Development of Global Operational Snow Analysis at the US Air Force 557th Weather Wing

The outdated SNODEP snow depth retrieval algorithm is replaced by the Foster et al. (1997; 2005) approach, which considers the effects of variations in forest cover. The simple blending algorithm (IDW) is replaced by the Bratseth scheme, a successive correction algorithm that converges to the solution provided by Optimal Interpolation (OI). Outdated quality control datasets are updated and quality control algorithms are reorganized to ensure the performance of the snow analysis. The spatial resolution of snow and ice estimates are increased from 25-km to 10-km.USAF-SI are fully integrated into the global operational land analysis configuration at the USAF 557th WW

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes

Presentation, evaluation and sensitivity of a discharge algorithm for remotely sensed river measurements : Test cases on Sacramento and Garonne Rivers

We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the ‘‘true’’ hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Manning’s equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

Metal-Oxide Nanomaterials Synthesis and Applications in Flexible and Wearable Sensors

© 2022 by the Author(s).Metal-oxide nanomaterials (MONs) have gained considerable interest in the construction of flexible/wearable sensors due to their tunable band gap, low cost, large specific area, and ease of manufacturing. Furthermore, MONs are in high demand for applications, such as gas leakage alarms, environmental protection, health tracking, and smart devices integrated with another system. In this Review, we introduce a comprehensive investigation of factors to boost the sensitivity of MON-based sensors in environmental indicators and health monitoring. Finally, the challenges and perspectives of MON-based flexible/wearable sensors are considered.N

Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System

This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations.Key PointsThe skill of a hyper‐resolution, off‐line terrestrial modeling system used for the High Mountain Asia region is presentedThe study emphasizes the importance of using hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrainThe study emphasizes the importance of an accurate hyper‐resolution precipitation product used to drive model simulationsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167423/1/jgrd56955_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167423/2/jgrd56955.pd

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation

Bioinspired Soft Robotic Fish for Wireless Underwater Control of Gliding Locomotion

Animal locomotion offers valuable references as it is a critical component of survival as animals adapting to a specific environment. Especially, underwater locomotion poses a challenge because water exerts a high antagonistic drag force against the direction of progress. However, marine vertebrates usually use much lower aerobic energy for locomotion than aerial or terrestrial vertebrates due to their unique intermittent gliding locomotion. None of the prior works demonstrate the locomotive strategies of marine vertebrates. Herein, an untethered soft robotic fish capable of reconstructing the marine vertebrates’ effective locomotion and traveling underwater by controlling localized buoyancy with thermoelectric pneumatic actuators is introduced. The actuators enable both heating and cooling to control a localized buoyancy while providing a substantial driving force to the system. Besides mimicking the locomotion, the bidirectional communication system enables the untethered delivery of commands to the underwater subject and real‐time acquisition of the robotic fish's physical information. Underwater imaging validates the fish's practical use as a drone, allowing for inspecting the aquatic environment that is not easily accessible to humans. Future work studies the operation of the robotic fish as a collective swarm to examine a broader range of the underwater area and conduct various strategic missions