Flood Insurance Rate Maps (FIRMs) managed by the Federal Emergency Management Agency (FEMA) have been providing ongoing flood information to most of the communities in the United States over the past half century. However, the uncertainty associated with the modeling of the FIRMs, some of which are created by using a single HEC-RAS one-dimensional (1D) steady flow model, may have adverse effects on the reliability of flood stage and inundation extent. Therefore, a systematic understating of the uncertainty in the modeling process of FIRMs is important and necessary. The Bayesian model averaging (BMA), which is a statistical approach that can combine estimations from multiple models and produce reliable probabilistic predictions, is applied to evaluating the uncertainty associated with the FIRMs. In this study, both the BMA and HBMA approaches are used to quantify the uncertainty within the detailed FEMA models of the Deep River and the Saint Marys River in the state of Indiana based on water stage predictions from 150 HEC-RAS 1D unsteady flow model configurations that incorporate four uncertainty sources including the bridges, channel roughness, floodplain roughness, and upstream flow input. The BMA weight and the variance for each model member are obtained given the ensemble predictions and the observed water stage data in the training period, and then the BMA prediction ability is validated for the observed data from the later period. The results indicate that BMA prediction is more robust than the original FEMA model as well as the ensemble mean. Different types of uncertainty coefficients based on the BMA prediction distribution are also proposed to evaluate the FEMA models. Furthermore, the HBMA framework shows that both the channel roughness and the upstream flow input have a larger impact on prediction variance than bridges, and hence provides some insights for modelers into the relative impact of individual uncertainty sources in the flood modeling process.

Dominant processes in a watershed are those that most strongly control hydrologic function and response. Estimating dominant processes enables hydrologists to design physically realistic streamflow generation models, design management interventions, and understand how climate and landscape features control hydrologic function. A recent approach to estimating dominant processes is through their link to hydrologic signatures, which are metrics that characterize the streamflow timeseries. Previous authors have used results from experimental watersheds to link signature values to underlying processes, but these links have not been tested on large scales. This paper fills that gap by testing signatures in large sample datasets from the U.S., Great Britain, Australia, and Brazil, and in Critical Zone Observatory (CZO) watersheds. We found that most inter-signature correlations are consistent with process interpretations, i.e., signatures that are supposed to represent the same process are correlated, and most signature values are consistent with process knowledge in CZO watersheds. Some exceptions occurred, such as infiltration and saturation excess processes that were often misidentified by signatures. Signature distributions vary by country, emphasizing the importance of regional context in understanding signature-process links and in classifying signature values as ‘high’ or ‘low’. Not all signatures were easily transferable from small- to large-scale studies, showing that visual or process-based assessment of signatures is important before large-scale use. We provide a summary table with information on the reliability of each signature for process identification. Overall, our results provide a reference for future studies that seek to use signatures to identify hydrological processes.

Saltwater disposal (SWDs) has been linked to the recent increase of earthquakes in various regions of the United States. In some cases, the strong temporal and spatial associations have provided unequivocal evidences to the scientific community that wastewater injection is one of the dominant causal factors to the onset seismicity. In addition, numerous physical models have suggested that the increase in pore pressure from wastewater injection is capable to induce fault slips, providing further physical evidences. Another growing body of literature sorts to rigorously prove causality with statistical analysis where they propose statistical frameworks with parametric regression models to evaluate whether the observed earthquakes were occurring more often than by random chances and tested the statistical significance of the observed occurrences of earthquake to arrive at causal interpretations. We propose causal inference frameworks with the potential outcomes perspective to explicitly define what we meant by causal effect with mathematical formulations and declare necessary assumptions to ensure consistency between models for model comparison. In particular, we put considerations on two common difficulties in raster-based spatial statistical analysis, the spatial correlation, which can be described by Tobler’s first law of geography where near things are more related than distant things, and interference, a causal inference term, where treatments applied to some spatially indexed units affect the outcomes at other spatially indexed units, mostly due to complex physical processes. The study region, the Fort-Worth Basin of North Central Texas, is discretized into non-overlapping grid blocks. The first proposed workflow adopts a cross-sectional study design on aggregated earthquake catalog and injection data where two statistical methods are employed to test the significance of the causal effect between the presence or absence of saltwater disposals and the number of the earthquakes and to estimate the magnitude of the average causal effect. The second proposed workflow incorporates the temporal domain which holds more scientific interests. Finally, the analysis is repeated for different grid configurations to directly assess the sensitivity of statistical results.

