A-Train highlights: Scientific whole > sum of the parts

Launch – June 2011

Return to A-Train May 2012 until Feb 2018

Graeme Stephens
Director of Center for Climate Sciences,
Jet Propulsion Laboratory, California
Institute of Tech., Pasadena CA
Distinguished Emeritus Professor,
Colorado State University
Visiting Professor, Oceanic & Planetary
A-Train highlights withPhysics, Uni of Oxford
emphasis on CloudSat andC-Train Sept 2018 – Summer 2023
CALIPSO – Stephens et al., The arrival of CloudSat and CALIPSO, motivated to a large degree by a desire toBAMS 2018better understand clouds & aerosols and their impact on the radiation budget andhydrological cycle, provided the true integration of A-Train
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The CloudSat-CALIPSO-EarthCARE journey
It really began in 1993 at a WCRP
supported workshop (held at JPL) that
also included both the ESA and TRMM
(not yet launched) communities.
Out of it grew CloudSat and CALIPSO ,ERM →EarthCARE, and a wider vision atthat time that is now reflected in AOS,
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22What science achievements were enabled by
the A-Train?
Integration across different observations platforms & sensors (synergy)(i) Richer validation of key products & improvements of products(ii) Remove guess work, with important applications(iii) Develop new information from combinations of different matchedobservations,(iv) Combine information to yield new insights on processes(v) Offer a more integrated view of Earth far beyond that which had beenpossible.
But there are other perhaps even more profound achievements @ theobserving system level(i) Viability of disaggregated approaches for building observing systems(ii) Viability of active RS systems for sustained EO’s
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A more integrated view of Earth
Aerosol

Brewer-Dobson Circulation

Cloud
Meridional circulation

Precipitation

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BAMS, 201844

How often does it rain? Multi-sensor synergy reveals process.
Multi-year climatology

Insight on the rain process madepossible with multi-sensor views
A-Train obs grouped by MODIS ReWe now have a good
grasp of the occurrencesof different modes ofprecipitation

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5Series of paper by Suzuki, 5et al

How often does it rain? So what?
• Jing and Suzuki (2019) illustrate a seven-fold range of AIE purely
from the way warm rain is ‘made’ in models• Mulmenstat et al (2022) find “errors in the representation of cloudand precipitation processes bias cloud feedback estimates inmodels by as much as the feedback change of the opposite sign
between CMIP5 and CMIP6.”“The cooling of light rains in a warming world”;
Nat Clim Change, to appear May 2021

• Golaz et al. (2013) demonstrated a profound sensitivity inreplication the the historical warming to how rain is made – thissensitivity is a manifestation of aerosol indirect life-time effects
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rcrit= 6.0mm
rcrit= 8.2mm
rcrit= 10.6mm

Golaz et al. (GRL’13)

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Removing much of the guess work

Perhaps the most significant but generally overlooked
accomplishment of such a diverse observing system is that the active
instruments of CloudSat and CALIPSO offered a clear and more precise
understanding of what it is we are looking at - this has profoundinfluences on information extracted from other sensors
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77Coldness

Knowing what we are looking at changes ‘everything’ andpresents new opportunities

brightness

Operational products

Example in the west pacificMace et al.,2012

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Haynes et al. 2022;2023
88Viability~-30 dBZEIK-2~-28 dBZ

Instrument sensitivity
EIK-1 ~ ± 1 changes over time andcan be explicitly
monitoredThe instrument outlived
the spacecraft!!!!

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99CloudSat constant sensitivity data productestablishes a new climate data record and allows for
new climate trend studies (see Mark Richardson’stalk tomorrow)-30 dBZ

original

Constant sensitvity-25 dBZ

This is the high cloud bin
count drift as original
minus constant sensitivity

EarthCARE now offers us an opportunity
to continue the climate data record (CDR)
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1010Summary Comments1) TRMM, CloudSat, CALIPSO and now GPM: What the last two decades revealed was theviability of active systems - just as ‘affordable’ and just as reliable as passive systems. We
now have the beginning of a climate data record2) The A-Train - offered a ‘new’ paradigm in EO & demonstrated the value of an ‘integrated’
observing approach3) CloudSat in the A-Train demonstrated the viability of ‘formation flying’ as a way ofcreating a single virtual observatory from two separate spacecraft and payloads thus
demonstrating the credibility of building something large from disaggregated parts.

Outlook- some what uncertain, perhaps bleak- after EarthCARE comes AOS but there isnothing in these comparable to the A-Train and really no coherent plans, despite AOS’s best
efforts, to re-produce this golden age of EO.
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1111

Constellations in the coming decade with active sensors(Ka rain radars)
INCUS Mission (3 small Ka radar sats),

Tomorrow.io – commercial
Constellation of Ka-band radars

AOS(proposed)

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1212Why :Prestented at EarthCARE meeting March, Shuzenuji

km scale models with ‘explicit’ convection is here (e.g Philip Stier’s talk)
There is much the W-band radar can tell us about convection including deep convection (series of papers by Luo,
Takahashi etc )The convergences of new emerging obs (EarthCARE, INCUS, AOS) with these model advances means we cancontemplate
now more apples to apples comparison that have not been possible in the CloudSat era.
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The A-Train arrived with a ‘Big Bang (Theory)’

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AmazonAfrica

15
15

1.5
1.5

1010

15

0.5

0.5

00-40

-30

-20

-100

10

20-40-30-20-100

10200WPAmazon

40

20020-20-400050

100

150

200

250

300

Longitude

2040020-20-40(km)Altitude(km)AltitudeLatitude400050100150200

25030050.550-400-400-4002000-20-40050100150200

250300350-20-1001020-30-20-1001020-30-20-100

10

20

0

1.5
1.5GEOS115
5

0.5
0.5-30-30-20-20-10-100010
102020WP

10

150.5

0-40

0-30

-20-1001020

A series of papers fromLuo, Takahashi, Masunaga

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001.5

CloudSat

Longitude

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0[%]

Altitude (km)Latitude

4000

0.50-30WPAmazon

1010CloudSast DC

0110.515201.511050-400-40350

Longitude

40SHIELD

1515DYAMOND GEOS DC

1.5Africa

151010350

1.5[%]DYAMOND SHiELD DC(km)
Altitude
Altitude(km)(km)
AltitudeLatitude150[%] [%]1540[%][%]

5DYAMOND

1[%] [%](km)
Altitude(km)Altitude

The era of km –scale model and assessment – deep convection

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Multi-year average
Cloud cover >50%
At 1X 1 degree

Multi-year average
Opaque Cloud cover>20% at 1X 1 degree

Multi-year average
Precipitation
frequency >5%
At 1X 1 degree
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