The natural sound generated by an unmanned aerial vehicle is used in conjunction with tomography to remotely sense atmospheric temperature and wind profiles simultaneously. Sound fields recorded onboard the aircraft and by an array of microphones on the ground are compared and converted to sound speed estimates for the ray paths intersecting the intervening medium. Tomographic inversion is then used to transform these sound speed values into vertical cross-sections and 3D volumes of virtual temperature and wind vectors, which enables the atmosphere to be visualised and monitored over time up to altitudes of 1,200m and over baselines of up to 600m. This paper reports on results from two short campaigns during which 2D and 3D profiles of wind and temperature obtained in this way were compared to: measurements taken by co-located mid-range Doppler SODAR and LIDAR; and temperature measurements made by instruments carried by unmanned aircraft flying through the intervening atmosphere. Large eddy simulation of daytime atmospheric boundary layers were also used to examine the anticipated performance of the instruments and the nature of any errors. The observations obtained using all systems are shown to correspond closely.

Increase in concentration in the atmosphere of greenhouse gases (GHG) is considered as one of the possible reasons of climatic changes. Recently all their possible anthropogenic sources including artificial reservoirs are considered. The actual task is the inventory of GHG emissions various sources for possible reduction and parameterization of emission from the underlying surface to define the conditions of interaction with the atmosphere in climate models. The work concerned questions of time-spatial changes of contents and emission of methane from a surface of polytypic reservoirs. On the basis of comparison of the field observations on the Mozhaisk and Gorky reservoirs the distinctions of contents and specific fluxes of methane for reservoirs with various water residence time and hydrological regime are shown. Comparison with literary data has shown that emission rate from reservoirs of a boreal zone with slow water exchange can be underestimated.

Chukchi Sea benthic ostracode assemblages collected during a research cruise aboard the USCGC Healy in 2017 are compared to collections from past years, primarily 2009 and 2010, with a goal of understanding recent species changes related to temperature, total organic carbon (TOC) and sediment grain size. The study area includes the continental shelf region influenced by the Alaska Coastal Current and the northward extension of the Bering Sea Shelf waters that flow through Bering Strait. Significant temporal (decadal, interannual) and spatial variability in the proportions of dominant species in the assemblage were observed, including an increase in subarctic species, particularly, Normanicythere leioderma, which is typically dominant in the Bering Sea, but which showed a notable range expansion in 2017 into the Chukchi Sea (20% of the 2017 Chukchi Sea assemblage). Secondary subarctic species with increasing abundance include Schizocythere ikeyai (8%) and Munseyella kiklukhensis (7%). A corresponding decline in dominance of Paracyprideis pseudopunctillata (4%), a common Arctic species in Chukchi, Beaufort and Laptev Sea assemblages, is another significant change. Continued monitoring of temperature-sensitive ostracode species in the Bering and Chukchi Seas is planned to provide additional information on annual and decadal variability in species dominance.

Spectral entropy (Hs) is an index which can be used to measure the structural complexity of time series data. When a time series is made up of one periodic function, the Hs value becomes smaller, while Hs becomes larger when a time series is composed of several periodic functions. We hypothesized that this characteristic of the Hs could be used to quantify the water stress history of vegetation. For the ideal condition for which sufficient water is supplied to an agricultural crop or natural vegetation, there should be a single distinct phenological cycle represented in a vegetation index time series (e.g., NDVI and EVI). However, time series data for a vegetation area that repeatedly experiences water stress may include several fluctuations that can be observed in addition to the predominant phenological cycle. This is because the process of experiencing water stress and recovering from it generates small fluctuations in phenological characteristics. Consequently, the value of Hs increases when vegetation experiences several water shortages. Therefore, the Hs could be used as an indicator for water stress history. To test this hypothesis, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data for a natural area in comparison to a nearby sugarcane area in seasonally-dry western Costa Rica. In this presentation, we will illustrate the use of spectral entropy to evaluate the vegetative responses of natural vegetation (dry tropical forest) and sugarcane under three different irrigation techniques (center pivot irrigation, drip irrigation and flood irrigation). Through this comparative analysis, the utility of Hs as an indicator will be tested. Furthermore, crop response to the different irrigation methods will be discussed in terms of Hs, NDVI and yield.

