We discuss an efficient implementation of the analog ensemble algorithm, the distilled analog ensemble, which is achieved by distilling the post-processing transformation generated by the analog ensemble into a deep neural network. While the analog ensemble has been shown to be able to improve deterministic forecasts and create calibrated probabilistic predictions in many contexts, a common issue with operationalizing a large, global analog ensemble-based system is the amount of data (a corpus of historical forecasts) and post-processing latency required to process that data in the time-critical path of producing a forecast. Deep neural networks are high capacity function approximators, and we demonstrate that we are able to train a network that memorizes the post-processing behavior of the analog ensemble on a particular corpus of forecasts. This technique breaks the scale factor between the size of the historical forecast corpus (larger is better for forecast skill improvements) and the calculation required to post-process the current forecast in real-time operations. We show that the distilled analog ensemble is able to improve European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution deterministic forecasts of winds in the lower stratosphere using as ground-truth either the ECMWF analysis or observations from Loon high altitude balloons. In this case, rather than requiring terabytes of historical forecast data to apply the conventional analog ensemble, we can perform the post-processing that improves forecast quality on the fly doing computationally efficient forward passes through a pre-trained network that has a data size of only 100’s of kilobytes.

The diamondback terrapin (Malaclemys terrapin), the official state reptile of Maryland, is an estuarine turtle found along the eastern and Gulf coasts of the United States. The diamondback terrapin may experience risks due to increasing global temperatures because they display temperature-dependent sex determination (TSD). Terrapins, through TSD, produce more female offspring than male offspring if their nest temperatures are relatively warm, leading to ecological issues should global temperatures get too high. This study used temperatures from several individual field, monitored nests, along with air temperatures at the nesting site, recorded in the summer of 2018, to select a predictive model with the R software package rstanarm. The model was then used to predict the average nest temperature in the same nesting site using air temperatures that were recorded during the summer of 2019. This prediction was then compared to temperatures recorded from five nests within the nesting site in 2019 to evaluate its predictive capabilities. A regression spline mixed model (RSMM) was selected for having the lowest leave-one-out information criterion at 50905.95 LOOIC, with a standard error of 0.64 LOOIC. The prediction of average nest temperature developed by the RSMM was given a predictive interval of 95%. We found that the actual temperature of the average nest in 2019 had a prediction coverage of 92.97% from the prediction model, with a root squared mean error of 1.89oC and a predictive mean absolute error of 2.10oC. Benefits of the study involve the ability to predict the future diamondback terrapin nest temperatures within a specific nesting site so long as future air temperatures within that site are known.

The fragility of human life has been tragically demonstrated through history by numerous natural hazards (e.g. earthquakes, tsunami, drought, floods, volcanic eruptions and landslides) leading in some cases to large-scale disasters. Given that the natural processes that underpin such hazards will continue into the foreseeable future, there is widespread international agreement about the need to focus on reducing risk and increasing resilience. In line with such objectives, Indigenous knowledge (IK) in Disaster Risk Reduction (DRR) is increasingly being recognised as an alternative domain of understanding and knowing, that is highly relevant to present-day societal challenges such as natural hazards and disasters. In Aotearoa-New Zealand, rapid social, political and environmental changes have led a growing chorus of Māori leaders expressing the need to have their perspectives and histories surrounding diverse natural hazards and risk heard. Some of the contributions that Māori knowledge can make to the management and mitigation of natural hazards in Aotearoa-New Zealand have already been identified, but much remains to be done before plural knowledge learning and development can take place. In this presentation we will explore some of the benefits of epistemological and empirical differences in knowledge about natural hazards, and consider how scientific and Māori complexes of knowing and being can inform one another to contribute to effective DRR and Recovery.

