The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.
OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/
This project aims at the improvement and testing of a modeling tool which will allow the simulation of impacts of on-going and projected changes in land use/ management on the dynamic exchange of C and N components between diversifying rice cropping systems and the atmosphere and hydrosphere. Model development is based on the modeling framework MOBILE-DNDC. Improvements of the soil biogeochemical submodule will be based on ICON data as well as on results from published studies. To improve simulation of rice growth the model ORYZA will be integrated and tested with own measurements of crop biomass development and transpiration. Model development will be continuously accompanied by uncertainty assessment of parameters. Due to the importance of soil hydrology and lateral transport of water and nutrients for exchange processes we will couple MOBILE-DNDC with the regional hydrological model CMF (SP7). The new framework will be used at field scale to demonstrate proof of concept and to study the importance of lateral transport for expectable small-scale spatial variability of crop production, soil C/N stocks and GHG fluxes. Further application of the coupled model, including scenarios of land use/ land management and climate at a wider regional scale, are scheduled for Phase II of ICON.