Imaging spectroscopy data is becoming more readily available from different satellite and airborne platforms. As this data becomes more prolific, there is a need for shared data tools and code for wrangling, cleaning, and analyzing it. The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC) pioneers an on-demand science data processing platform with scalable back-end compute. It considers user experience and facilitates open science. ImgSPEC enables users to create data products in areas of interest using default workflows from registered algorithms, while also enabling users to customize scripts and workflows. ImgSPEC seamlessly interfaces with NASA Earthdata Search and tracks appropriate metadata for reproducibility when generating data products to share with others. Users can work in their preferred workspace (e.g., Rstudio, Jupyterlab, or command line) thereby facilitating use of open science software packages and collaborative coding through Git. ImgSPEC leverages existing NASA-funded information technologies such as the hybrid on-premise/cloud science data system (HySDS) and the Multi-mission Algorithm and Analysis Platform (MAAP). It also creates seamless interfaces with NASA-funded ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the NASA Surface Biology and Geology (SBG) Science and Applications Communities. As this technology is more widely adopted the interface with Amazon Web Services and NASA Earthdata search will enable broader use of more data (publicly available or loaded by the user) across more domains.

We demonstrate a new approach for climate model tuning in a realistic situation. Our approach, described in detail in Part I, systematically uses a single-column configuration of a global atmospheric model on a series of test cases for which reference large-eddy-simulations are available. The space of free parameters is sampled running the single-column model from which metrics are estimated in the full parameter space using emulators. The parameter space is then reduced by retaining only the values that are consistent with the metrics computed on large eddy simulations within a given tolerance to error. The approach is applied to the recently designed 6A version of the LMDZ model, itself the result of a long investment in the development of physics parameterizations and by-hand tuning. The boundary layer is revisited by increasing the vertical resolution and varying parameters that were kept fixed so far. The approach allows us to automatically reach a tuning as good as that of the 6A version, after some improvements are done at process scale. This approach helps accelerate the introduction of new parameterizations, by avoiding a tedious manual tuning process and preventing some of the error compensations that could occur if calibration was carried out directly with the full atmospheric model. This way of using machine learning techniques allows us to maintain the physical foundations of the model and to ensure that the improvement of global metrics is obtained for a reasonable behavior at process level. That is, we get things right for the right reasons.

Mount Early and Sheridan Bluff (87 degrees south) are the above-ice expression of Earth’s southernmost volcanic field and are isolated by more than 1000 km from any other exposed Cenozoic volcano in Antarctica. These monogenetic, Early Miocene volcanoes are constructed mostly of olivine-phyric basaltic pillow lavas and breccias (Mount Early) and pāhoehoe lavas (Sheridan Bluff) whose differentiation is controlled by the fractional crystallization of olivine with lesser quantities of clinopyroxene, plagioclase and magnetite. Fractional crystallization or contamination by crust cannot account for the coexistence of olivine tholeiite and alkaline compositions but their relationship can be explained by change from higher (5-6%) to lower (1.5-2%) degrees of mantle partial melting, respectively, of a source that is heterogeneous on a small-scale. Both alkaline and subalkaline magma types have geochemical and isotopic signatures that differentiate them from volcanism of the West Antarctic rift system. Data trends in Sr-Nd-Pb isotope space along with major and trace element characteristics indicate mixing of at least two-distinct mantle sources; 1) a relatively depleted component similar to sources for mid-ocean ridge basalt from the extinct Antarctic-Phoenix spreading center, and 2) an enriched component similar to sources for mafic magmas of the Jurassic Karoo‒Ferrar large igneous provinces. The availability of these two mantle source types was facilitated by the detachment, sinking and heating of metasomatized continental lithosphere (enriched source) that released volatiles into the surrounding asthenosphere (depleted source) to promote flux melting. Volcanism triggered by lithospheric detachment is therefore uniquely applied to Mount Early and Sheridan Bluff to explain their isolation and enigmatic tectonic setting but can also account for source heterogeneity and the ephemeral change in degree of mantle partial melting recorded in their mafic compositions.

