Precipitation flag (precipitating or not; stratiform or convective) is a key parameter for us to make betterretrieval of precipitation characteristics as well as to understand the cloud-precipitation physicalprocesses. The Global Precipitation Measurement (GPM) Core Observatory’s Microwave Imager (GMI)and Dual-Frequency Precipitation Radar (DPR) together provide ample information on globalprecipitation characteristics. As an active sensor in particular, DPR provides an accurate precipitationflag assignment, while passive sensors like GMI were traditionally believed not to be able to tell apartprecipitation types. Using collocated precipitation flag assignment from DPR as the “truth”, this project employs machinelearning models to train and test the predictability and accuracy of using passive GMI-only observationstogether with ancillary atmosphere information from reanalysis. Precipitation types are classified intothe following classes: convective, stratiform, convective-stratiform mixed, no precipitation, and otherprecipitation. Sub-sampling with different probabilities is employed to construct a balanced trainingdataset. A variety of classification algorithms are tested, including Support Vector Machines, NaiveBayes, Random Forests, Gradient Boosting, and Neural Networks (Multilayer Perceptron Network), andtheir results are evaluated and compared. The trained model has ~ 85% of prediction accuracy for everytype of precipitation. High-frequency channels (166 GHz and 183 GHz channels) and 166 GHzpolarization difference are found among the most important factors that contribute to the modelperformance, which shed light on future instrument channel selection.

Aerosol-cloud interactions are both uncertain and important in global and regional climate models, and especially in the southeast Atlantic Ocean. This uncertainty in the region is largely due to two correlated factors---the expansive, bright, semi-permanent stratocumulus cloud deck and the fact that southern Africa is the largest source of biomass-burning aerosols in the world. We study this region using the WRF-Chem model with CAM5 aerosols and in situ observations from the ORACLES and LASIC field campaigns in August-October of 2016 through 2018. We compare aerosol and cloud properties to measure and improve model performance and expand upon observational findings of aerosol-cloud effects. Relevant comparison variables include aerosol number concentration, mean particle diameter and spread, CCN activation tendency, hygroscopicity, and cloud droplet number concentrations. Specifically, our approach is to analyze colocated model data along flight tracks to resolve aerosol-cloud interactions. Within and between single-day flights, there is high spatiotemporal variability that can get lost to large-scale averaging analyses. We have found that CCN is substantially under-represented in the model compared to observations. For a given aerosol number concentration, size, supersaturation and hygroscopicity, the model will consider fewer particles as CCN than observations indicate. We plan to explore this result further, diagnosing the model-observation differences more consistently and updating the model with more physically accurate values of aerosol size, concentration, or hygroscopicity based on observations. We will also intercompare multiple instrument platforms involved with the ORACLES and LASIC campaigns. With improved small-scale aerosol-cloud interactions, this work also shows promise to substantially improve that representation in climate models.

Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). Although solar flare-driven HF absorption is a well-known impact of SWF, the occurrence of a frequency shift on radio wave signal traversing the lower ionosphere in the early stages of SWF, also known as “Doppler Flash”, is newly reported and not well understood. Some prior investigations have suggested two possible sources that might contribute to the manifestation of Doppler Flash: first, enhancements of plasma density in the D and lower E regions; second, the lowering of the reflection point in the F region. Observations and modeling evidence regarding the manifestation and evolution of Doppler Flash in the ionosphere are limited. This study seeks to advance our understanding of the initial impacts of solar flare-driven SWF. We use WACCM-X to estimate flare-driven enhanced ionization in D, E, and F-regions and a ray-tracing code (Pharlap) to simulate a 1-hop HF communication through the modified ionosphere. Once the ray traveling path has been identified, the model estimates the Doppler frequency shift along the ray path. Finally, the outputs are validated against observations of SWF made with SuperDARN HF radars. We find that changes in refractive index in the D and lower E regions due to plasma density enhancement are the primary cause of Doppler Flash.

