Cloud Products and Applications: moving from POES to NPOESS

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Cloud Products and Applications moving from POES
to NPOESS (A VIIRS/NOAA-biased perspective)
Andrew Heidinger, Fuzhong Weng NOAA/NESDIS Offic
e of Research and Applications (STAR)
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Outline

• Review of POES Cloud Products and Applications
• EDRs
• NWP Applications
• Climate Applications
• Expected Improvements in EDRs with NPOESS
• Expected Improvements in CDRs with NPOESS
• Conclusions

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NOAA is producing many of the NPOESS Cloud EDRs
now from POES and our customers should be ready
for the improvements offered by NPOESS.

• AVHRR (CLAVR-x)
• Cloud Detection using the CLAVR-x cloud mask
• Cloud Typing (water, cirrus, opaque ice,
  multi-layer cirrus)
• Cloud Optical thickness and particle size during
  the day for ice and water clouds separately.
• Cloud Temperature and emissivity from a
  split-window approach day/night insensitive.
• Products from MSPPS (AMSU A/B)
• Cloud liquid water path
• Ice water path
• Ice crystal size

• Products from HIRS
• Cloud Top Pressure
• Effective Cloud Amount

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Where are we with the current observing system
(POES)
The following slides will review the abilities of
the NOAA AVHRR processing to derive cloud
properties produced by VIIRS for the following
scene from the Eastern Tropical Pacific.
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• Cloud Detection
• We can detect clouds well enough over ice-free
  oceans for SST estimation
• We estimate our detection level requires a cloud
  of optical depth 0.3 0.5.
• Thin cirrus contamination is an issue for some
  POES products
• Detect of cloud over the poles is difficult and
  requires advanced algorithms.

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• Cloud Typing / Phase
• Outside of terminator conditions, the AVHRR does
  well on opaque clouds.
• AVHRR approaches often have difficulty uniquely
  detecting thin cirrus
• Cirrus detection actually better at night
  without solar component to 3.75 mm channel
• Detection of multi-layer clouds but only thin
  and high over thick and low.

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• Cloud Optical Depth / Particle Size
• Most common technique retrieve these
  simultaneously from 0.65 and 3.75 mm
  observations.
• Split-window approaches offer some night-time
  capability though limited in optical depth range.
• Separation of 3.75 mm component into solar and
  thermal contributions is commonly done and is a
  major source of error in particle size.
• Lack of on-board calibration is another source
  of error in AVHRR retrievals.
• At 0.65 and 0.86 mm, optical depth retrieval over
  snow difficult for optically thin cloud.

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• Cloud Top Height
• Without CO2 slicing, AVHRR can only estimate
  cloud temperature directly.
• Most common approaches (also VIIRS) to retrieve
  Tc with t and re during the day.
• Split-window approaches can be used for
  day/night independent estimations.

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Comparison between Microwave (AMSU) and VIS/IR
Cloud Products

• AMSU-A (left) can see cloud water that is under
  cloud ice AVHRR can not. This explains why most
  high values seen by AMSU are missing in AVHRR. A
• AVHRR can detect smaller amounts of cloud water
• Both EDRS are not redundant and complement each
  other (same for CMIS/VIIRS)

AMSU data from MSPPS site
AVHRR data from CLAVR-x site
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Comparison between Microwave (AMSU) and VIS/IR
Cloud Products

• AMSU-B (left) is less sensitive to presence of
  some ice than AVHRR (right) but is more to
  uniquely detect ice signatures.
• Much of the signal detected by AVHRR as Cloud
  Ice Water Path is due to the presence of Cloud
  Liquid Water underneath the ice. This holds true
  for VIIRS.

AVHRR data from CLAVR-x site
AMSU data from MSPPS site
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Reasons to Expect VIIRS Cloud EDRs to surpass
AVHRR EDRs

• Cloud Detection/Typing.
• 1.38 mm channel will aid in thin cirrus
  detection and typing
• lack of H2O or CO2 channels will hinder polar
  cloud detection
• DNB and more infrared channels will help at
  night compared to AVHRR
• Spatial resolution also a big benefit to cloud
  detection
• Cloud Optical Properties
• More reflectance channels will lead to better
  particle size and optical dep.
• More infrared channels (8.5 mm) and DNB will
  greatly enhance nighttime COP
• Cloud Top Parameters
• Lack of H2O and CO2 channels causes Cloud Top
  Parameters to rely on Cloud Optical Properties
  therefore performance suspect in some regions
  (poles/termin.)
• Nighttime performance should be better than
  AVHRR with 8.5 mm channel
• VIIRS will produce a cloud base product for the
  first time.