Nonmigrating tidal diagnostics of SABER temperature observations in the ionospheric dynamo region reveal a large amount of variability on time-scales of a few days to weeks. In this paper, we discuss the physical reasons for the observed short-term tidal variability using a novel approach based on Information theory and Bayesian statistics. We diagnose short-term tidal variability as a function of season, QBO, ENSO, and solar cycle and other drivers using time dependent probability density functions, Shannon entropy and Kullback-Leibler divergence. The statistical significance of the approach and its predictive capability is exemplified using SABER tidal diagnostics with emphasis on the responses to the QBO and solar cycle. Implications for F-region plasma density will be discussed.

Horizontally-extensive lightning flashes occur frequently in association with mature and late phases of multicellular thunderstorms, both in trailing stratiform regions and horizontally-extensive anvils. The spatial relationship between these flashes and the parent cloud volume is of importance for space launch operational decision making, and is of broader scientific interest. Before this question can be accurately addressed, there is a need to understand the degree to which current lightning observation systems can depict the spatial extent of these long flashes. In this ongoing work, we will intercompare the depiction of horizontally-extensive flashes using several ground-based lightning locating systems (LLSs) located at Kennedy Space Center (KSC) with space-based observations observed by the recently-launched Geostationary Lightning Mapper (GLM) onboard the GOES-16 satellite. Ground-based datasets include the KSC Lightning Mapping Array (KSCLMA), the operational narrowband digital interferometer network MERLIN, and the combined cloud-to-ground and cloud lightning dataset produced by the U.S. National Lightning Detection Network (NLDN). The KSCLMA system is a network of VHF time-of-arrival sensors that preferentially report breakdown processes, and MERLIN is a network of VHF interferometers that point to the discharges in the horizontal plane. Observations to date indicate that MERLIN and the KSCSLMA provide similar overall descriptions of the spatial and temporal extent of these flashes, while the NLDN does not provide adequate spatial mapping of these flashes. The KSC LMA system has much better location accuracy, and provides excellent 3-dimensional representation within ~100 km of KSC. It also has sufficient sensitivity to provide 2-dimensional flash mapping within ~250 km of KSC. The MERLIN system provides a more-detailed representation of fast leader propagation (in 2 dimensions) with ~100 km of KSC. Earlier work during the CHUVA campaign in Brazil with similar systems and the (orbital) Lightning Imaging System (LIS) has shown that the interferometric data correlated much better in space and time with the LIS optical observations. We are currently investigating this relationship at KSC, where both the LMA and interferometer perform much better than the systems used during CHUVA.

Spatial and temporal flux data coverage have improved significantly in recent years, due to standardization, automation and management of data collection, and better handling of the generated data. With more stations and networks, larger data streams from each station, and smaller operating budgets, modern tools are required to effectively and efficiently handle the entire process. These tools should produce standardized verifiable datasets, and provide a way to cross-share the standardized data with external collaborators to leverage available funding, and promote data analyses and publications. In 2015, new open-path and enclosed flux measurement systems1 were developed, based on established gas analyzer models2,3, with the goal of improving stability in the presence of contamination over older models4, refining temperature control and compensation5,6, providing more accurate gas concentration measurements1, and synchronizing analyzer and anemometer data streams in a very careful manner7. In late 2017, the new open-path system was further refined to simplify hardware configuration, to significantly reduce power consumption and cost, and to prevent or considerably minimize flow distortion8 in the anemometer to increase data coverage. Additionally, all new systems incorporate complete automated on-site flux calculations using EddyPro® Software9 run by a weatherized remotely-accessible microcomputer to provide standardized traceable data sets for fluxes and supporting variables. This presentation will describe details and results from the latest field tests of the new flux systems, in comparison to older models and control reference instruments. References: 1 Burba G., W. Miller, I. Begashaw, G. Fratini, F. Griessbaum, J. Kathilankal, L. Xu, D. Franz, E. Joseph, E. Larmanou, S. Miller, D. Papale, S. Sabbatini, T. Sachs, R. Sakai, D. McDermitt, 2017. Comparison of CO2 Concentrations, Co-spectra and Flux Measurements between Latest Standardized Automated CO2/H2O Flux Systems and Older Gas Analysers. 10th ICDC Conference, Switzerland: 21-25/08 2 Metzger, S., G. Burba, S. Burns, P. Blanken, J. Li, H. Luo, R. Zulueta, 2016. Optimization of an enclosed gas analyzer sampling system for measuring eddy covariance fluxes of H2O and CO2. AMT, 9: 1341-1359 3 Burba, G., 2013. Eddy Covariance Method for Scientific, Industrial, Agricultural and Regulatory Applications. LI-COR Biosciences: 331 pp. 4 Fratini, G., McDermitt, D.K. and Papale, D., 2014. Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction. Biogeosciences, 11(4), pp.1037-1051. 5 McDermitt, D., J. Welles, and R. Eckles, 1993. Effects of temperature, pressure, and water vapor on gas phase infrared absorption by CO2. LI-COR, Inc. Lincoln, NE. 6 Welles, J. and D. McDermitt, 2005. Measuring carbon dioxide in the atmosphere. In: Hatfield J. and J. Baker (Eds.) Micrometeorology in Agricultural Systems. ASA-CSSA-SSSA, Madison, W

