Enhancing CAMS Radiation Service with APOLLO_NG Cloud Retrieval

1. APOLLO_NG A new cloud retrieval for the CAMS radiation service Niels Killius1, Lars Klüser1, Shengyin Li1, Marion Schroedter-Homscheidt1, Philippe Blanc2 1German Aerospace Center (DLR), Wessling, Germany 2MINES Paristech / Armines, Sophia-Antipolis, France

2. Outline • Copernicus atmospheric monitoring service (CAMS) and its radiation service • APOLLO/APOLLO_NG • How can APOLLO_NG improve CAMS radiation service?

3. CAMS and its radiation service • CAMS services: Air quality & composition, climate forcing, eimissions & surface fluxes, … • CAMS radiation service: provides UV information, solar radiation (clear sky + total sky) • Free of charge + well documented / easy to recompute

4. How to compute solar radiation – Heliosat-4 method

5. Heliosat-4 cloud properties • APOLLO (Avhrr Processing scheme Over Land, cLouds and Ocean, Saunders & Kriebel, 1983)  uses AVHRR heritage channels (0.6, 0.8, 1.6/3.9, 10.8, 12µm) • Five different cloud tests (Dynamic visible test, infrared gross temperature test, shortwave reflectance ratio, spatial coherence test, brightness temperature difference) • Cloud mask decision: bit adding scheme of fixed thresholds

6. APOLLO_NG: a probabilistic cloud scheme • Uses the same five cloud tests as APOLLO • Probability instead of binary threshold • Start cloud probability is subsequently updated with cloud test results

7. Which cloud probability threshold fits best to the old world? • Count number of pixels for which APOLLO and APOLLO_NG agree in cloud/no cloud decision • Number of agreeing pixels is highest for an APOLLO_NG cloud probability threshold of 40% in most cases.

8. Comparison APOLLO_NG vs GFSC cloud product • Three golden days out of Cloud Retrieval Evaluation Workshops (CREW) dataset: 13th June, 22nd June and 3rd July 2008

9. Comparison: cold desert surface 15. January 2008 08:00 UTC RGB composite Cloud mask comparison pthresh = 40%

10. Comparison with ground measurements • APOLLO_NG vs original APOLLO cloud properties as HS-4 input • Comparison with ground measurements (Plataforma solar de Almería(PSA) & BSRN stations IZA, LIN, PAY, CAM, CAR, SBO, TAM)

11. References • Hoyer-Klick, C., Lefèvre, M., Schroedter-Homscheidt, M., Wald, L., USER’S GUIDE to the MACC-RAD Services on solar energy radiation resources, MACC III project report D57.5, version v4.0, public report accessible via http://www.atmosphere.copernicus.eu, 2015 • Klüser, L., Killius, N., & Gesell, G. (2015). APOLLO_NG–a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels. Atmospheric Measurement Techniques, 8(10), 4155-4170. • König-Langlo, G. , Sieger, R. , Schmithüsen, H. , Bücker, A. , Richter, F. and Dutton E.G. 2013The Baseline Surface Radiation Network and its World Radiation Monitoring Centre at the Alfred Wegener Institute. • Qu, Z., Oumbe, A., Blanc, P., Espinar, B., Gschwind, G., Lefèvre, M., ... & Klueser, L. (2016). Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat-4 method. Meteorol. Z. • Roebeling, R., B. Baum, R. Bennartz, U. Hamann, A. Heidinger, A. Thoss, and A. Walther (2012): Evaluating and Improving Cloud Parameter Retrievals. Bulletin of the American Meteorological Society, 94, ES41–ES44, http://journals.ametsoc.org/doi/full/10.1175/BAMS-D-12-00041.1 • Saunders, R. W., & Kriebel, K. T. (1988). An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9(1), 123-150.

12. Thank you