Land surface temperature retrieval for Himawari 8 Advanced Himawari Imager (AHI) data
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In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.
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This paper presents an algorithm for retrieving surface precipitation rate for the Advanced Himawari Imager (AHI). AHIs are aboard the Japanese geostationary meteorological satellites Himawari 8 and 9. The algorithm employs the neural network method developed for land and sea separately. The coincidentally overlapping AHI observations and the surface precipitation rates from the AMSU MIT Precipitation (AMP) retrieval products are employed for training neural networks and evaluating retrieval accuracies. Results show that AHI retrievals agree well with AMP surface precipitation rates, where the correlation coefficient is 0.65. The retrievals are useful for all ranges of surface precipitation rate for both land and sea, where those over sea are more accurate. The precipitation retrieval algorithm developed in this study can provide useful retrieved surface precipitation rates every 10 minutes, which are particularly useful for observing convective precipitation and tropical cyclones.
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Himawari-8, a next-generation geostationary meteorological satellite, was successfully launched by the Japanese Meteorological Agency (JMA) on 7 October 2014 and has been in official operation since 7 July 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 channels from 0.47 to 13.3 μm and performs full-disk observations every 10 min. This study describes AHI aerosol optical property (AOP) retrieval based on a multi-channel algorithm using three visible and one near-infrared channels (470, 510, 640, and 860 nm). AOPs were retrieved by obtaining the visible surface reflectance using shortwave infrared (SWIR) data along with normalized difference vegetation index shortwave infrared (NDVISWIR) categories and the minimum reflectance method (MRM). Estimated surface reflectance from SWIR (ESR) tends to be overestimated in urban and cropland areas. Thus, the visible surface reflectance was improved by considering urbanization effects. Ocean surface reflectance is obtained using MRM, while it is from the Cox and Munk method in ESR with the consideration of chlorophyll-a concentration. Based on validation with ground-based sun-photometer measurements from Aerosol Robotic Network (AERONET) data, the error pattern tends to the opposition between MRMver (using MRM reflectance) AOD and ESRver (Using ESR reflectance) AOD over land. To estimate optimal AOD products, two methods were used to merge the data. The final aerosol products and the two surface reflectances were merged, which resulted in higher accuracy AOD values than those retrieved by either individual method. All four AODs shown in this study show accurate diurnal variation compared with AERONET, but the optimum AOD changes depending on observation time.
AERONET
Shortwave
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A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47µm and 0.51µm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET.
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This study developed a retrieval algorithm for reflected shortwave radiation at the top of the atmosphere (RSR). This algorithm is based on Himawari-8/AHI (Advanced Himawari Imager) whose sensor characteristics and observation area are similar to the next-generation Geostationary Korea Multi-Purpose Satellite/Advanced Meteorological Imager (GK-2A/AMI). This algorithm converts the radiance into reflectance for six shortwave channels and retrieves the RSR with a regression coefficient look-up-table according to geometry of the solar-viewing (solar zenith angle, viewing zenith angle, and relative azimuth angle) and atmospheric conditions (surface type and absence/presence of clouds), and removed sun glint with high uncertainty. The regression coefficients were calculated using numerical experiments from the radiative transfer model (SBDART), and ridge regression for broadband albedo at the top of the atmosphere (TOA albedo) and narrowband reflectance considering anisotropy. The retrieved RSR were validated using Terra, Aqua, and S-NPP/CERES data on the 15th day of every month from July 2015 to February 2017. The coefficient of determination (R2) between AHI and CERES for scene analysis was higher than 0.867 and the Bias and root mean square error (RMSE) were −21.34–5.52 and 51.74–59.28 Wm−2. The R2, Bias, and RMSE for the all cases were 0.903, −2.34, and 52.12 Wm−2, respectively.
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The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.
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Moderate-resolution imaging spectroradiometer
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2014年10月に打ち上げられた「ひまわり８号」は，従来より大幅な観測機能の向上が図られた次世代静止気象衛星である．地球全体を10分，日本付近においては2.5分間隔で観測を行い，地球表面温度の日変化を詳細に監視することが出来る．一方，1999年12月に打ち上げられた米国地球観測衛星TERRAに搭載されたセンサASTERは15年にわたる観測実績および検証実験の成果から地表面温度情報に補正するアルゴリズムを確立し，処理したプロダクトを提供している． 「ひまわり８号」が観測する温度情報は大気の影響を受けたもので，従来機においても地球表面温度を推定するため種々補正手法が開発されている．簡単な手法としては，近接した観測波長帯域の観測値から大気の影響を推定し補正するスプリットウィンドウ法が知られている．本研究は同時刻に観測されたASTERデータを参照して「ひまわり８号」データにおける本手法の有効性について検証したもので，得られる地表面温度分布や日変化等は都市の熱環境調査へ応用が期待できる.
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AERONET
Moderate-resolution imaging spectroradiometer
Spectroradiometer
Deep blue
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