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Comprehensive Analysis of Cloud Types Using AVHRR and AMSU Data: Processing, Comparisons, and Challenges

This document provides a detailed overview of the methodologies and challenges surrounding cloud type classification using AVHRR and AMSU satellite data. It discusses data acquisition, standard processing techniques, and the integration of ground-based measurements for validation. Key contributors Adam Dybbroe and Heike Hausschildt highlight significant issues encountered, including antenna failures and retrieval assumptions. The goal is to present a synthesized analysis of cloud characteristics over time, comparing satellite observations with atmospheric models and enhancing retrieval accuracy using neural networks for improved quantitative analysis.

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Comprehensive Analysis of Cloud Types Using AVHRR and AMSU Data: Processing, Comparisons, and Challenges

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  1. 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 m-wave data Comparison with Atmospheric Models

  2. 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

  3. Cloud Type Classifications Adam Dybbroe Goal: Give overview of synoptic cloud classes Products: All overpasses analyzed Monthly statistics of cloud characteristics Time series of cloud characteristics per station

  4. AMSUHeike 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

  5. AVHRR Quantitative Cloud Analysis Dominique Jolivet Goal Spatial distribution of LWP Problems Calibration NOAA-15 Retrieval assumptions Number of cases (Supervized mode)

  6. 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

  7. AVHRR versus m-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 ..?

  8. AVHRR versus m-wave radiometers

  9. Comparison with Atmospheric Models

  10. 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 m-wave data

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