{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:29:26Z","timestamp":1778171366760,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.<\/jats:p>","DOI":"10.3390\/s24196254","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T03:44:13Z","timestamp":1727408653000},"page":"6254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Temporally Correlated Deep Learning-Based Horizontal Wind-Speed Prediction"],"prefix":"10.3390","volume":"24","author":[{"given":"Lintong","family":"Li","sequence":"first","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"given":"Jose","family":"Escribano-Macias","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"given":"Mingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Air Traffic Management System, Nanjing 210007, China"}]},{"given":"Shenghao","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Air Traffic Management System, Nanjing 210007, China"}]},{"given":"Mingyang","family":"Huang","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6618-1331","authenticated-orcid":false,"given":"Xiangmin","family":"Yang","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5647-7121","authenticated-orcid":false,"given":"Tianyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5530-2503","authenticated-orcid":false,"given":"Yuxiang","family":"Feng","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"given":"Mireille","family":"Elhajj","sequence":"additional","affiliation":[{"name":"Astra-Terra Limited, London HA0 1HD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6332-7858","authenticated-orcid":false,"given":"Arnab","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6778-8264","authenticated-orcid":false,"given":"Panagiotis","family":"Angeloudis","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]},{"given":"Washington","family":"Ochieng","sequence":"additional","affiliation":[{"name":"Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","unstructured":"McKinsey Company (2022). McKinsey\u2019s Global Energy Perspective Is a Collaboration between Energy Insights and Adjacent Practices, McKinsey Company. Technical Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.renene.2003.11.009","article-title":"Support vector machines for wind speed prediction","volume":"29","author":"Mohandes","year":"2004","journal-title":"Renew. Energy"},{"key":"ref_3","first-page":"1","article-title":"A review on weather impact on aviation operations: Visibility, wind, precipitation, icing","volume":"2","author":"Gultepe","year":"2003","journal-title":"J. Airl. Oper. Aviat. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/0376-0421(89)90004-3","article-title":"Effect of wind shear on flight safety","volume":"26","author":"Hahn","year":"1989","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105872","DOI":"10.1016\/j.conengprac.2024.105872","article-title":"Impact of Wind and Wind Shear on Sliding Mode Controller Assisted Aircraft Spin Recovery","volume":"145","author":"Salahudden","year":"2024","journal-title":"Control Eng. Pract."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.1007\/s00704-024-04968-w","article-title":"Aviation accidents related to atmospheric instability in the United States (2000\u20132020)","volume":"155","author":"Nita","year":"2024","journal-title":"Theor. Appl. Climatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/S0960-1481(98)00001-9","article-title":"A neural networks approach for wind speed prediction","volume":"13","author":"Mohandes","year":"1998","journal-title":"Renew. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1016\/j.energy.2010.12.063","article-title":"A corrected hybrid approach for wind speed prediction in Hexi Corridor of China","volume":"36","author":"Guo","year":"2011","journal-title":"Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.apenergy.2010.10.031","article-title":"ARMA based approaches for forecasting the tuple of wind speed and direction","volume":"88","author":"Erdem","year":"2011","journal-title":"Appl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.renene.2008.09.006","article-title":"Day-ahead wind speed forecasting using f-ARIMA models","volume":"34","author":"Kavasseri","year":"2009","journal-title":"Renew. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jweia.2015.02.004","article-title":"An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system","volume":"141","author":"Liu","year":"2015","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2732","DOI":"10.1016\/j.renene.2010.04.022","article-title":"Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA\u2013ANN model","volume":"35","author":"Cadenas","year":"2010","journal-title":"Renew. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.apenergy.2012.04.001","article-title":"Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction","volume":"98","author":"Liu","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"127525","DOI":"10.1016\/j.energy.2023.127525","article-title":"High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM","volume":"275","author":"Zhou","year":"2023","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.renene.2022.09.114","article-title":"A combined short-term wind speed forecasting model based on CNN\u2013RNN and linear regression optimization considering