Antecedent Soil moisture (ASM) is one of the most important factors affecting the rainfall-runoff modelling process. Its existence and influence in computing runoff using Soil Conservation Service Curve Number (SCS-CN) method have been a point of discussion for many decades. In this study, a novel procedure has been proposed to estimate the ASM by modifying the SCS-CN method and verify its applicability by comparing the ASM with the observed soil moisture. Natural rainfall, runoff and soil moisture data from eight small experimental farms with similar land use and varying slopes viz. 1%, 3% , 5% and 8%, located at Roorkee, India have been utilized. The ASM is computed by optimizing two parameters i.e., absolute maximum potential retention (Sabs) and initial abstraction coefficient (λ), and the optimization is carried out by minimizing Root mean square error (RMSE). Results show that there exists a good agreement between observed and computed ASM for slopes 1%, 3% and 5% (R2 > 0.5). A fair correlation for slope of 8% (R2 of 0.31) is observed, which may be due to the inadequacy of data. This study will be helpful in calculating ASM for ungauged catchments and will further broaden the applications of SCS-CN method.

As it was recently predicted, the kinetic ballooning/interchange instability (BICI) can provoke reconnection onsets that lead to detached azimuthally thin earthward intrusions (heads) of depleted plasma tubes when 𝛽eq ⩽ 100. Such detached BICI heads would be seen as localized earthward-propagating dipolarization fronts. Using Time History of Events and Macroscale Interactions during Substorms observations in the plasma sheet at XGSM ≈ −11 Re and conjugate All-Sky Imager and magnetometer networks observations on the ground, we show four examples when prominent dipolarization fronts with moderate earthward flows were observed amidst azimuthally drifting interchange heads and concurrently with the ionospheric current intensifications near Time History of Events and Macroscale Interactions during Substorms footprints and auroral bright spots originating from dimmer azimuthal beads/rays. These events support the idea that some of the BICI heads detach from the region with reversed radial gradient of Bz. The detached BICI heads propagate earthward-driving ionospheric pseudo-breakups.

The loss of biodiversity and the associated decline of ecosystem services vital for sustaining human life demand a comprehensive monitoring of plant biodiversity. Measuring biodiversity in the field on large areas generates issues like the need of a robust sampling design, the high demand on human and monetary resources and different biases introduced by humans and environmental conditions. These circumstances have recently triggered an extended use of remote sensing data to quantify biodiversity in a cost- and time-efficient way. Remotely sensed datasets represent the Earth surface at a certain point in time. Yet, it is not well studied what the use of a single dataset in time implies for biodiversity estimates. The functional dimension of biodiversity, expressed through functional traits within or between species, varies according to the phenological cycle. Further in grasslands, mowing and grazing events lead to temporal variations in the remotely sensed diversity. We provide an approach in which we integrate the temporal dimension in the quantification of biodiversity from space. Functional diversity is partitioned into a spatial and a temporal component. In particular, Sentinel-2 satellite datasets are well suited for this purpose, providing a complete landscape picture with high revisit time. In our study case, the incorporation of the temporal dimension and the interaction between spatial and temporal diversity by employing multiple datasets improves the retrieval of functional diversity in differently managed alpine grasslands. In comparison to the use of a single dataset, our approach provides more reliable recommendations for conservation and restoration decision-making on a regional scale.

Estimations of the surface soil moisture and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats that are characteristically wet and periodically submerged by sea water. However, existing techniques have limitations of measurement. Along with the three-dimensional (3D) point cloud, long-range terrestrial laser scanners (TLSs) can simultaneously record the co-located intensity value of each echo that internally contains physical reflectance characteristics of the scanned point. Most long-range TLSs emit near-infrared lasers that can be strongly absorbed by the water. Thus, the intensity values of the areas with high water moisture are theoretically smaller than those of the regions with low moisture. In this study, the intensity data of long-range TLSs are corrected for the incidence angle and distance effects and the corrected intensity data are utilized to quantitatively estimate the surface soil moisture of intertidal mudflats. A case study is conducted for a mudflat in Chongming Island, Shanghai, China, using a long-range TLS (Riegl VZ-4000) that can measure distance up to 4000 m with a near-infrared wavelength of 1550 nm. Results show that compared with traditional techniques (e.g., gravimetric measurements) the corrected intensity data of long-range TLSs are a very effective data source for a quick, accurate, and detailed estimation of surface soil moisture for large-area mudflats. The estimation accuracy is approximately 97%. Additionally, by combining the point cloud of the mudflats the 3D distribution of the moisture can be accurately mapped to potentially analyze the intrinsic association between moisture and topography, vegetation coverage, and habitation of creatures in mudflats.