Coal strip mining has left degraded soils throughout the southeastern United States. These soils tend to have low pH, high bulk density, impacted hydraulic processes, and an accumulation of heavy metals that limit revegetation and reforestation efforts. Shortleaf pine (Pinus echinata) has the adaptability to grow on post-mined sites due to being able to tolerate soils with a low pH. It also has the largest native range of pines in the southeastern United States, making it an ideal species for such restoration efforts. Furthermore, soil restoration using a combination of biochar and mycorrhizal amendments can provide many benefits for degraded soils ranging from increasing carbon sequestration, reducing erosion, promoting plant growth, and immobilizing heavy metals. However, limited empirical field trials have been conducted on the success of these soil amendments on both soil health and tree productivity. To provide restoration recommendations to land managers and landowners, we established a field trial in Winston County, Alabama at a reclaimed mining site. In Spring 2021 we planted Shortleaf pine in a complete randomized block design with 30.5x30.5 m spacing with two treatments: biochar and microbial inoculation in four replicates. We measured soil bulk density, pH, heavy metal content, electrical conductivity, carbon content, and nitrogen content both before and after planting every three months. We will also monitor shortleaf pine survival and growth. Our preliminary results for pH, bulk density, and electrical conductivity are within the expected range for shortleaf pine to do well on this post-mined site. Prior to soil treatments and planting, soil pH was 5.55 ± 0.54 pH, dry bulk density was 1.46 ± 0.14 g/cm3, wet bulk density was 1.74 ± 0.12 g/cm3, and electrical conductivity was 273.19 ± 141.33 µS. Soil nitrogen content was 0.15 ± 0.04% and soil carbon content was 2.31 ± 0.76%. The average C:N ratio was 15.8:1. Survival of planted seedlings after three months was 98%. Changes in soil physical and chemical conditions relative to restoration treatments are pending. This study will help support our understanding of biochar’s interaction with mycorrhizal fungi inoculation, role in restoration, and use in southeastern United States soils.

Using principal component (PC) analysis of 16 years of monthly series of nitrogen dioxide (NO2) from the Ozone Monitoring Instrument on the Aura satellite, we show that it is the third PC (PC3) from the deseasonalized hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40% of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO2 variability. We thus conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in the NO2 departures from the seasonal averages. Once we identified PC3 as the NO2 mode that is coupled with the economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of NO2 variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board of NASA Aqua satellite, decompose series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of NO2 PC3 on these climate variables.

A new diver-held magnetometer has been developed that directly senses the total magnetic gradient as a scalar quantity, independent of the orientation of the device. It therefore provides a direct signal if a magnetic (e.g. ferrous) object is in its vicinity, regardless of other ambient geomagnetic effects. The small (7cm diameter x75cm length), battery-powered device is neutrally buoyant, and provides a simple and intuitive audio interface to the diver. The device was field tested in July 2019 during the Lost Ships of Cortés Project offshore Villa Rica, Mexico, an area with strong underlying volcanic geology and large quantities of magnetic sediment that mask buried archaeological materials. Magnetic anomalies were recorded during boat-towed total-field magnetometer surveys, but divers could not identify their source using traditional underwater metal detectors. The new magnetometer decisively and precisely guided divers to an early 16th-century anchor at a range of several meters that was completely buried in magnetite-rich sand with its shallowest point at 50cm below the sea floor.

The American Geophysical Union (AGU) is committed to inspiring and educating present and future generations of diverse, innovative, and creative Earth and space scientists. To meet our commitment, AGU provides career and educational resources, webinars, mentoring, and support for students and professionals at each level of development to reduce barriers to achievement and to promote professional advancement. AGU is also working with other organizations and educational institutions to collaborate on projects benefiting the greater geoscience community. The presentation will include an overview of current Pathfinder efforts, collaborative efforts, and an appeal for additional partnerships.

The American Association for the Advancement of Science’s Leshner Leadership Institute for Public Engagement with Science, founded in 2015, empowers scientists and engineers to develop and implement public engagement activities, mentor other scientists in their communities and promote public engagement within their institutions. Each year the AAAS Leshner Leadership Institute brings together 15 mid-career researchers working on a topic at the nexus of science and society for a week of intensive training, public engagement practice, and planning. During the following year at their home institutions, AAAS Leshner Fellows implement their plans, interact via regular virtual meetings and receive additional support from staff and experts in the field of public engagement. One of the goals of the AAAS Leshner Leadership Institute is to catalyze institutional change: building understanding about, capacity for and support of public engagement within the various institutions with which researchers are affiliated. AAAS Leshner Fellows have sought to identify opportunities for institutional change within a variety of settings, including universities, government agencies and professional societies. This presentation will provide examples of institutional change from the first three cohorts of AAAS Leshner Fellows (2016-17, climate change; 2017-18, infectious disease; 2018-19, food and water security), including the ways in which they have worked to integrate public engagement into the purpose of the institutions, build processes to support public engagement, and empower people within the institutions to participate in public engagement. It will also discuss the opportunities and challenges for encouraging institutional change via a distributed network of individuals who are linked by discipline but not (necessarily) by institution.

