Solar, auroral, and radiation belt electrons enter the atmosphere at polar regions leading to ionization and affecting its chemistry. Climate models usually parametrize this ionization and the related changes in chemistry based on satellite particle measurements. Precise measurements of the particle and energy influx into the upper atmosphere are difficult because they vary substantially in location and time. Widely used particle data are derived from the POES and GOES satellite measurements which provide electron and proton spectra. We present the electron energy and flux measurements from the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instruments on board the Defense Meteorological Satellite Program (DMSP) satellites. This formation of now three operating satellites observes the auroral zone in the UV from which electron energies and fluxes are inferred in the range from 2 keV to 20 keV. We use these observed electron energies and fluxes to calculate ionization rates and electron densities in the upper mesosphere and lower thermosphere (≈ 80–200 km). We present our validation study of the SSUSI-derived electron densities to those measured by the ground-based EISCAT radar stations. We find that with the current standard parametrizations, the SSUSI-derived auroral electron densities (90–150 km) agree well with EISCAT measurements, with differences between +/- 20% for F18, and +/- 50 % for F17. The largest differences are at the lower end of the altitude range because there the electron densities decline very rapidly.

The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies. Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR). Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data. This presentation introduces international community guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product. Supportive case studies demonstrate the applicability of the proposed guidelines.

We developed a space-time model to project seasonal streamflow extremes on a river network for at several lead times. In this, the extremes – 3-day maximum streamflow - at each gauge location on the network are assumed to be realized from a Generalized Extreme Value (GEV) distribution with temporal non-stationary parameters. The parameters are modeled as a linear function of suitable covariates. In addition, the spatial dependence of the extremes across the network is modeled via a Gaussian copula. The parameters of the non-stationary GEV at each location are estimated via maximum likelihood, whereas those of the Copula are estimated via maximum pseudo-likelihood. Best subset of covariates are selected using AIC. Ensembles of streamflow in time, which are based on the varying temporal covariates and from the Copula, are generated, consequently, capturing the spatial and temporal variability and the attendant uncertainty. We applied this framework to project spring (May-Jun) season 3-day maximum flow at seven gauges in the Upper Colorado River Basin (UCRB) network, at 0 ~ 3 months lead time. In this basin, almost all of the annual flow and extremes that cause severe flooding, arrives during the spring season as a result of melting of snow accumulated during the preceding winter season. As potential covariates, we used indices of large scale climate teleconnection – ENSO, AMO, and PDO, regional mean snow water equivalent and temperature from the preceding winter season. The skill of the probabilistic projections of flow extremes is assessed by rank histograms and skill scores such as CRPSS and ES for marginal and spatial performance. We also evaluate the utility of Gaussian Copula by computing spatial threshold exceedance probabilities compared to a model without the Copula – i.e. independent model at each gauge. The validation indicates that the model is able to capture the space-time variability of flow extremes very well, and the skills increase with decreasing lead time. Also the use of climate variables enhances skill relative to using just the snow information. The median projections and their uncertainties are highly consistent with the observations with a Gaussian copula than without it, indicating the role of spatial dependence. This framework will be of use in long leading planning of flood risk mitigation strategies.

In this talk, we present our machine learning efforts, which show great promise towards early predictions of solar flare events. (1) We present a data pre-processing pipeline that is built to extract useful data from multiple sources – Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and SDO/Atmospheric Imaging Assembly (AIA) – to prepare inputs for machine learning algorithms. (2) For our strong/weak flare classification model, case studies show a significant increase in the prediction score around 20 hours before strong solar flare events, which implies that early precursors appear at least 20 hours prior to the peak of a flare event. (3) We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window. (4) Our ongoing and future work will also be briefly mentioned.

