Articles | Volume 10, issue 2
Atmos. Meas. Tech., 10, 409–429, 2017
https://doi.org/10.5194/amt-10-409-2017
Atmos. Meas. Tech., 10, 409–429, 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 et al.

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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.
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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.