Monitoring Earth Energy Imbalance (EEI) is absolutely fundamental to understand and prevent climate change as it corresponds to the Earth’s excess heat. In particular, measuring radiative forcing in an environment with a wide range of conditions entails a better understanding of its impact on the climate.The UltraViolet and infrared Sensors at high Quantum efficiency on-board a small SATellite (UVSQ-SAT) aims to improve measurements of those radiations at Top Of the Atmosphere (TOA). It becomes a pathfinder for Radiation Budget measurements on-board a CubeSat demonstrating a disruptive technology, implementing miniaturized sensors on a small satellite (twelve miniaturized thermopiles sensors and four photodiodes). This project is promoted by the University of Saint-Quentin-en-Yvelines with the support of the International Satellite Program in Research and Education (INSPIRE). The satellite will be launched and in-orbit in December 2020.One of the main purposes of the mission is to measure the Essential Climate Variables (ECV) more precisely using multi-point observation, in an effort to improve spatiotemporal coverage through future constellation. The scientific goals of the mission are first to observe incoming solar radiation and outgoing terrestrial radiations at Top Of the Atmosphere. The second purpose is to measure solar spectral irradiance in the Herzberg continuum. Finally, the third objective is to improve the technological readiness of a sensor.This session will focus on the ability to define the spatiotemporal accuracy required to monitor the variability of those Essential Climate Variables. Therefore, it will allow us to determine the number of satellites and characteristics of the future constellation meeting these specifications.

ICESat-2 (IS2), launched in September 2018, has been providing high-resolution measurements of Arctic sea ice for two years. IS2’s main instrument, the Advanced Topographic Laser Altimeter System (ATLAS), is a photon-counting lidar with a pulse frequency of 10 kHz, translating to 0.7 m along-track sampling. We use high-resolution ATLAS measurements with a new algorithm, to derive sea ice surface elevations for all available IS2 reference ground tracks over sea ice for the 2019-2020 winter period. Sea ice surface elevations are used to calculate sea ice parameters including surface roughness, ridge sail height, and ridge frequency for the period October 2019 to April 2020. Near coincident Airborne Topographic Mapper (ATM) lidar data were collected along IS2 orbits during the Spring 2019 NASA Operation IceBridge flight campaign. Using ATM data as a validation tool, we show that, when compared to the existing ATL07 sea ice surface height product, our algorithm discerns pressure ridge frequency and height more accurately while maintaining measurement precision at under 2 cm. We show monthly variability in the probability distributions for these sea ice parameters, with respect to ice type. Following our previous studies, we show that pressure ridges have distinct characteristics depending on the ice type in which the ridge was formed. These results demonstrate the utility of photon-counting lidar for deriving new parameterizations of sea ice surface roughness, ridge sail height, and ridge frequency, that may be used to advance high-resolution sea ice modeling.

Despite recent advances in sensors, hydro-meteorological data remain scarce in urban watersheds. In the current, reactive approach to stormwater management, whether or not an urban flood event is documented, as well as where and how it actually occurred, is highly dependent on the level of monitoring. Even though there are many methods for observing flooding extent and predicting flooding vulnerability, issues with data availability and accuracy persist at the local level. Urban watersheds are spatially and temporally complex and flash floods, while of particular interest and importance to both hydrologists and communities, are hard to characterize, given that they are rare, spatially localized, short-lived, and often occur in locations without formal monitoring. On the other hand, identifying vulnerable areas in a large city using hydraulic/hydrological modeling would be very difficult, either because models have parameters that need to be calibrated against mostly non-existent data (in the case of conceptual models), or else we do not know all of the actual physical processes at work, or how to quantify them (in the case of physically-based models). Community-based monitoring activities can support the characterization of urban watersheds, as well as stormwater management, because they provide valuable spatial and temporal knowledge about the behavior of water flow and other related issues at a local level. An urban watershed study was conducted to demonstrate the value of community-based observations for understanding and characterizing urban pluvial flash flooding, by addressing the following questions: (i) How can communities feasibly monitor their local watershed using low-cost, straightforward approaches? (ii) How can local knowledge help us to better define and characterize urban pluvial flooding vulnerability? (iii) Are community-based data reliable and meaningful to urban watershed management and the decision-making process? To pursue these questions, participatory research was applied in a low-resourced community in the City of Paternò, in Sicily, Italy. We collected as unbiased as possible information about flash flooding events through 300 surveys. The locally-gathered data were compared to the results of advanced hydrodynamic models on smaller scales.