Historically, location algorithms have relied on simple, one-dimensional (1D, with depth) velocity models for fast, seismic event locations. The speed of these 1D models made them the preferred type of velocity model for operational needs, mainly due to computational requirements. Higher-dimensional (2D-3D) seismic velocity models are becoming more readily available from the scientific community and can provide significantly more accurate event locations over 1D models. The computational requirements of these higher-dimensional models tend to make their operational use prohibitive. The benefit of a 1D model is that it is generally used as travel-time lookup tables, one for each seismic phase, with travel-time predictions pre-calculated for event distance and depth. This simple, lookup structure makes the travel-time computation extremely fast. Comparing location accuracy for 2D and 3D seismic velocity models tends to be problematic because each model is usually determined using different inversion parameters and ray-tracing algorithms. Attempting to use a different ray-tracing algorithm than used to develop a model almost always results in poor travel-time prediction compared to the algorithm used when developing the model. We will demonstrate that using an open-source framework (GeoTess, www.sandia.gov/geotess) that can easily store 3D travel-time data can overcome the ray-tracing algorithm hurdle because the lookup tables (one for each station and phase) can be generated using the exact ray-tracing algorithm that is preferred for a specified model. The lookup surfaces are generally applied as corrections to a simple 1D model and also include variations in event depth, as opposed to legacy source-specific station corrections (SSSCs), as well as estimates of path-specific travel-time uncertainty. Having a common travel-time framework used for a location algorithm allows individual 2D and 3D velocity models to be compared in a fair, consistent manner.

As scientists are confronted with increasingly massive datasets from observations to simulations, one of the biggest challenges is having the right tools to gain scientific insight from the data and communicate the understanding to stakeholders. Recent developments in web technologies make it easy to manage, analyze, visualize, and share large data sets with the public. Web technologies, intelligent systems, artificial intelligence, and virtual and augmented reality techniques provide advanced capabilities for big data analytics, knowledge discovery and smart communication platforms. This talk provides an overview of developments in web systems for hydrological analysis and communication, and presents real-world applications of these techniques in water resources and disaster mitigation.

Hurricanes cause severe impacts on the ecosystem, which substantially affects the carbon cycle at the local or regional scale. During the hurricanes, the loss of many vegetation/trees in the forest and agricultural lands causes more carbon to be released into the atmosphere. Studying the effects of hurricanes on the terrestrial carbon cycle, which includes gross primary product (GPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), and their interactions with land-use change, flood, and others are critical to understand the effect on the terrestrial ecosystem. The main objective of this research was to evaluate the impact of three hurricanes (Harvey, Irma, and Maria in 2017) on the carbon cycle and study the interactions among the flood events, land uses, and terrestrial carbon cycling in the state of Texas, Florida, Puerto Rico using satellite measurements. This study analyzed the GPP, NEE, and Rh distributions in the coastal climate zones in Texas, Florida, and Puerto Rico during hurricane season using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were evaluated using CO2 flux data measured at EC flux site on the Prairie View A&M University Research Farm, Texas. Results showed Florida (Irma) had higher carbon emissions and lower GPP during the hurricane compared to Texas (Harvey), and Puerto Rico (Maria). For example, hurricanes Harvey (08/26/2017), Irma (09/10/2017), and Maria (09/20/2017) caused 2.6, 4.1, and 3.03 gC/m2, of carbon emissions when the recorded daily precipitations were 162, 135, and 241 mm, respectively. However, mostly carbon uptakes or low (<1 gC/m2) carbon emissions were observed on the same day in 2016 and 2018. The analysis showed that the amount of precipitation is not the only driving factor causing increased carbon emission; the characteristics of the drainage area also affect the carbon cycle and emission. Overall, the results showed that hurricanes increase carbon emissions. This study helps to understand the impact of hurricanes on the carbon cycle through analyses of spatial and temporal variations of carbon emission and uptake during the hurricane season.

Stories are an essential part of our everyday life, vehicles to understand how the world around us works, both physically and emotionally. They allow us to organise otherwise random facts and events into a cohesive and logical structure, making them easier to understand and remember. Science itself is also full of facts and processes, often seemingly disconnected but that, when put in context, pave the road for scientific discoveries. We propose that classical story-telling strategies can also be used to communicate science to a variety of audiences, specialist and non-specialist alike, and present a few practical examples of how this can be achieved. We focus on what we call the “story circle” narrative structure (see Fig. 1), a distillation of the “Hero’s Journey”(Campbell, 1949; Harmon, 2009). In this storytelling framework, the circle begins with a hero who, posed with a question, chooses to venture beyond their familiar space in a quest for answers. When the hero returns to familiar territory, they have been forever changed by their journey. Firstly, we discuss how this story circle can be directly mapped onto the structure of a research paper, enabling researchers to write up their work in a way that makes it easier to follow for the reader. Then we apply the story circle strategy to a real research example aimed at explaining large-scale mantle convection, in a story where silica is the “hero” who descends into the deep in a subducting slab to then rise back up to the surface in an upwelling plume. This approach to communicating science by exploiting its story-like qualities is key when explaining complex deep Earth processes to the non-academic public who, understandably, can struggle to grasp these concepts due to their abstract nature and detachment from everyday life. Ultimately, the scientific process is an expression of the most fundamental story of humanity – researchers look at the world as it is, see questions that need answering and go on a voyage of discovery to find the answers. When they return, the world has changed because of what was found on that journey. And so the cycle repeats, the circle keeps turning, and the ideas keep changing after every iteration. In science, however, we will never truly write “The End”.

