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http://dx.doi.org/10.5532/KJAFM.2021.23.4.415

A Statistical Correction of Point Time Series Data of the NCAM-LAMP Medium-range Prediction System Using Support Vector Machine  

Kwon, Su-Young (National Center for AgroMeteorology)
Lee, Seung-Jae (National Center for AgroMeteorology)
Kim, Man-Il (National Forestry Cooperative Federation)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.23, no.4, 2021 , pp. 415-423 More about this Journal
Abstract
Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.
Keywords
Support Vector Machine; LAMP; Statistical correction;
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