Robust validation of the space–time structure of remotely sensed precipitation estimates is critical to improving their quality and confident application in water cycle–related research. In this work, the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) precipitation product is evaluated against warm season precipitation observations from the North American Monsoon Experiment (NAME) Event Rain Gauge Network (NERN) in the complex terrain region of northwestern Mexico. Analyses of hourly and daily precipitation estimates show that the PERSIANN-CCS captures well active and break periods in the early and mature phases of the monsoon season. While the PERSIANN-CCS generally captures the spatial distribution and timing of diurnal convective rainfall, elevation-dependent biases exist, which are characterized by an underestimate in the occurrence of light precipitation at high elevations and an overestimate in the occurrence of precipitation at low elevations. The elevation-dependent biases contribute to a 1–2-h phase shift of the diurnal cycle of precipitation at various elevation bands. For reasons yet to be determined, the PERSIANN-CCS significantly underestimated a few active periods of precipitation during the late or “senescent” phase of the monsoon. Despite these shortcomings, the continuous domain and relatively high spatial resolution of PERSIANN-CCS quantitative precipitation estimates (QPEs) provide useful characterization of precipitation space–time structures in the North American monsoon region of northwestern Mexico, which should prove useful for hydrological applications.

We describe studies of the pressure driven flow of several classical fluids through lithographically produced channels in which one dimension, the channel height h, is in the micron or nanometer size range. The measured flow rates are compared with theoretical predictions assuming no-slip boundary conditions at the walls of the channel. The results for water agree well with this prediction for h as small as 40 nm (our smallest channels). However, for hexane, decane, hexadecane, and silicone oil we find deviations from this theory when h is reduced below about 100 nm. The observed flow rates for small h are larger than theoretical expectations, implying significant slip at the walls, and values of the slip length are estimated. The results are compared with previous experimental and theoretical work.

We investigated water vapor condensation on a two-tier superhydrophobic surface in an environmental scanning electron microscope (ESEM) and in a customer-designed vapor chamber. We have observed continuous dropwise condensation (DWC) on the textured surface in ESEM. However, a film layer of condensate was formed on the multiscale texture in the vapor chamber. Due to the filmwise condensation, the condensation heat transfer coefficient of the superhydrophobic surface is lower than that of a flat hydrophobic surface especially under high heat flux situations. Our studies indicate that adaptive and prompt condensate droplet purging is the dominant factor for sustaining long-term DWC.

We report an electrowetting-controlled optofluidic system for adaptive beam tracking and agile steering. With two immiscible fluids in a transparent cell, we can actively control the contact angle along the fluid-fluid-solid tri-junction line and hence the orientation of the fluid-fluid interface via electrowetting. The naturally formed meniscus between the two liquids can function as an optical prism. We have fabricated a liquid prism module with an aperture size of 10 mm × 10mm. With 1 wt. % KCl and 1 wt. % Sodium Dodecyl Sulfate added into deionized water, the orientation of the water-silicone oil interface has been modulated between −26° and 26° that can deflect and steer beam within the incidence angle of 0°–15°. The wide-range beam tracking and steering enables the liquid prism work as an electrowetting solar cell.