In this project, I study families of deep learning neural networks that are trained on publicly available chest X-ray datasets to identify the best image classification algorithm for automating the diagnosis of respiratory illnesses. Specifically, the learned networks will be used to classify anonymized chest X-ray images to three classes: healthy, COVID-19 and non-COVID pneumonia. As in most real-world applications, publicly available chest X-ray image datasets are not abundant, and ground truth data of COVID-19 diagnosis is especially hard to come by. In this project, the first variable implemented to improve the predictive power of the neural networks is through pretraining on a domain-relevant and much larger than the transfer learning dataset. To address the imbalance within training data, the second variable implemented is to customize the data sampling configuration using the equal-weight-per-epoch method or fixed- fraction-per-batch method. As control for each neural network, pretrained weights learned from the classic ImageNet dataset are used, and no customized training data sampling method is applied. In regard to transfer learning, two scikit-learn functions, average precision and F1 score, are computed during training. Then Precision and Recall are manually calculated based on the confusion matrix for each neural network along with the hyperparameters. The most significant observation is that the Recall metric for the control group is consistently less than 0.6, which is a clear indicator of the underperformance on COVID-19 prediction. The family with significantly higher performance is DenseNet; surprisingly, DenseNet169 has one of the highest Precision and Recall of 0.870 and 0.837. With more than 82 million COVID-19 cases worldwide, the need for efficient, accurate and mass diagnosis of patients is apparent and growing. The utilization of chest X-ray images in medical diagnosis is both a cost-effective and widespread technique for early screening of respiratory illnesses.

Profiling water vapor in the marine boundary layer (MBL) is critical to marine weather prediction, maritime communications, and understanding feedbacks relevant to multi-decadal climate prediction, yet profiling the MBL remotely has proven extraordinarily difficult because of the spatial scales involved and the proximity of the ocean surface. Collocated radio occultation (RO) and nadir passive microwave (MW) soundings can be combined in retrieval to profile water vapor with the vertical resolution of RO and with super-refraction and the wet-dry ambiguity inherent to RO resolved by the MW. We have constructed a retrieval technique that considers collocated RO and MW soundings that yields profiles of water vapor in the MBL with unprecedented precision, accuracy, and vertical resolution. We have also performed RO and MW collocation studies that consider many current RO missions and MW instruments. The joint RO+MW retrieval technique mines the information in MW soundings for an inference of the microwave refractivity in the MBL surface air, removes the biasing effect of super-refraction following the approach of Xie et al. (2006, doi:10.1175/JTECH1996.1), and resolves the wet-dry ambiguity inherent to RO using the MW sounding as a constraint or a weather forecast as a prior in 1DVAR. We constructed a simulation-retrieval demonstration system that uses a multi-phase screen propagator to simulate RO amplitude and phase and the Optimal Spectral Sampler (OSS) to simulate AMSU-A radiances. In its current state, the retrieval technique is capable of producing MBL water vapor profiles with 2% accuracy and 100-meter vertical resolution. Our collocation study shows that existing RO satellites and orbiting MW instruments achieve approximately 1,300 collocations daily, defined with a temporal window of 10 minutes. To facilitate this study, we have formulated a time-dependent rotational transformation that is applied to RO geolocations. It is three orders of magnitude more efficient than a brute force approach to finding collocations and is valid to 4% precision. We have found that the greatest yield for collocations in low latitudes would come from RO satellites that would fly in tandem with the TROPICS MW CubeSats, potentially producing 1,500 daily RO+MW collocations in the Tropics and Subtropics.

Heterogeneous reactions occurring at the surface of atmospheric aerosol particles regulate the production and lifetime of a wide array of atmospheric gases. Aerosol surface area plays a critical role in setting the rate of heterogeneous reactions in atmospheric chemistry. Despite the central role for aerosol surface area, there are few assessments of the accuracy of aerosol surface area concentrations in regional and global models. In this study, we compare aerosol surface area concentrations in the EPA’s CMAQ (Community Multiscale Air Quality) model with commensurate observations from the 2011 NASA flight-based DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign. The study region for the 2011 DISCOVER-AQ campaign focused primarily on the Baltimore and Washington, DC region. Dry aerosol surface area was measured aboard the NASA P-3B aircraft using a combination of a Scanning Mobility Particle Sizer (SMPS), Aerodynamic Particle Sizer (APS), and Ultra-High Sensitivity Aerosol Spectrometer (UHSAS). In this study, we focus on the continuous 1s UHSAS measurements in size range of 60-1000nm, as it captures the majority of the dry surface area distribution. Over the course of 13 flight campaigns, we show strong agreement between measured and modeled aerosol number concentration (CMAQ Number/UHSAS Number= 0.88). In contrast, the total surface area showed a larger discrepancy (CMAQ surface area/UHSAS surface area = 0.44). We hypothesize that the emissions and chemistry in CMAQ relating to the production and loss of each moment play a large part in the model/measurement discrepancy. Despite the disagreement, this analysis suggests that modeled aerosol surface area is accurate to within a factor of two, highlighting that uncertainty in the rate of heterogeneous reactions is largely driven by uncertainty in the reactive uptake coefficients. We would like to thank the DISCOVER-AQ NASA Langley Aerosol Research Group Experiment (LARGE) research team, including Richard Moore, Bruce Anderson, Andreas Beyersdorf, Luke Ziemba, Lee Thornhill, and Edward Winstead for the use of their data in this study.