Structural imaging using body-wave energy present in ambient seismic data remains a challenging task, largely because these wave modes are commonly much weaker than surface wave energy. In a number of situations body-wave energy has been extracted successfully; however, (nearly) all successful body-wave extraction and imaging approaches have focused on cross-correlation processing. While this is useful for interferometric purposes, it can also lead to the inclusion of unwanted noise events that dominate the resulting stack, leaving body-wave energy overpowered by the coherent noise. Conversely, wave-equation imaging can be applied directly on non-correlated ambient data that has been preprocessed to mitigate unwanted energy (i.e., surface waves, burst-like and electromechanical noise) to enhance body-wave arrivals. Following this approach, though, requires a significant preprocessing effort on often Terabytes of ambient seismic data, which is expensive and requires automation to be a feasible approach. In this work we outline an automated processing workflow designed to optimize body wave energy from an ambient seismic data set acquired on a large-N array at a mine site near Lalor Lake, Manitoba, Canada. We show that processing ambient seismic data in the recording domain, rather than the cross-correlation domain, allows us to mitigate energy that is inappropriate for body-wave imaging. We first develop a method for window selection that automatically identifies and removes data contaminated by coherent high-energy bursts. We then apply time- and frequency-domain debursting techniques to mitigate the effects of remaining strong amplitude and/or monochromatic energy without severely degrading the overall waveforms. After each processing step we implement a QC check to investigate improvements in the convergence rates - and the emergence of reflection events - in the cross-correlation plus stack waveforms over hour-long windows. Overall, the QC analyses suggest that automated preprocessing of ambient seismic recordings in the recording domain successfully mitigates unwanted coherent noise events in both the time and frequency domain. Accordingly, we assert that this method is beneficial for direct wave-equation imaging with ambient seismic recordings.

Ocean General Circulation Models (OCGM) have been used for ocean forecasts and reanalysis in the past; successfully reproducing realistic large scale features such as Western Boundary Currents (WBC) which evolve slowly in time. Recent developments of three dimensional OCGM’s include the incorporation of tidal forcing embedded in the numerical integration. With the inclusion of higher frequency tidal forcing it is now possible to study the impact of tides on the larger scale features of the ocean reproduced in the OCGM’s such as WBC’s through comparison of simulations with and without tides. We compare two 1/12.5° simulations of the Hybrid Coordinate Ocean Model (HYCOM) and report on differences in the mean position, transports, and warm/cold core eddy production in the Gulf Stream and Kuroshio Current and their extensions across the Atlantic and Pacific Oceans.

