(Towards) A New AVHRR Cloud Climatology
Andrew Heidinger, Mitch Goldberg, Dan Tarpley
NOAA/NESDIS Office of Research and Applications
Michael Pavolonis
UW/CIMSS, Madison, Wisconsin
Aleksandar Jelenak
IMSG, Inc
This work is funded by ORA and leveraged heavily off IPO and OSD funding
SPIE Asia-Pacific Symposium, 8-11 November 2004, Honolulu Hawaii
Outline
• ORA’s AVHRR Reprocessing Project
•The AVHRR Cloud Climatology (Pathfinder Atmospheres
Extended Project – PATMOS-x)
•Comparison of PATMOS-x Cloud Climatology with other
climatologies (ISCCP, NOAA + UW HIRS)
•Comparison with new observing systems (GLAS LIDAR
data)
•Conclusions
•Future Work
Why use the The AVHRR for Climate Studies
The Advanced Very High Resolution Radiometer (AVHRR) was launched
in the 1979 for non-quantitative cloud imagery and SST. It flies on the
NOAA Polar Orbiting Satellites (POES)
1. AVHRR Provides enough spectral
information for several applications
2. AVHRR provides enough
spatial resolution (1 or 4 km)
to resolve many atmospheric
and surface features
3. Combined with its long data record (1982-2012) make the AVHRR dataset appealing for decadal climate studies
AVHRR Data Stewardship Project
For these reasons, the NOAA/NESDIS Office of Research
and Applications has embarked a pilot Data Stewardship
Initiative Project to reprocess the entire AVHRR GAC dataset (1982-2004)
The activities within this project include:
•Improving the level 1b data (navigation/calibration)
•Reprocessing for a new AVHRR SST climatology
•Regeneration of Global Vegetation Index Climatology
(GVI) at higher spatial resolution
•Polar Winds
•Regeneration of a new PATMOS-x Cloud Climatology
AVHRR Data Improvement Activities
Due to clock errors and other effects, the AVHRR GAC navigation can be
off by many kilometers. We have been provided the clock corrections
from the University of Miami and are re-navigating the data.
These corrections will be made available to the public as ancillary data
after
before
Note, this is a worst-case example
The AVHRR Pathfinder Atmospheres Extended (PATMOS-x)
The PATMOS-x Cloud Climatology is our attempt to provide a new
cloud climatology to the satellite climate research community. This
builds upon our previous effort (PATMOS) completed in 1998.
PATMOS-x is an extension of the original PATMOS into
• the NOAA-klm AVHRR/3 series (1998-2004)
• AVHRR from morning orbits (higher temporal resolution)
• higher spatial resolution (0.5o or 0.25o versus 1.0o)
• many more products with new algorithms and data files in HDF
(with metadata)
PATMOS-x also differs from PATMOS in that we are now able to
reprocess from the level 1b data multiple times. PATMOS allowed only
one pass through the data.
Reasons for Deriving a New Cloud Climatology from AVHRR
•Uncertainties in climate
predictions often attributed to
cloud uncertainties.
• In some ways, the existing
cloud climatologies show little
consensus (i.e. The trend and
magnitude of the July Total
Cloud Amount time series)
•
PATMOS-x will provide another data-set that is mostly independent.
•
PATMOS-x will provide more information to better understand observed
cloud amount climatologies.
•
PATMOS-x is being developed to be physically consistent with MODIS
and NPOESS/VIIRS – this is critical for time series continuity beyond
2010.
PATMOS-x Cloud Products
• Pixel level cloud detection and cloud type*
• Pixel level cloud optical depth, particle size, lwp, iwp
• Pixel level cloud top temperature and emissivity*
• Gridded means and standard deviations of all pixel level products
• Cloud amounts: total, high, mid, low, ice, water, multilayer
• Mean and standard deviations of the radiances for each cloud mask
category
•Products are similar to those from MODIS and VIIRS, lack of spectral
information does hamper AVHRR’s performance in stressing conditions
such as terminator conditions and polar regions. For most regions for
most clouds, AVHRR can do well for many important climate data
records.