Autonomous large-scale detection of whistler-ode chorus elements in the Van Allen radiation belts has been an open computational challenge. This is primarily due to: (i) Variability of the spectral morphology of chorus elements, and (ii) Structured background interference from hiss-like chorus that can make elements difficult to detect using traditional signal processing and pattern recognition techniques. We will present computational techniques drawing on pixel connectivity, signal-to-noise (SNR) considerations as well as supervised and unsupervised pattern recognition techniques. Specifically, we will explore the efficacy of popular machine learning techniques trained on unfiltered spectral images versus those trained on culled features generated by unsupervised feature extraction techniques. Representative results will be presented based on magnetic field measurements taken by the EMFISIS instrument suite in the Van Allen probes mission.

The mechanical coupling between a planet and its atmosphere enables the conversion of seismic waves into infrasound waves, i.e. low-frequency pressure perturbations (< 20Hz), which propagate to the upper atmosphere. Since the characteristics of the seismically-induced pressure perturbations are connected to their seismic counterparts, they provide a unique opportunity to investigate the atmospheric and interior structures of a planet or to constrain source properties. However, in Earth’s remote regions, deploying seismic or infrasound networks at the surface can be a difficult task. Stratospheric balloon platforms equipped with pressure sensors have therefore gained interest since they provide a unique and inexpensive way to record pressure signals in the atmosphere with a low noise level. Yet, infrasound observations of Earthquakes on balloon platforms have never been reported in the literature. In this study, we investigate the seismo-acoustic wavefield generated by the aftershocks of the 2019 Ridgecrest sequence and other regional low-magnitude Earthquakes on July 22 and August 9, 2019 using four free-flying balloons equipped with pressure sensors. We observed a strong signal coherence after the largest event between seismic motions at the surface and balloon pressure variations which matches our numerical simulations. A first atmospheric earthquake detection is crucial to demonstrate the viability of this novel technique to monitor infrasound from natural and artificial seismicity on Earth, and the study of seismic activity on planets such as Venus.

Communities in Fiji rely on provisioning services from landscape resources such as agricultural and forestry-related production, and climate regulation determined by the mix of landscape resources across space. Accurate mapping and monitoring of patterns of land use and land cover (LULC) over time at scales relevant to livelihood processes is important for informing landscape management, land use policies, and climate-smart sustainable development. A methodology developed collaboratively with landscape stakeholders to produce an inter-annual LULC map that addresses natural resource, agricultural, and forestry management use cases is presented here. Key requirements identified by stakeholders were that the LULC methodology was robust, relatively easy to reproduce, and could be applied to other Fijian landscapes with different dynamics. Using publicly available remotely sensed data and geospatial tools, we applied the LULC methodology for two locations in the Ba Catchment, Fiji. Field orientation and key validation data were collected using the QField open-source mobile GIS, and labelled training and accuracy assessment data were collected in Google Earth. Annual median multispectral surface reflectance and seasonal NDVI-based phenology metrics derived from Sentinel-2, and topographic variation from SRTM DEM provided the best discrimination between vegetation classes across the catchment from low-lying coastal areas to the highlands (> 1000 m ASL). A random forest model was trained and validated in Google Earth Engine to produce an inter-annual LULC map with a 10m spatial resolution. An important outcome from our work was the transfer of skills and building of local stakeholder capacity to continue to update the LULC map, and to expand the map to include other communities, catchments and forestry areas across Fiji. This capacity building included iterative stakeholder consultation, co-development of online training materials, workshops, and collaborative fieldwork.

