A hybrid system for identifying the presence of clouds on the horizon line

Arkadiusz Rychlik

a:1:{s:5:"en_US";s:15:"UWM w Olsztynie";}

Arkadiusz Olejnik




Abstract

Common passive surveys for the presence of clouds in the sky are carried out using the digital processing of photographs taken using fish-eye type lenses. Another type of survey is carried out using active measuring systems, e.g. thermal imaging, active laser distance sensors, etc. A common feature of these sensors is the high cost of the measuring apparatus, which is hardly acceptable for small local short-term weather forecasting stations. The main task of these weather stations is to forecast local dynamic weather phenomena that have (or may have) an impact on the use or operation of technical facilities (e.g. photovoltaic farms, wind farms, cranes, etc.) or agricultural plant protection measures. This paper proposes a hybrid method for detecting clouds and predicting changes in cloud cover on the horizon line. The proposed method is based on an analysis of colour photographs of the cross-section of the sky using chrominance distribution by the RGB method. In the next step, the CCT colour temperature and the ALS-based ambient light intensity resulting from spectral analysis are determined for the cross-section of the sky being identified. The survey results indicate the dependence of the variability of CCT and ALS parameters for classifying objects on the horizon line. The above analysis makes it possible to distinguish the sky on the horizon, a cloud, or agglomeration elements. The data obtained from the surveys show that the proposed structure of the hybrid system for analysing the presence and movement of clouds on the horizon line can provide the basis for further development of data processing algorithms for a passive sensor of the apparatus for detecting clouds and other objects on the horizon line.


Keywords:

colour detection, passive optical detection, skyline, horizon, spectral characteristics of objects


BAKER M.B., BLYTH A.M., CHRISTIAN H.J., LATHAM J., MILLER K.L., GADIAN A.M. 1999. Relationships between lightning activity and various thundercloud parameters: satellite and modeling studies. Atmospheric Research, 51: 221-236. https://doi.org/10.1016/S0169-8095(99)00009-5   Google Scholar

BARTHE C., DEIERLING W., BARTH M.C. 2010. Estimation of total lightning from various storm parameters: A cloud-resolving model study. Journal of Geophysical Research, 115: D24202. https://doi.org/10.1029/2010JD014405   Google Scholar

BORECKI M., OLEJNIK A., RYCHLIK A., KORWIN-PAWLOWSKI M.L., SZMIDT J. 2019. A passive sensing device for a cloud on the skyline detection. Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, 111763A. https://doi.org/10.1117/12.2536616   Google Scholar

BOUSQUET O., BARBARY D., BIELLI S., KEBIR S., RAYNAUD L., MALARDEL S., FAURE G. 2020. An evaluation of tropical cyclone forecast in the Southwest Indian Ocean basin with AROME-Indian Ocean convection-permitting numerical weather predicting system. Atmospheric Science Letters, 21(3): e950.   Google Scholar

BRABEC M., CRACIUN A., DUMITRESCU A. 2021. Hybrid numerical models for wind speed forecasting. Journal of Atmospheric and Solar-Terrestrial Physics, 220: 105669.   Google Scholar

CEKUS D., GNATOWSKA R., KWIATOŃ P. 2019. Impact of Wind on the Movement of the Load Carried by Rotary Crane. Applied Sciences, 9: 3842. https://doi.org/10.3390/app9183842   Google Scholar

CHEN G., LOMBARDO F.T. 2020. An automated classification method of thunderstorm and non-thunderstorm wind data based on a convolutional neural network. Journal of Wind Engineering & Industrial Aerodynamics, 207: 104407. https://doi.org/10.1016/j.jweia.2020.104407   Google Scholar

CIE 1931 Colour-Matching Functions, 2 Degree Observer. 2019. International Commission on Illumination. https://doi.org/10.25039/CIE.DS.xvudnb9b   Google Scholar

DE TROCH R., HAMDI R., VAN DE VYER H., GELEYN J.F., TERMONIA P. 2013. Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium. Journal of Climate, 26(22): 8895-8915. https://doi.org/10.1175/JCLI-D-12-00844.1   Google Scholar

DOUBRAWA P., BARTHELMIE R.J., WANG H., PRYOR S.C., CHURCHFIELD M.J. 2016. Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements. Remote Sensing, 8: 939. https://doi.org/10.3390/rs8110939   Google Scholar

FIGURSKI M., NYKIEL G., PROFICZ J. 2021. WRF-METEOPG: numerical weather forecast data for Poland - Days 36-42, Year 2021 (1–) [dataset]. Gdańsk University of Technology. https://doi.org/10.34808/9rfy-5b07   Google Scholar

FUMIÈRE Q., DÉQUÉ M., NUISSIER O., SOMOT S., ALIAS A., CAILLAUD C., LAURANTIN O., SEITY Y. 2020. Extreme rainfall in Mediterranean France during the fall: added value of the CNRM-AROME Convection-Permitting Regional Climate Model. Climate Dynamics, 55: 77-91.   Google Scholar

GUTIÉRREZ A., FOVELL R.G. 2018. A new gust parameterization for weather prediction models. Journal of Wind Engineering and Industrial Aerodynamics, 177: 45-59. https://doi.org/10.1016/j.jweia.2018.04.005   Google Scholar

HOLLE R.L. 1987. International cloud atlas. Vol. 2. World Meteorological Organization.   Google Scholar

HUANG G., JIANG Y., PENG L., SOLARI G., LIAO H., LI M. 2019. Characteristics of intense winds in mountain area based on field measurement: Focusing on thunderstorm winds. Journal of Wind Engineering and Industrial Aerodynamics, 190: 166-182. https://doi.org/10.1016/j.jweia.2019.04.020   Google Scholar

KWON D.K., KAREEM A. 2019. Towards codification of thunderstorm/downburst using gust front factor: Model-based and data-driven perspectives. Engineering Structures, 199: 109608. https://doi.org/10.1016/j.engstruct.2019.109608   Google Scholar

MERCER A.E., DYER J.L. 2014. A New Scheme for Daily Peak Wind Gust Prediction Using Machine Learning. Procedia Computer Science, 36: 593-598. https://doi.org/10.1016/j.procs.2014.09.059   Google Scholar

MUGUME I., BASALIRWA C., WAISWA D., NSABAGWA M., NGAILO T.J., REUDER J., SEMUJJU M. 2018. A comparative analysis of the performance of COSMO and WRF models in quantitative rainfall prediction. International Journal of Marine and Environmental Sciences, 12(2): 130-138.   Google Scholar

NARASIMHA R., BHAT G.S. 2008. Recent Experimental and Computational Studies Related to the Fluid Dynamics of Clouds. IUTAM Symposium on Computational Physics and New Perspectives in Turbulence, p. 313–320. https://doi.org/10.1007/978-1-4020-6472-2_48   Google Scholar

OLEJNIK A., BORECKI M., RYCHLIK A. 2019. A sensing device for color immediate detection of medium-distant objects on the horizon. Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 111760N. https://doi.org/10.1117/12.2536753   Google Scholar

PIROOZ A.A.S., FLAY R.G.J., MINOLA L., AZORIN-MOLINA C., CHEN D. 2020. Effects of sensor response and moving average filter duration on maximum wind gust measurements. Journal of Wind Engineering and Industrial Aerodynamics, 206: 104354. https://doi.org/10.1016/j.jweia.2020.104354   Google Scholar

SCHAAR R. 2019. Designing the VEML6040 RGBW Color Sensor Into Applications. Vishay Semiconductors. Document Number: 84331. Retrieved from chrome-extension://efaidnbmnnnibpcaj pcglclefindmkaj/https://www.vishay.com/docs/84331/designingveml6040.pdf   Google Scholar

SHAMEY R., KUEHNI R.G. 2020. Pioneers of Color Science. Springer, Berlin.   Google Scholar

SHU Z.R., LI Q.S., HE Y.C., CHAN P.W. 2017. Vertical wind profiles for typhoon, monsoon and thunderstorm winds. Journal of Wind Engineering and Industrial Aerodynamics, 168: 190-199. https://doi.org/10.1016/j.jweia.2017.06.004   Google Scholar

SIMLEY E., FÜRST H., HAIZMANN F., SCHLIPF D. 2018. Optimizing Lidars for Wind Turbine Control Applications – Results from the IEA Wind Task 32 Workshop. Remote Sensing, 10: 863. https://doi.org/10.3390/rs10060863   Google Scholar

TASZAREK M., KENDZIERSKI S., PILGUJ N. 2020. Hazardous weather affecting European airports: Climatological estimates of situations with limited visibility, thunderstorm, low-level wind shear and snowfall from ERA5. Weather and Climate Extremes, 28: 100243. https://doi.org/10.1016/j.wace.2020.100243   Google Scholar

TERRÉN-SERRANO G., BASHIR A., ESTRADA T., MARTÍNEZ-RAMÓN M. 2021. Girasol, a sky imaging and global solar irradiance dataset. Data in Brief, 35: 106914. https://doi.org/10.1016/j.apenergy. 2021.116656   Google Scholar

WANG H., ZHANG Y.M., MAO J.X., WAN H.P. 2020. A probabilistic approach for short-term prediction of wind gust speed using ensemble learning. Journal of Wind Engineering & Industrial Aerodynamics, 202: 104198.2020. https://doi.org/10.1016/j.jweia.2020.104198   Google Scholar

WILCZAK J.M., GOSSARD E.E., NEFF W.D., EBERHARD W.L. 1996. Ground-based remote sensing of the atmospheric boundary layer: 25 years of progress. Boundary-Layer Meteorology, 78: 321-349. https://doi.org/10.1007/BF00120940   Google Scholar

ZHENG Y., ROSENFELD D., ZHU Y., LI Z. 2019. Satellite-Based Estimation of Cloud Top Radiative Cooling Rate for Marine Stratocumulus. Geophysical Research Letters, 46: 4485–4494. https://doi.org/10.1029/2019GL082094   Google Scholar

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Published
2023-12-19

Cited by

Rychlik, A., & Olejnik, A. (2023). A hybrid system for identifying the presence of clouds on the horizon line. Technical Sciences, 26(26), 249–262. https://doi.org/10.31648/ts.9726

Arkadiusz Rychlik 
a:1:{s:5:"en_US";s:15:"UWM w Olsztynie";}
Arkadiusz Olejnik 




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