Japan’s new geostationary satellite Himawari-8, the first of a series of the third-generation geostationary meteorological satellites including GOES-16, has been operational since July 2015. Himawari-8 produces high-resolution observations with 16 frequency bands every 10 min for full disk, and every 2.5 min for local regions. This study aims to assimilate all-sky every-10-min infrared (IR) radiances from Himawari-8 with a regional numerical weather prediction model and to investigate its impact on real-world tropical cyclone (TC) analyses and forecasts for the first time. The results show that the assimilation of Himawari-8 IR radiances improves the analyzed TC structure in both inner-core and outer-rainband regions. The TC intensity forecasts are also improved due to Himawari-8 data because of the improved TC structure analysis.
[1] In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation ≥1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation ≥90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation ≥90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.
In this study, the impact of global climate change and anticipated urbanization over the next 70 years is estimated with regard to the summertime local climate in the Tokyo metropolitan area (TMA), whose population is already near its peak now. First, five climate projections for the 2070s calculated with the aid of general circulation models (GCMs) are used for dynamical downscaling experiments to evaluate the impact of global climate changes using a regional climate model. Second, the sensitivity of future urbanization until the 2070s is examined assuming a simple developing urban scenario for the TMA. These two sensitivity analyses indicate that the increase in the surface air temperature from the 1990s to the 2070s is about 2.08C as a result of global climate changes under the A1B scenario in the Intergovernmental Panel on Climate Change's Special Report on Emissions Scenarios (SRES) and about 0.58C as a result of urbanization. Considering the current urban heat island intensity (UHII) of 1.08C, the possible UHII in the future reaches an average of 1.58C in the TMA. This means that the mitigation of the UHII should be one of the ways to adapt to a local temperature increase caused by changes in the future global climate. In addition, the estimation of temperature increase due to global climate change has an uncertainty of about 2.08C depending on the GCM projection, suggesting that the local climate should be projected on the basis of multiple GCM projections.
In this study, the frequency of surface cyclogenesis and the surface cyclone track in East Asia are investigated using high-resolution and long-term reanalysis data, which are ERA40 data in a reduced Gaussian grid system. A cyclone center was identified in the surface pressure anomaly using a modified method after Serreze (1995). Surface cyclogenesis frequently occurs in several specific areas in all seasons. The broadening of the frequent areas is mostly narrower than those shown in previous studies. The areas of high-frequency cyclogenesis are distributed in the lee of mountains, basins, the Pacific Ocean to the east of Japan, the Japan Sea, the Kuroshio Current region to the south of Japan, and the East China Sea. In particular, this study showed newly several findings; the inactive cyclogenesis in the lee of Tahsinganling in summer; the frequent cyclogenesis in the Hebei Plain except for summer; and the cyclogenesis maxima around the mouth of the Yangtze River and in the East China Sea to the northeast of Taiwan in winter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.