error","volume":"200","author":"Duan","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"125556","DOI":"10.1016\/j.energy.2022.125556","article-title":"A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions","volume":"262","author":"Yu","year":"2023","journal-title":"Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"120904","DOI":"10.1016\/j.energy.2021.120904","article-title":"A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers","volume":"230","author":"Liang","year":"2021","journal-title":"Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113559","DOI":"10.1016\/j.enconman.2020.113559","article-title":"Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history","volume":"227","author":"Chen","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"05011","DOI":"10.1051\/matecconf\/202030905011","article-title":"Multi-time scale wind speed prediction based on WT-bi-LSTM","volume":"Volume 309","author":"Xiang","year":"2020","journal-title":"Proceedings of the MATEC Web of Conferences"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123848","DOI":"10.1016\/j.energy.2022.123848","article-title":"A novel offshore wind farm typhoon wind speed prediction model based on PSO\u2013Bi-LSTM improved by VMD","volume":"251","author":"Li","year":"2022","journal-title":"Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2350","DOI":"10.1016\/j.renene.2006.12.001","article-title":"Application of artificial neural networks for the wind speed prediction of target station using reference stations data","volume":"32","author":"Bilgili","year":"2007","journal-title":"Renew. Energy"},{"key":"ref_23","unstructured":"Kalogirou, S.A., Neocleous, C., Pashiardis, S., and Schizas, C.N. (2022, January 01). Wind Speed Prediction Using Artificial Neural Networks. Available online: https:\/\/www.researchgate.net\/publication\/228967008_Wind_speed_prediction_using_artificial_neural_networks."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wei, C.C., and Chang, H.C. (2021). Forecasting of typhoon-induced wind-wave by using convolutional deep learning on fused data of remote sensing and ground measurements. Sensors, 21.","DOI":"10.3390\/s21155234"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiao, X., Zhang, D., Wang, X., Tian, Y., Liu, W., and Xin, L. (2023). Wind speed prediction based on error compensation. Sensors, 23.","DOI":"10.3390\/s23104905"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tarade, R.S., and Katti, P.K. (2011, January 28\u201330). A comparative analysis for wind speed prediction. Proceedings of the 2011 IEEE International Conference on Energy, Automation and Signal, Bhubaneswar, India.","DOI":"10.1109\/ICEAS.2011.6147167"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vassallo, D., Krishnamurthy, R., Sherman, T., and Fernando, H.J. (2020). Analysis of random forest modeling strategies for multi-step wind speed forecasting. Energies, 13.","DOI":"10.3390\/en13205488"},{"key":"ref_28","unstructured":"Frank, E.H. (2015). Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, Springer."},{"key":"ref_29","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107908","DOI":"10.1016\/j.epsr.2022.107908","article-title":"CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production","volume":"208","author":"Agga","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1093\/bioinformatics\/15.11.937","article-title":"Exploiting the past and the future in protein secondary structure prediction","volume":"15","author":"Baldi","year":"1999","journal-title":"Bioinformatics"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2019, January 9\u201312). The performance of LSTM and BiLSTM in forecasting time series. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1175\/2009JCLI2613.1","article-title":"The effects of SST-induced surface wind speed and direction gradients on midlatitude surface vorticity and divergence","volume":"23","author":"Chelton","year":"2010","journal-title":"J. Clim."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1002\/qj.4474","article-title":"Vertical divergence profiles as detected by two wind-profiler mesonets over East China: Implications for nowcasting convective storms","volume":"149","author":"Guo","year":"2023","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"King, G.P., Portabella, M., Lin, W., and Stoffelen, A. (2022). Correlating extremes in wind divergence with extremes in rain over the tropical Atlantic. Remote Sens., 14.","DOI":"10.3390\/rs14051147"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1175\/1520-0426(1996)013<0948:HDAVVR>2.0.CO;2","article-title":"Horizontal divergence and vertical velocity retrievals from Doppler radar and wind profiler observations","volume":"13","author":"Cifelli","year":"1996","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_37","first-page":"590","article-title":"Impact of wind speed and direction on low cloud cover over Baghdad City","volume":"21","author":"Abbood","year":"2021","journal-title":"Curr. Appl. Sci. Technol."},{"key":"ref_38","unstructured":"Davis, S., and Mader, T.L. (2003). Adjustments for Wind Speed and Solar Radiation to the Temperature-Humidity Index, University of Nebraska."