Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a a NOAA/NESDIS Office of Research and Applications b UW/Cooperative Institute for Meteorological Satellite Studies (CIMSS), Madison, WI P2.6 Validation of the Cloud Detection/ Clear Radiance Quality 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) (c) (d) (b) Introduction 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 includes many algorithms for new cloud and surface products 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. 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. 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* * developed but not yet implemented Conclusions ORA is developing an improved AVHRR data-set ( ?) 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 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) Continuity in the PATMOS-x and EOS/MODIS Climate Records Validation of the Cloud Products (Cloud Type) Other PATMOS-x Products 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 CO 2 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) 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 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. PATMOS-x has over 100 products. Here is a sample of some of the more common ones. These are monthly averages from July Global Precipitation Index Normalized Vegetation Index Multilayer Cloud Fraction Cloud Top Temperature We are in the process of publishing and validating all cloud algorithms used in CLAVR- x/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. RADAR data showing a multilayer cloud during a MODIS overpass Histogram of multilayer detection results s: