Middleton, W.E.K.: Random Reflections on the History of Atmospheric Optics. J. Opt. Soc. Am. 50(2), 97–100 (1960)
Evald, F., Kölling, T., Baumgartner, A., Zinner, T., Mayer, B.: Design and characterization of specMACS, a multipurpose hyperspectral cloud and sky imager. Atmos. Meas. Tech. 9, 2015–2042 (2016)
Shields, J.E., Karr, M.E., Johnson, R.W., Burden, A.R.: Day/night whole sky imagers for 24-h cloud and sky assessment: history and overview. Appl. Opt. 52(8), 1605–1616 (2013)
Zhu, T., Zhou, H., Wei, H., Zhao, X., Zhang, K., Zhang, J.: Inter-hour direct normal irradiance forecast with multiple data types and time-series. J. Mod. Power Syst. Clean Energy 7(5), 1319–1327 (2019)
National Weather Service: National Oceanic and Atmospheric Administration, US Dept. of Commerce: https://www.weather.gov/cle/CWOP. Accessed 20 Nov 2022
Richardson, W., Jr., Krishnaswami, H., Vega, R., Cervantes, M.: A low cost, edge computing, all-sky imager for cloud tracking and intra-hour irradiance forecasting. Sustainability 9, 482 (2017)
Wilkes, T.C., Mcgonigle, A.J.S., Willmott, J.R., Pering, T.D., Cook, J.M.: Low-cost 3D printed 1 nm resolution smartphone sensor-based spectrometer: instrument design and application in ultraviolet spectroscopy. Opt. Lett. 42(21), 4323–426 (2017)
Chang, C.-C., Wu, C.-T., Choi, B.I., Fang, T.-J.: MW-PPG Sensor: an on-Chip Spectrometer Approach. Sensors 19, 3698 (2019)
Snik, F., Rietjens, J.H.H., Apituley, A., Volten, H., Mijling, B., Noia, A.D., Heikamp, S., Heinsbroek, R.C., Hasekamp, O.P., Smit, J.M., Vonk, J., Stam, D.M., Harten, G., Boer, J., Keller, C.U., and 3187 iSPEC citizen scientists: Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters. Geophysical Research Letters 41, 7351–7358 (2014)
Nou, J., Chauvin, R., Eynard, J., Thil, S., Grieu, S.: Towards the intrahour forecasting of direct normal irradiance using sky-imaging data. Heliyon 4, e00598 (2018)
Xu, J., Liu, Z.: Enhanced all-weather precipitable water vapor retrieval from MODIS near-infrared bands using machine learning. Int. J. Appl. Earth Observat. Geoinform. 114, 103050 (2022)
Sigernes, F., MIKKO Syrjäsuo, M., Storvold, R., Fortuna, J., Grøtte, M.E., TOR ARNE Johansen, T.A.: Do it yourself hyperspectral imager for handheld to airborne operations. Opt. Express 26(5), 6021–6035 (2018)
Ohtera, Y.: “Automated NIR spectrometer for the investigation of the correlation between sky spectra and weather parameters,” 13th International Conference on Optics-photonics Design and Fabrication (ODF’ 22), P-OTh-24, Sapporo, August 4th (2022)
Michalsky, J., Beauharnois, M., Berndt, J., Harrison, L., Kiedron, P., Min, Q.: O\(_2\)-O\(_2\) absorption band identification based on optical depth spectra of the visible and near-infrared. Geophys. Res. Lett. 26(11), 1581–1584 (1999)
He, Q., Fang, Z., Shoshanim, O., Brown, S.S., Rudich, Y.: Scattering and absorption cross sections of atmospheric gases in the ultraviolet-visible wavelength range (307–725 nm). Atmos. Chem. Phys. 21, 14927–14940 (2021)
Manago, N.: “Development of measurement and analysis methods for tropospheric aerosol optical properties using solar spectrum”, Doctoral thesis of Chiba University, (January 2012). (https://opac.ll.chiba-u.jp/da/curator/900116318/Manago_Naohiro.pdf). Accessed 10 Jan 2023
Gowen, A.A., Downey, G., Esquerre, C., O’Donnell, C.P.: Use of spectral pre-processing methods to compensate for the presence of packaging film in visible-near infrared hyperspectral images of food products. J. Spectral Imaging 1, a1 (2010)
Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
Stigell, P., Miyata, K., Hauta-Kasari, M.: Wiener estimation method in estimating of spectral reflectance from RGB images. Pattern Recognit. Image Anal. 17(2), 233–242 (2007)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
MathSciNet MATH Google Scholar
Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc. B 58(1), 267–288 (1996)
MathSciNet MATH Google Scholar
Bishop, C.M.: Pattern recognition and Machine learning. Springer, New York (2006)
Middleton, W.E.K.: The color of the overcast sky. J. Opt. Soc. Am. 44(10), 793–798 (1954)
Lee, R.L., Jr.: Measuring overcast colors with all-sky imaging. Appl. Opt. 47(34), H106–H115 (2008)
Lee, R.L., Jr., Hernández-Andrés, J.: Colors of the daytime overcast sky. Appl. Opt. 44(27), 5712–5722 (2005)
Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)
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