Laboratory data are essential for testing and refining of theories and models in Earth sciences. Recent developments in data mining techniques and machine learning have made it feasible to utilize and digest large amounts of information; yet such data must be initially prepared and structured in a meaningful way. Recognizing the potential and challenges of data access, multiple efforts are underway in the development of digital data repositories (Strabospot, Epos). Currently most information in experimental Geophysics is not accessible in digital, searchable form. Such information may include: equipment capabilities and configurations, original and edited experimental data, laboratory calibrations as well as information regarding testing protocols and procedures. The LAPS project aims to design and develop resources to facilitate data workflow and access. It specifically focuses on the needs of laboratory researchers, students and managers to prepare data for use in digital data repositories. As laboratories use a wide variety of hardware and software solutions to acquire and process data, we focus our efforts on the development of web based tools that do not require specific local infrastructure and software. One of the main objectives of LAPS is to establish a coherent and effective way to describe equipment and experiments across a variety of testing rigs and devices. To simplify the workflow we are proposing a combination of selecting pre-configured equipment and experimental profiles and manual data entry via web form. A completed test protocol containing all experimental metadata may then be saved locally (e.g., as JSON file) or (optionally) to a database. Such test protocols can be re-loaded and modified as needed. To complete the workflow of a successful test, a link to original or processed data files may be inserted. The data file layout can be defined in the experimental profile. Upload to a digital data repository is optional but by adhering to the proposed Strabo/Epos data model it will not require additional input. While the web based workflow will be accessible to all users, we also recognize the need to integrate the system into specific work flow solutions in rock deformation laboratories. We therefore provide the framework that simplifies local data management systems and analytical applications.

We assessed the effect of temperature and tropospheric concentration of Nitrogen dioxide (NO2) and Carbon monoxide (CO) before and during the COVID 19 curfew/lockdown period (22 March-19 May 2020) on mortality due to COVID-19 in Indian Punjab. Time series daily data of TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor was used to study the spatio-temporal changes in NO2 and CO from 15 March to 19 May 2020. Visible Infrared Imaging Radiometer Suite onboard Suomi NPP satellite was used to detect the active fires (from 15 April to 19 May 2020) due to crop residue burning. The COVID-19 mortality was calculated from number of deaths relative to number of cases preceding two weeks of deaths due to COVID-19 virus. The weekly (15-21 March 2020) averaged tropospheric concentration of NO2 and CO was higher before the first day of curfew (22 March 2020). The concentration of NO2 decreased during the curfew period followed by increasing concentration from 11 April to 19 May 2020 and the concentration of CO increased from 19 April to 19 May 2020. There was a continuous increase in daily air temperature from 15 March to 19 May 2020. Mortality due to COVID-19 virus was significantly negatively correlated with NO2, CO and temperature. These results show that increasing temperature and concentration of NO2 and CO decrease COVID-19 mortality, but further studies should be conducted in different continents of the world to verify the impact of NO2, CO and temperature on COVID-19 mortality.

Addressing the need for convergence of different sources of knowledge to deal with complex issues such as global change, this paper presents the results of collaboration between artists and scientists to study social-ecological-technological systems (SETS). We focus on informal roads as an example of SETS. In the absence of public roads local, mostly indigenous communities and others use these forestry roads, seismic line clearings and oil and gas service roads for mobility in Siberian taiga affected by extractive industry. In 2020, with COVID-19, we had to increase our emphasis on virtual forms of data gathering, interpretation, and representations of the results. Presented in this paper forms of transmedia storytelling are designed to allow audience and users as well as the local and indigenous communities to get familiar with the research results, give feedback, and provide their own perspectives, interrelations and interdependencies between different SETS components.