It has long been recognized that the energy source for major solar flares and coronal mass ejections (CMEs) is the solar magnetic field within active regions. Specifically, it is believed to be the release of the free magnetic energy (energy above the potential field state) stored in the field prior to eruption. For estimates of the free energy to provide a prognostic for future eruptions, we must know how much energy an active region can store – Is there a bound to this energy? The Aly-Sturrock theorem shows that the energy of a fully force-free field cannot exceed the energy of the so-called open field. If the theorem holds, this places an upper limit on the amount of free energy that can be stored. In recent simulations, we have found that the energy of a closely related field, the partially open field (POF), can place a useful bound on the energy of an eruption from real active regions, a much tighter constraint than the energy of the fully open field. A database of flare ribbons (Kazachenko et al., ApJ 845, 2017) offers us an opportunity to test this idea observationally. A flare ribbon mask is defined as the area swept out by the ribbons during the flare. It can serve as a proxy for the region of the field that opened during the eruption. In this preliminary study, we use the ribbon masks to define the POF for several large events originating in solar cycle 24 active regions, and compute the energy of the POF. We compare these energies with the X-ray fluxes and CME energies for these events. Work supported by NSF, NASA, and AFOSR.

The degree of damage on buildings due to earthquakes is strongly dependent on the properties of the subsurface at that specific site. The shallow geology of the Netherlands consists of an heterogeneous soft sediment cover, which has a strong effect on seismic wave propagation characteristics and in particular the amplitude of ground shaking. Where seismic velocities are lower, seismic wave amplitudes are higher. By studying local velocity and amplitude variations from seismic waves, we can obtain constraints on the seismic hazard. Gas extraction in the Groningen field, in the northern part of the Netherlands, is regularly causing shallow (3 km), low magnitude (Mw max= 3.6), induced earthquakes. This region forms an excellent study area due to the presence of a permanent borehole network and detailed subsurface knowledge. The earthquake wavefield consists of shear and compressional waves. Whereas a lot of research has been carried out on the shallow behaviour of the shear waves, this project includes the characterisation of the compressional waves in the shallow subsurface. In this way, ground motions in the vertical direction can be determined in order to support the re-enforcement design for buildings in the areas affected by induced seismicity. The Groningen borehole network is continuously measuring since 2015 and besides earthquakes, it records a wealth of background signals, which is usually called ‘noise’. This noise contains low-energetic elastic waves which also resonate within the sedimentary layers. The local earthquake recordings are used to assess how the shallow unconsolidated subsurface geology influences the amplification of compressional waves, amplification can directly be measured because there are geophones on multiple depth levels. For compressional waves we observe a strong relationship between locations with high amplitudes and the presence of peat in the subsurface. Peat is generating biogas, resulting in a partly gas-saturated soil, hence very low seismic velocities. The noise resonance and earthquake amplification patterns are well-matched. Therefore, the learnings from the densely sampled Groningen region are of interest for other areas in the Netherlands with risk of seismicity since the noise resonance can be used as a first proxy to assess wave amplification.

Using the magnetic field observed by Galileo during two flybys of Callisto, Khurana et al. (1998) demonstrated that Callisto generates a strong induction response to the time-varying primary field, indicative of the presence of a subsurface ocean. In contrast, Hartkorn and Saur (2017) modeled the atmosphere and ionosphere of Callisto and suggested that the ionosphere could be responsible for a significant part of the observed magnetic fields. Thus, they concluded that the water ocean might be located much deeper than previously thought or might not exist at all. While Khurana et al. (1998) did not account for the induction within a conductive ionosphere, Hartkorn and Saur (2017) overestimated the conductivity of the ionosphere by using Cowling conductivity which is not applicable for the situation at Callisto. In this paper, we re-analyzed the S-band open-loop one-way Doppler data of the Galileo spacecraft with the aim to derive the electron density (ED) and neutral density (ND) profiles of Callisto and address its implication in terms of moon’s conductivities and interiors. Using modern orbit determination software, MONTE, and the most up-to-date information on the Jovian system, we reconstructed the Galileo orbit with a full dynamical approach. The estimated rms values of the Doppler residuals for baseline measurement vary from 0.01-0.08 Hz, well within the expected noises of the radio signals. We used these residuals to derive the ED profiles using the technique discussed in Verma et al., (2019). We found an appreciable ionosphere for C22 and C23 Ingress occultations with peak densities of 15600±900 cm-3 and 17700±600 cm-3, respectively. For other cases, the detections do not exceed the 3-σ level. While the general features of the EDs are consistent with Kliore et al. (2002), our estimated 1-σ formal uncertainties are 2-3 times better presumably because of the constrained Galileo’s orbit. Assuming O2 as the major component of the Callisto’s atmosphere, the estimated ND (weighted mean) at the surface is 2.0±0.33 x10-10 cm-3 which corresponds to a column density of 3.9±0.35 x10-16 cm-2 (see Figure). Finally, we will use these density profiles to constrain the ionospheric conductivities and address their implications in terms of the presence of a subsurface ocean.

