• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.4
• /
• pp.105-113
• /
• 2016

Fire radiative power(FRP), which means the power radiated from wildfire, is used to estimate fire emissions. Currently, the geostationary satellites of East Asia do not provide official FRP products yet, whereas the American and European geostationary satellites are providing near-real-time FRP products for Europe, Africa and America. This paper describes the first retrieval of Himawari-8 FRP using the mid-infrared radiance method and shows the comparisons with MODIS FRP for Sumatra, Indonesia. Land surface emissivity, an essential parameter for mid-infrared radiance method, was calculated using NDVI(normalized difference vegetation index) and FVC(fraction of vegetation coverage) according to land cover types. Also, the sensor coefficient for Himawari-8(a = 3.11) was derived through optimization experiments. The mean absolute percentage difference was about 20%, which can be interpreted as a favourable performance similar to the validation statistics of the American and European satellites. The retrieval accuracies of Himawari FRP were rarely influenced by land cover types or solar zenith angle, but parts of the pixels showed somewhat low accuracies according to the fire size and viewing zenith angle. This study will contribute to estimation of wildfire emissions and can be a reference for the FRP retrieval of current and forthcoming geostationary satellites in East Asia.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.1
• /
• pp.28-36
• /
• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.1101-1117
• /
• 2018

The clear sky radiance (CSR) is one of the baseline products of the Himawari-8 which was launched on October, 2014. The CSR contributes to numerical weather prediction (NWP) accuracy through the data assimilation; especially water vapor channel CSR has good impact on the forecast in high level atmosphere. The focus of this study is the quality analysis of the CSR of the Himawari-8 geostationary satellite. We used the operational CSR (or clear sky brightness temperature) products in JMA (Japan Meteorological Agency) as observation data; for a background field, we employed the CSR simulated using the Radiative Transfer for TOVS (RTTOV) with the atmospheric state from the global model of KMA (Korea Meteorological Administration). We investigated data characteristics and analyzed observation minus background statistics of each channel with respect to regional and seasonal variability. Overall results for the analysis period showed that the water vapor channels (6.2, 6.9, and $7.3{\mu}m$) had a positive mean bias where as the window channels(10.4, 11.2, and $12.4{\mu}m$) had a negative mean bias. The magnitude of biases and Uncertainty result varied with the regional and the seasonal conditions, thus these should be taken into account when using CSR data. This study is helpful for the pre-processing of Himawari-8/Advanced Himawari Imager (AHI) CSR data assimilation. Furthermore, this study also can contribute to preparing for the utilization of products from the Geo-Kompsat-2A (GK-2A), which will be launched in 2018 by the National Meteorological Satellite Center (NMSC) of KMA.

• Korean Journal of Remote Sensing
• /
• v.33 no.6_1
• /
• pp.1029-1040
• /
• 2017

Wildfires release a large amount of greenhouse gases (GHGs) into the atmosphere. Fire radiative power (FRP) data obtained from geostationary satellites can play an important role for tracing the GHGs. This paper describes an estimation of the Himawari-8 FRP and fire emissions for Samcheock and Gangnueng wildfire in 6 May 2017. The FRP estimated using Himawari-8 well represented the temporal variability of the fire intensity, which cannot be captured by MODIS (Moderate Resolution Imaging Spectroradiometer) because of its limited temporal resolution. Fire emissions calculated from the Himwari-8 FRP showed a very similar time-series pattern compared with the AirKorea observations, but 1 to 3 hour's time-lag existed because of the distance between the station and the wildfire location. The estimated emissions were also compared with those of a previous study which analyzed fire damages using high-resolution images. They almost coincided with 12% difference for Samcheock and 2% difference for Gangneung, demonstrating a reliability of the estimation of fire emissions using our Himawari-8 FRP without high-resolution images. This study can be a reference for estimating fire emissions using the current and forthcoming geostationary satellites in East Asia and can contribute to improving accuracy of meteorological products such as AOD (aerosol optical depth).

