A simplified, axisymmetric, one-layer model of tropical cyclone intensification is presented. The model is based on the Salmon wave-vortex approximation, which can describe flows with low Froude number and arbitrary Rossby number. After introducing an additional approximation designed to filter propagating inertia-gravity waves, the problem is reduced to the prediction of potential vorticity (PV) and the inversion of this PV to obtain the balanced wind and mass fields. This PV prediction/inversion problem is solved analytically for two types of forcing: a two-region model in which there is nonzero forcing in the cyclone core and zero forcing in the far-field and a three-region model in which there is nonzero forcing in both the cyclone core and the eyewall, with zero forcing in the far-field. The solutions of the two-region model provide insight into why tropical cyclones can have long incubation times before rapid intensification and how the size of the mature vortex can be influenced by the size of the initial vortex. The solutions of the three-region model provide insight into the formation of hollow PV structures and the inward movement of angular momentum surfaces across the radius of maximum wind.
The most severe large-scale flood on record occurred on the Amur River and its main tributaries (the Songhua, the Zeya, and the Bureya Rivers) in August-early September 2013. Prolonged heavy rainfalls over the vast territory of the Amur River basin produced the flood during the summer of 2013. During the flood monitoring, it was noted that observed precipitation data from the Amur River observational network had not represented areal precipitation over drainage basins of the Amur River and its tributaries well enough. Therefore, operational Weather Research and Forecasting (WRF)-Advanced Research WRF (WRF-ARW) model with grid distance of 15 km was applied for prediction of areal precipitation over that territory. The results of the simulation were compared with observed precipitation and water level data from the outlet points of partial drainage basins of the Amur River in June-September 2012 and 2013 to discuss the possibility of using numerically simulated precipitation in hydrological applications related to the Amur River basin. During the summer months of those years, an extreme flood occurred in 2013, while the hydrological situation was normal in 2012 on the Amur River. The results of the comparison show that the amount of precipitation simulated on grid points of partial basins of the Amur River and its tributaries are in better agreement with major flood peaks than precipitation data obtained from the observational network. Additionally, if the five-day total areal precipitation averaged over the territory of a partial drainage basin exceeds 20 mm, the water level on an outlet point of a partial drainage basin of the Amur River monotonically increases independent of any variations of precipitation at an amount above the 20 mm value.
Himawari-8/9—a new generation of Japanese geostationary meteorological satellites-carry state-of-the-art optical sensors with significantly higher radiometric, spectral, and spatial resolution than those previously available in the geostationary orbit. They have 16 observation bands, and their spatial resolution is 0.5 or 1 km for visible and near-infrared bands and 2 km for infrared bands. These advantages, when combined with shortened revisit times (around 10 min for Full Disk and 2.5 min for sectored regions), provide new levels of capacity for the identification and tracking of rapidly changing weather phenomena and for the derivation of quantitative products. For example, fundamental cloud product is retrieved from observation data of Himawari-8 operationally. Based on the fundamental cloud product, Clear Sky Radiance and Atmospheric Motion Vector are processed for numerical weather prediction, and volcanic ash product and Aeolian dust product are created for disaster watching and environmental monitoring. Imageries from the satellites are distributed and disseminated to users via multiple paths, including Internet cloud services and communication satellite services.
This study evaluated the accuracy of gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP_Gauge version V5.222.1, hereafter G_Gauge) data in Japan’s Tone River basin during 2006-2009. Specifically, the accuracy of a gauge non-adjusted product, GSMaP Moving Vector with Kalman Filter (GSMaP_MVK, hereafter G_MVK), was also evaluated. Both products were also evaluated against ground observation data from rain gauge-radar combined product Radar-Automated Meteorological Data Acquisition System (Radar-AMeDAS) in terms of temporal and spatial variability. Temporal analyses showed that G_Gauge had better accuracy than G_MVK at sub-daily time scales (1, 3, 6, 9, 12, and 24 h) within any range of precipitation intensity and better detection capabilities of rainfall event. Linear regressions with Radar-AMeDAS showed better performance for G_Gauge than G_MVK at any time scales in terms of Pearson’s correlation coefficient and the slope of regression. At an hourly scale, in particular, Pearson’s correlation coefficient for G_Gauge (0.84) was higher than that for G_MVK (0.72) as well as the slope of linear regression (0.87 and 0.65, respectively). The probability of detection (POD) improved from 0.48 (G_MVK) to 0.70 (G_Gauge) when gauge-adjusted data were used. However, spatial analysis detected that G_Gauge still underestimated the precipitation intensity in high-elevation regions and slightly overestimated it in low elevation regions. The POD and false alarm ratio had a linear relationship with log-transformed elevation data, and the relationships were stronger in the winter seasons than in the summer seasons. At any spatial and temporal scale, the evaluation of these products should consider seasonal changes (especially in winter) and the topographic effects. For further improvements of G_Gauge, we suggest including higher resolution gauge-based network data than the Climate Prediction Center unified gauge-based analysis of global daily precipitation, which is used for G_Gauge.
This study discovered a decreasing phenomenon of interdecadal variation in winter precipitation averaged over southern China since 1998. This study analyzed the difference in the average precipitation in the periods of 1999-2014 and 1980-1998 to determine the cause of reduction in winter precipitation in recent years. The spatial distribution of difference in winter precipitation between the two periods showed that negative anomalies were distributed from the equatorial eastern Pacific to the equatorial central Pacific, whereas positive anomalies were distributed in the subtropical western Pacific, Maritime Continent, and northern part of Australia. This pattern is a typical spatial distribution of precipitation anomalies displayed at La Niña events. On the other hand, most of the northern parts (i.e., above 20°N) in East Asia showed negative anomalies, the centers of which were distributed over southern China. To determine the characteristic of the large-scale atmospheric circulations that caused a difference of spatial distribution in precipitation between the two periods, a difference in 850 hPa streamlines was analyzed between the two periods. In the tropical Pacific, the Walker circulation, in which air ascended from the Maritime Continent and tropical western Pacific and descended at the equatorial central Pacific, was strengthened. Thus, this was caused by a spatial distribution of sea surface temperature, La Niña-like pattern. In the East Asia region, the Hadley circulation, in which air ascended at the southern China Sea and descended at mid-latitude regions in East Asia, was strengthened. This circulation was related to an anomalous pressure system pattern of the west high east low type in East Asia and the strengthening of the East Asian winter monsoon, which can be confirmed through the snow depth, which increased in mid-latitude regions in East Asia in recent years.