Articles | Volume 10, issue 2
https://doi.org/10.5194/amt-10-409-2017
https://doi.org/10.5194/amt-10-409-2017
Research article
 | 
02 Feb 2017
Research article |  | 02 Feb 2017

Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants

Tobias Sirch, Luca Bugliaro, Tobias Zinner, Matthias Möhrlein, and Margarita Vazquez-Navarro

Related authors

Machine learning for improvement of upper-tropospheric relative humidity in ERA5 weather model data
Ziming Wang, Luca Bugliaro, Klaus Gierens, Michaela I. Hegglin, Susanne Rohs, Andreas Petzold, Stefan Kaufmann, and Christiane Voigt
Atmos. Chem. Phys., 25, 2845–2861, https://doi.org/10.5194/acp-25-2845-2025,https://doi.org/10.5194/acp-25-2845-2025, 2025
Short summary
Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights
Lea Volkmer, Tobias Kölling, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6807–6817, https://doi.org/10.5194/amt-17-6807-2024,https://doi.org/10.5194/amt-17-6807-2024, 2024
Short summary
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024,https://doi.org/10.5194/amt-17-6025-2024, 2024
Short summary
Influence of Temperature and Humidity on Contrail Formation Regions in EMAC: A Spring Case Study
Patrick Peter, Sigrun Matthes, Christine Frömming, Patrick Jöckel, Luca Bugliaro, Andreas Giez, Martina Krämer, and Volker Grewe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2142,https://doi.org/10.5194/egusphere-2024-2142, 2024
Short summary
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024,https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025,https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025,https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025,https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025,https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025,https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary

Cited articles

Accadia, C., Mariani, S., Casaioli, M., Lavagnini, A., and Speranza, A.: Sensitivity of Precipitation Forecast Skill Scores to Bilinear Interpolation and a Simple Nearest-Neighbor Average Method on High-Resolution Verification Grids, Weather Forecast., 18, 918–932, https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2, 2003.
Anderson, G., Clough, S., Kneizys, F., Chetwynd, J., and Shettle, E.: AFGL Atmospheric Constituent Profiles (0–120 km), Tech. Rep. AFGL-TR-86-0110, AFGL (OPI), Hanscom AFB, MA 01736, 1986.
Baum, B., Uttal, T., Poellot, M., Ackermann, T., Alvarez, J., Intrieri, J., Starr, D., Titlow, J., Tovinkere, V., and Clothiaux, E.: Satellite remote sensing of multiple cloud layers, J. Atmos. Sci., 52, 4210–4230, 1995.
Baum, B., Heymsfield, A., Yang, P., and Bedka, S.: Bulk scattering models for the remote sensing of ice clouds. Part 1: Microphysical data and models, J. Appl. Meteor., 44, 1885–1895, https://doi.org/10.1175/JAM2308.1, 2005.
Blanc, P., Espinar, B., Geuder, N., Gueymard, C., Meyer, R., Pitz-Paal, R., Reinhardt, B., Renne, D., Sengupta, M., Wald, L., and Wilbert, S.: Direct normal irradiance related definitions and applications: The circumsolar issue, Sol. Energy, 110, 561–577, 2014.
Download
Short summary
A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the geostationary satellite MSG is presented. The basis of the algorithm is an optical flow method to derive cloud motion vectors for low and high level clouds separately. DNI is calculated from the forecasted optical thickness of the clouds. Validation against MSG observations shows good performance: compared to persistence an improvement of forecast horizon by a factor of 2 is reached for 2 h forecasts.
Share