The Global Multi-Resolution Topography (GMRT) Synthesis is an elevation model that includes curated deep-water multibeam bathymetry data at ~100 m resolution covering more than 9% of the ocean. GMRT is built with a scalable tiled raster architecture that efficiently stores and presents high-resolution elevation data nested within low resolution data. A set of tools are available for users to access the compilation through simple user interfaces (e.g. GMRT MapTool) and web services, while also providing full attribution and access to source swath files. The availability of raw/unprocessed multibeam sonar data in the National Centers for Environmental Information (NCEI) archive has increased dramatically over the last decade, but transforming these data into high-quality integrated products suitable for use by scientists and the public alike requires significant effort. The GMRT Team has built workflows and tools for data preparation and review that are optimized for cleaning and integrating sparse globally distributed multibeam data, enabling the addition of ~60-80 research cruises per year. Once raw swath data files are cleaned and corrected, they are gridded/tiled with the GMRT Tiling tools so they can be reviewed and quality controlled in the context of other data in the GMRT Synthesis. Working with processed swath files generated by the community, we have observed that this process frequently reveals issues that are overlooked during data processing. In order to accelerate the rate of data integration and leverage the data processing efforts of the community, GMRT Tiling tools are being adapted for distributed use. Ocean Exploration Trust is an initial partner in this effort, and all processed swath files from the 2017-2019 Nautilus field seasons were prepared with GMRT Tiling tools and reviewed by the OET team. This revealed problems in processed swath data files from several cruises that were addressed prior to submission to NCEI, thereby improving the quality of data in the archive. We are now working to include the GMRT Tiling tools into at-sea standard operating procedures of the Nautilus as a testbed for broader community distribution, to ensure consistent quality of processed multibeam data, and to accelerate the production of high-quality integrated data products including GMRT and Seabed 2030.

2019 was both the hottest and driest year on record for Australia, leading to large forest fires in the southeast from November 2019 to January 2020. However, in early 2020, the fires and hot-dry conditions dissipated with above average rainfall and below average temperatures along Australia’s southeast coast. In this study, we utilize space-based measurements of trace gases (TROPOMI XCO, OCO-2 XCO2) and vegetation function (OCO-2 SIF, MODIS NDVI) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019/2020 growing season. During the austral spring, we find anomalous reductions in primary productivity and large biomass burning emissions in excess of bottom-up estimates from GFAS. This is then followed by a remarkable recovery and greening during early 2020, coincident with cooler and wetter conditions. We will further discuss different behaviors of recovery over fire-devasted and non-fire regions. This study showcases the capability of combining observations from multiple satellites to monitor the carbon and ecosystem anomalies resulting from extreme events. Finally, we will discuss the remaining challenges in monitoring the carbon cycle from space.

The microstructures, textures and seismic anisotropy of a foliated blueschist-eclogite rock from Naga Hills Ophiolite (NHO) complex, North-East India has been investigated to understand the fabric relationship and deformation mechanism prevailed during the metamorphism and subduction of the Neo-Tethys oceanic crust. The Naga Hills blueschist-eclogite rock essentially contains omphacite, phengite, glaucophane, and garnet, representing an oceanic crust that experienced P-T stability field of blueschist and further eclogite metamorphism before it resurfaced accreting in the Naga Hills. Omphacite and glaucophane show weak shape preferred orientation (SPO), while phengite displays a strong SPO. The Crystallographic Preferred Orientations (CPO) of omphacite is characterized by the [001]-axes gridles within the foliation, and the (010)-poles concentrated sub-perpendicular to the foliation. For glaucophanes, the [001] axis aligns parallel to lineation and the [100] axis and (110) pole plunge perpendicular to foliation. These CPOs correspond to SL-type fabrics, related to a deformation geometry within the plain strain field and they developed from plastic deformation through dislocation creep. The seismic anisotropies of the individual minerals, blueschist-eclogite domains and their contributions in the bulk rock anisotropy has been discussed. The calculated seismic anisotropies (AVP and AVS) of bulk rock are 12.8% and 8.1%, respectively. This strong seismic anisotropy is due to the presence of phengite and glaucophane and can contribute to the observed seismic anisotropy in the subduction zone. The average low P-wave velocity of whole rock from NHO compared to blueschist is probably due to the low P-wave velocity of phengite (avg VP: 6.2 kms-1). Therefore, the low seismic velocity in the upper layer and the strong seismic anisotropy of the subducting oceanic crust can be attributed to the presence of glaucophane and phengite.