Tourmaline is a common mineral in Sn-W mineralizations. It is commonly strongly zoned, both optically and chemically, and may host a range of trace elements. High levels of Sn have been observed in tourmaline throughout the world, whereas W is typically very low, even in mineralized areas. Tourmaline from subeconomic cassiterite mineralizations and hydrothermally altered granite from the Land’s End granite, SW England, was analyzed by EPMA in order to identify Sn-rich zones. LA-ICP-MS is typically the go-to instrument for trace element analyses, but spatial resolution is strongly compromised compared to EPMA. The behavior of Sn in tourmaline varies from patchy to concentric zoning between different samples, and the highest recorded value was 2.48 wt% SnO2 in a patchily zoned rim. A high resolution WDS map of the area did not show any major Sn spikes, and TiO2, a common minor element in cassiterite, is low in the Sn-rich regions. Also, cassiterite in these samples are commonly light brown in plane polarized light, and the Sn-rich zones can be related to dark green zones. This indicates that the high Sn-values are not due to cassiterite inclusions, and may be structurally bound in tourmaline, most likely substituting into the Y-site. The highest Sn-values (>0.4 wt% SnO2) are coupled with a substantial substitution of Fe for Al in the Z-site, and the structural formula calculation indicate that some Fe is present as Fe3+ in the analyses with over ca. 20 wt% FeO. Trends between Sn and other elements are erratic, except for a weak positive correlation with Sr. The Sn-content in tourmaline combined with the inferred Fe3+ and the observed cassiterite in the samples may indicate that oxidation of the ore fluid caused cassiterite precipitation.

We investigate the relationship between NO infrared radiation and x-ray radiation at different local time (LT) and latitudes of NO average fluxes. The NO volume emission rates obtained by SABER instrument on board TIMED satellite and x-ray fluxes on long (0.1-0.8 nm) and short (0.05-0.4) channels measured by GOES 12 satellite were used. NO rates were vertically integrated to obtain fluxes, which are then separated into day (8

Tikhonov’s regularization method is the standard technique applied to obtain models of the subsurface conductivity dis- tribution from electric or electromagnetic measurements by solving UT (m) = ||F(m) - d||^2 + P(m): The second term correspond to the stabilizing functional, with P(m) = ||m||^2 the usual approach, and the regularization parameter. Due to the roughness penalizer inclusion, the model developed by Tikhonov’s algorithm tends to smear discontinuities, a feature that may be undesirable. An important requirement for the regularizer is to allow the recovery of edges, and smooth the homogeneous parts. As is well known, Total Variation (TV) is now the standard approach to meet this requirement. Recently, Wang et.al. proved convergence for alternating direction method of multipliers in nonconvex, nonsmooth optimization. In this work we present a study of several algorithms for model recovering of Geosounding data based on Inmal Convolution, and also on hybrid, TV and second order TV and nonsmooth, nonconvex regularizers, observing their performance on synthetic and real data. The algorithms are based on Bregman iteration and Split Bregman method, and the geosounding method is the low-induction numbers magnetic dipoles. Non-smooth regularizers are considered using the Legendre-Fenchel transform.

Eutrophication-induced hypoxia in a coastal estuarine system results in the deterioration of water quality, which is a growing issue worldwide. In early August 2016, an anoxic event caused the mortality of up to 75 percent of cultured oysters in aquaculture leases of northern Tracadie Bay, New Brunswick (TNB). Field observations revealed that, although the occurrence of summer hypoxia in north TNB is linked with anthropogenic stressors, changes in the geomorphology of one of the bay’s inlet are likely to play a significant role in escalating the eutrophic conditions. However, adequate information on the circulation dynamics of TNB estuarine system was lacking. To address this knowledge gap, a high-resolution spatially-explicit hydrodynamic model was developed for TNB to evaluate the possible physical processes involved in generation of hypoxia leading to oyster mortality. The model showed high skill (0.93) in south TNB in simulating water level elevations and slightly lower skill (0.85) in north TNB. Findings from modeling scenarios based on past, present and future inlet geomorphology suggest that it has strong consequences on intricate circulation patterns, renewal of water and transport. Further, the model is coupled with a volume advection-dispersion tracer module to track the dissemination of effluent and identify areas at risk of hypoxia. Residual flow estimates revealed poorly-circulated stagnant areas, consistent with hot spots in sediment organic matter content. Outcomes from this study will be of relevance for the management of water quality and aquaculture practices.