The Sandur greenstone belt (SGB) is a distinctive greenstone belt as it is perched within the Closepet granitoid rocks (CG). The emplacement of the CG is attributed to intrusion in a crustal scale transcurrent shear zone towards the end of Archaean (Moyen et al., 2003). The Chitradurga shear zone that forms the eastern margin of the Chitradurga greenstone belt, located west of the Closepet granite, is considered as the boundary between WDC and EDC. As per the division of the Dharwar craton, the domination of volcano-sedimentary sequences in SGB with abundant BIF and considerable greywacke-argillite lithologies, attest their similarity to the greenstone belts of WDC. Closer to the SGB, the rocks of the CG are known to host excellent mafic microgranular enclaves (MME) which might indicate interaction between the granitic magma with the older greenstone belt lithologies during intrusion. It is interesting to note that there is a progressive increase in crustal thickness from north towards south in the Dharwar craton and the SGB is found associated with the CG in the shallow zones of the north (Moyen et al., 2003). The mafic volcanic rocks are predominantly basaltic in composition and are composed of amphibole, pyroxene, plagioclase and quartz with titanite and magnetite as accessory minerals. The rocks are classified as tholeiitic basalts that were metamorphosed to amphibolite grade. Preliminary geochemical studies on these rocks show significant differences in their trace element distribution. The chondrite-normalized REE patterns show moderate to high contents of REE and have unfractionated pattern. The basalts show a flat to slightly LREE enriched pattern. Some samples show slight negative Eu anomaly and some do not show any significant anomaly. Some associated rocks also have complementary enrichment-depletion of certain elements. All of these point to multiple petrogenetic processes involved in the generation of these magmatic precursor rocks.

In March 2019 cyclone Idai led to a compound flooding event in Mozambique combining high river runoff and storm induced water level extremes and causing damages up to 2 billion USD and more than 1200 fatalities. The co-occurrence of storm surges, wind waves, and flooding through heavy precipitation and runoff increases the risk of flooding and exacerbates the impacts along the vulnerable Southern African coasts. To mitigate the associated high-impacts, it is essential to know the probability of theses compound events (multivariate extreme event analysis) and understand the processes driving them (Wahl et al. 2015). In the project CASISAC*, we propose a regionalized multivariate assessment of extreme events to model extremes as combination of storm surge, waves, river discharge and high precipitation at southern African coasts (Namibia, South Africa, Mozambique). We develop a multivariate statistical model based on copulas to represent and analyze the physical mechanism underlying compound events and their return periods under present day climate conditions. The African regions are particularly poor sampled by tide gauges and available records include large gaps. To overcome the data scarcity, ancillary data from high-resolution ocean model hindcasts (Schwarzkopf et al. 2019) based on the NEMO model will complement the analysis. An integrated analysis of observational records from tide gauges in combination with ocean model hindcasts allows us to regionalize compound extreme events corresponding to certain return periods along the entire coastline. In this presentation, we will show preliminary results of the multivariate extreme value model analysis chain. *CASISAC (Changes in the Agulhas System and its Impact on Southern African Coasts: Sea level and coastal extremes) is funded by the German Federal Ministry of Education and Research (BMBF) under the grant number 03F0796C. Schwarzkopf et al. (2019): The INALT family – a set of high-resolution nests for the Agulhas Current system within global NEMO ocean/sea-ice configurations. In: Geoscientific Model Development, Vol. 12, 7, 3329-3355, doi: 10.5194/gmd-12-3329-2019. Wahl et al. (2015): Increasing risk of compound flooding from storm surge and rainfall for major US cities. In: Nature Climate Change, Vol. 5, 12, 1093-1097, doi: 10.1038/nclimate2736.