Recently a new approach to change point analysis was presented in the statistical literature. This approach is based on the construction of confidence sets. One way to apply it is to take existing homogeneity tests as a basis. In this study Pettitt and CUSUM based homogeneity tests are used to derive distribution-free change point analysis methods. These are applied to a large number of synthetic data series, and the results are analyzed. The results are compared to results of the application of the classical Pettitt and CUSUM methods, and with a Bayesian approach. It is shown that the new methods perform as least as well as the classical methods and the Bayesian method. Unlike the classical methods, the Bayesian and confidence set based methods provide information on the uncertainty of the change point location. The methods are tested on normally distributed synthetic time series and on synthetic time series with a type II Generalized Extreme Value distribution.

The northernmost part of the Mediterranean Sea, the northern Adriatic shelf, is a complex area where the intensity of dense water formation and the consequent Adriatic-Ionian thermohaline circulation are shaped by a combination of extreme wintertime bora winds and substantial freshwater loads. To better understand the impact of global warming on extreme bora dynamics and the associated sea surface cooling, we applied the Adriatic Sea and Coast (AdriSC) kilometer-scale modelling suite to the far future climate (2060-2100) period. Under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 greenhouse emission scenarios, the AdriSC simulations are carried out via the combination of a statistical approach – consisting of an ensemble of 3-day simulations for 22 extreme bora events, and a pseudo-global warning (PGW) methodology – imposing a climatological change to the forcing used to produce the evaluation (present climate) runs. Despite a noteworthy decrease in intensity of the bora winds (by up to 3 m/s), the intensity of the negative latent heat fluxes is simulated to increase (by up to 150 W/m2) due to the reduction in relative humidity in the northern Adriatic (by up to 3%). Consequently, the sea surface cooling associated with severe bora events and preconditioning the dense shelf water formation in the northern Adriatic is projected to not significantly change compared to present climate. Although these results need to be further confirmed, this study thus provides a new view on the future of processes driven by sea surface cooling, such as the dense shelf water formation or the Ionian-Adriatic thermohaline circulation, that were projected to decrease in the future climate by regional climate models an order of magnitude coarser than the AdriSC simulations.

Observations from the LADEE, Apollo and Surveyor missions have provided evidence of an active lunar dust environment where dust is ejected from the surface into the exosphere by natural physical mechanisms (e.g., meteoroid impacts and possibly electrostatic lofting). Characterizing the spatial and temporal distribution of different exospheric dust populations, and how they are coupled to other components of the dynamic lunar environment, is important to understanding the surface evolution of the Moon. Additionally, anthropogenic dust ejected from the surface by human/robotic exploration activities can be a significant source of exospheric dust. Experience from the Apollo missions showed that dust has the potential to be hazardous as it can interfere with the operation of mechanical, thermal and optical systems. Therefore, it is vitally important to be able to monitor the dust environment to ensure mission safety for future exploration, especially in the Artemis era. An effective method for observing any dust populations in the lunar exosphere is to measure the intensity of sunlight scattered by the dust. This can reveal the dust abundance and spatial distribution, as well as constrain the average grain size by measuring the angular width of the forward scattering lobe. The largest uncertainties in such measurements lies in the grain scattering properties, namely: scattering coefficient, phase function shape and polarization. All of these become more important at larger scattering angles, where diffraction no longer dominates. Typically, the dust size distribution cannot be measured uniquely, and must be constrained by a set of forward simulations at multiple wavelengths and scattering angles. We present a precomputed grid of light scattering properties for irregularly shaped grains (Richard et al., 2011), which are a more realistic representation of lunar dust than the Mie models commonly employed. The grid spans UV to near-IR wavelengths and encompasses a wide range of grain size. Scattering from smaller grains is computed using the Discrete Dipole (DDA) method (DDSCAT) and at larger sizes using the Hapke-Equivalent Slab with Allen diffraction (this will soon be replaced by ray tracing with diffraction). Applications of this grid include observation interpretation and modelling of sunlight scattered by meteoroid impact plumes, lunar horizon glow, and exploration activities.