Amazonian rivers are highly interconnected and dynamic systems. Their behavior depends, to a large extent, on their geomorphology, being classified in 1) meandering rivers (MR), characterized by high rates of migration and sinuosity, and 2) anabranching rivers (AR), known for forming several permanent channels and islands. A planimetric characterization of the main rivers of the Peruvian Amazon (Huallaga, Ucayali, Marañon, and Amazonas), spanning from the Andes to the Amazon lowland region, was carried out to understand their physical dynamics. By a multi-temporal analysis from 1987 to 2017 using Landsat images, a segmentation was made for each river based on 1) the characterization of the geological valley, 2) the confluence of important tributaries, 3) changes of the main channel through the years, and 4) planimetric variables such as confinement, bend length, amplitude, sinuosity, and asymmetry. As a result, a total of 160 sections were obtained, in which a new set of 25 metrics was applied, filtered from an initial set of 31 variables and their statistics (i.e. mean, variance, kurtosis, and skewness), calculated through different approaches (i.e. half-meander, full-meander, and full-river). The variables were standardized and principal component analysis (PCA) was performed. The resulting biplot showed a distinction between AR and MR, with a shared area consisting predominantly of Marañon and Huallaga sections. The average value of sinuosity was found more associated with the MR, while higher length and asymmetry variance values were more oriented to the AR. This study also indicated the similarity in the behavior of some river sections of different types, based exclusively on their morphometric characteristics. At the same time, revealed how some sections could not be differentiated from others despite being nominally different. In this scenario, the PCA highlighted the need for a complete set of statistics that can recognize different features of these rivers, capturing greater complexity. Thus, the evaluation and segmentation of these planimetric variables, according to their planform characteristics, allows a better understanding of their dynamics, providing accurate information for coherent decision-making.

The Undergraduate Student Instrumentation Project (USIP) was a NASA program to engage undergraduate students in rigorous scientific research, for the purposes of innovation and developing the next generation of professionals in space research. The program is student led and executed from initial ideation of research objectives to the design and deployment of scientific payloads. The University of Houston was selected twice to participate in the USIP programs. The first program (USIP_UH I) ran from 2013 to 2016. USIP_UH II ran from 2016 to 2019. USIP_UH I (USIP_UH II) at the University of Houston was composed of eight (seven) research teams developing six (seven), distinct, balloon-based scientific instruments. This project was a for-credit course two years in duration. The program has been so successful in terms of improved student career outcomes the University has decided to continue the project with purely local funding. The pandemic has produced a substantial instructional challenge since this project is a lab class! The virtual classroom that we designed to meet this need provides tools for ongoing collaboration, revisions, storage, project planning, systems engineering, and a tool to request immediate feedback from faculty and fellow researchers. Additionally, the classroom provides an ongoing place to store data from different students for many years. New students can use this continuity in a consistent and secure way. We also provided tools for conferencing and communication. A combination of several tools were selected and customized to meet this need. These tools include Google Classroom, Microsoft Teams, Slack, Git, Groupme, and Zoom.