Formed by Mississippi River sediments, south Louisiana is a flat, low-lying coastal region with high-clay content soils and heavy annual precipitation that is particularly susceptible to damage caused by extreme storms and flooding. In August 2016, a stationary storm system caused over 50 cm of rain to fall across much of southeast Louisiana. Largely rural, the major trends in the hard-hit Amite River Basin have been conversion of agricultural land to forest beginning in the mid-20th century and rapid urbanization and development spurred by economic and population growth in the Baton Rouge area following the oil boom of the 1970s, as well as later waves of migration following Hurricane Katrina and the 2016 floods. This analysis examines the effects of spatially and temporally changing land use on runoff and flooding within the watershed and is part of a larger research project which seeks to also quantify the relative impacts of changes in precipitation and planform geometry. To quantify the effects of land-use change on flooding, runoff curve number (CN) maps were created using NRCS soil type data and USGS land cover data. Areas with a higher CN experience less interception and infiltration of surface water and the flood risk is consequently greater. While CN for the Basin overall dropped from 86 to 79 between 1938 and 2018, CN dropped from 82 to 70 in rural areas due to reforestation and increased from 86 to 90 in the southern portion of the Basin due to urbanization. These data were then input into the HEC-HMS and coupled 1D/2D HEC-RAS components of a numerical model of the Amite River Basin. Flooding behavior under different design storms and land cover conditions was then observed and quantified. In examining the major contributing factors to flooding in south Louisiana, this research project aims to create a more comprehensive understanding of flooding and propose potential mitigation strategies and design interventions for alleviating the worst effects.

Coastal aquifers are prone to saltwater intrusion as increased demand for freshwater leads to heavy, unregulated pumping from these coastal aquifers. Under sustainable pumping conditions, the saltwater-freshwater interface is stable but rarely monitored. When saltwater intrusion is first detected, saltwater-freshwater interface is already disturbed and starts moving inland. Any numerical simulation-based methodology to manage saltwater intrusion requires the initial saltwater-freshwater interface to be known. Numerical models need to satisfy the boundary conditions and the initial conditions (Cauchy–Riemann equation) for the solution to exist. However, in the absence of any monitoring, the initial position of the saltwater-freshwater interface is unknown, which forms the initial condition for numerical models and thus fall in the category of initial value problem. Pool and Carrera’s correction factor for the sharp interface approach is used to estimate the initial saltwater-freshwater interface. Generally, groundwater head information is readily available, which can be used to locate the saltwater-freshwater interface. Ghyben-Herzberg relation is used for estimating the initial position of the interface, but the level of accuracy of the information derived from Ghyben-Herzberg relation is low. Hence, in this study Pool and Carrera’s correction factor for density ratio is used in Ghyben-Herzberg relation for accurate estimation initial interface location. The developed method is applied to a real coastal site in Puri, India, where the aquifer is encroached by saltwater intrusion. The performance evaluation results show the applicability of this methodology

Atmospheric General Circulation Models (GCMs) continue to increase in complexity which is especially true for their computationally-demanding physical parameterizations. This work explores whether, and how, computationally-efficient machine learning (ML) techniques can become an option for replacing physical parameterization schemes in GCMs. We test this idea in a model hierarchy with NCAR’s Community Atmosphere Model version 6 (CAM6) which is part of NCAR’s Community Earth System Model (CESM 2.1). In particular, dry and idealized-moist CAM6 model configurations are considered which employ simplified physical forcing mechanisms for radiation, boundary layer mixing, surface fluxes, and precipitation (in the moist setup). Several ML models are developed, trained, and tested offline using CAM6 output data. The assessed ML techniques include linear regression, random forests, and neural networks with and without convolutional layers. Using a variety of ML hyperparameter choices, all of the ML methods are able to capture the general structure of the CAM6 physical forcing. However, in order to capture the details in the physical forcing patterns, the ML hyperparameters must be tuned. Once tuned, we compare different ML techniques against one another in order to assess their strengths and weaknesses. Future work will explore the online coupling of the ML-generated physical tendencies to the CAM6 atmospheric dynamical core.

