A unique dataset of targeted dropsonde observations was collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in the autumn of 2008. The campaign was supplemented by an enhancement of the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. For the first time, up to four different aircraft were available for typhoon observations and over 1500 additional soundings were collected.This study investigates the influence of assimilating additional observations during the two major typhoon events of T-PARC on the typhoon track forecast by the global models of the European Centre for MediumRange Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction (NCEP), and the limited-area Weather Research and Forecasting (WRF) model. Additionally, the influence of T-PARC observations on ECMWF midlatitude forecasts is investigated.All models show an improving tendency of typhoon track forecasts, but the degree of improvement varied from about 20% to 40% in NCEP and WRF to a comparably low influence in ECMWF and JMA. This is likely related to lower track forecast errors without dropsondes in the latter two models, presumably caused by a more extensive use of satellite data and four-dimensional variational data assimilation (4D-Var) of ECMWF and JMA compared to three-dimensional variational data assimilation (3D-Var) of NCEP and WRF. The different behavior of the models emphasizes that the benefit gained strongly depends on the quality of the first-guess field and the assimilation system.

Atmospheric motion vectors (AMVs) derived from 5-min rapid scan (RS) imagery of the Multi-functional Transport Satellite are expected to capture small-scale distributions of airflows better than typical AMVs derived from 30-min imagery because the observation interval of RS-AMV is shorter. The impact of these high-frequency data on the numerical forecasting of a heavy rainfall near a stationary front was investigated by conducting data assimilation experiments. As a part of preparation for the assimilation, RS-AMVs were compared with the firstguess field obtained from the Japan Meteorological Agency (JMA) nonhydrostatic model (NHM). The comparison result indicated that the RS-AMVs were of good quality and could be used in the JMA's operational NHM with 4D variational data assimilation (JNoVA). Assimilation experiments investigating a heavy rainfall event were conducted using different lengths of assimilation time slot and time intervals of spatial thinning for the assimilation of the RS-AMV data. The assimilation of RS-AMVs caused the initial wind fields to enhance the upper-level divergence and low-level convergence around the front. Consequently, the forecast of the rainfall amount was increased near the front, and the verification scores were slightly improved over the control experiment in the early forecast hours.

Microscale needle-electrode devices offer neuronal signal recording capability in brain tissue; however, using needles of smaller geometry to minimize tissue damage causes degradation of electrical properties, including high electrical impedance and low signal-to-noise ratio (SNR) recording. We overcome these limitations using a device assembly technique that uses a single needle-topped amplifier package, called STACK, within a device of ∼1 × 1 mm2. Based on silicon (Si) growth technology, a <3-µm-tip-diameter, 400-µm-length needle electrode was fabricated on a Si block as the module. The high electrical impedance characteristics of the needle electrode were improved by stacking it on the other module of the amplifier. The STACK device exhibited a voltage gain of >0.98 (−0.175 dB), enabling recording of the local field potential and action potentials from the mouse brain in vivo with an improved SNR of 6.2. Additionally, the device allowed us to use a Bluetooth module to demonstrate wireless recording of these neuronal signals; the chronic experiment was also conducted using STACK-implanted mice.

A compact low power dynamic memory‐in‐pixel circuit has been integrated into a 1.9″ QCIF+ LTPS mobile AMLCD. The power consumed by the active matrix array and the row and column drive circuits can be reduced to a value of just ∼100μW. This low power consumption is possible because the refresh circuit is integrated within the pixel, which reduces the power to charge the columns and common electrode during each refresh cycle.

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