The aim of this study is to determine the hydrochemical properties of water resources in the Seyfe Basin and to understand processes involving water chemistry using statistical techniques such as principal component analysis (PCA) and cluster analysis. Seyfe Lake closed basin is located west of Kirsehir (Turkey) in the Central Anatolia. Considering the importance of the Seyfe Lake Basin for its water resources potential and due to its fragile ecosystem, a detailed hydrogeological characterization of the basin has been undertaken. In order to characterize hydrogeological system accurately, water samples were collected from springs, wells, drainage channels during September and December 2019 field campaigns from the Seyfe basin. The temperature, pH, total dissolved solids (TDS) and specific electrical conductance (EC25°C) were measured at the field and spatio-temporal changes of major ions are evaluated. The waters have neutral to slightly alkaline character, and most of the water samples are saturated with calcite, dolomite and aragonite minerals. According to the results obtained from Gibbs, Piper plot, and PCA, the water-rock interaction takes place without any remarkable ion exchange reactions. Water facies identified with the Piper diagram; supports this view. Water types are determined as Ca-HCO3 for springs; Na-HCO3 for wells; Na-SO4 for drainage channel in the dry season and Na-Cl in the rainy season. In Ihe study area; ionic compositions of the springs are not affected by seasonal changes. The high sulfate concentration is probably related to the weathering of evaporitic rocks in the basin. The waters are grouped according to the results, and the difference observed within the groups are related to the lithology of aquifers and flow path of the waters. KEYWORDS: Hydrochemistry, Major ions, Kırsehir, Central Anatolia, Seyfe Closed Basin, Spatio-temporal Changes.

Turbulent kinetic energy dissipation rates in the Indonesian throughflow (ITF) exit passages to the Indian Ocean, the Lesser Sunda waters, are inferred from archived CTD measurements and recent high-resolution time series data sets. Dissipation rates from archived data sets are inferred using an improved Thorpe scale method validated against microstructure measurements. Elevated dissipation rates ~[10-6-10-7] m2s-3 were observed in the straits, where internal tides are generated. Tidal variations seemingly influence the dissipation rates and diffusivities as has been suggested from the yoyo profiling data sets. The spatial pattern of dissipation rates inferred from the high-resolution 3D hydrodynamics model output of Nagai et al (2015) shows a general agreement with the observations in the location of the mixing hot spots and suggests that the M2 internal tide is the dominant factor driving the turbulent kinetic dissipation rates in this region. The bias in the model is possible due to the lack of representation of the ITF and mesoscale circulation in the model.

When the COVID-19 pandemic forced institutions to close their physical spaces, many researchers and professors were required to work from home. Media outlets and individuals wondered if women specifically would be shouldering the childcare and/or homeschooling responsibilities, decreasing the time they had for professional activities. Preliminary analyses show mixed results: some have seen decreased submissions from women (Vincent-Lamarre et al., 2020; Del Boca et al., 2020), with at least one other showing an increase in submissions from women (Dolan & Lawless, 2020); and another suggests the rate of male-athored submissions to preprint archives are growing faster than those from women (Frederickson 2020). AGU has been monitoring our monthly submission data to understand how the pandemic and stay-at home orders are affecting our submissions from specific demographics—based on author gender, age, and country. Our analysis finds that the pandemic so far has not resulted in a decrease in submissions to AGU journals; nor has there so far been a decrease in the participation of women as submitting authors. Instead, we have seen an increase in submissions from most author groups compared to year-to-date numbers in previous years.

Between 5 and 25% of the total momentum transferred between the atmosphere and ocean is transmitted via the growth of long surface gravity waves called ‘swell’. In this paper, we use large eddy simulations to show that swell-transmitted momentum excites near-inertial waves and drives turbulent mixing that deepens a rotating, stratified, turbulent ocean surface boundary layer. We find that swell-transmitted currents are less effective at producing turbulence and mixing the boundary layer than currents driven by an effective surface stress. Overall, however, the differences between swell-driven and surface-stress-driven boundary layers are relatively minor. In consequence, our results corroborate assumptions made in Earth system models that neglect the vertical structure of swell-transmitted momentum fluxes and instead parameterize all air-sea momentum transfer processes with an effective surface stress.