Climate models generally require that results between runs are bit-for-bit reproducible. This becomes impossible when switching the computational platform or compiler that the model is run on. An ensemble of the Geophysical Fluid Dynamics Laboratory (GFDL) Atmosphere Model 4.0 (AM4, Zhao et al. 2018a,b)) is created by perturbing the temperature at a random point on the order of 10-13 (in the rounding error of the system). Previous results show that three different compilers on the same computing platform results in a spread of the global mean temperature of 0.14 K (Robinson et al. 2018). The current ensembles are run on three different computing platforms with different processors: the main production computer of GFDL with Intel broadwell/haswell, one with Intel knights landing, and the other with Intel skylake. The ensemble means and standard deviations for global surface temperature are compared in order to see if the spread of rounding error in the model is platform dependent. The means are also compared to see if they lie within the spread of each modeling platform.

Aerosols interact with radiation and clouds. Substantial progress made over the past 40 years in observing, understanding, and modeling these processes helped quantify the imbalance in the Earth’s radiation budget caused by anthropogenic aerosols, called aerosol radiative forcing, but uncertainties remain large. This poster presents the outcome of an international workshop and subsequent review paper, which quantify the likely range of aerosol radiative forcing over the industrial era based on multiple lines of evidence, including modelling approaches, theoretical considerations, and observations. Improved understanding of aerosol absorption and the causes of trends in surface radiative fluxes narrow the range of the forcing from aerosol-radiation interactions compared to the latest assessment by the Intergovernmental Panel on Climate Change (IPCC). A robust theoretical foundation and convincing evidence constrain the forcing caused by aerosol-driven increases in liquid cloud droplet number concentration. However, the influence of anthropogenic aerosols on cloud liquid water content and cloud fraction and on mixed-phase and ice clouds remains poorly constrained. Observed changes in surface temperature and radiative fluxes provide additional constraints. These multiple lines of evidence lead to total aerosol radiative forcing ranges that are of similar width to the last IPCC assessment but more clearly based on physical arguments.

The IRIS Data Management Center (DMC) maintains a large assemblage of pre-computed and dynamically generated quality assurance metrics for seismic data by means of its MUSTANG system. Freely accessible through web services at http://service.iris.edu/mustang, this collection of measurements includes basic statistics, data latency, data availability, miniSEED flag counts, Power Spectral Densities (PSD), Probability Density Functions (PDF), and more. The metrics produced are suitable for a wide range of uses such as data selection for research projects, identification of data and metadata problems, assessment of station health and upgrades, and characterization of environmental noise, among others. Currently, MUSTANG measurements for seismic channels span our entire primary data repository from the year 1972 to the present. We are expanding MUSTANG’s utility to the broader geosciences community by calculating measurements on our active source, PH5 data repository and by working towards including other types of data such as infrasound. We will also present improvements to metric visualization and quality assessment tools like the MUSTANG Databrowser (basic metric plots and boxplots), MUSTANGular (map-based metric plots), and ISPAQ (a stand-alone Python utility for calculating metrics for miniSEED data stored locally or at any FDSN data center).

Researchers using climate data are facing challenge to analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often not a possibility anymore. A platform called climate4impact (C4I) has been designed and developed to enhance the use of research data, to support researchers with analytics and to support other climate portals. It is currently under development within the European Project IS-ENES3 and builds on previous developments from previous IS-ENES projects, CLIPC and C3S-Magic. C4I offers a front-end and standard services (with APIs) on top of the climate data infrastructure, and it can be visited at https://climate4impact.eu. The current version provides processing services include climate indicator calculations, country based statistics and polygon extraction. C4I makes use of the DKRZ Birdhouse framework, which is an extendable and modular processing framework based on PyWPS. Data is obtained from various ESGF nodes using secure OpenDAP. C4I provides a personal basket where users can upload their own data and do research with the provided tools. The software is open, reusable, modular and packaged. Components are available via docker containers to enable easy re-use. The on-demand calculations are taking place on the front-end server, and this is not scalable and can lead to performance problems. Within the DARE project, delegation of the calculations on the DARE Platform using the DARE API has been implemented and tested in a prototype, using EUDAT B2DROP as an intermediate storage service. It is to be noted that the DARE Platform as well as the EUDAT B2 Services should be interoperable with the European Open Science Cloud (EOSC). This prototype service delegation will be made operational during the upcoming year. In the IS-ENES3 project, the web portal will be redesigned with a completely new architecture using a micro-services and containerized approach, building on experience gained during the previous projects. The next version of the portal will be built using the React framework, which allows for creating large web applications which can change data, without reloading the page. We are actively seeking input from current as well as potential users at this time, to make the next version of C4I useful to as many people as possible. The material presented here is made possible because the IS-ENES3 project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.

