In order to study airborne bacterial community dynamics over Tokyo, including fine-scale correlations between airborne microorganisms and meteorological conditions, and the influence of local versus long-range transport of microbes, air samples were collected on filters for periods ranging from 48 to 72 h. The diversity of the microbial community was assessed by next generation sequencing. Predicted source regions of airborne particles, from back trajectory analyses, changed abruptly from the Pacific Ocean to the Eurasian Continent in the beginning of October. However, the microbial community composition and the alpha and beta diversities were not affected by this shift in meteorological regime, suggesting that long-range transport from oceanic or continental sources was not the principal determinant controlling the local airborne microbiome. By contrast, we found a significant correlation between the local meteorology, especially relative humidity and wind speed, and both alpha diversity and beta diversity. Among four potential local source categories (soil, bay seawater, river, and pond), bay seawater and soil were identified as constant and predominant sources. Statistical analyses point toward humidity as the most influential meteorological factor, most likely because it is correlated with soil moisture and hence negatively correlated with the dispersal of particles from the land surface. In this study, we have demonstrated the benefits of fine-scale temporal analyses for understanding the sources and relationships with the meteorology of Tokyo’s “aerobiome.”

A classification of snow clouds, called the "snowfall mode," is proposed based on Doppler radar observations at 10-minute intervals at Nagaoka in 1999/2000 winter season. Using 795 hours of data at an altitude of 1.6 km, six snowfall modes were defined: longitudinal line (Lmode), transversal line (T-mode), spreading precipitation (S-mode), meso-scale vortex (V-mode), mountainslope precipitation (M-mode), and local-frontal (discontinuity) band (D-mode). In migrating snow clouds, a subclass, referred to as snowfall with coastal intensification (xI-mode, where x is L, T, S and V) was defined. A sample snapshot and the mean Ze are shown for each snowfall mode. The frequency of occurrence of the snowfall modes indicated that both of the longitudinal cloud streets and the mesoscale disturbances occupied about 1/3 of the analysis period. About 18% of the precipitation in the analysis period was considered to be under orographic effects. The prevailing wind direction differed between the snowfall modes although a west-northwesterly wind dominated. IntroductionSnow clouds developing over the Sea of Japan generate a wide variety of radar echo patterns, which suggests that there are a number of mechanisms involved in the development of snow clouds. In the 1970s, some classifications of snow clouds were made using conventional radars (e.g., Nanasawa 1975). However, their time resolution was low that the motion and duration of the specific patterns were not analyzed. Since then, many theoretical and observational case studies have been conducted. There are several wellknown structures of snow clouds. Longitudinal (Lmode) snowbands often correspond to "cloud streets" that appear during cold outbreaks. The structure of the transversal (T-mode) snowbands was recently elucidated (Murakami et al. 2002). Vortex disturbances often appear around the Japan Sea Polar-Airmass Convergence Zone (JPCZ) (Asai 1988; Tsuboki and Asai 2004). They were also observed as radar echoes (e.g., Asai and Miura 1981). Moreover, land breezes contribute to the formation of snowbands and significantly affect the snowfall (e.g., Ishihara et al. 1989;Ohigashi and Tsuboki 2005).Thus, various structure and development processes of the snow clouds have been analyzed. However, systematic morphological terminology has not been established, and the frequency of occurrence has not been thoroughly analyzed.The Nagaoka Institute of Snow and Ice Studies (NISIS) locates in the central part of the Niigata Prefecture (Fig. 1). The NISIS makes it possible to observe snow clouds throughout the winter season. In this paper, we propose a classification of snow clouds or "snowfall modes" based on Doppler radar winter observations. ObservationAn X-band Doppler radar, X-POL (Iwanami et al. 1996), was set up on the roof of the NISIS. The observation area was a northwestern-side semicircle with a radius of 64 km. The radar operation consisted of 15 steps of a PPI scan, repeated at about 10-minute intervals. Three-dimensional distributions of the equivalent radar reflect...

Three-year semi-operational observations of rainfall distributions with NIED X-band multiparameter (or polarimetric) radar started in the Kanto area of Japan from July 2003. The purposes and outlines of the radar observations with networks of rain gauges and disdrometers for ground validations are described. Preliminary results of validation analysis of polarimetric rain rate estimators show the usefulness of X-band multi-parameter radar for hydrological and meteorological applications in a small area.

Abstract. The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass–dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; and coalescence, riming, and aggregation. To evaluate the model's performance, a 2-D large-eddy simulation of a cumulonimbus was conducted, and the life cycle of a cumulonimbus typically observed in nature was successfully reproduced. The mass–dimension and velocity–dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128 per cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

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14Recent progress in Next Generation Sequencing allows us to explore the diversity of airborne 15 microorganisms across time and space. However, few studies have used consecutive short-16 period samples to explore correlations between the seasonal variation of the microbiota and 17 meteorology. In order to understand airborne bacterial community dynamics over Tokyo, 18including fine-scale correlations between airborne microorganisms and meteorological 19 conditions, and the influence of local versus long-range transport of microbes, air samples 20 were continuously taken from a platform at the 458-m level of the Tokyo Skytree (a 634-m-21 high broadcasting tower in Tokyo) from August 2016 to February 2017. Predicted source 22 regions of airborne particles, from back trajectory analyses, changed abruptly from the 23 Pacific Ocean to the Eurasian Continent in the beginning of October. However, microbial 24 community composition and alpha and beta diversities were not affected by this 25 meteorological regime shift, suggesting that long-range transport from ocean or continent 26 was not the principal determinant controlling the local airborne microbiome. By contrast, 27 local meteorology, especially relative humidity and wind speed, had significant relationships 28 with both alpha diversities and beta diversity. Among four potential local source categories 29 (soil, bay seawater, river, and pond), bay seawater and soil were constant and predominant 30 sources. Statistical analyses suggest humidity is the most influential meteorological factor, 31 most likely because it is correlated with soil moisture and hence negatively correlated with 32 the dispersal of particles from the land surface. 33

The heavy rainfall event that occurred on 5–6 July 2017 in Northern Kyushu, Japan, caused extensive flooding across several mountainous river basins and resulted in fatalities and extensive damage to infrastructure along those rivers. For the periods before and during the extreme event, there are no hydrological observations for many of the flooded river basins, most of which are small and located in mountainous regions. We used the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, a physically based model, to acquire more detailed information about the hydrological processes in the flood-affected ungauged mountain basins. We calibrated the GSSHA model using data from an adjacent gauged river basin, and then applied it to several small ungauged basins without changing the parameters of the model. We simulated the gridded flow and generated a map of the possible maximum flood depth across the basins. By comparing the extent of flood-affected areas from the model with data of the Japanese Geospatial Information Authority (GSI), we found that the maximum flood inundation areas of the river networks estimated by the GSSHA model are sometimes less than those estimated by the GSI, as the influence of landslides and erosion was not considered in the modeling. The model accuracy could be improved by taking these factors into account, although this task would be challenging. The results indicated that simulations of flood inundation in ungauged mountain river basins could contribute to disaster management during extreme rain events.

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