The high temporal and spatial resolutions of geostationary satellite observations achieved by recent technological advancements have facilitated the derivation of atmospheric motion vectors (AMVs), even in a tropical cyclone (TC) wherein the winds abruptly change. This study used TCs in the western North Pacific basin to investigate the ability of upper tropospheric AMVs to estimate the TC intensity and structure. We first examined the relationships between the cloud-top wind fields captured by 6-hourly upper tropospheric AMVs derived from images of the Multi-functional Transport Satellite (MTSAT) and the surface maximum sustained wind (MSW) of the Japan Meteorological Agency's best-track data for 44 TCs during 2011-2014. The correlation between the maximum tangential winds of the upper tropospheric AMVs (UMaxWinds) and MSWs was high, approximately 0.73, suggesting that the cyclonic circulation near the cloud top was intensified by the upward transport of absolute angular momentum within the TC inner core. The upper tropospheric AMVs also revealed that the mean radii of UMaxWinds and the maximum radial outflows shifted inward as the TC intensification rate became large, implying that the low-level inflow was strong for TCs undergoing rapid intensification. We further examined the possibility of estimating the MSW using 30-min-interval UMaxWinds derived from Himawari-8 target observations, which have been used to track TCs throughout their lifetimes. A case study considering Typhoon Lionrock (1610) showed that the UMaxWinds captured the changes in the cyclonic circulation near the cloud top within the inner core on a timescale shorter than 1 day. It was apparent that the increase in the UMaxWind was associated with the intensification of the TC warm core and the shrinkage of UMaxWind radius. These results suggest that Himawari-8 AMVs include useful information about TC intensification and related structural changes to support the TC intensity analysis and structure monitoring.

 An algorithm for retrieving the macroscopic, physical, and optical properties of clouds from thermal infrared measurements is applied to the Himawari-8 multiband observations. A sensitivity study demonstrates that the addition of the single CO₂ band of Himawari-8 is effective for the estimation of cloud top height. For validation, retrieved cloud properties are compared systematically with collocated active remote sensing counterparts with small time lags. While retrievals agree reasonably for single-layer clouds, multilayer cloud systems with optically thin upper clouds overlying lower clouds are the major source of error in the present algorithm. Validation of cloud products is critical for identifying the characteristics, advantages, and limitation of each product and should be continued in the future.

 As an application example, data are analyzed for eight days in the vicinity of the New Guinea to study the diurnal cycle of the cloud system. The present cloud property analysis investigates cloud evolution through separation of different cloud types and reveals typical features of diurnal cycles related to the topography. Over land, middle clouds increase from 0900 to 1200 local solar time (LST), deep convective clouds develop rapidly during 1200-1700 LST with a subsequent increase in cirrus and cirrostratus cloud amounts. Over the ocean near coastlines, a broad peak of convective cloud fraction is seen in the early morning. The present study demonstrates the utility of frequent observations by Himawari-8 for life cycle study of cloud systems, owing to the ability to capture their continuous temporal variations.

 Land surface emissivity (LSE) in the thermal infrared (TIR) is an essential parameter in the retrieving land surface temperature (LST) from space. This paper describes the LSE maps in three TIR bands (centered at 10.4, 11.2 and 12.4 μm) used for retrieving the LST from Himawari-8. Himawari-8, a next-generation geostationary satellite has high spatial and temporal resolutions compared to previous geostationary satellites. Because of these improvements, the Himawari-8 LST product is expected to contribute to the observation of small-scale environments in high-frequency. In this study, the LSE is estimated by a semi-empirical method, which is a combination of the classification based method and the normalized difference vegetation index (NDVI) thresholds method. The land cover classification information is taken from the Global Land Cover by National Mapping Organizations version3 (GLCNMO 2013). Material emissivities of soil, vegetation and others are taken from the MODIS UCSB emissivity library and the ASTER spectral library. This method basically follows the semi-empirical methods developed by the previous studies, but advanced considerations are added. These considerations are the phenology of vegetation, flooding of paddy fields, snow/ice coverage, and internal reflections (cavity effect) in urban areas. The average cavity effect on LSE in urban canopies is approximately 0.01, but it reaches 0.02 in built-up areas. The sensitivity analysis shows that the total LSE errors for the three bands are less than 0.02. The LSE estimation is especially stable at the vegetation area, where the error is less than 0.01.

