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Observations of atmospheric conditions and processes in cities are fundamental to understanding the interactions between the urban surface and weather/climate, improving the performance of urban weather, air quality, and climate models, and providing key information for city end users (e.g., decision makers, stakeholders, public). In this paper, Shanghai’s Urban Integrated Meteorological Observation Network (SUIMON) and some examples of intended applications are introduced. Its characteristics include being multipurpose (e.g., forecast, research, service), multifunction (e.g., high-impact weather, city climate, special end users), multiscale (e.g., macro/meso, urban, neighborhood, street canyon), multivariable (e.g., thermal, dynamic, chemical, biometeorological, ecological), and multiplatform (e.g., radar, wind profiler, ground based, satellite based, in situ observation/sampling). Underlying SUIMON is a data management system to facilitate exchange of data and information. The overall aim of the network is to improve coordination strategies and instruments, to identify data gaps based on science- and user-driven requirements, and to intelligently combine observations from a variety of platforms by using a data assimilation system that is tuned to produce the best estimate of the current state of the urban atmosphere.

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Implicit waveform-specific (IWS) learning during a typical continuous tracking task paradigm has been reported for decades, as evidenced by better tracking improvement on the repeated segment of a specific target waveform than random segments. However, the occurrence of the IWS learning in such a task paradigm has been challenged by several unsuccessful results in recent literature. This research concerns reliable detection of the induced IWS learning and to this end, proposes to use the similarity between the cursor and the target along the direction corresponding to the waveform pattern as the performance measure. A 3-day experiment designed with full examination on IWS learning including a practice phase, an immediate test phase and a consolidation test phase after 24 hours was conducted to validate the feasibility and sensitivity of the Pearson’s correlation coefficient on the vertical movement r v in this study. Experiment results indicate that r v is more sensitive in detecting the IWS learning in all phases compared to the conventional root mean square error (RMSE) performance measure. The findings confirm the importance of the performance measure in implicit learning research and the similarity measure in accordance with the waveform could be promising for waveform-specific learning detection in this paradigm.

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