* - our primary focus for algorithm validation
Examples of PATMOS-x Cloud Products
Cloud Detection
PATMOS-x uses the CLAVR-x cloud detection algorithm which
serves as NOAA’s operational AVHRR cloud mask. CLAVR-x is well
suited for climate work because it requires little ancillary data and is
stable over time. CLAVR-x has been designed to minimize
day/night/terminator artifacts. Spatial uniformity plays a big role.
Cloud mask similar to MODIS: clear, prob. clear, prob. cloudy, cloudy
Examples of PATMOS-x Cloud Climate Records (Cloud Type)
Each cloudy pixel is classified into one of following cloud types: (0)
clear, (1) fog, (2) water , (3) super-cooled water (4) opaque ice, (5)
cirrus, (6) multilayer cirrus
Validated against ARM cloud type products (Tanneil Uttal/NOAA) and
a version developed for VIIRS with IPO support
Knowledge of Multilayer Cloud may help resolve discrepancies between
climatologies which do not contain this information (ISCCP and HIRS)
Cloud Temperature/Emissivity
• PATMOS-x uses a split-window optimal estimation approach to estimate
both cloud temperature and emissivity. Pure IR approach helps achieve
consistent performance for all orbits/solar geometries
•Results being validated against MODIS. Goal is to achieve consistency
with MODIS and VIIRS for thin cirrus – difficult from AVHRR.
MODIS (MOD06) cloud temp.
AVHRR cloud temp
AVHRR cloud emissivity
Comparison with Other Cloud Climatologies
•
Two existing global satellite derived climatologies are:
1. International Satellite Cloud Climatology Project
(ISCCP) (GEO imagers+AVHRR)
2. University of Wisconsin / NOAA HIRS
•
We can learn a lot about PATMOS-x from these comparisons.
For example, we can test the inter-annual variability, annual
and diurnal cycles of PATMOS-x compared to the others.
•
Though the MODIS time series is short, it offers superior
performance in some situations that can provide point out
PATMOS-x deficiencies and guide future development.
•
New active cloud observing systems (GLAS, CloudSat,
CALIPSO) offer direct validation of many PATMOS-x
parameters and allow us to characterize what clouds under
what conditions we are missing.
Comparison of Total Cloud Amount Trends
The figure shows Total Cloud
Amount time series from 60S to
60N for July
•PATMOS-x does not exhibit
the downward trend scene in
ISCCP
•Differences in magnitude
are likely due to PATMOS-x
weighting of partly cloudy
pixels. ISCCP and HIRS do
no weighting of partly cloudy
pixels
PATMOS-x trends are preliminary
until calibration work is finished
Spatial Pattern in the Total Cloud Amount Trends
The following images show the linear trend from the time series based on the
July High cloud amounts (roughly 20 years)
ISCCP
PATMOS-x
HIRS
•More agreement in patterns in PATMOS-x and ISCCP than HIRS
•Agreement in the positive trends in Philippine and Arabian Seas
•Agreement in the negative trends in the Caribbean Sea
•HIRS show more wide spread distribution of positive trends (consistent
with trend in 20S to 20N result)
July High Cloud Amount Trends in Tropics
• HIRS High Cloud Amounts are
much greater than PATMOS-x and
ISCCP
•HIRS shows a slight positive trend
while PATMOS-x shows little trend
and ISCCP shows a trend opposite to
HIRS.
July High Cloud Amount Trends in Tropics (Continued)
• If we compare the time series of
the ratio of the High to Total Cloud
Amount, HIRS and PATMOS-x
are very similar
•This ratioing may remove effects
in cloud amount due to partly
cloud pixels
Correlation of the PATMOS-x Cloud Climatology with ENSO
Our goal in this analysis to verify that PATMOS-x cloud
climatology behaves as expected where expected under ENSO
The following images show to mean July High Cloud Amount and its
Anomaly correlation with ENSO
Positive correlation of Tropical Pacific Cloud is expected as is the
distribution over North America. Similar correlation seen in Total Cloud
Amount. These anomalies probably explain spatial pattern of trends.
Comparison of the Annual Cycle in High Cloud
Other checks of the PATMOS-x cloud climatologies are if they
reproduce the expected diurnal and annual cycles
• All show the same
annual cycle
• ISCCP shows a very
large diurnal cycle that
is likely an algorithm
artifact
•PATMOS-x shows
little diurnal cycle in
tropical high cloud.
