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At least 199 records · Page 11

Tethered Satellites as an Enabling Platform for Operational Space Weather Monitoring Systems

Tethered satellites offer the potential to be an important enabling technology to support operational space weather monitoring systems. Space weather "nowcasting" and forecasting models rely on assimilation of near-real-time (NRT) space environment data to provide warnings for storm events and deleterious effects on the global societal infrastructure. Typically, these models are initialized by a climatological model to provide "most probable distributions" of environmental parameters as a function of time and space. The process of NRT data assimilation gently pulls the climate model closer toward the observed state (e.g., via Kalman smoothing) for nowcasting, and forecasting is achieved through a set of iterative semi-empirical physics-based forward-prediction calculations. Many challenges are associated with the development of an operational system, from the top-level architecture (e.g., the required space weather observatories to meet the spatial and temporal requirements of these models) down to the individual instruments capable of making the NRT measurements. This study focuses on the latter challenge: we present some examples of how tethered satellites (from 100s of m to 20 km) are uniquely suited to address certain shortfalls in our ability to measure critical environmental parameters necessary to drive these space weather models. Examples include long baseline electric field measurements, magnetized ionospheric conductivity measurements, and the ability to separate temporal from spatial irregularities in environmental parameters. Tethered satellite functional requirements are presented for two examples of space environment observables.

Gilchrist, Brian E.↗

Understanding Low Level Jets in the US Atlantic Offshore

Low level jets (LLJs) in the atmosphere exhibit a local windspeed maximum in the boundary layer, with positive shear beneath the jet and negative shear above the jet. Wind turbines tend to experience increased loads with varying wake recovery characteristics in the presence of an LLJ, therefore understanding the mechanism and impact of LLJs is crucial to wind energy development. The US Mid-Atlantic offshore region is a huge potential wind energy resource, yet LLJs in this area are poorly understood. In particular, the coastal offshore environment does not exhibit the same diurnal cycle that leads to strong LLJs in the well-characterized Great Plains region. In this study, we use the Weather Research and Forecasting model (WRF) plus lidar buoy data to identify case studies for LLJ events in 2020 in the New York Bight. A reduced order model is presented to explain the onset of these events based on the competing effects of baroclinicity and eddy diffusivity. Finally, using the macro-scale WRF, we drive a micro-scale large eddy simulation (LES) to generate a more detailed characterization of the Marine Boundary Layer during an LLJ. Gaining a better understanding of LLJs and their impacts on offshore wind in the mid-Atlantic is crucial for a transition toward renewable energy.

atmospheric boundary layer↗

Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper of Findings and Recommendations

Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high cadence, high-resolution multiwavelength observations, growing amounts of data from realistic modeling, and operational needs for uninterrupted science-quality data coverage generate the demand for a solar metadata standardization and overall healthy data infrastructure. This white paper is prepared as an effort of the working group “Uniform Semantics and Syntax of Solar Observations and Events” created within the “Towards Integration of Heliophysics Data, Modeling, and Analysis Tools” EarthCube Research Coordination Network (@HDMIEC RCN), with primary objectives to discuss current advances and identify future needs for the solar research cyberinfrastructure. The white paper summarizes presentations and discussions held during the special working group session at the EarthCube Annual Meeting on June 19th, 2020, as well as community contribution gathered during a series of preceding workshops and subsequent RCN working group sessions. The authors provide examples of the current standing of the solar research cyberinfrastructure, and describe the problems related to current data handling approaches. The list of the top-level recommendations agreed by the authors of the current white paper is presented at the beginning of the paper.

SMD↗

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Shuttle imaging radar-B (SIR-B) data analysis for identifying rainfall event occurrence and intensity

The utility of SIR-B data were evaluated for the detection and measurement of rainfall events, and applications of SIR-B data were developed to the improvement of existing rainfall models. During the SIR-B mission, EarthSat monitored rainfall events occurring within the conterminous United States. The GOES scenes form showed rainfall activity within the conterminous U.S. during the SIR-B mission. Swaths of the actual SIR-B data taken were plotted onto the GOES satellite scenes most closely representing the time of the Shuttle overpass. The JPL provided EarthSat with available SIR-B imagery in paper print form representing the appropriate requested data takes. EarthSat identified the collateral data required for site characterization during subsequent SIR-B contracts with JPL.

