an impact (e.g. US surface
temperature)• A good regime indicator forinhomogeneity?

Identifying core cloud
Tested several methods but using method based on
Luo et al 2009.
A column is designated as a core if:• less than 5 layers between 0 dBZ echo top height andcloud top
• less than 9 layers between 10dBZ echo top height andcloud top
• at least 3 layers between cloud base and 10dBZ echo top
height.

A convective cloud observed by CloudSat on
19/11/2009 at about 5:20 UTC above Brazil.

Battaglia et al (2011)

Using convective core
% of columns with cores
information

Observed liquid FSD

A-Train Constellation Evolution

Aqua(May 2002)

CALIPSO(April 2006)

Aura(July 2004)

PARASOL(Dec 2004; lowered
orbit Dec 2009)

CloudSatGCOM-W1/Shizuku(April 2006; battery(May 2012)
management
orbit lowering June 2011,
re-entered train May 2012)• Glory launch failure on 4 March 2011
• OCO launch failure on 24 Feb 2009

OCO-2(~July 2014)

Aerosol effects on clouds – largest uncertainty in climate
forcing and these too is shaped by the thermodynamic
properties of the boundary layer/free troposphere