• Proceedings of the KSRS Conference
• /
• 2006.03a
• /
• pp.337-340
• /
• 2006

태풍의 경우, 주요 자연재해 중의 하나로 태풍의 상황을 정확하게 파악하는 것은 기상예측의 정도를 높이고, 재해를 방지하는데 중요한 역할을 할 수 있다. 일반적으로 태풍의 동향을 감시하는데 있어, 히마와리 등의 기상위성이 주로 활용되고 있다. 근년 인공위성의 원격탐사를 이용하여 광범위의 해양에 대한 해상풍과 파랑의 관측이 가능하게 되었다. 본 연구에서는, QuickSCAT위성에 의한 해상풍 관측의 현상을 조사하고, 위성으로부터 얻어진 2000년의 데이터를 사용해서 한반도 주변해역에 대한 해상풍의 월변동 특성을 조사하고, 2000년 7월에 한반도에 영향을 준 태풍 카이탁내의 해상풍을 검토하였다. 추가로 RSMC 동경 태풍 센터에서 발행하는 태풍자료를 이용하여, 태풍 비교를 수행하였다. 풍속은 제주도 주변해역, 특히 제주도 동쪽해역에서의 풍속이 연중 강하며, 9월에서 2월 기간에는 북풍 계열의 바람이 우세하고, 6월-8월에는 남풍계열의 바람이 지배적이다. 봄의 기간인 3월-5월에는 북풍에서 남풍으로 바뀌는 과정으로 다양한 방향의 바람이 혼재한다. 태풍 카이탁의 해상풍 조사를 통하여, 위험반원의 형상이 보다 복잡하며 그 범위가 크다는 점이 확인되었다.

• Korean Journal of Remote Sensing
• /
• v.32 no.6
• /
• pp.681-691
• /
• 2016

Using various satellite measurements in UV, visible and IR, diverse algorithms to retrieve aerosol information have been developed and operated to date. Advanced Himawari Imager (AHI) onboard the Himawari 8 weather satellite was launched in 2014 and has 16 channels from visible to Thermal InfRared (TIR) in high temporal and spatial resolution. Using AHI, it is very valuable to retrieve aerosol optical properties over dark surface to demonstrate its capability. To retrieve aerosol optical properties using visible and Near InfRared (NIR) region, surface signal is very important to be removed which can be estimated using minimum reflectivity method. The estimated surface reflectance is then used to retrieve the aerosol optical properties through the inversion process. In this study, we retrieve the aerosol optical properties over dark surface, but not over bright surface such as clouds, desert and so on. Therefore, the bright surface was detected and masked using various infrared channels of AHI and spatial heterogeneity, Brightness Temperature Difference (BTD), etc. The retrieval result shows the correlation coefficient of 0.7 against AERONET, and the within the Expected Error (EE) of 49%. It is accurately retrieved even for low Aerosol Optical Depth (AOD). However, AOD tends to be underestimated over the Beijing Hefei area, where the surface reflectance using the minimum reflectance method is overestimated than the actual surface reflectance.

• Journal of the Korean earth science society
• /
• v.39 no.2
• /
• pp.119-130
• /
• 2018

Aerosol Optical Properties (AOPs) are retrieved using the geostationary satellite instruments such as Geostationary Ocean Color Imager (GOCI), Meteorological Imager (MI), and Advanced Himawari Imager (AHI) through Yonsei AErosol Retrieval algorithm (YAER). In this study, the retrieved aerosol optical depths (AOD)s from each instrument were intercompared and validated with the ground-based sunphotometer AErosol Robotic NETwork (AERONET) data. As a result, the four AOD products derived from different instruments showed consistent results over land and ocean. However, AODs from MI and GOCI tend to be overestimated due to cloud contamination. According to the comparison results with AERONET, the percentage within expected errors (EE) are 36.3, 48.4, 56.6, and 68.2% for MI, GOCI, AHI-minimum reflectivity method (MRM), and AHI-estimated surface reflectance from shortwave Infrared (ESR) product, respectively. Since MI AOD is retrieved from a single visible channel, and adopts only one aerosol type by season, EE is relatively lower than other products. On the other hand, the AHI ESR is more accurate than the minimum reflectance method as used by GOCI, MI, and AHI MRM method in May and June when the vegetation is relatively abundant. These results are explained by the RMSE and the EE for each AERONET site. The ESR method result show to be better than the other satellite product in terms of EE for 15 out of 22 sites used for validation, and they are better than the other product for 13 sites in terms of RMSE. In addition, the error in observation time in each product is found by using characteristics of geostationary satellites. The absolute median biases at 00 to 06 Universal Time Coordinated (UTC) are 0.05, 0.09, 0.18, 0.18, 0.14, 0.09, and 0.10. The absolute median bias by observation time has appeared in MI and the only 00 UTC appeared in GOCI.