•PATMOS-x does
show more cloud over
land during day
data provided by H. Zhang and D. Wylie
Using New Cloud Observing Systems (GLAS) to estimate
optical depth sensitivity of cloud climatologies
By filtering out GLAS results with
optical depths below some
minimum, we can estimate the
sensitivity of our passive cloud
climatologies:
Minimum GLAS optical depth to
match observed High Cloud Amount:
AVHRR Day – 0.23
AVHRR Night – 0.1
MODIS/TERRA – 0.12
ISCCP Day – 0.27
ISCCP Night – 0.40
HIRS Day/Night – 0.04
AVHRR can identify thinner high cloud at
night than day – opposite of ISCCP.
If we can’t get reduce day/night
differences, we want to characterize
them.
Conclusions
• NOAA/NESDIS ORA is working on improving the AVHRR GAC data
record (Please contact us is you want to participate)
•PATMOS-x will use this improved data and provide a new global cloud
climatology (along with radiances and selected non-cloud products)
•PATMOS-x cloud amounts show many similarities in spatial distribution
but not much consensus in trends with ISCCP and HIRS
•PATMOS-x annual cycles appear consistent with MODIS/HIRS/ISCCP
•GLAS observations are useful in estimating the cloud missed by passive
sensors (derive errors bars for passive cloud climatologies)
•PATMOS-x cloud amount shows some expected variations with ENSO.
Future Work
• Finish navigation and calibration improvements to AVHRR GAC
•Validate remaining cloud algorithms (Cloud Temperature, Cloud LWP)
•Make preliminary data-set available for testing
•Finish intercomparison with other climatologies
•Extend GLAS analysis to CALIPSO and CloudSat
•Continue to explore AVHRR/EOS/NPOESS climate continuity
We welcome any feedback and seek collaboration with others
Andrew.Heidinger@noaa.gov
Goals for PATMOS-x
• Provide a low resolution radiance data-set that allows
the climate community access to ORA’s Level 1b
improvements.
• Provide critical climate records derived from accepted
algorithms, on a consistent grid in an easy to read format
(HDF). We focus on products that we (ORA) have
experience with (NDVI,aerosol, SST, OLR, Clouds)
•Serve as a new cloud climatology with information that
compliments and builds upon the existing climatologies
(ISCCP and UW/HIRS).
Using Cloud New Observing Systems to Validate Cloud Climatologies
NASA’s GLAS LIDAR optical depth / cloud
layer data has been made publicly available for
one month (Oct/Nov 2003). LIDAR provides a
direct measurement of the presence of cloud
and the vertical profile for thin clouds.
In comparison to PATMOS-x, GLAS detects many more clouds.
Previous Cloud Climatologies
• International Satellite Cloud
Climatology Project (ISCCP)
•NOAA + University of
Wisconsin HIRS (UWHIRS)
•Original AVHRR Pathfinder
Atmospheres (PATMOS)
•Cloud climatologies exist for
most missions (NIMBUS-7,
SAGE, EOS)
•Other AVHRR climatologies
includes several region studies
(APP-x – Polar Regions,
SCANDIA - European)
Cloud Amounts
• Using the Pixel level cloud mask, cloud type and cloud temperature, the
following cloud amounts are produced: Total, Low, Mid, High, Ice and
Water
•Being compared to ISCCP and UW/HIRS
•NCEP comparing them against other real-time products (USAF)
Total Cloud
Water Cloud
High Cloud (P < 440 Hpa)
Spatial Pattern in the High Cloud Amount Trends
The following images show the linear trend from the time series based on the
July High cloud amounts (roughly 20 years)
ISCCP
PATMOS-x
HIRS
• Agreement in the positive trends in Philippine and Arabian Seas
•HIRS show more wide distribution of positive trends (consistent with
trends in 20S to 20N result)
Using GLAS to estimate sensitivity of the AVHRR cloud detection
By filtering out clouds with optical depths lower than some threshold, we
can determine the lower limit of the optical depth of clouds observed in
the AVHRR/PATMOS-x and other cloud climatologies
Using this analysis applied to the
Total Cloud Amounts:
AVHRR = 0.33
MODIS/TERRA = 0.05
ISCCP = 0.08
HIRS = 0.015
This does not take into account
the differences in cloud amount
that are not due to cloud
detection (partly cloudy pixels)