[논문]고해상도 규모상세화모델 KMAPP의 농업지역 기온 및 일사량 예측 성능: 맑은 날 철원 및 전북 사례 연구

신설은; 이승재; 노일석; 김수현; 소윤영; 이서연; 민병훈; 김규랑

doi:10.5532/kjafm.2020.22.4.312

고해상도 규모상세화모델 KMAPP의 농업지역 기온 및 일사량 예측 성능: 맑은 날 철원 및 전북 사례 연구
Temperature and Solar Radiation Prediction Performance of High-resolution KMAPP Model in Agricultural Areas: Clear Sky Case Studies in Cheorwon and Jeonbuk Province 원문보기

한국농림기상학회지 = Korean Journal of Agricultural and Forest Meteorology, v.22 no.4, 2020년, pp.312 - 326

신설은 (국가농림기상센터) , 이승재 (국가농림기상센터) , 노일석 (국가농림기상센터) , 김수현 (국가농림기상센터) , 소윤영 (국가농림기상센터) , 이서연 (국가농림기상센터) , 민병훈 (국립기상과학원) , 김규랑 (국립기상과학원)

초록
AI-Helper

KMAPP은 규모상세화 과정을 통해 100 m 단위의 초고해상도 기상 예측을 산출하는 체계로써 최근 수문, 농업, 신재생에너지 등 다양한 분야에서 활용되기 시작됨에 따라 각 분야별로 예측성능을 검증할 필요가 있다. 철원 지역과 전북 지역은 산지가 많은 우리나라에서 비교적 넓은 범위에 걸쳐서 수평면을 보유하고 있으며, 특히 철원은 대규모 벼 논 재배지역 중에서 실측 및 원격탐사 생물계절 자료가 많은 지역으로 KMAPP 예측 성능을 검증하는데 필요한 관측자료를 사용하기에 적절한 지점으로 판단된다. 이번 연구에서는 철원 내 농경지역의 생태적 변화에 따라 변화하는 KMAPP 기온 예측 성능을 AWS와 ASOS 관측자료를 이용하여 비교 검증하였다. 그리고 전북지역 폭염 기간 동안 가축 고온스트레스 모델과 같은 응용모델에 KMAPP 예측 자료를 입력자료로 활용하는 것을 검토하고자 일사량 예측을 ASOS 자료를 이용하여 검증하였다. 더 많은 사례의 수집과 선정이 필요하다는 한계가 있지만 농경지역에서 추수 후 기온 예측 성능이 일반 주택지 에서보다 더 크게 향상된 것을 통해 생물리적 효과가 예측 정확도에 미치는 영향을 간접적으로 추측해 볼 수 있었다. 한편, 일사량 예측의 경우 단위 변환에 따른 오차가 발생하지만 관측값과 일치하는 경향을 보여 KMAPP 자료가 지역규모의 상세 예측 자료로 응용모델에 활용될 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.

주제어

표/그림 (17)

그림 Fig. 1. Korea Meteorological Administration(KMA) AWS sites in Cheorwon (blue dots in A) and Jeonbuk Province (red dots in B). An AWS site (blue dot in panel A) is almost collocated with CRK site (refer to Park et al. (2018)).
표 Table 1. Locations of KMA observation sites in Cheorwon
표 Table 2. Locations of KMA observation sites in North Jeolla Province
표 Table 3. General work schedule in rice paddy in Korea (Rural Development Administration)
그림 Fig. 2. Agricultural work schedule and growth status in 2019 at NCAM Cheorwon rice paddy site (CRK).
그림 Fig. 3. Spatial distribution of satellite leaf area index (gray color) in Cheorwon area around (a) August 5 (before harvest), (b) September 25, and (c) October 30, 2020. White dots indicate inland water mass or missing data.
그림 Fig. 4. Local variation of MODIS satellite leaf area index around CRK (red balloon) site in 2019. White dots indicate missing data. Monthly variation of LAI was computed near Jan. 25., Feb. 20, Mar. 30, Apr. 23, May 25, Jun. 26, Jul. 28, Aug. 29, Sep. 30, Oct. 24, Nov. 25, and Dec. 27, 2019). Each panel corresponds to the white dotted box in Fig. 1.
그림 Fig. 5. Measured LAI as a function of day of year at CRK in Cheorwon area for three years (2015-2017) (Huang et al. 2018). The blue, orange, and gray dots denote 2015, 2016, and 2017 data, respectively.
그림 Fig. 6. The satellite image composites from the PlanetScope of Planet labs over the Cherowon-Jangheung AWS station (47095) (upper panels: 25 Aug. 2019, lower panels: 30 Sep. 2019).
그림 Fig. 7. Visible satellite image of GK2A for 00 UTC 03 Aug. 2019 (left) and VDAPS analysis at 200 hPa (right). Images are from KMA.
그림 Fig. 8. ASOS temperature in Gochang and Gochang-gun observation sites.
표 Table 4. Description of variables in hourly KMAPP dataset
그림 Fig. 9. Scatter plots of KMAPP temperature forecasts against the observed values at Cheorwon AWS (left) and NCAM CRK site (right) for 25~26 Aug. 2019 (upper) case and 29~30 Sep. 2019 case (lower).
그림 Fig. 10. Scatter plots of KMAPP temperature forecasts against the observed values at Cheorwon-Jangheung AWS (left) and NCAM site (right) for 25~26 Aug. 2019 case (upper) and 29~30 Sep. 2019 case (lower).
표 Table 5. Statistics of KMAPP predictions of temperature at Cheorwon sites (Figs. 9-10)
그림 Fig. 11. Scatter plots of KMAPP forecasts of solar radiation against the observed values at ASOS in Jeonbook areas; Sunchang, Jeonju, Gochang-gun, Gochang (Clockwise) for the 02~03 Aug. 2019 case.
표 Table 6. Statistics of KMAPP predictions of solar radiation in Jeonbook areas (Fig. 11)

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