Synthetic aperture radar (SAR) is an important means of obtaining battlefield information. Correct identification of SAR images is essential. Hence, we propose a new method of SAR image recognition based on multi-feature fusion. Convolutional neural networks (CNNs) are based on local pixels and fuse this information at deep layers to obtain deep features. The moment method focuses on the entire image and obtains global moment features of different orders. We fuse the two feature types and choose a suitable feature fusion. First, we propose a Q_sigmoid function to enhance SAR image contrast, and then we separate the target area and remove noise interference. Next, we design a convolutional neural network to obtain the deep features of the target from the separated image. Then, we use four moment methods to produce the moment features of the image and sort them according to recognition rate. Finally, the above features are fused and sorted according to recognition rate to find the optimal combination. The recognition rate of our optimal fusion is 98.63%, which is 2.77% higher than is obtained by CNNs and 6.32% higher than moment methods.

Radio environment map (REM) is an efficient enabler for practical cognitive radio networks by sensing the electromagnetic information within regions of interest dynamically. Most of works on Kriging-based method have proven that separate estimation for pathloss and shadowing can obtain more accurate REM construction. But these methods have some shortcomings that prior information is required for construction or disability for multiple transmitters scenario. In order to overcome the problems of urban REM construction mentioned above, this paper propose a residual Kriging algorithm based on generalized regression neural network (GRNN-RK) for that. The performance of proposed algorithm has been evaluated by the analysis of simulation results, and experiments show that GRNN is capable of improving Kriging in accuracy. Additionally, the influence of spread on REM construction is also experimented.

Gravity waves (GWs) are key drivers of atmospheric dynamics, with major impacts on climate and weather processes. However, they are challenging to measure in observational data, and as a result no large-area multi-decadal GW time series yet exist. This has prevented us from quantifying the interactions between GWs and long-timescale climate processes. Here, we exploit temperatures measured by commercial aircraft since 1994 as part of the IAGOS atmospheric chemistry research programme to produce a novel 26-year time series of upper troposphere/lower stratosphere (UTLS) GW measurements across most of the northern hemisphere. We analyse 90\,342 flight-hours (76.2 million flight-kilometres) of data, typically at a temporal resolution of seconds and with high temperature precision. We show that GW activity in the northern-hemisphere UTLS is consistently strongest north of and above the upper tropospheric jet. We also show that GW sources not typically observed in stratospheric data but assumed in model schemes, such as the Rocky Mountains, are visible at these altitudes, suggesting that wave momentum from these sources is deposited specifically between $\sim$200–50\,hPa. Our data shows no significant impact of the Quasi Biennial Oscillation, the Northern Annular Mode, or climate change. However, we do see strong evidence of links with the El Ni\ no-Southern Oscillation, which modulates the measured GW signal by $\sim$25\%, and weak evidence of links with the 11-year solar cycle. These results have important implications for atmospheric process modelling and for understanding large-scale climate teleconnections.

Much of the water that once flowed on the surface of Mars was lost to space long ago, and the total amount lost remains unknown. Clues to the amount lost can be found by studying hydrogen (H) and its isotope deuterium (D), both of which are produced when atmospheric water molecules H$_2$O and HDO dissociate. The freed H and D atoms then escape to space at different rates due to their different masses, leaving an enhanced D/H ratio. The rate of change of D/H is referred to as the fractionation factor $f$. Both the D/H ratio and $f$ are necessary to estimate water loss; thus, if we can constrain the range of $f$, we will be able to estimate water loss more accurately. In this study, we use a 1D photochemical model of the Martian atmosphere to determine how $f$ depends on assumed temperature and water vapor profiles. We find that for most Martian atmospheric conditions, $f$ varies between $10^{-1}$ and $10^{-5}$; for the standard Martian atmosphere, $f=0.002$ for thermal escape processes, and $f\approxeq0.06$ when both thermal and non-thermal escape are considered. Using these results, we estimate that Mars has lost at minimum 66-123 m GEL of water. Our results demonstrate that the value of $f$ is almost completely controlled by the amount of non-thermal escape of D, and that photochemical modeling studies that include fractionation must thus model both neutral and ion processes throughout the atmosphere.

Lake Powell is a major reservoir responsible for providing water resources throughout the Colorado River Basin. Here we develop a hybrid model to perform 1-4 month forecasts of the inflow to Lake Powell. First, observational data are used in early December for a binary prediction of high- or low-flow based on peak monthly inflow or April-July inflow, and our prediction is much better than the Colorado Basin River Forecast Center (CBRFC)’s forecasts for the 1982-2016 study period. Second, we calibrate the NCEP Climate Forecast System seasonal forecasts of temperature, precipitation, and snow mass, and use them to perform 1-4 month inflow prediction through a regression equation, with the coefficients determined for the high- and low-flow years separately. Our early April forecasts for the critical April-July inflow are better than those of the CBRFC from 1982-2016. Sensitivity tests have also demonstrated the value of including snow mass in our prediction.