[논문]MODIS 대기자료를 활용한 남북한 기상관측소에서의 냉방도일 추정

유병현; 김광수; 이지혜

doi:10.5532/kjafm.2019.21.2.97

MODIS 대기자료를 활용한 남북한 기상관측소에서의 냉방도일 추정
The use of MODIS atmospheric products to estimate cooling degree days at weather stations in South and North Korea 원문보기

한국농림기상학회지 = Korean Journal of Agricultural and Forest Meteorology, v.21 no.2, 2019년, pp.97 - 109

유병현 (서울대학교 식물생산과학부) , 김광수 (서울대학교 식물생산과학부) , 이지혜 (국가농림기상센터)

초록
AI-Helper

적산 온도는 작물 재배 의사결정 지원을 위해 대상지역 주변 기상 관측소의 자료를 활용하여 산정되어 왔다. 한편 Moderate Resolution Imaging Spectroradiometer (MODIS) 자료로부터 공간적인 온도 자료를 바탕으로 특정 지점의 적산 온도 자료를 생산할 수 있다. 본 연구의 목적은 MODIS 자료를 처리하는 도구를 개발하고 이를 바탕으로 작물의 고온 피해도 및 시설의 냉방 요구도 분석에 활용될 수 있는 냉방도일을 계산하고자 하였다. R 스크립트를 사용하여 특정지역의 MODIS 기온자료를 생성하는 모듈들을 작성하였다. 해당 스크립트들은 격자자료의 좌표계 변환과 자료들의 공간적인 통합 기능들을 가지고 있었다. 온도 수직 분포 자료로부터 지표 기압에 해당하는 온도를 추출하는 기능은 rgdal과 RcppArmadillo등의 패키지를 활용하여 구현되었다. 또한 냉방도일 및 일평균온도 추정을 위해 MODIS 기온 자료, day of year, 및 위도를 입력 자료로 사용하는 random forest (RF) 모형을 남한 지역의 24개 지점에 대하여 훈련하였다. 인공위성 자료 별로 훈련된 RF 모형을 사용하여 한반도 지역의 일별 냉방도일을 계산하였다. 특히, 북한지역에 24개 지점에 대해 검증한 결과, MODIS 자료를 바탕으로 추정된 지역별 평균 연간 냉방도일은 관측값 변이의 96%를 설명할 수 있었다. 이러한 결과는 MODIS 자료로부터 유효적산온도 및 난방도일 등 다른 농림 기상 모형의 입력자료 생산을 지원할 수 있다는 것을 암시하였다.

Abstract ▼ AI-Helper

Degree days have been determined using temperature data measured at nearby weather stations to a site of interest to produce information for supporting decision-making on agricultural production. Alternatively, the data products of Moderate Resolution Imaging Spectroradiometer (MODIS) can be used for estimation of degree days in a given region, e.g., Korean Peninsula. The objective of this study was to develop a simple tool for processing the MODIS product for estimating cooling degree days (CDD), which would help assessment of heat stress conditions for a crop as well as energy requirement for greenhouses. A set of scripts written in R was implemented to obtain temperature profile data for the region of interest. These scripts had functionalities for processing spatial data, which include reprojection, mosaicking, and cropping. A module to extract air temperature at the surface pressure level was also developed using R extension packages such as rgdal and RcppArmadillo. Random forest (RF) models, which estimate mean temperature and CDD with a different set of MODIS data, were trained at 34 sites in South Korea during 2009 - 2018. Then, the values of CDD were calculated over Korean peninsula during the same period using those RF models. It was found that the CDD estimates using the MODIS data explained >74% of the variation in the CDD measurements at the weather stations in North Korea as well as South Korea. These results indicate that temperature data derived from the MODIS atmospheric products would be useful for reliable estimation of CDD. Our results also suggest that the MODIS data can be used for preparation of weather input data for other temperature-based agro-ecological models such as growing degree days or chill units.

주제어

표/그림 (11)