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"201","DOI":"10.2151\/jmsj.2018-025","article-title":"Cloud fractions estimated from shipboard whole-sky camera and ceilometer observations between East Asia and Antarctica","volume":"96","author":"Kuji","year":"2018","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ding, Y., Liu, Q., Lao, P., Li, M., Li, Y., Zheng, Q., and Peng, Y. (2023). Spatial Distributions of Cloud Occurrences in Terms of Volume Fraction as Inferred from CloudSat and CALIPSO. Remote Sens., 15.","DOI":"10.3390\/rs15163978"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.isprsjprs.2021.09.013","article-title":"Towards a novel approach for Sentinel-3 synergistic OLCI\/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation","volume":"181","author":"Alonso","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Frey, R.A., Ackerman, S.A., Holz, R.E., Dutcher, S., and Griffith, Z. (2020). The continuity MODIS-VIIRS cloud mask. Remote Sens., 12.","DOI":"10.3390\/rs12203334"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Spera, D., and Richards, T. (1979, January 19\u201321). Modified power law equations for vertical wind profiles. Proceedings of the Conference and Workshop on Wind Energy Characteristics and Wind Energy Siting, Portland, OR, USA.","DOI":"10.2172\/5946342"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1002\/2016JD025902","article-title":"The power of vertical geolocation of atmospheric profiles from GNSS radio occultation","volume":"122","author":"Steiner","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s11869-020-00822-w","article-title":"Two decades of ozone standard exceedances in Santiago de Chile","volume":"13","author":"Seguel","year":"2020","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11129","DOI":"10.1029\/JD093iD09p11129","article-title":"Intercomparison of total ozone measured by the Brewer and Dobson spectrophotometers at Toronto","volume":"93","author":"Kerr","year":"1988","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1364\/AO.22.000522","article-title":"NASA multipurpose airborne DIAL system and measurements of ozone and aerosol profiles","volume":"22","author":"Browell","year":"1983","journal-title":"Appl. Opt."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"ACH-8","DOI":"10.1029\/2001JD000557","article-title":"Electrochemical concentration cell (ECC) ozonesonde pump efficiency measurements and tests on the sensitivity to ozone of buffered and unbuffered ECC sensor cathode solutions","volume":"107","author":"Johnson","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.5194\/acp-19-3257-2019","article-title":"Trends in global tropospheric ozone inferred from a composite record of TOMS\/OMI\/MLS\/OMPS satellite measurements and the MERRA-2 GMI simulation","volume":"19","author":"Ziemke","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s10236-015-0913-z","article-title":"Monitoring tidal currents with a towed ADCP system","volume":"66","author":"Sentchev","year":"2016","journal-title":"Ocean Dyn."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ravi, S., and D\u2019Odorico, P. (2005). A field-scale analysis of the dependence of wind erosion threshold velocity on air humidity. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL023675"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1800969","DOI":"10.1002\/admi.201800969","article-title":"Organic thin-film capacitive and resistive humidity sensors: A focus review","volume":"5","author":"Najeeb","year":"2018","journal-title":"Adv. Mater. Interfaces"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"445","DOI":"10.5194\/amt-6-445-2013","article-title":"Comparison between MODIS and AIRS\/AMSU satellite-derived surface skin temperatures","volume":"6","author":"Lee","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_54","unstructured":"Yue, Q., Jiang, J.H., Kangaslahti, P., Chien, S., Swope, J., Wu, L., Ogut, M., and Deal, W.R. (February, January 28). Remote Sensing of Vertical Profiles of Clouds and In-cloud Humidity Using a Combined Platform of Radar and Sub-Millimeter Microwave Radiometers. Proceedings of the 104th AMS Annual Meeting, Baltimore, MD, USA."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mitnik, L.M., Mitnik, M.L., Gurvich, I.A., Vykochko, A.V., Pichugin, M.K., and Cherny, I.V. (2012, January 22\u201327). Water vapor, cloud liquid water content and wind speed in tropical, extratropical and polar cyclones over the Northwest Pacific Ocean. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351122"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5827","DOI":"10.3390\/s100605827","article-title":"Using automated point dendrometers to analyze tropical treeline stem growth at Nevado de Colima, Mexico","volume":"10","author":"Biondi","year":"2010","journal-title":"Sensors"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2007JD009766","DOI":"10.1029\/2007JD009766","article-title":"Deriving snow cloud characteristics from CloudSat observations","volume":"113","author":"Liu","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lyzenga, D.R. (2009). Estimation of Ocean Surface Wind Speed and Direction from Polarimetric Radiometry Data, Michigan Univ Ann Arbor Dept of Naval Architecture and Marine Engineering. Technical Report.","DOI":"10.21236\/ADA533831"},{"key":"ref_59","first-page":"31","article-title":"Snow evolution in Sierra Nevada (Spain) from an energy balance model validated with Landsat TM data","volume":"Volume 8174","author":"Herrero","year":"2011","journal-title":"Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII"},{"key":"ref_60","unstructured":"Prasad, D., and Nath, V. (2019, January 29\u201330). An overview of temperature sensors. Proceedings of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017), Ranchi, India."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6254\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:04:34Z","timestamp":1760112274000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":60,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196254"],"URL":"https:\/\/doi.org\/10.3390\/s24196254","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]}}}