P2.6
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set
Andrew Heidingera, Michael Pavolonisb and Mitch Goldberga
aNOAA/NESDIS Office of Research and Applications
bUW/Cooperative Institute for Meteorological Satellite Studies (CIMSS), Madison, WI
Emails: Andrew.Heidinger@noaa.gov, mpav@ssec.wisc.edu, Mitch.Goldberg@noaa.gov
Other PATMOS-x Products
Introduction
Validation of the Cloud Detection/ Clear Radiance Quality
Validation of the Cloud Products (Cloud Type)
PATMOS-x is…
• an extension of the original AVHRR Pathfinder Atmospheres (PATMOS)
•extends PATMOS by processing the NOAA-klm data and the data from
AVHRR’s in morning orbits
We have compared the SST’s from PATMOS-x computed daily(a) and monthly
averaged (b) to the Reynolds Optimally Interpolated SST climatology (d). The
histogram of the SST – OISST for the 4 cloud mask values show the desired behavior
with little indication of cloud contamination in the clear radiances (no cold tail).
(a)
• includes many algorithms for new cloud and surface products
PATMOS-x has over 100 products.
Here is a sample of some of the more
common ones. These are monthly
averages from July 1987.
We are in the process of publishing and validating all cloud algorithms used in CLAVRx/PATMOS-x. One of the algorithms already published is the cloud type algorithm.
We derived 6 cloud types for each pixel (fog, water, supercooled water, opaque ice,
cirrus, multilayer). The validation shown below was based on MODIS and RADAR
overpasses compiled by Jay Mace of University of Utah.
Global Precipitation Index
(b)
•Is part of a larger NESDIS Data Stewardship Initiative which also include
activities aimed at improving the AVHRR calibration and navigation
GOALS of PATMOS-x
• Use the improved AVHRR observations to make a data-set useful for
satellite climatology work within NESDIS and others based on accepted
procedures.
(c)
(d)
•Contribute to bringing consensus to satellite cloud climatologies (where
there is little now)
•Work with NPOESS and EOS to develop AVHRR climatologies that are
consistent with the future climate records.
Normalized Vegetation Index
Histogram of multilayer
detection results
RADAR data showing a
multilayer cloud during a
MODIS overpass
P2.6
PATMOS-x Products
• Radiance: Mean and Standard Deviations of all channels for all cloud
mask values (clear, probably clear, probably cloudy, cloudy + all-sky)
•Cloud – Amounts (total, high, mid, low, ice and water), 6 Types
(including multilayer), cloud temperature, emissivity, optical depth,
particle size and liquid/ice water path
•Surface – Sea, Land and Ice Surface Temperature, NDVI
•Aerosol – Optical depths using NOAA’s operational algorithm*
•Precipitation – Global Precipitation Index (GPI)
•Other – Fire, Dust and Volcanic Ash*
Multilayer Cloud Fraction
Comparison with other Satellite Climatologies (ISCCP)
We are comparing our cloud climatologies to those from other satellite derived
climatologies (ISCCP, UW/HIRS). While “philosophical” differences often prevent
close agreement in the absolute values, we do see agreement in annual cycles (here
July – January) and other relative measures of cloudiness
* developed but not yet implemented
AVHRR Data Improvement Activities
A large part of ORA’s effort is focused on improving the radiometric and geolocation
accuracy. Some of these activities are:
• using simultaneous nadir observations between AVHRR and MODIS to transfer
MODIS’s on-board reflectance calibration to AVHRR (see below)
•Using advanced hyperspectral sensors such as Hyperion on NASA’s EO1 satellite to
improve our spectral knowledge of radiometric targets (i.e. desert sites) used for
reflectance calibration
•Using AVIRIS data for characterizing and removing artifacts in climate records from
the spectral differences between AVHRR’s
Comparison of MODIS versus AVHRR
(0.63 micron)
Using Hyperion to improve our knowledge of AVHRR
and its relation to other sensors (i.e. MODIS)
Improvements over PATMOS
One of the problems apparent in the
PATMOS data were the large jumps in
some cloud product time series during
transitions from one satellite to the next
(vertical lines in figure to the right).
CLAVR-x (and therefore PATMOS-x)
has reduced this problem by improving
the physical basis of the cloud mask.
This also allowed for processing
morning satellite data in a consistent
way.
Cloud Top Temperature
Conclusions
Continuity in the PATMOS-x and EOS/MODIS Climate Records
While developing the PATMOS-x algorithms we have tried to
ensure physical continuity with the comparable climate records
from EOS/MODIS
For example, we use a split-window algorithm to estimate cloud
temperature and cloud emissivity while MODIS uses a better
CO2 slicing approach. While AVHRR is spectrally limited, we
feel we can produce comparable climatologies of cloud
temperature and emissivity in many regions. Comparing to
MODIS (see right) helps us characterize the weakness and
strengths of the PATMOS-x products.
AVHRR Cloud Temperature
MODIS Cloud Temperature
(MOD06)
•ORA is developing an improved AVHRR data-set (1982-200?)
•PATMOS-x will use this improved data to develop a new climate data-set
•PATMOS-x data will made available as orbital, daily and monthly averages in a
self describing format (HDF4)
•Work is ongoing to finish publication of all algorithms but initial results and
comparison are encouraging and show PATMOS-x adds new information to the
existing satellite climatologies
•We actively seek collaboration with others on the use of this data