EGU Vienna, Matthias Jerg on behalf of Cloud CCI Deutscher Wetterdienst The ESA Cloud CCI project.

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Presentation transcript:

EGU Vienna, Matthias Jerg on behalf of Cloud CCI Deutscher Wetterdienst The ESA Cloud CCI project Generation of Multi Sensor consistent Cloud Properties with an Optimal Estimation based Retrieval Algorithm

EGU Vienna, Contents Motivation, overview, objectives of the project Algorithm comparison effort: Round Robin Community Retrieval Products, L3 algorithm Validation strategy Summary, outlook

EGU Vienna, Trenberth, K. E., 2009: An imperative for adapting to climate change: Tracking Earth’s global energy. Current Opinion in Environmental Sustainability, 1, DOI /j.cosust Motivation Clouds are … affecting the energy budget a coupling mechanism to hydrological cycle highly variable in space and time easy to observe? … but not fully understood nor modelled.

EGU Vienna, ESA Cloud CCI overview The ultimate objective is to provide long-term coherent cloud property data sets exploiting the synergic capabilities of different Earth observation missions allowing for improved accuracies and enhanced temporal and spatial sampling better than those provided by the single sources. European consortium: Newsletter twice a year

EGU Vienna, The generation of two consistent global data sets for cloud property including uncertainty estimates based on intercalibrated radiances from: 1) AVHRR heritage measurements of MODIS, AATSR, AVHRR 2) Combined AATSR + MERIS measurements with GCOS requirements in mind for Development of a coherent physical retrieval framework for cloud properties as an open community retrieval framework, publicly available and usable by all scientists. Produce multi-year multi-instrument time series. Objectives

EGU Vienna, Round Robin Algorithm Comparison Select most suitable algorithm and develop it into a community retrieval framework, identify areas for further development. Participating algorithms: CM-SAF (CPP+PPS),ORAC,CLAVR-X Apply to AVHRR NOAA18 and MODIS Aqua L1b in AVHRR channels of 5 days in Collocate and compare cloud retrieval results with respect to A-Train observations of CLOUDSAT (CTH, CMa), CALIPSO (CTH, CMa, CPH) and AMSR-E (LWP). CLAVR-XCM-SAFORAC CTH: CLAVR-X MODIS -CLOUDSAT LWP: CM-SAF MODIS-AMSR-E

EGU Vienna, CTH/LWP: MODIS L1b of heritage channels vs. CLOUDSAT/AMSR-E CTH LWP CTH Result of Round Robin: OE retrieval ORAC as basis for future community retrieval scheme for Cloud CCI.

EGU Vienna, Community Retrieval ORAC selected based on retrieval performance and potential of optimal estimation (OE) for further development: Main OE advantageous features: Consistency (sensors, channels) Simultaneity (state space parameters) Uncertainty (derived for state space parameters) Flexibility (additional sensors, number of channels, information content) Main development points presently: Cloud mask improvements. Cloud phase determination. Surface treatment. Processing environment and speed. Development among RAL, University of Oxford, DWD so far but… Available to the community via svn:

EGU Vienna, Final Products L2 and L3 products will be publicly available via ftp server. Product suite COT,CTP,REF,CPH,CWP,CMa L2: pixel based results including uncertainty estimates. L2b: 0.1 deg. daily L2 composite. L3: monthly 0.5 deg. Av., St. Dev.,Median incl. uncert. est., 2D COT-CTP Histograms. Prel. results based on CM-SAF ‘ s AVHRR GAC data for

EGU Vienna, Validation Validation for L2 and L3 data using tools used for RR. References: Ground based (ARM,Cloudnet,synop) and Spaceborne (CALIOP,CPR,AMSRE,AVHRR,TMI,MODIS,MERIS,SEVIRI,ATOVS,AIRS,IASI) as well as established climatologies (ICCP,PATMOSX,CM-SAF).

EGU Vienna, Summary and Outlook ESA Cloud CCI will produce two datasets spanning exploiting synergistic capabilities of different sensors. Optimal Estimation technique employed improving homogeneity and stability of time series. Second Phase of CCI 2013+:further retrieval development and processing of the full AATSR, AVHRR and MODIS time series. Additional project components start soon: Validation over mountaneous and polar areas (Meteo Swiss) Advanced Cloud Screening for AATSR-MERIS (Univ. Valencia) Cloud detection under presence of aerosol with Bayesian approach.(OU,RAL,DWD) 1D Var for SSMI to produce LWP dataset for CCI validation (DWD)