Water sample collection is a simple, but fundamental approach for measuring water properties that cannot currently be sensed in situ with instruments or for conducting experiments requiring water samples. Conventional approaches to water sampling typically employ research vessels which are costly, limited by sea state, and are often restricted by scheduling and other logistics. These factors can limit the ability to sample transient phenomena in inland and coastal waters. They can also restrict sampling frequency for time series measurements. We describe the use of aerial drones and newly developed sampling bottles that allow sub-surface water collection without the use of research vessels. The sampling bottles are similar in operation to conventional Niskin bottles, but with the different methods for closing the bottles. We have experimented with two closing methods. One uses a float and mechanical linkage to close bottles at fixed depths and the other uses pressure sensors to close bottles at programmed depths. Drone-based water sampling is currently employed in the Santa Barbara Coastal Long Term Ecological Research (SBC LTER) project to obtain weekly time series of water samples for pH and total alkalinity at a long-term oceanographic mooring. The water samples are also being used to calibrate pH sensors on the mooring and assess data quality. Drone sampling will be expanded to other SBC LTER moorings in the future. Aerial drones offer a new approach for sampling the coastal ocean and inland waters. Drone-based sampling is in its infancy, but we envision the development of a suite of specialized instrumentation and water collection devices that take advantage of the capabilities of aerial drones. This will allow rapid response for sampling transient events such as harmful algal blooms and toxic spills in a wide range of environmental conditions.

The so-called regularized Biot-Savart laws (RBSLs, Titov et al. 2018) provide an efficient and flexible method for modeling pre-eruptive magnetic configurations whose characteristics are constrained by observational image and magnetic-field data. This method allows one to calculate the field of magnetic flux ropes (MFRs) with small circular cross-sections and an arbitrary axis shape. The field of the whole configuration is constructed as a superposition of (1) such a flux-rope field, (2) an ambient potential field determined, for example, by the radial field component of an observed magnetogram, and (3) a so-called compensating potential field that counteracts deviations of the radial field caused by the axial current of the MFR. The RBSL kernels are determined from the requirement that the MFR field for a straight cylinder must be exactly force-free. For a curved MFR, however, the magnetic forces are generally unbalanced over the whole path of the MFR. To reduce this imbalance, we apply a modified Gauss-Newton method to minimize the magnitude of the residual magnetic forces per unit length and the unit axial current of the MFR. This is done by iteratively adjusting the MFR axis path and axial current. We then try to relax the resulting optimized configuration in a subsequent line-tied zero-beta MHD simulation toward a force-free equilibrium. By considering several examples, we demonstrate how this approach works depending on the initial parameters of the MFR and the ambient magnetic field. Our method will be beneficial for both the modeling of particular eruptive events and theoretical studies of idealized pre-eruptive magnetic configurations. This research is supported by NSF, NASA’s HSR, SBIR, and LWS Programs, and AFOSR

Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. Required forest carbon data consist of integrated: (1) baseline mapping of contemporary carbon stocks, (2) projections of future carbon stocks for planning, and (3) annual monitoring for assessment. Previously, we developed high-resolution mapping of contemporary carbon stocks and 300-yr projections of annual carbon sequestration potential (CSP) for Maryland at 90m resolution by integrating airborne LiDAR with mechanistic ecosystem modeling (Ecosystem Demography (ED) model). Here we extend this work to Delaware and present the first consistent, annual monitoring results for both states (Maryland and Delaware). For monitoring, we intersect annual carbon stock estimates with 30m Landsat-derived changes in forest area to compute realized carbon gains and losses over the period 2011—2019. Moving forward, we expect to extend this pilot system developed for Maryland and Delaware to an additional 9 U.S. states. As the framework is flexible, developing a nationwide or global system is increasingly feasible, particularly with the recent availability of GEDI LiDAR observations from space.

