GOES-R AWG Product Validation Tool Development

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3 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

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1

GOES
-
R AWG Product Validation
Tool Development


Cloud Products


Andrew Heidinger (STAR)

Michael Pavolonis (STAR)

Andi Walther (CIMSS)

Pat Heck and Pat Minnis (NASA Larc)

William Straka (CIMSS)




Cloud Products

While the cloud team as 10 products from 5 algorithms, we really have 14 when
doing validation.


1.
Clear sky mask (binary, 4
-
level, and test results)

2.
Cloud Phase

3.
Cloud Type

4.
Cloud
-
top Height

5.
Cloud
-
top Temperature

6.
Cloud
-
top Pressure

7.
Daytime Cloud Optical Depth

8.
Daytime Cloud Particle Size

9.
Daytime Liquid Water Path

10.
Daytime Ice Water Path

11.
Nighttime Cloud Optical Thickness

12.
Nighttime Cloud Particle Size

13.
Nighttime Liquid Water Path

14.
Nighttime Ice Water Path

2

Validation Strategies


ABI Cloud Mask (ACM)


CALIPSO cloud layer product.


Comparison to other clouds masks (NASA, EUMETSAT)


ABI Cloud Phase/Type (ACT)


A
-
train


CALIPSO and potentially CloudSat


ABI Cloud Height Algorithm (ACHA)


CALIPSO cloud
-
top height


Comparison to other sensors (GOES, SEVIRI, MODIS)


Cloud Optical and Microphysical Properties (DCOMP and NCOMP)


Comparison to same products from other algorithms on similar sensors (DCOMP)


Microwave radiometers (AMSRe) provide Liquid Water Path


Various data
-
sets from field campaigns.


CALIPSO to provide cloud optical depth for thin cirrus (NCOMP).


Validation Tools


All Deep
-
Dive tools developed in IDL


Routine validation imagery done in IDL and prototype websites exist showing
images using Java Applets and Google Earth.


McIdas
-
V interaction beginning.

3


Routine Validation Tools



Visual analysis


Cloud Type


Easiest to perform in an operational manner. Visualization below from
the CIMSS GEOCAT website. Images generated in IDL.


Cloud phase and type should visually correlate with features seen in false
color images that include the appropriate channels.


Visual analysis of all cloud products is an important tool.



4

5

Deep Dive:


ACM Comparison to other Masks



The MODIS cloud mask from
NASA (developed at CIMSS)
provides a well
-
characterized mask
designed for an advanced imager.




The image on the right shows a
comparison of the ACM run on data
from AQUA as compared to the
MODIS cloud mask from that
scene.




This can be done with any polar
orbiting satellite (NASA EOS,
JPSS, Metop) that has a cloud
mask product.

6



The EUMETSAT Meteorological
Product Extraction Facility (MPEF)
Cloud Mask cloud mask provides
a well
-
characterized mask
designed for the imagery used as
proxy




The animation on the right shows
a comparison of the ACM run on
data from SEVIRI as compared to
the EUMETSAT cloud mask from
that scene.




Inter
-
satellite comparisons of
cloud mask (and other products)
can provide insight as to
deficiencies and improvement in
both products (next slide)

Deep Dive:


ACM Comparison to other Masks

7


Deep Dive: ACHA Comparison
with CALIPSO/CALIOP



CALIPSO/CALIOP
remains our main
source of cloud height
validation.




CALIPSO results
compared to MODIS,
GOES, SEVIRI and
AVHRR



See ACHA Poster for
more details.

Example Comparison of CALIPSO and ACHA for
one AQUA/MODIS Granule .

See ACHA poster for more CALIPSO/CALIOP comparisons.

8

Deep
-
Dive Validation

DCOMP Liquid water path with
AMSRe

8


Image shows result of four
arbitrary chosen days in October
2006 and April 2007.




Accuracy and precision specs
are met.



DCOMP LWP (SEVIRI)

AMSRe LWP

See DCOMP poster for more AMSRe and MODIS comparisons.

Deep Dive: Field Campaigns

9


The SSFR is a shortwave
spectrometer operated by U.
Colorado / LASP.



During CALNEX 2010 it was
operated on a ship looking up.