Source record↗

A TRMM Microwave Radiometer Rain Rate Estimation Method with Convective and Stratiform Discrimination

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima (T85min) in a regional map containing a Mesoscale Convective System (MCS). This is because of relatively small footprint size (approximately 5.5 km) and strong extinction properties in this channel of the TMI. A map of surface rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85(sub min), produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we infer empirically from TMI data the presence of three different kinds of thunderstorms or Cbs. These Cbs are classified as young, mature, and decaying types, and are assumed to have a scale of about 20 km on the average. Two parameters are used to classify these three kinds of Cbs based on the T85 data: a) the magnitude of scattering depression deduced from local T85(sub min) and b) the mean horizontal gradient of T85 around such minima. Knowing the category of a given Cb, we can estimate the rain rate associated with it. Such estimation is done with the help of relationships linking T85min to rain rate in each Cb type. Similarly, a weak background rain rate in all the areas where T85 is less than 260 K is deduced with another relationship linking T85 to rain rate. In our rain retrieval model, this background rain constitutes stratiform rain where the Cbs are absent. Initially, these relationships are optimized or tuned utilizing the PR and TMI data of a few MCS events. After such tuning, the model is applied to independent MCS cases. The areal distribution of light (1-10 mm/hr), moderate (10-20 mm/hr), and intense (>= 20 mm/hr) rain rates are retrieved satisfactorily. Accuracy in the estimates of the light, moderate, and intense rain areas and the mean rain rates associated with such areas in these independent MCS cases is on the average about 15 %. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics.

Prabhakara, C.↗

Characterization of Extreme Hydroclimate Events in Earth System Models using ML/AI

Focal Area(s): (1) We put forward the concepts of data assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization and unsupervised learning applied to downscale information within Earth System Models (ESMs). (2) We discuss predictive modeling through the use of AI techniques and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection) to improve the characterization of extreme hydroclimate events in ESMs. The AI-based models will run five-six order of magnitude times faster, yet will provide similar accuracy, allowing us to provide range bounds on uncertainty faster and thus enabling faster extreme event identification. (3) Further, we consider the insight gleaned from complex data (observed/simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI to improve the characterization of extreme hydroclimate events in ESMs.

54 ENVIRONMENTAL SCIENCES↗

TMI Rain Rate Estimation Over Land and Ocean Utilizing Convective and Stratiform Discrimination

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima in a regional map containing a Mesoscale Convective System (MCS). This is because of relatively small footprint size (approximately 5.5 km) and strong extinction properties in this channel of the TMI. A map of rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85 minima, produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we infer from TMI data the presence of three different kinds of thunderstorms or Cbs. They are young, mature, and decaying Cbs that have a scale of about 20 km on the average. Two parameters enable us to infer these three kinds of Cbs objectively: a) the magnitude of scattering depression deduced from local T85 minima and b) the mean horizontal gradient of T85 around such minima. Knowing the category of a given Cb, we can estimate the rain rate associated with it. Such estimation is done with the help of relationships linking T85 minimum to rain rate in each Cb type. Similarly, a weak background rain rate in all the areas where T85 is less than 260 K is deduced with another relationship linking T85 to rain rate. In our rain retrieval model, this background rain constitutes the stratiform rain where the Cbs are absent. Initially, these relationships are optimized or tuned utilizing the PR and TMI data of a few MCS events. After such tuning, the model is applied to independent MCS cases. The areal distribution of light (1-10 mm/hr), moderate (10-20 mm/hr), and intense (> 20 mm/hr) rain rates are retrieved satisfactorally. Accuracy in the estimates of the light, moderate and intense rain areas and the mean rain rates associated with such areas in these independent MCS cases is on the average about 15%. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics.

Prabhakara, C.↗

Combined analysis of soft and hard X-ray spectra from flares

An attempt is made to develop a self-consistent model which accounts for the line and continuum data generated by the three X-ray imaging instruments on the SMM satellite. The intensities measured covered the 4-500 kV energy range. The model is based on a differential emission measure and electron beam parameters and is used to predict absolute signals detected by the 15 channels of the SMM sensors. Consideration is given to the thermal contribution, instrumental characteristics, thin target excitation and thick target bremsstrahlung. In comparison with data from a flare event on June 29, 1980, model predictions provide a good fit, including the identification of hard electrons with a 5.3 index during the impulsive phase.

Gabriel, A. H.↗

Describing hadronization via histories and observables for Monte-Carlo event reweighting

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a q\bar{q} q q ‾ string fragmenting into pions, and extract a modified Lund string fragmentation function f(z) f ( z ) . We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that f(z) f ( z ) can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted f(z) f ( z ) suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Assessing Simulations of Forest Hurricane Disturbance and Recovery in Puerto Rico by ELM-FATES Using Field Measurements

In the past three decades, Puerto Rico (PR) experienced five hurricanes that met or exceeded category three, and they caused severe forest structural damage and elevated tree mortality. To improve our mechanistic understanding of hurricane impacts on tropical forests and assess hurricane-affected forest dynamics in Earth system models, we use in situ forest measurements at the Bisley Experimental Watersheds in Northeast PR to evaluate the Functionally Assembled Terrestrial Ecosystem Simulator coupled with the Energy Exascale Earth System Model Land Model (ELM-FATES). The observations show that before Hurricane Hugo, 77.3% of the aboveground biomass (AGB) is from the shade-tolerant plant function type (PFT). The Hugo-induced mortality rates are over ~50%, and they induce a ~39% AGB reduction, which recovers to a level like the pre-Hugo condition in 2014, following a second, lower intensity hurricane, Georges. We perform numerical experiments that simulate damage from Hugo and Georges on the forests, including defoliation, sapwood and structural biomass damage, and hurricane-induced mortality. ELM-FATES can reasonably represent coexistence between the two PFTs–light-demanding and shade-tolerant–for both the pre-Hugo and post-Hugo conditions. The model represents a reasonable size distribution of mid-and large-sized trees although it underestimates AGB, likely due to the overestimated nonhurricane mortality. ELM-FATES temporarily stimulated leaf biomass and diameter increment after Georges, an effect that should be tested with observations of future hurricane defoliation events. This research indicates that addressing model-data mismatches in tree mortality and understory dynamics are essential to simulation of more extreme hurricane effects under climate change.

58 GEOSCIENCES↗

The effect of matrix properties and fiber properties on impact failure mechanics

The low-velocity impact problem in graphite/epoxy composite sheets must be solved before large amounts of that material can be used in commercial aircraft. Many of the low-velocity impacts that affect aircraft parts occur during normal ground operations and maintenance. Service equipment and tools have masses above 1 kg, and at velocities of less than 3 m/s can impact structural parts with energies higher than composites can endure without degradation of stiffness or strength. Simple solutions were developed for large-mass, low-velocity impacts which can be modeled as quasi-static events. Static test data and impact data show that the fiber properties control the impact energy which can be absorbed before penetration. Matrix shear strength and peel resistance control the extent of delamination. Comparison of results from tough matrix and brittle matrix composites show that although tough matrices reduce the extent of delamination, they lead to more fiber damage in the contact area.

Elber, W.↗

Autocommander: A Supervisory Controller for Integrated Guidance and Control for the 2nd Generation Reusable Launch Vehicle

This paper presents a hierarchical architecture for integrated guidance and control that achieves risk and cost reduction for NASA's 2d generation reusable launch vehicle (RLV). Guidance, attitude control, and control allocation subsystems that heretofore operated independently will now work cooperatively under the coordination of a top-level autocommander. In addition to delivering improved performance from a flight mechanics perspective, the autocommander is intended to provide an autonomous supervisory control capability for traditional mission management under nominal conditions, G&C reconfiguration in response to effector saturation, and abort mode decision-making upon vehicle malfunction. This high-level functionality is to be implemented through the development of a relational database that is populated with the broad range of vehicle and mission specific data and translated into a discrete event system model for analysis, simulation, and onboard implementation. A Stateflow Autocoder software tool that translates the database into the Stateflow component of a Matlab/Simulink simulation is also presented.