Arctic summer sea ice has decreased dramatically over the last few decades, with a substantial part of this decline attributed to internal variability. However, models show large differences in their simulated internal variability, increasing projection uncertainty and complications with model-observation comparisons. Here we will present results that aim to quantify the contribution of internal variability in different models which provide large ensemble simulations, and compare them with estimates from observations. In particular, we are comparing five models from the CLIVAR multi-model large ensemble (CanESM2, CESM1, CSIRO MK36, GFDL ESM2M, and MPI ESM1) with observations. So far, we have found a large range in simulated pan-Arctic sea ice area standard deviation from 0.35 million km2 (CSIRO MK36) to 0.74 million km2 (CESM1) for mean September areas between 4.00-4.25 million km2. Spatially, the detrended standard deviation in the central Arctic is consistently over-represented in models compared to observations. Conversely, the marginal seas are simulated to have slightly below to several times below observed detrended standard deviation. Further analysis on a more regional scale will be done over the coming months to further characterize the realism of simulated internal variability in Arctic sea ice.

The Gravity Recovery and Climate Experiment (GRACE) mission and the Follow-On mission (GRACE-FO) have been widely used to estimate Groundwater (GW) at different scales. With improved releases and data longevity of these missions it becomes possible to analyze changes on GW and its link with global climate patterns such as El Niño-Southern Oscillation (ENSO), which can contribute to better management of this resource, especially in zones where it is crucial for the livelihoods and food security of the community. In the current study, TWS-derived from GRACE, Soil Moisture and Snow simulated by the Global Land Data Assimilation System (GLDAS) were used in the period 2002-2020 to estimate fluctuations in High Plains Aquifer (HPA). Trend and annual cycle were analyzed, and then Principal Component Analysis (PCA) was carry out to observe spatio-temporal oscillation modes and its relationship with Oceanic Niño Index (ONI) through Spectral Analysis. Furthermore, to validate GRACE-derived GW and evaluate a larger period pattern such as Pacific Decadal Oscillation (PDO), Depth to Water data from United States Geological Survey (USGS) wells distributed in the aquifer were selected with a longer period looking for teleconnections. Results shows increasing trend in Northern High Plains while a decreasing in the central and southern part of it. GW peak occurs in August in most of HPA, three month later than rainfall peak. First four components explain 77% of total variance. GW derived from GRACE and GRACE-FO as well as GW from wells could detect periodicities that suggest connections of GW variability with PDO more than with ENSO in HPA.

Ceboruco (2280 m.a.s.l.), in the western Trans-Mexican Volcanic Belt, is considered among the most hazardous volcanoes in Mexico. Some 55,000 people and important infrastructure (e.g. hydroelectric dams; highways; railways) lie within the area covered by deposits of Holocene eruptions. A diverse activity over the past 1000 years spans from effusive (e.g. andesite lava flows, dacitic domes) to explosive (e.g. Strombolian, Vulcanian and Plinian eruptions). With a poor monitoring network, a first hazard map was published in 2019. Here we present the first probabilistic hazard maps for Ceboruco, which constitute a progression towards a more quantitative hazard assessment. We conduct a probabilistic hazard assessment using the pyBET_VH (Bayesian Event Tree for Volcanic Hazard) tool (i.e. software implementation of the event-tree scheme), which allows the user to estimate and visualize the probabilities and uncertainties associated with volcanic phenomena. pyBET_VH merges information from eruptive history, expert elicitation, and the output of other computer models to produce probabilistic hazard maps (i.e. absolute and conditional probabilities and associated uncertainties). We present the probability hazard maps for each eruptive scenario (i.e. Scenario 1 – small magnitude effusive eruption; Scenario 2 – medium magnitude effusive and/or explosive eruption (VEI<3), and Scenario 3 – large magnitude Plinian eruption) and the associated uncertainties. Such maps can be used by civil authorities and stakeholders for the purpose of crisis management as well as for long-term development strategies by visualizing the probabilities of areas around the volcano likely to be impacted by volcanic phenomena. Using pyBET_VH has advantages and disadvantages: the reliability of the output maps is directly related to the quality of the input data, but the tool allows easy estimation and visualization of the uncertainties; being an interactive tool, the user can continuously update the probability maps as new information becomes available.

