TY - JOUR
T1 - Nowcasting algorithm for wind fields using ensemble forecasting and aircraft flight data
AU - Kikuchi, Ryota
AU - Misaka, Takashi
AU - Obayashi, Shigeru
AU - Inokuchi, Hamaki
AU - Oikawa, Hiroshi
AU - Misumi, Akeo
N1 - Funding Information:
This work was supported by the Japan Society for the Promotion of Science Grant-in-Aid for JSPS Research Fellow (grant number 26-7391). The computation in this research was conducted using an SGI AltixUV2000 computer at the Institute of Fluid Science, Tohoku University. The dataset of flight data was supplied by Japan Airlines Co., Ltd.
Publisher Copyright:
© 2017 Royal Meteorological Society
PY - 2018/7
Y1 - 2018/7
N2 - This study proposes an algorithm that combines ensemble numerical weather-prediction model data and aircraft flight data in a wind nowcasting system for safe and efficient aircraft operation. It uses an ensemble-weighted average method based on sequential importance sampling (SIS), which is a particle filter method for forecasting the wind field in real time. SIS is applied to the ensemble forecast data and control run data of the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP) and United Kingdom Met Office (UKMO) for the two case studies that use flight data from 72 commercial aircraft flights. The results show that SIS can forecast better than the other four methods: direct ensemble average (DEA), elite strategy (ES), and selective ensemble average (SEAV) and weighted average (SEWE), with average improvements in forecast performance of about 10–15%, even at 300 min ahead. In addition, the overall forecast performance between the forecast wind and observation of the radiosonde of SIS was slightly better than DEA. In both cases, the forecast performance was significantly improved on points along the flight path of the aircraft used for this study. Case analyses and the impact of differences in the hyper-parameters of SIS on forecast performance are also presented in this study.
AB - This study proposes an algorithm that combines ensemble numerical weather-prediction model data and aircraft flight data in a wind nowcasting system for safe and efficient aircraft operation. It uses an ensemble-weighted average method based on sequential importance sampling (SIS), which is a particle filter method for forecasting the wind field in real time. SIS is applied to the ensemble forecast data and control run data of the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP) and United Kingdom Met Office (UKMO) for the two case studies that use flight data from 72 commercial aircraft flights. The results show that SIS can forecast better than the other four methods: direct ensemble average (DEA), elite strategy (ES), and selective ensemble average (SEAV) and weighted average (SEWE), with average improvements in forecast performance of about 10–15%, even at 300 min ahead. In addition, the overall forecast performance between the forecast wind and observation of the radiosonde of SIS was slightly better than DEA. In both cases, the forecast performance was significantly improved on points along the flight path of the aircraft used for this study. Case analyses and the impact of differences in the hyper-parameters of SIS on forecast performance are also presented in this study.
KW - TIGGE
KW - aircraft flight data
KW - data assimilation
KW - real-time prediction
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U2 - 10.1002/met.1704
DO - 10.1002/met.1704
M3 - Article
AN - SCOPUS:85036556931
VL - 25
SP - 365
EP - 375
JO - Meteorological Applications
JF - Meteorological Applications
SN - 1350-4827
IS - 3
ER -