The Goddard Earth Observing System (GEOS) global atmospheric model tracks and transports several individual aerosol species. A key contribution by these aerosols is to interact with solar radiation. When calculating the aerosol-radiation interactions, pre-computed look-up tables of aerosol optical properties are used.We have recently finished an effort to update the aerosol optical properties used in the GEOS model. This has been accomplished with a full rewrite of the optical property simulation code into a Python-based version. In addition to structural changes to the code (outlined below), there are some concrete changes to the aerosol particle definitions. First, truncated lognormal size distributions have been replaced with non-truncated ones, and sub-bin size resolutions of all simulations have been enhanced. Additionally, non-spherical dust accuracy has been improved due to switching to a different spheroidal kernel database. Finally, dust size distribution has been changed from a continuous power law distribution to a bin-specific lognormal distributions.There are also significant changes in the convenience and flexibility of adding or modifying aerosol types for future work and other investigations. Particle properties are now defined exclusively in resource files rather than in the code. This allows for easy addition or modification of aerosol particles, with no need to touch the code. Relatedly, the code can be run either with a provided command-line interface, or via a custom script or notebook. That is, no Python knowledge is needed to generate aerosol particle optics, making the tool easy to use. Additionally, particle types are fully decoupled from specific size distributions, hydration schemes, and other type-specific properties. This also means changing, e.g., a particle size distribution type for a given particle is simple. Performance is significantly improved due to novel Mie simulation optimizations. This enables the aforementioned high size resolutions to be used without concerns for processing time.

Natural and human-made extreme events can alter residential electricity demand in urban areas and stress the electricity grid. Different types of residential electricity consumers, which in some cases account for more than 30% of customers, can present different consumption patterns. Residential electricity demands have been widely analyzed considering single-family consumers; however, multi-family consumers remain comparatively understudied. The deployment of smart electricity meters enables the identification of single- and multi-family residential electricity consumption patterns at high temporal resolution. Using smart electricity meter data for the greater Chicago area, we compare electricity demand profiles reported by smart meters from single- and multi-family consumers in a large and diverse urban environment. The study provides a comprehensive analysis of daily electricity demand profiles of these two types of residential consumers to identify peak electricity consumption times and magnitudes. The analysis also presents correlations of the electricity demand with socioeconomic data at the zip code level. Preliminary results show that median building age, percent of occupancy, and mean commute time are statistically significant predictors of multi-family electricity consumption. Results suggest that single-family consumers have comparable correlation when using the same socioeconomic data with respect to the multi-family users. Uncovering differences between single- and multi-family electricity demands can assist city planners and utility managers to develop tailored demand management strategies.

Internal waves and vortical mode are ubiquitous in the ocean, providing a conduit for energy transfer from tides and wind-driven circulation to fine- and micro-scale turbulence associated with internal-wave breaking and dissipation. Numerical simulations of a Garrett-Munk internal-wave field are used to study the interactions between internal waves and vortical mode, and the generation of turbulent mixing events and semi-permanent finestructure (vortical mode). Regions of reduced stratification are found to occupy between ~5% and 25% of the model domain, depending on the strength of the anomalies. Decomposition of 3-dimensional model fields into linear internal-wave and vortical-mode components shows that both influence stratification, with ~88% of the anomalies due to linear internal waves and ~40% due to vortical mode, the overlap in percentiles representing regions that are a combination of the two. The time evolution of anomalies is examined, from the initial generation to eventual dissipation. Results further describe the preconditions of these regions, including their relation to a subcritical Richardson number and overturning / mixing events as quantified via Thorpe scales and turbulent dissipation rate.