Every year millions of people in the US are affected by power outages, disrupting the economy and daily life. Many of these outages are caused by events such as strong winds, heavy rains, thunderstorms, floods, tropical storms and hurricanes. At the University of Connecticut an outage prediction model (OPM) has been developed for forecasting outages during storms. The OPM has been operational since 2015 serving utilities in the Northeastern US. It uses variables describing weather events, infrastructure, land cover and elevation. Non-parametric machine learning (ML) ensembles generate the predictions. The first version of the model served Connecticut exclusively and was characterized by large uncertainty in predictions due to the dataset limitations of a small service territory and limited historical dataset. Over time, the model expanded to include utility territories in Massachusetts and New Hampshire, the dataset grew, the understanding between environmental forcing and outages improved, and probabilistic operational forecasts began to be produced. The relationship between UConn and the utility stakeholders has grown to where operational forecasts are now used as part of response planning to storm events by the utility. This work leverages knowledge from the UConn OPM and utilizes a similar ML framework in combination with non-utility-owned customer outage data to build a community OPM for predicting customer outages along the US Eastern Seaboard for large scale events. Proxies for proprietary infrastructure are used including road and publicly available transmission line data. Variables including tree type and ecoregion data are used to account for regional diversity of the larger domain. To validate the customer outage reference data, correlations are shown between customer outages and utility trouble spots in the Northeast where outage data from utilities is known. Model performance evaluated at county and state levels shows that the model is capable of predicting the peak number of customer outages with great accuracy, demonstrating promise for the ultimate goal of determining return periods of outages under current and future climate scenarios to help the public and utilities with resiliency and response planning.

The Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework considers the interactions between the environment, societal impact, human decision-making, and technology design to support decision making. EVDT has been expanded to include a public health model in the Vida Decision Support System, which will help local leaders understand the relationships between societal factors relating to COVID-19. Key to the development of Vida are collaborative design and mutual learning with international and interdisciplinary teams. Collaborations with researchers and government officials (including public health, economics, environmental, and demographic data collection officials) in Angola, Brazil, Chile, Indonesia, Mexico and the United States provide in-depth understanding of local contexts. Lessons learned from these collaborations include the value of dialogues with teams from the same region but different topic areas (such as a space agency compared to a public health agency), allowing for time to learn the best way to combine diverse data types and find the tools each collaborator prefers, and encouraging the use of the preferred language of collaborators. During Vida’s development, each collaborator has worked to create their own version of Vida using local data sources, the US team has provided prototype analyses and models, and collaborators have shared individual insights among the whole network. These partnerships have yielded promising initial results to support decision making, with prototype tools incorporating local data on COVID cases, the environment, and socio-economic factors from Rio De Janeiro and Chile being evaluated. This collaborative design process will develop insights for decision-making, create a network of international collaborators that can exchange technical methods beyond the pandemic, and emphasize the principles of inclusive innovation and decoloniality by submitting to the preferences of local leaders in each country.

The Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission is NASA’s next great investment in Earth Science, continuing NASA’s legacy of over forty years of satellite ocean color measurements. PACE, expected to launch in 2023, will advance our Earth-observing and monitoring capabilities through hyperspectral imaging and multi-angle polarimetric observations of ocean, atmosphere, and land ecosystems. PACE will give us an unprecedented view of our home planet and will support user-driven environmental applications through research and applied science to address societal challenges and inform decision-making. An integral component of actionable applied science is Design Thinking - an iterative, problem-solving framework that integrates human perspectives, needs, and experiences at every step of process. In this session, we will present the design process, collaborative activities, and outcomes of the 2021 PACE Applications Water Quality community focus session. A Design Thinking methodology was used in event planning as well as during day-of ideation breakout sessions. To foster empathy and better illuminate the goals, concerns, and needs of the diverse PACE user community, eight draft user personas were created to represent a range of water industry users from research to government to the private sector. Attendees worked together to complete the various personas by identifying different user challenges and pain points, ideal data experiences, and realistic, tailored Earth Observation and PACE Mission specific solutions and opportunities to satisfy users’ needs and goals. As a result, the eight archetype personas and co-production of knowledge will help ensure that PACE data are usable and accessible for a variety of possible users, thereby expanding the eventual reach and societal benefit of PACE. Lastly, we will highlight how Design Thinking will inform future stakeholder engagement efforts and actionable science via the PACE Mission.