 This paper presents a method for estimating the land surface temperature (LST) from Himawari-8 data. The Advanced Himawari Imager onboard Himawari-8 has three thermal infrared bands in the spectral range of 10-12.5 μm. We developed a nonlinear three-band algorithm (NTB) that makes the best use of these bands to estimate the LST. The formula of the algorithm includes 10 coefficients. The optimum values of these coefficients were derived using a statistical regression method from the simulated data, as obtained by a radiative transfer model. The simulated data sets correspond to a variety of values of LST, as well as surface emissivity, type and season of temperature and water vapor profiles. Viewing zenith angles (VZAs) from 0° to 60° were considered. For the coefficients obtained in this way, we verified the root-mean-square error (RMSE) in terms of the VZA, LST and precipitable water dependence. We showed that the NTB can accurately estimate the LST with an RMSE less than 0.9 K compared with the nonlinear split-window algorithm developed by Sobrino and Romaguera (2004). Moreover, we evaluated the sensitivities of the LST algorithms to the uncertainties in input data by using the dataset independent of the dataset used to obtain coefficients. Consequently, we showed that the NTB has the highest robustness against the uncertainties in input data. Finally, the stepwise LST retrieval method was constructed. This method includes a simple cloud mask procedure and the land surface emissivity estimation. The LST product was evaluated using in-situ data over the Tibetan Plateau, and the validity was confirmed.

 We developed an atmospheric gas absorption table for the Advanced Himawari Imager (AHI) based on the correlated k-distribution (CKD) method with an optimization method, which was used to determine quadrature weights and abscissas. We incorporated the table and band information of the AHI into a multi-purpose atmospheric radiative transfer package, RSTAR. We updated the package so that users could easily specify the satellite and band number. Use of this update made it possible to carry out calculations rapidly and accurately using the optimized CKD method. RSTAR is easy for beginners to use and facilitates comparison of results. Cloud retrieval tests using different numbers of quadrature points showed that cloud retrievals could be significantly affected by the CKD model's accuracy.

 The new geostationary (GEO) meteorological satellite of the Japan Meteorological Agency (JMA), Himawari-8, entered operation on 7 July 2015. Himawari-8 features the new 16-band Advanced Himawari Imager (AHI), whose spatial resolution and observation frequency are improved over those of its predecessor MTSAT-series satellites. These improvements will bring about unprecedented levels of performance in nowcasting services and short-range weather forecasting systems. In view of the essential nature of navigation and radiometric calibration in fully leveraging the imager's potential, this study reports on the current status of calibration for the AHI. Image navigation is accurate to within 1 km, and band-to-band coregistration has also been validated. Infrared (IR) band calibration is accurate to within 0.2 K with no significant diurnal variation and is being validated using an approach developed under the Global Space-based Inter-Calibration System (GSICS) framework. Validation approaches are currently being tested for the visible and near-IR (NIR) bands. Two such approaches were compared and found to produce largely consistent results.

 Rapid scan atmospheric motion vectors (RS-AMVs) were derived using an algorithm developed by the Meteorological Satellite Center of the Japan Meteorological Agency (JMA) from Himawari-8 rapid scan imagery over the area around Japan. They were computed every 10 min for seven different channels, namely, the visible channel (VIS), near infrared and infrared channels (IR), three water vapor absorption channels (WV), and CO₂ absorption channel (CO₂), from image triplets with time intervals of 2.5 min for VIS and 5 min for the other six channels. In June 2016, the amount of data was increased by more than 20 times compared to the number of routinely used AMVs. To exploit these high-resolution data in mesoscale data assimilation for the improvement of short-range forecasts, data verification, and assimilation experiments were conducted. The RS-AMVs were of sufficiently good quality for assimilation and consistent overall with winds from JMA's mesoscale analyses, radiosonde, and wind profiler observations. Errors were slightly larger in WV than in VIS and IR channels. Significant negative biases relative to sonde winds were seen at high levels in VIS, IR, and CO₂, whereas slightly positive biases were noticeable in WV at mid- to high levels. Data assimilation experiments with the JMA's non-hydrostatic model based Variational Data Assimilation System (JNoVA) on a cold vortex event in June 2016 were conducted using RS-AMVs from seven channels. The wind forecasts improved slightly in early forecast hours before 12 hours in northern Japan, over which the vortex passed during the assimilation period. They also showed small improvements at low levels when averaged over the whole forecast period. The results varied slightly depending on the channels used for assimilation, which might be caused by different error characteristics of RS-AMVs in different channels.