The Sun’s deep-seated convective flows must ultimately sustain not just the efficient EMF from which solar magnetism derives, but also the large-scale shearing and circulatory flows thought to imbue that magnetism with its remarkable spatiotemporal ordering. Exploration of the dynamo process ultimately requires knowledge of how these flows are structured, and indeed, near-surface convective motions are imaged regularly using helioseismic observations. Those techniques reliably image flows throughout only the upper 15% of the convection zone, however, leaving us with a description of the interior that is heavily predicated on theoretical-computational results. Ideally, computational models would be validated directly against, or even applied in tandem with, laboratory experiments. Such investigations have been limited in the solar context due to the difficulty in creating a central force outside of a microgravity environment. Recent advances in acoustics have enabled the construction of an experimental apparatus that employs resonant sound waves to confine a plasma in spherical geometry. The plasma is heated internally using microwaves that are pulsed in resonance with the bulb’s fundamental acoustic mode. The resonant soundwave excited results in a central force over 1,000 times stronger than Earth’s gravity. In analog to the Sun, the resulting plasma state possesses layers that are both stable and unstable to convection. We discuss how this system’s behavior compares with numerical models run in comparable parameter regimes. Using the 3D-convection code, Rayleigh, we first model the spherically symmetric acoustic radiation pressure as a pseudo gravity. Following this initial incompressible treatment examine the behavior of a fully compressible model wherein the sound field is simulated directly. For each set of simulations, we examine the initial onset of thermal buoyancy instability and characterize the most unstable modes and their growth rate during the initial transient phase.

Satellite based remote sensing data are increasingly used for urban meteorological applications, particularly to study urban heat island impacts. However, the land surface temperature, a critical variable used to characterize the urban thermal state has never been calibrated for urbanized landcover. This will in turn escalate the uncertainties in various applications (like weather forecasting in urban areas) which use remote sensing data. This research focuses on the development and testing of an Arduino-based, GPS-enabled, non-contact passive infrared temperature sensor that provides ground-truth temperature validation of the Geostationary Operational Environmental Satellite, GOES-16, and its LST operational product. It is posited that high-resolution, multi-point, near-surface temperature information will improve LST algorithms and ultimately advance the application of satellite data to study urban climate. New York City (NYC) is used as a test site for the temperature sensor along with its geographically-respective satellite calibration points. The analysis anticipates expansion into several U.S. cities, pending preliminary evaluation and testing in NYC. GIS tools will be used to visualize data points atop geographic maps, with the intention of correlating more built-up landcover regions with temperature differences quantified by the ground-based sensor and the GOES-16 LST data. The Arduino sensor is equipped with a thermocouple to provide real-time calibration measurements on the encountered surfaces to ensure that parameters such as emissivity are captured, as well as accurate and repeatable infrared temperature readings. The enhancement of satellite information improves the well-being of the general public, which can save lives during extreme weather events such as heat waves. The research presented here intends to broaden the LST calibration network available to satellites by providing a ground-based, portable sensor framework that is implementable across cities and urban areas.

