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.