 The Japan Meteorological Agency (JMA) launched a next-generation geostationary meteorological satellite (GMS), Himawari-8, on October 7, 2014, which began its operation on July 7, 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 observational bands that enable the retrieval of full-disk maps of aerosol optical properties (AOPs), including aerosol optical thickness (AOT) and the Ångström exponent (AE), with unprecedented spatial and temporal resolutions. In this study, we combined an aerosol transport model with the Himawari-8 AOT using the data assimilation method and performed aerosol assimilation and forecasting experiments on smoke from an intensive wildfire that occurred over Siberia between May 15 and 18, 2016. To effectively utilize the high observational frequency of Himawari-8, we assimilated 1-h merged AOTs generated through the combination of six AOT snapshots taken over 10-min intervals, three times per day. The heavy smoke originating from the wildfire was transported eastward behind a low-pressure trough and covered northern Japan from May 19 to 20. The southern part of the smoke plume then traveled westward, in a clockwise flow associated with high pressure. The forecast without assimilation reproduced the transport of the smoke to northern Japan; however, it underestimated AOT and the extinction coefficient compared with observed values mainly because of errors in the emission inventory. Data assimilation with the Himawari-8 AOT compensated for the underestimation and successfully forecasted the unique C-shaped distribution of the smoke. In particular, the assimilation of the Himawari-8 AOT in May 18 greatly improved the forecast of the southern part of the smoke flow. Our results indicate that the inheritance of assimilation cycles and the assimilation of more recent observations led to better forecasting in this case of a continental smoke outflow.

 The present study implements long-term surface observed radiation data (pyranometer observed global flux and sky radiometer observed spectral zenith transmittance data) of multiple SKYNET sites to validate water cloud optical properties (cloud optical depth COD and effective radius Re) observed from space by MODIS onboard TERRA and AQUA satellites and AHI onboard Himawari-8 satellite. Despite some degrees of differences in COD and Re between MODIS and AHI, they both showed common features when validated using surface based global flux data as well as cloud properties retrieved from sky radiometer observed zenith transmittance data. In general, CODs from both satellite sensors are found to overestimated when clouds are optically thin. Among a number of factors (spatial and temporal variations of cloud, sensor and solar zenith angles), the solar zenith angle (SZA) is found to have an impact on COD difference between reflectance based satellite sensor and transmittance based sky radiometer. The Re values from the sky radiometer and satellite sensor are generally poorly correlated. The difference in Re between the sky radiometer and satellite sensor is negatively correlated with COD difference between them, which is likely due to the inherent influence of Re retrieval precision on COD retrieval and vice versa in transmittance based sky radiometer.

 This article reports on the impacts of Himawari-8 Clear Sky Radiance (CSR) data assimilation in the global and mesoscale numerical weather prediction (NWP) systems of the Japan Meteorological Agency (JMA). Adoption of the Advanced Himawari Imager (AHI) on board JMA's Himawari-8 and -9 satellites has enhanced observational capabilities in terms of spectral, horizontal, and temporal resolution. Improvements brought by the switchover from the Multi-functional Transport Satellite-2 (MTSAT-2) to the new-generation Himawari-8 satellite include an upgrade to the horizontal resolution of CSR data from 64 to 32 km and an increase in the number of available water vapor bands from one to three. CSR products are obtained every hour and distributed to the NWP community. The improved horizontal and spectral resolution of Himawari-8 CSR data provides new information on horizontal water vapor distribution and vertical profiles in data assimilation.

 In data assimilation experiments using JMA's global NWP system, the assimilation of Himawari-8's three water vapor bands significantly improved the tropospheric humidity field in analysis, especially in the lower troposphere, as compared to assimilation of the single MTSAT-2 water vapor channel. First-guess (FG) departure statistics for microwave humidity sounders indicated an improvement in the water vapor field, especially over Himawari-8 observation areas. Improved forecasting of tropospheric temperature, humidity, and wind fields for Himawari-8 observation areas was also seen.