Around the world there is a consensus of the importance of knowladge the ice thickness distribution and total ice volume for estimating future glacier change for both glaciological and hydrological application, especially the Polar Regions for being important regulators of the global climate system and response rapidly to climate change. This work aimed to estimate the ice thickness distribution and total ice volume of Znosko glacier (King George Island, Antarctic Peninsula) using Glacier Bed Topography (Glabtop) model and ground data. Ground penetrating radar (GPR) measurements were collected in two field campaigns. GPR measurements were used to validate, calibrate (shape factor) and improve the accuracy of the ice thickness distribution model. The ice thickness (GPR) distribution is not uniform over the glacier, which varies from 0 to 155 m. The total stored ice volumen of Znosko glacier in 2020 is estimated to be 0.11 km3. Additionally, the model bounded areas in the bedrock topography below sea level, as a sign of future lake formation. Sensitivity analysis reveal that Glabtop model with a shape factor (f) = 0.5 gave best results when compared with ground penetrating radar (GPR) data measured on Znosko glacier and has an uncertainty range of ± 20.5%.

Seaports in Africa are expanding rapidly to meet an increasing demand for imported goods in Africa and for natural resources and manufactured goods from the continent. Emissions from shipping and anthropogenic activities at seaports, in particular nitrogen oxides (NOx), are challenging to estimate. This impacts our ability to determine the impacts of shipping activities on ozone air pollution for a continent that is NOx limited. Here we develop an approach to oversample tropospheric column observations of nitrogen dioxide (NO2) from the recently launched high spatial resolution TROPOMI instrument to determine NOx emissions from shipping activities at major seaports along the African coastline from August 2019 to July 2020. We use these to evaluate state-of-science emission inventories and determine temporal variability in these emissions for improved implementation in global and regional models.

The solar polar fields play a crucial role in sustaining the solar dynamo mechanism. Observations and computational modelling indicates that the surface dynamics of active regions mediated via plasma flows, which leads to the polar field build-up, is a fundamental aspect of the solar dynamo mechanism; in fact, this surface mediated flux dynamics also known as the Babcock-Leighton mechanism, is now implicated to be the leading contributor to the Sun’s polar field, and thus also provides a window to the future solar cycle. This emergent understanding is one of the major drivers of novel mission concepts aiming to image the solar high latitudes from out of the ecliptic locations. Based on numerical simulations of the near-surface magnetic field dynamics and the Sun’s polar landscape, here we discuss what new insights may be provided by out-of-the ecliptic solar physics missions.

Gulf Stream frontal eddies, cyclonic features that form in the trough of meanders in the South Atlantic Bight, provide a natural mesocosm for investigating questions around submesoscale spatial and temporal variability. These frontal eddies drive important nutrient fluxes into the euphotic zone which generate considerable productivity on the outer shelf of the Southeast US coastline. Modeling suggests these nutrient fluxes are partly due to submesoscale physics, but current observational approaches miss much of these dynamics and the biological response to them. As a result, current global biogeochemical models are not parameterized to account for potential submesoscale contributions to productivity and carbon export. We present work observing these frontal eddies from new satellite platforms such as ECOSTRESS which provides an order of magnitude higher resolution SST compared to MODIS, GOES-16 which provides an order of magnitude higher temporal cadence SST compared to MODIS, and VIIRS for ocean color. Characterizing spatial patterns and temporal variation of SST and chlorophyll-a within frontal eddies at finer spatiotemporal resolution provides a better understanding of whether their submesoscale variability is captured by current remote sensing products, and insight into their overall role in ocean productivity and biodiversity.

