Four teleconnection patterns that are possibly associated with the anomalous summer climate in Japan and the surrounding regions, were extracted by applying empirical orthogonal function and regression analyses to stream function anomalies. The two teleconnection patterns prevailing over northern Eurasia, especially in early summer, called the Europe-Japan (EJ) 1 and EJ2, are linked with the variability of the Okhotsk high. The third teleconnection pattern, called the West Asia-Japan (WJ), is a stationary wave-train pattern along the upper-level subtropical jet from West Asia to the central North Pacific, which is possibly excited by the anomalous convective heating of the Indian summer monsoon. The final teleconnection pattern is identified with the Pacific-Japan (PJ), found by Nitta. Teleconnection indices that account for the variability of those patterns are also defined on a monthly basis. The PJ and WJ patterns, which are more influential teleconnection patterns than the others, are closely related to the summer temperature anomalies, especially in northern and western Japan, respectively. EJ1 and EJ2 were amplified in several extreme summers, and they played a vital role in the cool summer of 2003, along with PJ. A combination of two or three teleconnection patterns was also responsible for the occurrence of the recent extreme summers. Monitoring the major teleconnection patterns is very useful for understanding and forecasting the anomalous summer climate in East Asia.

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radar, obtaining detailed local information is still limited by cost and by bulky, complicated instrumentation. For future safety of human life, it is obvious that simple, attachable weather sensors can help to mitigate flood disasters. Currently, acquiring local weather information relies on personal input via websites or weather cams.One approach is to utilize a simple, lightweight, flexible sensor that can be attached conformably to a variety of surfaces, such as building and automobile roofs and even umbrellas. In fact, flexible sensors with multiple functionalities have been already reported for applications such as wearable healthcare devices, [1][2][3][4][5][6] robotics, [7][8][9][10] and plants. [11] Although the concept of attachable sensor sheets to collect local weather information is unique, it is not simple to detect precipitation and wind velocity simultaneously using a single device. It should be noted that power generation using raindrops has been proposed, [12] and this may also be able to estimate precipitation. However, the concept of monitoring local weather information has yet to be suggested, most likely because it is challenging to measure wind velocity due to complexity of signal changes.For simultaneous monitoring of precipitation and wind, waterdrop behavior must be understood to enable further analyses. Waterdrops, including rain, exhibit a variety of dynamics such as spreading, bouncing, and wetting, depending on droplet behavior and surface conditions at the time of surface impact. These behaviors include information at micro-to-macro scales about volume, velocity, etc. If dynamics of water behavior can be monitored in real time, weather information focused on precipitation and wind velocity may be readily extracted. Furthermore, using wireless networks and data sharing on the Internet of Things platform, local weather information can be shared automatically by attaching sensors to various objects. As a result, it will be possible to collect at the edge and analyze more data using cloud computing, to monitor dangerous weather in real time. In general, to gather multiple types of data in real-time, it is necessary to integrate multiple sensors for each information type. However, the use of a large number of sensors to collect information can limit applications, owing to device weight, size, cost, and complexity of the systems. It is important to analyze multiple features simply, with low power consumption.With sensor developments, complicated signal processing may be required regardless of the number of sensors and Natural disasters are reported globally, and one source of severe damage to cities is flooding caused by locally heavy rain. Sharing of local weather information can save lives. However, it is difficult to collect local weather information in real-time because such data collection requires bulky, expensive sensors. For local, real-time monitoring of heavy rain and wind, a sensor system should be simple and low-cost so that it can be attached to a variety of surfaces, includi...

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In artificial intelligence and deep learning applications, data collection from a variety of objects is of great interest. One way to support such data collection is to use very thin, mechanically flexible sensor sheets, which can cover an object without altering the original shape. This study proposes a thin, macroscale, flexible, tactile pressure sensor array fabricated by a simple process for economical device applications. Using laser-induced graphene, a transfer process, and a printing method, a relatively stable, reliable, macroscale, thin (∼300 μm), flexible, tactile pressure sensor is realized. The detectable pressure range is about tens to hundreds of kPa. Then, as a proof-of-concept, the uniformity, sensitivity, repeatability, object mapping, finger pressure distribution, and pressure mapping are demonstrated under bending conditions. Although many flexible, tactile pressure sensors have been reported, the proposed structure has the potential for macroscale, thin, flexible, tactile pressure sensor sheets because of the simple and easy fabrication process.