Overview WP3000 Arnout Feijt AVHRR and AMSU analysis Data acquisition Standard processing Cloud Type Classifications AMSU AVHRR Quantitative Cloud Analysis Comparing / Combining with Ground Based -wave data Comparison with Atmospheric Models
Data acquisition and standard processing Adam Dybbroe Problems Area definitions, Antenna failures Formats (hdf5, phyton, KNMI) Failure NOAA-15 (AMSU) Delayed launch NOAA-16 Upgrades ANA
Goal: Give overview of synoptic cloud classes Products: All overpasses analyzed Monthly statistics of cloud characteristics Time series of cloud characteristics per station Cloud Type Classifications Adam Dybbroe
AMSU Heike Hausschildt Goal Provide reference LWP values for AVHRR retrievals Problems Radiative Transfer Theory large gap Real Life Results Literature approach ===> unsatisfactory Neural Network-approach ===>? presentation
AVHRR Quantitative Cloud Analysis Dominique Jolivet Goal Spatial distribution of LWP Problems Calibration NOAA-15 Retrieval assumptions Number of cases (Supervized mode)
AVHRR Quantitative Cloud Analysis Dominique Jolivet Number of cases Selection, Help from University Kiel Extentions + Adaptions of KLAROS High speed access LUTs, Multiple viewing geometries, Area averages, 1.6micron images, Unexpected usefulness of the 1.6micron channel (N-16) Particle size, Phase discrimination (not part of KLAROS) Quality LWP fields is better than was expected for cloud fields of low variability
AVHRR versus -wave radiometers Dominique Jolivet, Heike Hausschildt Supervized Lot of work and subjective Russian doll method Comparing average and variance at increasingly larger scales The Heike method..?
AVHRR versus -wave radiometers
Comparison with Atmospheric Models
ProgramWP3000 Adam Dybbroe –Data acquisition –Standard processing –Cloud Type Classifications Heike Hausschildt –AMSU radiances into a go Neural Network Dominique Jolivet –AVHRR Quantitative Cloud Analysis Heike Hausschildt –Comparing AVHRR with Ground Based -wave data