{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T22:55:54Z","timestamp":1779144954307,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hainan","award":["2019RC165"],"award-info":[{"award-number":["2019RC165"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["SCSF201905"],"award-info":[{"award-number":["SCSF201905"]}]},{"name":"Young Talents Science and Technology Innovation Project of Hainan Association for Science and Technology","award":["QCXM202011"],"award-info":[{"award-number":["QCXM202011"]}]},{"DOI":"10.13039\/501100013142","name":"Key Research and Development Project of Hainan Province","doi-asserted-by":"publisher","award":["ZDYF2020041"],"award-info":[{"award-number":["ZDYF2020041"]}],"id":[{"id":"10.13039\/501100013142","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32060413"],"award-info":[{"award-number":["32060413"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Achieving high-performance numerical weather prediction (NWP) is important for people\u2019s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.<\/jats:p>","DOI":"10.3390\/s22010193","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T02:31:27Z","timestamp":1640745087000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method"],"prefix":"10.3390","volume":"22","author":[{"given":"Rongnian","family":"Tang","sequence":"first","affiliation":[{"name":"Electrical and Mechanical College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuke","family":"Ning","sequence":"additional","affiliation":[{"name":"Electrical and Mechanical College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuang","family":"Li","sequence":"additional","affiliation":[{"name":"Electrical and Mechanical College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Feng","sequence":"additional","affiliation":[{"name":"Hainan Meteorological Observatory, Haikou 570203, China"},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570203, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youlong","family":"Chen","sequence":"additional","affiliation":[{"name":"Hainan Meteorological Observatory, Haikou 570203, China"},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570203, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Xie","sequence":"additional","affiliation":[{"name":"Electrical and Mechanical College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s00376-007-1099-1","article-title":"Numerical weather prediction in China in the new century\u2014Progress, problems and prospects","volume":"24","author":"Xue","year":"2007","journal-title":"Adv. Atmos. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s00376-013-2274-1","article-title":"A forecast error correction method in numerical weather prediction by using recent multiple-time evolution data","volume":"30","author":"Xue","year":"2013","journal-title":"Adv. Atmos. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s00376-019-9023-z","article-title":"A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area","volume":"36","author":"Li","year":"2019","journal-title":"Adv. Atmos. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00376-020-0165-9","article-title":"Determination of Surface Precipitation Type Based on the Data Fusion Approach","volume":"38","author":"Kolendowicz","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kuni\u0107, Z., \u017denko, B., and Boshkoska, B.M. (2021). FOCUSED\u2013Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems. Sensors, 21.","DOI":"10.3390\/s21103405"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, L., Xu, Y., Yan, L., Wang, S., Liu, G., and Liu, F. (2020). A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model. Sensors, 20.","DOI":"10.3390\/s20113167"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, J., and Schmit, T.J. (2020). The impact of low latency satellite sounder observations on local severe storm forecasts in regional NWP. Sensors, 20.","DOI":"10.3390\/s20030650"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1002\/met.12","article-title":"Correction and downscaling of NWP wind speed forecasts","volume":"14","author":"Howard","year":"2010","journal-title":"Meteorol. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1109\/TGRS.2011.2180730","article-title":"Improved ASCAT Wind Retrieval Using NWP Ocean Calibration","volume":"50","author":"Verspeek","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dong, L., Ren, L., Gao, S., Gao, Y., and Liao, X. (2013, January 25\u201327). Studies on wind farms ultra-short term NWP wind speed correction methods. Proceedings of the 2013 25th Chinese Control and Decision Conference (CCDC), Guiyang, China.","DOI":"10.1109\/CCDC.2013.6561180"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1002\/qj.56","article-title":"Adaptive bias correction for satellite data in a numerical weather prediction system","volume":"133","author":"McNally","year":"2007","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, B., Lu, J., Yan, Z., Luo, H., Li, T., Zheng, Y., and Zhang, G. (2018, January 19\u201323). Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3292500.3330704"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.egypro.2014.10.089","article-title":"A Neural Network Post-processing Approach to Improving NWP Solar Radiation Forecasts","volume":"57","author":"Lauret","year":"2014","journal-title":"Energy Procedia"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1063\/1.4950972","article-title":"Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting","volume":"8","author":"Liu","year":"2016","journal-title":"J. Renew. Sustain. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1109\/TII.2016.2543004","article-title":"Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power Forecasts","volume":"12","author":"Buhan","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"McCandless, T., and Jim\u00e9nez, P.A. (2020). Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting. Energies, 13.","DOI":"10.3390\/en13071671"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1109\/TSTE.2018.2880615","article-title":"Ensemble Machine Learning-Based Wind Forecasting to Combine NWP Output With Data From Weather Station","volume":"10","author":"Du","year":"2019","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yanyan, K., Haochen, L., Jiangjiang, X., and Yingxin, Z. (2020, January 4\u20138). Post-processing for NWP Outputs Based on Machine Learning for 2022 Winter Olympics Games over Complex Terrain, EGU General Assembly 2020. Proceedings of the EGU General Assembly Conference, Online.","DOI":"10.5194\/egusphere-egu2020-10463"},{"key":"ref_19","first-page":"45","article-title":"Spatial and temporal analysis of land surface temperature change on new britain island","volume":"17","author":"Devi","year":"2020","journal-title":"Int. J. Remote Sens. Earth Sci. (IJReSES)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.rser.2019.04.059","article-title":"Wind energy potential analysis using Sentinel\u22121 satellite: A review and a case study on Mediterranean islands","volume":"109","author":"Nezhad","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_21","first-page":"118","article-title":"Jeju Island climate modeling using multiple linear regression analysis","volume":"13","author":"Kim","year":"2019","journal-title":"J. Anal. Appl. Math."},{"key":"ref_22","first-page":"97","article-title":"Effect of global climate (ENSO) on regional climate (rainfall and air temperature) in the Morotai Island region","volume":"12","author":"Muksin","year":"2020","journal-title":"Adv. Environ. Sci."},{"key":"ref_23","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhong, J., Zhang, X., Gui, K., Wang, Y., Che, H., Shen, X., and Zhang, W. (2021). Robust prediction of hourly PM2.5 from meteorological data using LightGBM. Natl. Sci. Rev.","DOI":"10.1093\/nsr\/nwaa307"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mousavi, S.M., and Beroza, G.C. (2019). Bayesian-deep-learning estimation of earthquake location from single-station observations. arXiv.","DOI":"10.1109\/TGRS.2020.2988770"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:54:54Z","timestamp":1760169294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,28]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010193"],"URL":"https:\/\/doi.org\/10.3390\/s22010193","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,28]]}}}