The mission of the National Geodetic Survey (NGS) is to define, maintain and provide access to the National Spatial Reference System (NSRS). The NSRS provides a consistent coordinate system that defines latitude, longitude, height, scale, gravity, and orientation throughout the United States and its territories. The current standards and specifications for acquiring and processing terrestrial gravity data date back to 1984, long before the now common use of portable absolute gravity meters and modern relative instruments. We present a proposal for a detailed update of these standards and specifications, which is consistent with the latest resolutions of the International Association of Geodesy: absolute gravity meter accuracies will be traced to international comparisons, and these can, in turn, be linked to other instruments through regional comparisons and fundamental gravity sites. Calibration procedures based on absolute observations will also quantify the accuracy of relative instruments as well.

Stream water temperature (T) is a variable of critical importance and decision-making relevance to aquatic ecosystems, energy production, and human’s interaction with the river system. Here, we propose a basin-centric stream water temperature model based on the long short-term memory (LSTM) model trained over hundreds of basins over continental United States, providing a first continental-scale benchmark on this problem. This model was fed by atmospheric forcing data, static catchment attributes and optionally observed or simulated discharge data. The model achieved a high performance, delivering a high median root-mean-squared-error (RMSE) for the groups with extensive, intermediate and scarce temperature measurements, respectively. The median Nash Sutcliffe model efficiency coefficients were above 0.97 for all groups and above 0.91 after air temperature was subtracted, showing the model to capture most of the temporal dynamics. Reservoirs have a substantial impact on the pattern of water temperature and negative influence the model performance. The median RMSE was 0.69 and 0.99 for sites without major dams and with major dams, respectively, in groups with data availability larger than 90%. Additional experiments showed that observed or simulated streamflow data is useful as an input for basins without major dams but may increase prediction bias otherwise. Our results suggest a strong mapping exists between basin-averaged forcings variables and attributes and water temperature, but local measurements can strongly improve the model. This work provides the first benchmark and significant insights for future effort. However, challenges remain for basins with large dams which can be targeted in the future when more information of withdrawal timing and water ponding time were accessible.

One key question about Mars is whether life has been or still is present on or beneath its surface. For life to flourish, it requires a habitable environment with the appropriate physical and chemical regolith parameters. To better understand the parameters that constitute a habitable environment, leachates of three martian simulants (JSC Mars-1, MMS-1 fine, and MGS-1) were analyzed for their soluble ionic composition, pH, and conductivity in order to determine the presence of any beneficial or toxic elements and their effects on the two bacteria E. coli and Eucapsis. E.coli was cultured in minimal media where acetic acid was the only organic source, and tested its requirements for a carbon source (acetic acid), nitrogen source ((NH4)2SO4) as well as trace elements (Ca, Mn, Zn, Cu, Fe, Co, Mo). In minimal culture media with all nutrients available for healthy growth, E. coli showed substantial growth. In the case of a carbon-only source or nitrogen-only source in MMS-1 soil leachate, E. coli showed limited growth compared to that observed in the full minimal culture media. Assessing Eucapsis growth, among the three leachates tested MMS-1 displayed the best growth. Additionally, we observed that MMS-1+Allen’s minimal and full Allen’s medium groups displayed similar growth curves, indicating that MMS-1 can provide all the trace elements needed for Eucapsis growth. Among all the leachates, MMS-1 showed the most promising results. MMS-1 + Allen’s medium provided the highest Eucapsis growth. MGS-1 + Allen’s minimal media also showed significantly higher growth than MGS-1 alone. However, JSC and JSC + Allen’s minimal did not show any significant difference regarding Eucapsis growth. The results indicated that Eucapsis grew best in both MMS-1 F leachate only and MMS-1 F leachate with Allen’s minimal medium.