Observations of atmospheric neutral density and associated dynamics within the lower thermosphere has been a challenge to the atmospheric science and space community due to its inaccessibility to balloon-borne or orbital in situ instruments. We present a methodology to study the latitudinal and temporal variation of neutral density in this region through a simultaneous campaign at geographically distinct high-power large-aperture (HPLA) radar facilities to observe meteor trajectories. These meteors are formed by meteoroids entering the Earth’s atmosphere; the deceleration they undergo due to atmospheric drag provides a source of information to determine the neutral density of the atmosphere at altitudes of 80 to 140 km. Through the measurement of meteor head echo trajectories using specialized radar waveforms and signal processing to enhance the range resolution, combined with novel statistical techniques to account for the distribution of meteoroid properties, the temporal evolution of atmospheric neutral density from 70 to 140 km can be characterized with sub-km altitude resolution. Initial results will be presented from the first of four observation campaigns planned during the 2019-2021 period using the HPLA radar sites at Jicamarca, Millstone Hill, and Resolute Bay, which span equatorial to polar latitudes at similar longitude. The simultaneous measurements across facilities complements other measurements of atmospheric composition and structure at similar altitudes to provide improved identification of latitudinal coupling and forcing from lower altitudes into the magnetosphere-ionosphere-thermosphere (MIT) system.

A wealth of new climate model simulations have recently become available through the Coupled Model Intercomparison Project, Phase 6 (CMIP6). Evaluation of the representation of the Antarctic ocean across CMIP6 models is critical: projections of near-ice sheet temperature change will be used as input into sea level projections, and previous CMIP ensembles show substantial biases with a wide inter-model and inter-region spread. However, the ocean over the Antarctic continental shelf remains sparsely sampled, posing challenges for model-data comparison. Here, we assess a new clustering-based, grid-independent, methodology to identify and compare regional water masses, focusing on the Pacific sector of the Antarctic continental shelf. We find that temperature is insufficient to differentiate water masses, given the complexity and diversity of hydrographic profiles on the continental shelf. In contrast, clustering approaches applied to World Ocean Atlas 2018 temperature and salinity profiles identify “source” and “mixed” regimes that have a physically interpretable basis. For example, meltwater-freshened coastal currents in the Amundsen Sea, and High Salinity Shelf Water formation regions in the western Ross Sea, emerge naturally from the algorithm. We compare the location and properties of observed regimes to those found in the modern hydrographic state of the Community Earth System Model, version 2. Although CESM2 biases can be substantial, the locations of distinct regimes, and inter-cluster differences in water mass properties, are relatively consistent with observations. Differences in the locations and properties of hydrographic regimes are consistent with those expected from missing or poorly-represented physical processes (e.g. katabatic winds, ice shelf basal melting). We note other applications of this method, including the assessment of seasonal variability, and model-data comparison with different CMIP6 simulations and higher resolution regional ocean models.

The Data Processing Unit (DPU) is the “heart” of the plasma suite SWA and is the only interface with the S/C. The DPU is interfaced with EAS, PAS and HIS sensors via SpW dedicated links and is in charge of supporting EAS and PAS with power, functionality control, temporary storage, communication and computational capability and, in addition, supports HIS with communication to the S/C. Its architecture derives from a trade-off analysis aiming to define a system able to perform the needed computational tasks while keeping mass, volume and power within the limits imposed by the constraints. Additionally, the DPU has been designed to be “single fault” tolerant and the “cold-spare” concept has been adopted as redundancy philosophy. It implements data and command interfaces with the S/C via the redundant SpaceWire (SpW) data links and the redundant power input HV-HPC command interface. Two independent, executable SW images represent the overall SWA DPU SW: the Boot SW (BSW) and the Flight SW (FSW). While the BSW manages the basic hardware initialization, the FSW manages TC/TM, controls all the processes related to the state of the sensors, validates and executes TC, acquires, processes, compresses and formats science data prior to downlink for EAS(1&2) and PAS sensors, while HIS autonomously processes its scientific data. In particular, FSW is in charge of data compression, moments calculation and telemetry generation restrictions to keep each sensor within its respective telemetry allocation. Remarkable level of data compression for EAS, which generates the largest data volume, is reached via customized implementation of lossless CCSDS 121.0 based on a “Complex Reordering” mechanism, which avoids periodical jumps among acquisition directions in phase space. Moments of proton and electron velocity distribution functions are computed onboard. Different Look-Up Tables (LUT) for PAS and EAS allow to perform moments calculation modulating the counts in each volume of phase space by a combination of the factors contained in these tables. Finally, since SWA data production greatly changes from normal to burst mode, a book keeping algorithm (BKA) will monitor and control, continuously along the orbit, the amount of burst mode, scheduled or triggered, used against the pro-rata expectation.