Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Minimizing Estimation Errors of a Wind Velocity Forecasting Technique That Functions as an Early Warning System in the Agricultural Sector

농업기상재해 조기경보시스템의 풍속 예측 기법 개선 연구

  • Received : 2022.02.22
  • Accepted : 2022.05.24
  • Published : 2022.06.30
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Abstract

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

농업기상재해 조기경보시스템에서 모의되는 농장 규모 풍속 예측자료의 추정오차를 개선하기 위해, 농촌진흥청 농업기상관측망의 2020년 1~12월 풍속 관측자료와 해당 지점에 대한 조기경보시스템 모의 풍속을 이용하여, 87지점 일 8시간대(00, 03, 06 … 21시) 각각 풍속 추정오차를 종속변수로, 추정풍속을 독립변수로 하는 일차 회귀식(Y=aX+b)을 도출하였다. 상관계수가 0.5를 초과하였을 때는 회귀식을 풍속 보정식으로 활용하고, 상관계수가 0.5 이하일 때는 회귀식 대신 해당 지점 및 시간대의 ME를 보정값으로 대체하였다. 풍속 모형을 전국적으로 적용할 수 있도록 87지점×8개 시간의 회귀계수 a와 b, 상관계수 R과 ME 값으로 거리역산가중법으로 공간내삽하여 250m 격자해상도의 분포도를 제작하였다. 모형의 검증을 위하여 회귀계수 a와 b, 상관계수 R과 ME 공간내삽 분포도로 부터 농산촌 지역 13개 기상관측지점의 격자값을 추출하고, 13곳의 2019년 1~12월의 조기경보시스템 모의 풍속(00, 03, 06 … 21시)를 보정한 다음, 기존 추정 풍속과 함께 추정오차를 비교하였다. 검증 지점 풍속의 평균 ME는 0.68m/s에서 보정 후 0.45m/s로 감소하였으며, 평균 RMSE는 1.30m/s에서 1.05m/s로 감소하였다. 조기경보시스템의 풍속은 전 시간대에서 모두 과대 추정되고 있는데, 보정 기법을 적용한 후에는 15시 경을 제외하고 모두 과대추정 경향이 감소하여 ME가 약 33%, RMSE는 19.2% 더 개선되었다. 농업기상재해 조기경보시스템에서 농작물의 풍해 위험 판단은 일 8회의 풍속 평균값으로부터 도출된 일 최대순간풍속을 기반으로 하는데, 풍속의 과대모의 현상을 개선하여 강풍 위험 경보의 오보를 감소시킬 것으로 기대된다.

Keywords

Acknowledgement

본 논문은 농촌진흥청 연구사업 신농업기후변화대응체계구축(과제번호: PJ01500703)의 지원에 의해 이루어진 것임.

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