No abstract

An extreme water-repellent surface is designed and fabricated with a hierarchical integration of nano- and microscale textures. We combined the two readily accessible etching techniques, a standard deep silicon etching, and a gas phase isotropic etching (XeF2) for the uniform formation of double roughness on a silicon surface. The fabricated synthetic surface shows the hallmarks of the Lotus effect: durable super water repellency (contact angle>173 degrees) and the sole existence of the Cassie state even with a very large spacing between roughness structures (>1:7.5). We directly demonstrate the absence of the Wenzel's or wetted state through a series of experiments. When a water droplet is squeezed or dropped on the fabricated surface, the contact angle hardly changes and the released droplet instantly springs back without remaining wetted on the surface. We also show that a ball of water droplet keeps bouncing on the surface. Furthermore, the droplet shows very small contact angle hysteresis which can be further used in applications such as super-repellent coating and low-drag microfludics. These properties are attributed to the nano/micro surface texture designed to keep the nonwetting state energetically favorable.

17 This paper evaluates the simulation of snow by the Community Land Model version 4 (CLM4), 18 the land model component of the Community Earth System Model (CESM1.0.4). We ran CLM4 19 in an offline mode forced with the corrected Modern-Era Retrospective Analysis for Research 20 and Applications meteorological (MERRA-Land) forcing and evaluated the output for the period 21 January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow 22 cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a 23 set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) 24 SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the 25 Canadian Meteorological Center (CMC) daily snow analysis products, snow depth from the U.S. 26 National Weather Service Cooperative Observer Program (COOP), and snowpack telemetry 27 (SNOTEL) SWE observations. CLM4 SCF was converted into snow cover extent (SCE) to 28 compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and 29 average bias of -1.54 x 10 2 km 2 . Overall, CLM4 agreed well with IMS snow cover, the 30 percentage of correctly modeled snow/no snow being 94%. CLM4 snow depth and SWE agreed 31 reasonably well with the CMC product, the average bias (and RMSE) of snow depth and SWE 32 being 0.044 m (0.19 m) and −0.010 m (0.04 m), respectively. CLM4 underestimated SNOTEL 33 SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow 34 estimates, and constitutes a benchmark against which improvement of the model through data 35 assimilation can be measured. 36 37 Keywords: snow model, snow depth, snow water equivalent, simulation, snow density. 38 39 40 Land surface models (LSMs) suffer from one of or a combination of the following: 1) errors in 64 forcing data, 2) improper model parameters, 3) simplified physical processes, and 4) simplified 65 numerical solutions or methods. The performance of the snow scheme in the Community Land 66 Model version 4 (CLM4), which is the land component of CESM, is unknown because there has 67 not been a comprehensive validation against observations. A few studies have investigated some 68 aspects of the CLM4 snow model including the snow cover fraction (SCF) estimates, the snow 69 albedo parameterizations, and the water budget. Swenson and Lawrence (2012) demonstrated 70 that the parameterization used to determine SCF based snow depth in CLM4 exhibits a bias 71 towards early melt when compared to satellite-observed SCF, and they proposed a new SCF 72 parameterization as a function of snow water equivalent (SWE) instead. The results showed an 73 improvement of the surface energy budget in snow-covered areas. Thackeray et al. (2014) 74 recently showed that the weak simulated snow albedo feedback over the boreal forest was 75 attributable to a poor parameterization of the CLM4 mechanism of snow removal from the forest 76 canopy. CLM4 was also assessed against its older version (CLM3.5) (Lawre...

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global Yoon et al.HMA Water Budget Evaluation studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.