It provides retrievals of
DCOMP cloud properties
using radiation that travelling
through the cloud (
not
reflected off the top like
DCOMP
).



This provides a more
independent validation.

Red lines are the DCOMP Error Bars

10

NCOMP

Validation Summary


Routine Validation



Tools using satellite
-
based sources for

LWP

and COD have been
developed with F&PS met.



Tools using satellite
-
based sources for CPS and
IWP have been
developed but sources are not well
-
understood and are subject to
the sources’ evolving retrieval algorithms
. Have met or are
approaching F&PS requirements.




Deep
-
dive Validation



Tools using satellite
-
based sources for LWP, COD, CPS and IWP have
been developed. Same caveats on satellite sources.



Tools using surface
-
based sources have been developed for usage within
non
-
GOES
-
R systems and are being adapted to GOES
-
R requirements.



Integrating multiple sources and tools for deep
-
dive looks is in
development.




Deep Dive Validation: NCOMP


11


Liquid Water Path











1

completed

2

in development


SEVIRI/ABI SIST near
-
realtime


Validation tool imminent, IDL
based, some
McIDAS

SIST LWP Retrieval 2 Apr 2011 00:00 UTC

L
WP from

SEVIRI SIST can be
compared to NCOMP
over entire disk






Retrievals from AMSR
-
E
1

NASA LaRC SEVIRI/ABI SIST
2

products


AMSR
-
E comparison meets
specs for thin water clouds


IDL
-
based



TBD: is AMSR
-
E a feasible
source for other water cloud
types? Is CloudSat LWP
appropriate?

L
WP from

SEVIRI SIST
compared to
AMSR
-
E for
thin water
clouds






Ground
-
based Retrievals
1

L
WP from ARM MWR
and radar/
lidar

are
compared to MODIS
SIST at ARM SGP







Need to transition NCOMP to MODIS


Subject to ground
-
based retrievals’ availability


IDL
-
based

Validation through Application


Another way to validate products is by tracking their impact on
applications that use cloud products.



GOES
-
R AWG cloud algorithms are currently being fed into
the following applications.



NWS Forecaster Support in GOES
-
R Proving Ground



Cloud
-
drift winds by the AMV team.



Cloud
-
top Height Assimilation by NWP (
potentially
)

Impact of Cloud
-
top Heights on AMV
Performance



AMV Performance is dependant on the accuracy of the cloud height


Image below from AMV team shows an impact of a cloud height change
(black line) on the wind speed bias (blue) and vector difference (green)

Wayne Bresky, AMV Team

Analysis Humidity RMS, Jan 4
-

13, 2010

oprRR:

w/ cloud analysis

devRR:

no cloud analysis

1
-
h Forecast Humidity RMS, Jan 4
-

13, 2010

Cloud Analysis Verification: Impact on moisture analysis & forecast

Inclusion of LaRC clouds yields more accurate moisture analyses & forecasts => ultimately
better forecasts of aviation hazards

oprRR => better
moisture forecasts
at all levels

Error

Error

*Ultimately, only Ztop is being used operationally because inconsistencies with other
parameters occurred when assimilating LWP/IWP. So, research continues.

Cloud
Assimilation

Impact of Cloud
-
top Heights in NWP
Data Assimilation:


Conclusions


Cloud team is making use of all available space
-
based validation
sources. (CALIPSO, CLOUDSAT, AMSRe, MODIS …)



Application of these algorithms to current sensors is an important
part of our algorithm validation and evolution. (
In start contrast to
JPSS
).



We are starting to get feedback from applications on our products by
other groups.



We hope some of the proposals we have submitted are funded or
we have additional opportunities so that we can benefit from the
data and insights offered by the airborne and field campaign
communities.

End of Talk, Thank You !

Deep Dive Comparison to MODIS
Example: DCOMP Comparison to NASA
MODIS MYD06 Products

N A S A M O D I S P R O D U C T S ( M Y D 0 6 )

A W G P R O D U C T S


(D C O M P )

18


Routine Validation Tools



Visual Analysis: DCOMP Cloud Water Path


Cloud Water Path (Liquid or Ice) should show a correlation with the visible
reflectance.


Areas of very low values over bright surface are indicative of false cloud.


Imagery shows performance of clouds near edges and across boundaries.