The COVID-19 pandemic has had a diverse range of both direct and indirect impacts on health (both physical and mental), the economy, and the environment. The relevant data sources used to inform pandemic-related decisions have been similarly diverse, though decision-makers have primarily relied upon data sets from non-satellite sources such as traditional public health data. As we move from initial crisis response to more long-term management, there is both an interest and a need for considering a wider diversity of data sources and impacts. It is difficult for any person to absorb and respond strategically to the broad sets of data that are relevant to the issues regarding COVID management. To address this, the authors propose a five part, integrated data visualization and modeling framework entitled the Vida Decision Support System. The goal of Vida is to create an accessible and openly available online platform that can be customized by the leadership team for a city or region and bring together knowledge from several areas of expertise. The five components of Vida, each of which serve to model a specific domain, include Public Health, Environment, Socio-economic Impacts, Public Policy, and Technology. This framework is currently being designed and evaluated with collaborators in Angola, Brazil, Chile, Indonesia, Mexico and the United States. The environmental data comes from sources such as in-situ sensors and both civil and commercial earth observation instruments (Landsat, VIIRS, Planet Labs’ PlanetScope, etc.) to track factors such as water quality, forest extent and health, air quality, human mobility, and nighttime urban lighting. Similarly, socioeconomic data derives from both in-situ sources, such as local statistical agencies, and from satellite products, such as those hosted by NASA’s Socioeconomic Data and Applications Center. The authors discuss the value provided by this framework to each of the collaborators, the process used to apply the framework to each local context, and future possibilities for Vida. Even though Vida was first developed and applied in response to COVID-19, it has applications in other public health contexts where policy, environment, and socio-economic impacts are closely tied.

Join us in an exploration of the climate of Northland, a world where the entire northern hemisphere is covered by a continent, and the entire southern hemisphere is covered by an ocean! On the continent, we will visit the seasonally moist tropics, the subtropical desert, and the Great Northern Swamp. We explore the interplay between water, energy, land, ocean, and atmosphere in this idealized climate model study. We find that the presence of a continent greatly increases the poleward extend of the ITCZ over both the land and ocean hemispheres compared to an aquaplanet, as a result of hemispheric energy imbalances introduced by (a) the small heat capacity of land and (b) large reductions in atmospheric water vapor (and thus reduced longwave trapping) over the continent. A combination of moisture transport from the tropics and local water recycling results in a polar swamp over the continent. We explore how the climate state responds to changes in the albedo and evaporative resistance of the continent. While making the land surface darker leads to warming, we find that decreasing evaporation from the land surface leads to global-scale cooling. This is in contrast to past studies, where reduced terrestrial evaporation leads to warming as a result of suppressed evaporative cooling of the land surface. In the case of Northland, the lack of an ocean to provide water to the northern hemisphere means that decreasing land evaporation leads to large reductions in water vapor over the northern hemisphere, in turn reducing strength of the greenhouse effect, resulting in cooling of near-surface air temperatures. This cooling signal is strongest over the continent, but cools air temperatures over the ocean hemisphere as well. We hypothesize that a threshold exists in the temperature response to reduced terrestrial evaporation: for small decreases in evaporation, reduced latent cooling dominates and near-surface temperatures warm, while for large decreases in evaporation, reduced longwave trapping from reduced atmospheric water vapor dominate, cooling near-surface temperatures. Through this idealized study of a hypothetical, Earth-like planet, we gain valuable insight into the connections between water, energy, land surface properties, and continental distribution in controlling global-scale climate.

Rakov and Uman pointed out (Lightning: Physics and Effects, 2003) that despite 5000 published reports of ball lightning (BL) and a scientific literature comparable in volume to the literature on conventional lightning, we still have no idea of what mechanisms create or power BL. Lightning characterization technology advances can and should be applied to BL studies. Keul and Diendorfer (2018) have correlated BL reports with European lightning-detection network data, but no such attempt has been made for US BL reports up to now. Using 31 BL reports from a now-defunct US website, we have attempted correlations with National Lightning Detection Network (NLDN) and radar data archived at the National Climactic Data Center. Of the 31 reports, 5 objects were indoors and 25 were reported to coincide with thunderstorms. Time information accurate to 30 minutes (1 sigma) and location information accurate within one to ten km was available from nine of the reports, and for these we obtained both NLDN and composite radar data covering the time frames indicated by the reports. For three of the nine reports, we found NLDN located strikes plausibly within the distance and time frames of the observer reports – thus qualifying as nearby (possibly causal) lightning. However, for the report for which the location of the ball was known within meters, the nearest lightning was 2.3 km away. 23 reports only associate BL with a storm within 25 km. Should it continue to hold in the face of more evidence, the relative lack of correlation of BL and nearby lightning suggests that the production of BL is associated more with static or changing electric fields in the vicinity of thunderstorms, than with the lightning plasma channel itself. While these results by themselves are limited, we are optimistic that one can learn more about the link between natural and ball lightning by fusing more precise eyewitness reports with lightning location and other archival meteorological data. To this end, we have launched a website to guide citizen scientists in future ball lightning reports. The site may be reached via https://tinyurl.com/BLReport.