Sound at frequencies below the nominal bound of human hearing (~20 Hz) is called “infrasound.” Infrasound at frequencies between 0.5 and 10 Hz has been observed to be emitted by tornado-producing storms throughout the life of the tornado, including tornadogenesis. Due in part to the low atmospheric attenuation at these low frequencies, infrasound monitoring is a good candidate for long-range passive tornado monitoring, especially in hilly terrain where line-of-sight limits radar (e.g. Southeast United States). However, the fluid mechanism(s) responsible for the infrasound must be identified to enable researchers to interpret these currently unused signals. This is the objective of the current research, which has used multiple infrasound measurement modalities to identify correlations between received infrasound signals and storm processes. This includes a fixed infrasound array, a mobile infrasound array, and a single infrasound microphone carried by a storm chaser. An overview of select events will be presented, including measurements from an EFU tornado on 11 May 2017 that was within 20 km of the fixed array. These tornadic and non-tornadic observations will then be used to identify potential physical mechanisms.

Across the last decades, various space missions have measured thetotal solar irradiance (TSI) such as the Variability of Irradiance andGravity Oscillations (VIRGO) experiment on the Solar and HeliosphericObservatory (SOHO) starting in 1996. Since the beginning of itsrecording time, one challenge is to correct the measurements from thedegradation of the TSI sensors in space. Various groupshave proposed different methodologies to produce a continuous TSI timeseries (TSI composite) which is essential to monitor the sun activityand its influence on the Earth’s climate. However, the benchmark to test all those solutions is source of adebate in the community. Moreover, the input data for the TSI compositeare the degradation-corrected measurements provided by each individualinstrument team. Here, we propose a different approach using amachine learning and data fusion algorithm to produce automatically thedegradation-corrected TSI time series based on a small number of genericassumptions. The algorithm is applied to the VIRGO/PMO6, VIRGO/DIARADand PREMOS/PMO6 data. The time series agree between each other in termsof mean value with a difference of ~ 0.14 W/m2 (PREMOS), ~ 0.23 W/m2(VIRGO) and ~ -0.18 W/m2 (DIARAD). Finally, taking a conservative valueof 0.3 W/m2 between our different TSI products, induces a variation ofthe global mean surface temperature of ~ 0.02 K based on global climatesimulations, which is within the uncertainties of simulated global meansurface temperatures, hence not impacting significantly any climateforcing scenarios.

Raindrop size distribution (DSD) measurements were taken with an onboard OTT Particle Size Velocity (Parsivel) disdrometer over the western Pacific during a marine survey from June to July 2014. Three subregions named south western Pacific (SWP), west western Pacific (WWP) and north western Pacific (NWP) were separated for a comparative study of the variability of DSD. In addition to disdrometer data, FY2E, MODIS, NCEP FNL and radiosonde data sets are used to illustrate the dynamical and microphysical characteristics associated with summer season rainfall of western Pacific. The DSD characteristics of six different rain rates and two rain types (convective and stratiform) were studied. Histograms of normalized intercept parameter log10(Nw) and mass-weighted mean diameter Dm indicated largest log10(Nw) values in WWP while largest Dm values in SWP, and the convective clusters in three regions could be indentified between maritime-like and continental-like. The constrained relations between shape µ and slope Λ, Nw and Dm of gamma DSDs are derived. An inverse relation of the coefficients and exponents of Z-ARb for convective rain were found in three regions. The R(ZH, ZDR) estimator is proved to be more accurate than Z–R relation algorithm. And the empirical relations between Dm and radar reflectivity factor in the Ku- and Ka-bands are also derived to improve the rainfall retrieval algorithms in the open sea of Pacific. Furthermore, the possible causative mechanisms for the significant DSD variability in three regions were investigated with respect to convective intensity, raindrop evaporation and other meteorological variables.