 In data assimilation experiments using JMA's mesoscale NWP system, a disastrous heavy precipitation event that took place in Japan's Kanto-Tohoku region in 2015 was investigated. A single water vapor band of Himawari-8 CSR corresponding to MTSAT-2 was assimilated, resulting in enhanced contrast of the water vapor field between moist and dry areas, as well as a realistic representation of moist air flows from the ocean in analysis. The changes also improved mesoscale model heavy precipitation forecasts.

 We developed a common algorithm to retrieve aerosol properties, such as aerosol optical thickness, single-scattering albedo, and Ångström exponent for various satellite sensors over land and ocean. The three main features of this algorithm are: (1) automatic selection of the optimum channels for aerosol retrieval by introducing a weight for each channel to the object function, (2) setting common candidate aerosol models over land and ocean, and (3) preparing lookup tables for every 1 nm in the range of 300 to 2500 nm in the wavelength and weighting the radiance using the response function for each sensor. This method was applied to the Advanced Himawari Imager (AHI) on board the Japan Meteorological Agency's geostationary satellite Himawari-8, and the results depicted a continuous estimate of aerosol optical thickness over land and ocean. Furthermore, the aerosol optical thickness estimated using our algorithm was generally consistent with the products of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET). In addition, we applied our algorithm to MODIS on board the Aqua satellite and compared the retrieval results to the results obtained from AHI. The comparisons of the aerosol optical thickness, retrieved from different sensors with the different viewing angles onboard the geostationary and polar-orbiting satellites, suggest an underestimation of aerosol optical thickness at the backscattering direction (or overestimated in other directions). The retrieval of aerosol properties using a common algorithm allows in identifying a weakness in the algorithm, such as the assumptions in the aerosol model (e.g., sphericity or size distribution).

 The combination of three visible bands of the Advanced Himawari Imager (AHI) aboard Japan Meteorological Agency's (JMA) new-generation Himawari-8 and Himawari-9 geostationary meteorological satellites enables the production of true color imagery. True color is intuitively understandable to human analysts and beneficial for monitoring surface and atmospheric features. It is particularly useful when applied to frequent observations from a geostationary platform. In this article, we report on an application of a color reproduction approach based on the International Commission on Illumination (CIE) 1931 XYZ color system to imagery rendering. This approach allows the consideration of primary color (RGB) differences among satellite and output devices, which in turn cause differences in the colors reproduced. The RGB signals observed by the AHI are converted to XYZ tristimulus values, which are independent of the devices themselves, and then reconverted to RGB signals for output devices via the application of 3 × 3 conversion matrices. This article also covers an objective technique for the evaluation of the accuracy of XYZ values. The evaluation indicated that the combination of AHI native RGB bands is suboptimal for obtaining XYZ values as is, whereas a combination in which the green band is replaced by a pseudo band with a central wavelength of around 0.555 μm is optimal. The pseudo band is generated via regression with existing visible and near-infrared bands as predictor variables. The imagery produced using this approach was termed True Color Reproduction (TCR). This approach is applicable to other satellites that have several bands in the visible to near-infrared spectral range, and it has the potential for development toward the production of standardized sensor-independent true color imagery.

 Two case studies of the mesoscale convective system (MCS) in the Indonesian region were conducted by applying an improved “Grab ‘em Tag ‘em Graph ‘em” (GTG) tracking algorithm and the Integrated Cloud Analysis System (ICAS) algorithm to Himawari-8 AHI infrared data. The first case over Java Island showed a land-originating MCS in the boreal winter, which coincided with a wet phase of Madden-Julian Oscillation (MJO) over the Maritime Continent. The second case showed the evolution of MCS under the influence of a strong vertical wind shear during the boreal summer. The cloud top height (CTH) of deep convective part in the first case was larger than that in the second case, while the temporal evolution of CTH was similar between the two cases. For the anvil part, the median CTH of the second case was relatively stable at around 13 km, while that of the first case showed a considerable temporal variation ranging from 14 to 16 km. The cloud-particle effective radius (CER) of the anvil increased after the period of maximum deep convective CTH in both cases, although the CER was slightly larger in the second case than in the first case. These differences in cloud properties between the two cases were attributable to the background wind profiles.