그림 Fig. 1. A map of the weather station sites where observation data were obtained. These sites consist of 34, 169 and 27 stations included in the synoptic observation system (SYNOP_SKOR), the meteorological information service system for disaster prevention (MISS_DP) in South Korea and the synoptic observation network in North Korea (SYNOP_NKOR), respectively.
그림 Fig. 2. Nassi-schneider diagram of MODIS-PIPET that calculates mean temperature using MOD07 level 2 product. RF represents a random forest model to estimate air temperature.
표 Table 1. Options for Random Forest (RF) models and variable importance of RF models
그림 Fig. 3. The proportion of cells where no missing data for the MODIS products were available by (a) month and (b) weather station. The weather station consists of the synoptic observation system (SYNOP_SKOR), the meteorological information service system for disaster prevention (MISS_DP) in South Korea and the synoptic observation network in North Korea (SYNOP_NKOR). MOD, MYD and MCD indicate Terra, Aqua and both Terra and Aqua respectively.
그림 Fig. 4. The training results of random forest models to estimate daily mean temperature (Tmean) using surface air temperature product from (a) Terra (MOD), (b) Aqua (MYD), and (c) both Terra and Aqua (MCD) satellites, respectively. Ground measurements of temperature (OBS) were obtained from 34 synoptic observation stations in South Korea. N indicates the total number of training data that had no missing value for the weather stations during the period of 2009-2018.
그림 Fig. 5. The training results of random forest models to estimate daily cooling degree (CDD) using surface air temperature product from (a) Terra (MOD), (b) Aqua (MYD), and (c) both Terra and Aqua (MCD) satellites, respectively. Ground measurements of temperature (OBS) were obtained from 34 synoptic observation stations in South Korea. N indicates the total number of training data that had no missing value for the weather stations during the period of 2009-2018.
그림 Fig. 6. Probability density of (a) coefficient of determination (R²) and (b) root mean square error (RMSE) of mean temperature estimated from random forest models. R² and RMSE were determined by weather station included in the meteorological information system for disaster prevention (MISS-DP) network in four provinces in South Korea. MOD, MYD and MCD indicate the random forest models of which training data were obtained from Terra, Aqua, and both Terra and Aqua satellites, respectively.
그림 Fig. 7. Probability density of (a) coefficient of determination (R²) and (b) root mean square error (RMSE) for daily cooling degree-days (CDD). The CDD values were calculated using the mean temperature estimated from random forest models. R² and RMSE were determined by weather station included in the meteorological information system for disaster prevention (MISS-DP) network in four provinces in South Korea. MOD, MYD and MCD indicate the random forest models of which training data were obtained from Terra, Aqua, and both Terra and Aqua satellites, respectively.
그림 Fig. 8. Probability density of (a) coefficient of determination (R²) and (b) root mean square error (RMSE) for daily cooling degree-days estimated using random forest models. R² and RMSE were determined by weather station included in the meteorological information system for disaster prevention (MISS-DP) network in four provinces in South Korea. MOD, MYD and MCD indicate the random forest models of which training data were obtained from Terra, Aqua, and both Terra and Aqua satellites, respectively.
그림 Fig. 9. Comparison of cooling degree days (CDD) obtained from weather stations and satellite products in North Korea. The daily values of CDD were compared only for the days when no missing occurred for the satellite data. The daily CDD values were estimated using the random forest model for mean temperature (TMEAN_RF; a, c, and e) and CDD (CDD_RF; b, d, and f), respectively. The random forest models were trained using data from Terra (a and b), Aqua (c and d) and both Terra and Aqua (e and f) satellites.
그림 Fig. 10. Comparison of average cooling degree days (CDD) obtained from weather stations and satellite products in North Korea. The daily values of CDD were summed only for the days when no missing occurred for the satellite data. These cumulative CDD values for each year were averaged for all of the weather stations available in North Korea. The daily CDD values were estimated using the random forest model for mean temperature (a, c, and e) and CDD (b, d, and f), respectively. The random forest models were trained using data from Terra (a and b), Aqua (c and d) and both Terra and Aqua (e and f) satellites.

질의응답

핵심어	질문	논문에서 추출한 답변
	인공위성 자료를 사용할 경우 장점은 무엇인가?	Neteler(2010)은 Moderate Resolution Imaging Spectroradiometer (MODIS) 자료를 활용하여 이탈리아 지역에 대한 작물 생육 추정을 위해 적산 온도 지도를 생산하였다. 특히, 인공위성 자료를 사용할 경우, 지상 관측자료를 확보하기 어려운 지역이나 넓은 지역에 대한 생태계 자료들을 확보할 수 있다(Luus et al., 2016).
	MODIS-PIPET의 기능은 무엇인가?	, 2017). 본 연구에서 개발된 MODIS-PIPET은 swath 형태로 제공되는 일단위의 MODIS 대기 자료로부터 한반도 지역의 자료를 추출 하고, 지상 기온 자료를 생성하는 전처리 과정과 함께, 처리된 대기 온도 자료를 사용하여 지도의 형태로 일평균기온을 출력하는 과정들을 자동화하여 처리하도록 개발되었다.
	MODIS 영상자료는 주로 어떤 좌표계로 제공되는가?	지표면 자료로는 지표 반사도 자료와 이로부터 추정되는 식생지수 등의 자료가 생산 되고 있다. 이러한 자료는 주로 Sinusoidal 좌표계를 가진 타일 형태로 제공된다. 대기 자료로는 에어로졸과 대기 온도 등의 대기 특성을 감시하기 위한 3차원 자료들이 생산되고 있다.

참고문헌 (22)

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Neteler, M., 2010: Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote sensing 2(1), 333-351.

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