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Fortunately, the MODIS has allowed to see what
VIIRS should provide (Cloud Optical Properties -
COP)
MODIS granule from the northeastern coast of
South America
Aqua platform 20 November 2000 1710 UTC
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Again, MODIS has provided a good experience of
Cloud Top Props (CTP) though lack of CO2 channels
requires different approaches.
MODIS granule from the northeastern coast of
South America
Cloud Top Temperature (K) Cloud Top
Pressure (hPa)
Aqua platform 20 November 2000 1710 UTC
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• NWP Applications Now and During NPOESS
• The complexity of cloud parameterization schemes
  in NWP models is increasing to the point of
  allowing meaningful comparisons between forecast
  clouds and satellite-derived clouds.
• JCSDA is pursuing microwave cloudy radiance
  assimilation methods and infrared radiance
  assimilation will follow.
• Direct assimilation of cloud EDRs seems less
  attractive to NWP centers than cloud EDR
  assimilation. This may change in the NPOESS era.

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Example Verification of NWP using Satellite
Radiances (11 mm)
While the global comparison indicate agreement on
the synoptic scales, there are difference
revealed in smaller scales.
AVHRR 11 mm BT at 6Z
GFS Simulated 11 mm BT at 6Z
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Comparison of products can explain differences
noticed in 11 mm radiances
AVHRR (CLAVR-x) Optical Depth
Derived NWP (GFS) Optical Depth
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Global Comparisons of SSM/I Cloud Liquid
Water Inter-comparison of Cloud Liquid Water
from SSM/I and NWP Model One Example of how NWP
models can be validated/tuned to
Satellite-derived cloud products.
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NWP Applications during NPOESS
Research is being conducted to explore
assimilation of cloudy microwave radiances and
derived LWP and IWP. Below are simulated LWP and
IWP fields from a model that assimilated AMSU
radiances (F. Weng)
Liquid Water Path
Ice Water Path
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• Climate Applications
• POES has provided roughly 25 years of data and
  is therefore a major source of data for decadal
  climate variability studies.
• The interest in satellite-derived climatologies
  is increasing and will certainly be a large
  application during NPOESS.
• NOAA has made climatologies of cloud products
  from AVHRR, SSM/I and HIRS.

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• Current State of Imager Cloud Climatologies.
• For the same CDR (i.e. high cloud fraction),
  different sensors produce time series with
  different magnitudes and signs. (see below)
• Many of the VIIRS cloud algorithms differ from
  those used AVHRR and MODIS and therefore making
  the AVHRR MODIS VIIRS time series consistent
  will require some effort.

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HIRS has also produced an excellent time series
of cloud heights and amounts (and so will
CrIS).Frequency of High Clouds (All CloudsTropics (20oS to 20oN) over ocean
Note that NOAA-15 NOAA-16 are out of family
(HIRS 3)
D. Wylie and P. Menzel
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Example of the difficulties in making a
consistent time series from POES and NPOESS
(AVHRR and MODIS)

• The time-series below is the mean water cloud
  particle size for a region off the coast of
  Western South America.
• In 2001-2003, NOAA-16 AVHRR used 1.6 mm instead
  of 3.75 mm channel. This effects the derived
  particle size.
• MODIS uses 2.1 mm for particle size
• Some of this difference is expected due to
  spectral differences, some is not.

NOAA-16 1.6 mm period
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NPOESS Improvements for Cloud Climatologies

• Lack of spectral characterization has limited our
  ability to make seamless time-series of some CDRs
• Orbital drift on the POES series has also
  hindered climate research
• NPOESS will offer several improvements
• Constant orbit times (POES/DMSP drifted)
• Onboard VIIRS reflectance calibration (missing
  on POES)
• Improved spectral characterization
• More overlap between sensors
• VIIRS DNB offers a chance for better day/night
  continuity in cloud products.

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Motivation to move beyond the Baseline VIIRS
products

• VIIRS channels do not span any h2o or co2
  absorption bands. This makes performance in
  polar regions marginal. In addition, the VIIRS
  cloud top height for thin clouds is very
  sensitive to errors in optical depths.
• Use of CrIS data will solve many of these issues
• Fusion with CrIS will
• improve cloud detection in the poles
• Provide the best cloud top height for thin
  cirrus and for all orbits.
• Provide cloud top particle and opacity
  information for thin clouds that is less
  sensitive to uncertainties in particle size than
  VIIRS CTP.
• Fusion with CMIS
• CMIS will benefit from sub-pixel cloud
  detection/type and height information.
• Fused microwave/visible/infrared approaches will
  certainly become mature before launch of NPOESS
• APS (at a minimum) will provide a basis for
  improving all VIIRS cloud products. Limited
  extent of APS limits utility of combined products.

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Conclusions

• While VIIRS will not essentially offer new cloud
  products (except for cloud base) from those we
  have been deriving from AVHRR, its products will
  be
• At a higher spatial resolution
• Better calibrated
• Available more quickly
• For cloud climatology research, VIIRS will
  improve upon POES with
• On board calibration
• Controlled orbits.
• The presence of CMIS, CrIS and VIIRS together on
  a single platform will provide a much better
  cloud observing system than possible with any one
  sensor.