The ESA Virtual Space Weather Modelling Centre (VSWMC )project was defined as a long term project including different successive parts. Parts 1 and 2 were completed in the first 4-5 years and designed and developed a system that enables models and other components to be installed locally or geographically distributed and to be coupled and run remotely from the central system. A first, limited version went operational in May 2019 under the H-ESC umbrella on the ESA SSA SWE Portal. Part 3 is the next development step before all objectives of the VSWMC are achieved. The goal of the ESA project “Virtual Space Weather Modelling Centre - Part 3” (2019-2021) is to further develop the Virtual Space Weather Modelling Centre, building on the Part 2 prototype system and focusing on the interaction with the ESA SSA SWE system. The objective and scope of this new project include, apart from maintaining the current operational system, the efficient integration of new models and new model couplings, including daily automated end-to-end (Sun to Earth) simulations, the further development and wider use of the coupling toolkit and front-end GUI, making the operational system more robust and user-friendly. The VSWMC-Part 3 project started on 1 October 2019. The new models that are being integrated are Wind-predict (a global coronal model from CEA, France), the Coupled Thermosphere/Ionosphere Plasmasphere (CTIP) model, Multi-VP (another global coronal model form IRAP/CNRS, France), the BIRA Plasma sphere Model of electron density and temperatures inside and outside the plasmasphere coupled with the ionosphere (BPIM, Belgium), the SNRB (also named SNB3GEO) model for electron fluxes at geostationary orbit (covering the GOES 15 energy channels >800keV and >2MeV) and the SNGI geomagnetic indices Kp and Dst models (University of Sheffield, UK), the SPARX Solar Energetic Particles transport model (University of Central Lancashire, UK), Spenvis DICTAT tool for s/c internal charging analysis (BISA, Belgium), the Gorgon magnetosphere model (ICL, UK), and the Drag Temperature Model (DTM) and operations-focused whole atmosphere model MCM being developed in the H2020 project SWAMI. We will provide an overview of the state-of-the-art and demonstrate the system.

Understanding the falling velocity of cloud droplets and ice crystals in a nonuniform flow is fundamental to cloud physics and precipitation. In this study, the mean settling velocity of a hydrometeor falling into a non-stationary fluid is examined. To investigate the possible particle-fluid interaction, a homogeneous and isotropic turbulent flow fields is considered. The study covers a wide range of flow velocities and hydrometeor density and size. The results show initially reduction of hydrometeor settling velocity and then enhancement in strong turbulence. The mean settling velocity depends on a) mean flow and turbulent intensity i.e., standard deviation of flow and b) particle terminal velocity i.e., the shape and size of hydrometeor, its density, the dynamic viscosity and the density of the fluid, and the gravitational acceleration. The non-dimensional parameters that are important in characterization of hydrometeor settling velocities are estimated using dimensional analysis. These non-dimensional parameters are then used to formulate the mean settling velocity.

Tephra fallout hazard assessment relies on accurate reconstruction of eruption source parameters (ESPs) from tephra deposits. Models of tephra transport and sedimentation from a volcanic plume use ESPs (e.g. erupted mass, column height, mass eruption rate, total grain size distribution) that characterize the processes and the properties of the plume, particles and the atmosphere. We use Tephra2, an Eulerian model of tephra dispersion that simplifies atmospheric dynamics to reconstruct ESPs from mapped deposits. Tephra2 works well in reconstructing ESPs for some deposits, however it does not account for the geometry (i.e. shape) of umbrella clouds of large explosive eruptions. Since the accumulation of particles on the ground is calculated with respect to their release point in the atmosphere, we hypothesize that a modification of Tephra2 that accounts for umbrella clouds would better explain the deposit variations observed in the field associated with some large eruptions. We developed a Python version of Tephra2 that uses the advection – diffusion equation to calculate the mass accumulation of tephra released from an umbrella cloud. We tested three different geometries (i.e. point, vertical line and horizontal disk) against field data from the deposit of the 2450 BP Pululagua (Ecuador) eruption that occurred in absence of wind. Our preliminary results indicate three important aspects of tephra modeling: i) a disk geometry characterizing an umbrella cloud fits the data better than the line and point sources, the last two being highly sensitive to the atmospheric diffusion coefficient; ii) a disk geometry is sensitive to the volume of tephra and the radius of the disk and, iii) different discretization of disk geometries show little sensitivity in deposit geometry with change in the release height, suggesting that disk radius is a more sensitive parameter in modeling large umbrella clouds than the release point or release height. Since large explosive eruptions are characterized by large laterally spreading umbrella clouds even when advected by wind and the umbrella diameter is controlled by eruption rate, as is plume height in vertical plumes,, we suggest the modeling of large deposits with alternative models of the cloud geometries is an important step in analysis of ESPs associated with mapped deposits.