Fisher, J. E.↗

The Circulation of the Plasmasphere Fluid during the Erosion Event on September 8, 2017

A strong solar wind pressure pulse triggered the magnetic storm on September 7, 2017. Near the end of September 7, the z-component of the interplanetary magnetic field (IMF Bz) dropped from 9 to -10 nT in 30 min. The IMF Bz remained at the level of -10 nT for 2 hours and then had another rapid drop to -31 nT in 30 min. The sudden plunge of IMF Bz and the associated strong convection electric field stirred up the storm main phase with Dst falling from 0 to -122 nT from 2200 UT on September 7 to 0200 UT on the 8th. Severe plasmasphere erosion was observed on September 8 by multiple spacecraft, such as the Van Allen Probes and the Arase satellite. In this study, we examine the fate of the eroded plasmasphere particles during this event by model simulation as well as satellite data analysis. The simulation tool we use is the Space Weather Modeling Framework (SWMF)/Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) model coupled with the Comprehensive Inner Magnetosphere-Ionosphere (CIMI) model. One of the distinctive capabilities of the SWMF/BATSRUS-CIMI model is that it treats the cold plasmas in the plasmasphere as a separate fluid in the MHD equations. As a result, the transport and circulation of the plasmasphere fluid in the global magnetosphere can be traced and the impacts of this cold fluid on the global magnetosphere can be evaluated. In this paper, we will show how the drainage plume is formed during the storm and how the plasmasphere fluid is transported to the flank and lobe regions and eventually to the plasma sheet and reenters into the plasmasphere. We will validate our simulation by plasmasphere signatures observed in both the inner and outer magnetosphere.

Mei-Ching Fok↗

The Circulation of the Plasmasphere Fluid during the Erosion Event on September 8, 2017

A strong solar wind pressure pulse triggered the magnetic storm on September 7-8, 2017. Near the end of September 7, the z-component of the IMF Bz dropped from 9 to -10 nT in 30 min and then had another rapid drop to -31 nT in 30 min. The sudden plunge of IMF Bz and the associated strong convection electric field stirred up the storm main phase with Dst falling from 0 to -122 nT from 2200 UT on September 7 to 0200 UT on the 8th. Severe plasmasphere erosion was observed on September 8 by multiple spacecraft, such as the Van Allen Probes and the Arase satellite. In this study, we examine the fate of the eroded plasmasphere particles during this event by model simulation as well as satellite data analysis. The simulation tool we use is the SWMF/BATS-R-US model coupled with the CIMI model. The cold plasmas in the plasmasphere is treated as a separate fluid in the MHD equations. As a result, the transport and circulation of the plasmasphere fluid in the global magnetosphere can be traced and the impacts of this cold fluid on the global magnetosphere can be evaluated. In this paper, we will show how the drainage plume is formed during the storm and how the plasmasphere fluid is transported to the flank and lobe regions and eventually to the plasma sheet and reenters into the inner magnetosphere. We will validate our simulation by plasmasphere signatures observed in both the inner and outer magnetosphere.

Mei-Ching Fok↗

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗

XRTD: An X-Windows based, real-time radiometric display and analysis system

XRTD is a graphical user interface (GUI) based tool for monitoring real time radiometric spacecraft data. The tool is designed to allow the navigation analyst to both view and analyze the characteristics of Doppler and ranging data. This capability is critical if ground personnel wish to verify the correct performance of ongoing maneuvers. The raw tracking data is transferred from Deep Space Network (DSN) computers to a local workstation, where the predicted value for the observable is subtracted from the actual observed value to create a residual. The tool then allows the navigation analyst to rescale and replot the data using simple GUI techniques. The navigator may then perform a number of data analysis and modeling techniques on the resulting residuals to allow for the real time characterization of spacecraft events. These techniques include the modeling of maneuvers, the compression and differencing of data, and Fast Fourier transforms of the data. This tool has shortened the amount of time required for initial characterization of spacecraft maneuvers from several hours to a few minutes.

Pollmeier, Vincent M.↗