Most countries around the world took actions to control COVID-19 spread that included social distancing, limiting air and ground travel, closing schools, suspending sports leagues, closing factories etc., leading to economic shutdown. The reduced traffic and human movement compared to Business as Usual (BAU) scenario was tracked by Apple and Android cellphone use; the data showed substantial reductions in mobility in most metropolitan areas. For example in Washington D.C., average distance traveled by people was ~13 km and by April when lockdown was in full effect, the distance reduced to ~5 km. Consistent with reduced mobility, air quality as indicated by satellite observations decreased substantially. Granted that year to year variability in weather patterns can have influence on observed NO2 and aerosol concentrations, but the drop in tropospheric nitrogen dioxide (NO2) observed by Sentinel 5P Tropospheric Ozone Monitoring Instrument (TROPOMI) and Suomi NPP Ozone Mapping Profiling Suite (OMPS) observations of NO2 was significant; reductions in observed NO2 were between 15% to 50% between February and April 2020 depending on location and similar reductions in NO2 amount in March and April 2020 compared to March and April 2019. Further, the changes in NO2 across the continental U.S. between 2020 and 2019 correlated well with on-road emissions but did not correlate with changes in emissions from power plants. In the first quarter of 2020, the total amount of NOx emitted on road were 200 times and 7 times larger than that from power plants in LA and NYC, respectively. These findings confirm that power plants are no longer the major source of NO2 in the United States. We also found positive correlation between NO2 and Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth measurements in these urban regions indicating common source sectors for NO2 and aerosols/aerosol precursors.

The study of lunar plasma environment’s response to the extreme solar wind condition is the main subject of our investigation in this report. The computational model includes the self-consistent dynamics of the light (H_2+) and (He+), and heavy (Na^+}) pickup ions. The electrons are considered as a fluid. The lunar interior is considered as a weakly conducting body. The input parameters are taken from the ARTEMIS observations. The modeling demonstrates a formation of the various plasma structures near the Moon: (a) bow shock wave with split shock transition in case of extreme solar wind density and standard bulk velocity; (b) hyper-sonic/Alfvenic Mach cone in case of extreme solar wind bulk velocity and moderate solar wind density. The modeling shows a strong asymmetry in the solar wind ion VDF which connected with a plasma compression and ion reflection at the bow shock wave/Mach cone front.

The South American Monsoon System (SAMS) is the dominant mode of hydroclimatic variability in South America and the associated dynamics have an important influence on atmospheric organization during pluvial and drought periods. Paleoclimate records from the monsoon domain have helped to describe the isotope hydrology of this region over the last millennium, but are insufficient to explain why the system has changed. Utilizing transient simulations of last millennium climate from isotope-enabled global climate models as well as experiments forced by isolated external forcings made available from the NCAR Community Earth System Model Last Millennium Ensemble (CESM-LME) project, we explore the dynamical drivers of variability documented in the isotopic record. We find that while models continue to underestimate the temporal variability of regional climate, they are able to reproduce documented spatial modes of variability. A Monte Carlo empirical orthogonal function (MCEOF) analysis is used to decompose major modes of isotope variability from both isotope proxy records and isotope-enabled global climate models over the last millennium (850 – 1850 CE). The principal component time series from the isotopic records show clear mean state departures during the early and late part of the record, corresponding to the Medieval Climate Anomaly and Little Ice Age periods. We explore the role of internal variability and different external forcings in driving these departures, and use the model space to describe changes to regional circulation patterns during these periods of extreme isotopic change. This enhanced understanding of the drivers of variability over the last millennium could provide a foundation upon which to interpret the sensitivity of this system to future changes due to external forcings, such as anthropogenic activity.