Two key parameters control the localization of deformation and seismicity along and surrounding crustal faults: the strength and roughness of the preexisting fault surface. Using three-dimensional discrete element method simulations, we investigate how the anisotropy and amplitude of roughness control the mechanical behavior of healed faults within granite blocks during quasi-static triaxial compression. The results show that the localization of fracture development into a damage zone surrounding the initially weak fault zone coincides with the macroscopic failure of the rock. Rougher faults produce more gouge than smoother faults, providing an explanation for the weak influence of roughness on compressive strength. The particles within smoother fault zones slip with higher maximum fault-parallel velocities than rougher faults during the quasi-static loading, likely because the asperities do not impede slip as effectively in the smoother fault zone. The maximum fault-parallel velocity occurs after the peak stress, and falls to a steady state value by the end of the simulation, highlighting the non-constant evolution of slip despite the constant axial strain rate loading conditions. Smoother faults develop stronger correlations between the fault topography and fault slip magnitudes, likely because smoother faults experience higher velocities than rougher faults. Thus, fault surface asperities control slip by acting as speed bumps that hinder fault-plane parallel slip and promote fault-plane normal opening. These numerical models provide insights into the evolution of damage localization, fault roughness, gouge production, asperity abrasion, fault slip and stress concentrations along initially healed faults of varying roughness.

A few friends from the big US space place, and two places where people get lots of learning, have started putting together a tiny flying box. This box will look at the tiny bits we can not see in the upper sky. We hope to learn why we see places where the tiny bits of the sky come together in large numbers and other places where the tiny bits try to get as far away from each other as possible. But before we can learn how and why parts of the sky come close together and sometimes far away from each other, we need to finish building our tiny flying box. Then our tiny box will go on a bit up goer to visit where people live in the sky. It will sit there until they have time to push it out and send it on its way around the world. We hope that our tiny flying box will stay working in the sky for at least 6 months. While this is not a long time, our short-lived tiny flying box will help us better understand the upper parts of our sky.

One of the largest sources of uncertainty in climate models comes from convective cloudssystems parametrizations, which are necessary at current horizontal resolutions, of about 25km.In order to fully resolve the effects of such systems, horizontal resolutions need to reach the 1-3km range, which entail a huge increase in computational costs. In recent years, the performance of the world largest supercomputers has steadilyimproved, to the point that we can now think of running a climate modelat cloud resolving resolution, with a Simulated Years Per Day (SYPD) throughputthat is suitable for decade or century long simulations.However, the architectures of the newest supercomputers are becoming more andmore heterogeneous, which makes the task of keeping a performant code base moreand more challenging. Here, we present our effort to upgrade the new non-hydrostatic atmosphere dycore ofE3SM to a version that is highly performant on a variety of architectures, includingGPUs, conventional CPUs, and many-core CPUs. When using GPUs, our implementation isone of the fastest, achieving 0.97 SYPD on the NGPPS benchmark, when running on the full Summit supercomputer.

Machine learning techniques have proven useful at predicting many variables of hydrologic interest, and often out-perform traditional models for univariate predictions. However, demonstration of multivariate output deep learning models has not had the same success as the univariate case in the hydrologic sciences. Multivariate prediction is a clear area where machine learning still lags behind traditional processed based modeling efforts. Reasons for this include the lack of coincident data from multiple variables, which make it difficult to train multivariate deep-learning models, as well as the need to capture inter-variable covariances and satisfy physical constraints. For these reasons process-based hydrologic models are still used to simulate and make predictions for entire hydrologic systems. Therefore, we anticipate that future state of the art hydrologic models will couple machine learning with process based representations in a way that satisfies physical constraints and allows for a blending of theoretical and data driven approaches as they are most appropriate. In this presentation we will demonstrate that it is possible to train deep learning models to represent individual processes, forming an effective process-parameterization, that can be directly coupled with a physically based hydrologic model. We will develop a deep-learning representation of latent heat and couple it to a mass and energy balance conserving hydrologic model. We will demonstrate its performance characteristics compared to traditional methods of predicting latent heat. We will also compare how incorporation of this deep learning representation affects other major states and fluxes internal to the hydrologic model.