Himawari-8 Advanced Himawari Imager Data Research Articles

Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) × 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 × 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 × 81 × 8) AOD values in all the time series samples, with filled values (−9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within ±0.05+15%∗AODsites for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within ±0.05+15%∗AODsites. The Ångström exponent (AE) derived from the multi-wavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network independent training and evaluation split to confirm the leave-one-station-out validation accuracy. Despite this, future study will focus on more spatially independent validation and more comprehensive comparison between the MAIAC and TMAT results.

Spatiotemporal distributions of cloud properties over China based on Himawari-8 advanced Himawari imager data

Abstract Using the Himawari-8 cloud product, this study investigated the seasonal, monthly, diurnal variations as well as spatial and vertical distributions of cloud properties over four typical regions, which are northwest (NW), north (N), southeast (SE) China and Tibetan Plateau (TP). The results show that the cloud occurs most frequently in spring and summer, and the seasonal average cloud top height (CTH) has clear seasonal differences while the cloud top temperature (CTT) is negatively correlated with CTH. A unimodal structure is found for the vertical distribution of occurrence frequency of CTH over SE and TP. By dividing clouds into four types, they show large differences in the probability of occurrence. The study also shows that the monthly cloud fraction (CF) is larger in summer and less in winter except in SE, the monthly CTH changes are relatively small in SE and TP, while the monthly CTT variation is the largest in TP. The total CF increases by 22%, 8%, 3%, and 16% from morning to afternoon in four study areas, respectively. The proportion of four types of cloud families has weak diurnal variation except over TP. In TP, the proportions of high cloud (HC) and low cloud (LC) have a diurnal variation of 30% and 22%, respectively. Note that the relative CF contribution (RCFC) of LC reaches the maximum values in the morning. With the enhancement of convection, the RCFC of DCC and HC increases rapidly. Associated with more active convections, the diurnal variations of CTH and CTT are larger in TP than those in other regions. The clouds in SE have a larger COT and smaller CTH than in other regions, implying more sufficient water supply with low clouds.

Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

Downward shortwave radiation (RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

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.

Development of GK-2A AMI Aerosol Detection Algorithm in the East-Asia Region Using Himawari-8 AHI Data

The GEO-KOMPSAT 2A (GK-2A) satellite is a next-generation geostationary meteorological satellite with multi-wavelength channels. The GK-2A Advanced Meteorological Imager (AMI) aerosol detection production (ADP) algorithm can detect aerosol types (e.g., dust, haze, volcanic ash), using visible and infrared channels. This algorithm is not employed in existing meteorological satellites and can detect four different aerosol types in the daytime and three at night. Here, cloud and aerosol detection were performed using Advanced Himawari Imager (AHI) data with channels similar to those of simulation data. Comparing the results with the Communication, Ocean and Meteorological Satellite (COMS) aerosol index (AI) and AHI RGB aerosol data revealed generally similar dust classification and excellent daytime haze and nighttime aerosol detection performances. Regarding detection of haze, dust, and undefined aerosols generated across East Asia, a percent correct (PC) of 0.76, probability of detection (POD) of 68%, and false alarm rate (FAR) of 33% were obtained for GK-2A ADP of dust when validated with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Vertical Feature Mask (VFM) aerosol type data. Furthermore, the detection performance was ≥87% for haze and undefined aerosols, and the FAR was <30%. When Moderate Resolution Imaging Spectroradiometer (MODIS) L2 aerosol optical depth (AOD) data were used for validation (with and without AOD), the POD was 0.66 (ocean) and 0.87 (land), while the FAR was 0.29 (ocean) and 0.35 (land). Thus, aerosol detection performances of GK-2A ADP are similar to those of existing satellites and superior to satellites that use limited optical channels.

Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

Retrieval of outgoing longwave radiation at top-of-atmosphere using Himawari-8 AHI data

This study proposes an algorithm to retrieve the outgoing longwave radiation at the top of the atmosphere (TOA OLR) using the advanced Himawari imager (AHI) unit that is a narrowband sensor installed in Himawari-8, a geostationary satellite providing window and water vapor imagery. The outgoing longwave radiation at the top of the atmosphere (TOA OLR) was retrieved by an empirical method where a radiative transfer model (RTM) was used to simulate various atmospheric conditions, such as the surface temperature, water vapor, and cloud characteristics (e.g., cloud optical thickness and cloud height). The results of an algorithm using a single channel (OLR12.4) and two channels (OLR6.2+12.4) were converted into Terra Cloud and Earth's Radiant Energy System (CERES) TOA OLR for the daytime (0105–0135 UTC) and nighttime (1325–1355 UTC) cases of a scene analysis (typhoon Goni on August 18, 2015). Long-term analysis (August 2015–July 2016) and statistical analysis (i.e., mean, bias, root mean square error (RMSE), and correlation coefficient) were conducted. The analysis results showed that the largest error was produced in the cloudy areas (overcast areas), and the minimum RMSE and a high correlation coefficient were observed over an ocean area. In the scene analysis, however, the daytime and nighttime average RMSE of the CERES TOA OLR and OLR6.2+12.4 was 12.21Wm−2 and showed the correlation coefficient of 0.971. It was an improved result over the RMSE (12.33Wm−2) and correlation coefficient (0.967) shown in the analysis of OLR12.4. In the long-term analysis, the average RMSE was 11.83Wm−2 and showed a correlation coefficient of 0.972 (OLR12.4 showed 12.17Wm−2 and 0.969). Furthermore, to minimize the difference in the cloud area that had the largest error, the study analyzed TOA OLR at the significance level of 95% (±2 RMSE). As a result, the scene analysis showed an RMSE of 10.54Wm−2 and correlation coefficient of 0.977, and the long-term data showed an RMSE of 10.23Wm−2 and correlation coefficient of 0.979.

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm Tb was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6–11.2 µm)–(11.2–12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20–40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI

AbstractAs the first of the next-generation geostationary meteorological satellites, Himawari-8 was successfully launched in October 2014 by the Japan Meteorological Agency (JMA) and placed over the western Pacific Ocean domain at 140.7°E. It carries the Advanced Himawari Imager (AHI), which provides full-disk images of Earth at 16 bands in the visible and infrared domains every 10 min. Efforts are currently ongoing at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) to assimilate Himawari-8 AHI radiance measurements into the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation analysis system (GSI). All software development within the GSI to allow for assimilation of Himawari-8 AHI radiance has been completed.This study reports on the assessment of AHI preassimilation data quality by comparing observed clear-sky ocean-only radiances to those simulated using collocated ECMWF analysis, as well as describing procedures implemented for quality control. The impact of the AHI data assimilation on the resulting analyses and forecasts is then assessed using the NCEP Global Forecast System (GFS). A preliminary assessment of the assimilation of AHI data from infrared water vapor channels and atmospheric motion vectors (AMVs) on top of the current global observing system shows neutral to marginal positive impact on analysis and forecast skill relative to an assimilation without AHI data. The main positive impact occurs for short- to medium-range forecasts of global upper-tropospheric water vapor. The results demonstrate the feasibility of direct assimilation of AHI radiances and highlight how humidity information can be extracted within the assimilation system.