The 2020 COVID-19 pandemic provided a unique opportunity to sample atmospheric gases during a period of very low industrial/human activity. Over 1000 Whole Air Samples were collected in over 30 cities and towns across the United States from April through July 2020 as part of the NASA Student Airborne Research Program (SARP). Sample locations leveraged the geographic distribution across the United States of the undergraduate and graduate students, faculty, and NASA personnel associated with the internship program (44 people total). Each person collected approximately 24 air samples in their city/town with the goal of characterizing local emissions with time during the pandemic. Samples were collected in 2-Liter stainless steel evacuated canisters at approximately 2 meters above ground level. The canisters were shipped to the Rowland/Blake Laboratory at the University of California Irvine and analyzed for methane, carbon dioxide, carbon monoxide, non-methane hydrocarbons, and halocarbons using the gas chromatographic system described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders across most of the United States while samples collected later in the spring and early summer reflect the easing of these measures in most locations. Overall trends in emissions with time across the United States during the pandemic (in several large metro areas as well as rural locations) will be discussed.

ICESat-2, the first photon counting satellite, maps the earth’s surface with unprecedented details and accuracy. However, the resulting photon clouds, distributed as the ATL-03 geocoded photon product, are vast, unstructured, and noisy datasets. The high density and four-dimensional nature of the photon dataset (location and time), coupled with different responses over different surfaces (e.g., ice, forest cover, water), pose a unique and challenging problem regarding surface detection’s overall objective of intelligently reducing the data volume. Multiscale models uncover hidden structures in data due to their ability to analyze the underlying processes at multiple scales. Besides the traditional wisdom of using multiple scales for improving local and global approximations, in this work, we show their application as an intelligent sampling mechanism for redundant and noisy datasets. Our proposed approach’s fundamental idea is the generation of data dependent and multiscale basis functions and corresponding representative sparse representations, which retain points essential for minimizing the error of reconstruction. Thereby, points associated with rapid spatial change are chosen, while those that are easily reconstructed using the smooth basis functions are discarded. As the final output, the algorithm provides an efficient sparse representation of the data that captures all relevant features for modeling and prediction with quantified uncertainty. Our presentation includes a detailed description of the algorithm and theory as applied to process the ATL-03 geolocated photon product of the ICESat-2 mission. We will demonstrate the efficiency of our approach by examples of different ice sheet surfaces, including heavily crevassed glaciers, that pose a challenge for currently used change detection methods.

Lithospheric yield stress is a key parameter in controlling tectonic processes. We calculate yield stress for a range of conditions appropriate to the Archean Earth, including hotter mantle potential temperatures and a range of Moho temperatures using 2D high resolution numerical geodynamic modelling techniques. This range of conditions are evaluated for generating felsic, tonalite-trondhjemite-granodiorite (TTG), crust with the results bench marked against the preserved rock record. The model results indicate that lithospheric yield stress slightly lower than the present-day Earth values (i.e. < 100 MPa) generates TTG melt volumes similar to those preserved in the rock record. In particular, large volumes of TTG melts form in the tails of lithospheric drips. Melting occurs profusely within the thinner portions of the drips as these regions are more ef�ciently heated by the enclosing hotter mantle. In contrast, only limited melting occurs in regions of thickened crust, in part because the weaker lithosphere cannot sustain crustal thickening for long time periods, resulting in its removal through drips. Our models highlight the dominance of non-plate tectonic mechanisms in producing TTGs under the conditions that operated on the hotter Archean Earth.