Cloud-top pressure data derived from the GOME-2 instruments aboard three MetOp satellites. The German Aerospace Center (DLR) generates these products for EUMETSAT's Atmospheric Chemistry Monitoring program, with near-real-time availability within two hours of sensing. Measurements began with MetOp-A in 2006, followed by MetOp-B in 2012 and MetOp-C in 2018.
Use Cases
- Analyze global cloud-top pressure trends over time using the multi-satellite record from 2006 onward.
- Validate climate model cloud parameterizations with the retrieved cloud fraction and cloud-top albedo data.
- Study atmospheric composition by correlating cloud-top pressure with GOME-2 trace gas measurements like ozone.
- Train neural networks for cloud retrieval using the ROCINN algorithm's methodology on O2-A band reflectivities.
Strengths
- Data continuity from three successive satellite missions spanning over 15 years.
- Near-real-time product availability within two hours of satellite sensing.
- Cloud properties derived using a neural network (ROCINN) trained on full polarization radiative transfer templates.
Limitations
- Specific row counts, spatial resolution, and file sizes are not provided.
- Cloud property accuracy is dependent on the OCRA cloud fraction input and neural network inversion.
Provenance
- Source
- DLR (German Aerospace Center) for EUMETSAT's AC-SAF.
- Collection Method
- Satellite remote sensing via GOME-2 instruments on MetOp-A, -B, -C, processed with OCRA and ROCINN algorithms.
- Time Range
- Operational from 2006 (MetOp-A) to present, with additional satellites launched in 2012 and 2018.
- Freshness
- Near-real-time (within two hours of sensing); operational data stream.
- Geography
- Global