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At least 1,531 records · Page 85Linked to original sources
USGS2011

Developing climate data records and essential climate variables from landsat data

The series of Landsat missions has compiled the longest record of satellite observation of the Earth’s land surface, extending for more than 38 years for most areas of the globe. Landsat data are particularly important as long term climate data records because the scale of observation is sufficient to differentiate between natural and human drivers of land cover change. The USGS has established consistent radiometric calibration across the Landsat TM and ETM+ sensors, and have extended the calibration back to the earlier MSS sensors. The USGS is developing capabilities to create fundamental climate data records (FCDRs), thematic climate data records (TCDRs), and essential climate variables (ECVs) from the Landsat data archive. Two high priority TCDRs were identified: surface reflectance and land surface temperature because they have direct application or are required as input to the generation of ECVs. We will focus development on a few of the terrestrial ECVs that have a high potential for being derived from Landsat data, that include land cover, albedo, fire disturbance, surface water, snow and ice, and leaf area index (LAI). We are collaborating with scientists who have demonstrated successful algorithm development and application of these science products to develop a framework of processing capabilities to support research projects and land management applications, along with an independent strategy for product validation. Our goal is to scale the creation and validation of these products from specific sites in the conterminous U.S. and Alaska, for extension to continental and global scales.

Conference Paper↗
USGS2012

Modelling ecosystem service flows under uncertainty with stochiastic SPAN

Ecosystem service models are increasingly in demand for decision making. However, the data required to run these models are often patchy, missing, outdated, or untrustworthy. Further, communication of data and model uncertainty to decision makers is often either absent or unintuitive. In this work, we introduce a systematic approach to addressing both the data gap and the difficulty in communicating uncertainty through a stochastic adaptation of the Service Path Attribution Networks (SPAN) framework. The SPAN formalism assesses ecosystem services through a set of up to 16 maps, which characterize the services in a study area in terms of flow pathways between ecosystems and human beneficiaries. Although the SPAN algorithms were originally defined deterministically, we present them here in a stochastic framework which combines probabilistic input data with a stochastic transport model in order to generate probabilistic spatial outputs. This enables a novel feature among ecosystem service models: the ability to spatially visualize uncertainty in the model results. The stochastic SPAN model can analyze areas where data limitations are prohibitive for deterministic models. Greater uncertainty in the model inputs (including missing data) should lead to greater uncertainty expressed in the model’s output distributions. By using Bayesian belief networks to fill data gaps and expert-provided trust assignments to augment untrustworthy or outdated information, we can account for uncertainty in input data, producing a model that is still able to run and provide information where strictly deterministic models could not. Taken together, these attributes enable more robust and intuitive modelling of ecosystem services under uncertainty.

Conference Paper↗
USGS2015

Using maximum topology matching to explore differences in species distribution models

Species distribution models (SDM) are used to help understand what drives the distribution of various plant and animal species. These models are typically high dimensional scalar functions, where the dimensions of the domain correspond to predictor variables of the model algorithm. Understanding and exploring the differences between models help ecologists understand areas where their data or understanding of the system is incomplete and will help guide further investigation in these regions. These differences can also indicate an important source of model to model uncertainty. However, it is cumbersome and often impractical to perform this analysis using existing tools, which allows for manual exploration of the models usually as 1-dimensional curves. In this paper, we propose a topology-based framework to help ecologists explore the differences in various SDMs directly in the high dimensional domain. In order to accomplish this, we introduce the concept of maximum topology matching that computes a locality-aware correspondence between similar extrema of two scalar functions. The matching is then used to compute the similarity between two functions. We also design a visualization interface that allows ecologists to explore SDMs using their topological features and to study the differences between pairs of models found using maximum topological matching. We demonstrate the utility of the proposed framework through several use cases using different data sets and report the feedback obtained from ecologists.

Conference Paper↗
USGS2011

Spatially explicit shallow landslide susceptibility mapping over large areas

Recent advances in downscaling climate model precipitation predictions now yield spatially explicit patterns of rainfall that could be used to estimate shallow landslide susceptibility over large areas. In California, the United States Geological Survey is exploring community emergency response to the possible effects of a very large simulated storm event and to do so it has generated downscaled precipitation maps for the storm. To predict the corresponding pattern of shallow landslide susceptibility across the state, we have used the model Shalstab (a coupled steady state runoff and infinite slope stability model) which susceptibility spatially explicit estimates of relative potential instability. Such slope stability models that include the effects of subsurface runoff on potentially destabilizing pore pressure evolution require water routing and hence the definition of upslope drainage area to each potential cell. To calculate drainage area efficiently over a large area we developed a parallel framework to scale-up Shalstab and specifically introduce a new efficient parallel drainage area algorithm which produces seamless results. The single seamless shallow landslide susceptibility map for all of California was accomplished in a short run time, and indicates that much larger areas can be efficiently modelled. As landslide maps generally over predict the extent of instability for any given storm. Local empirical data on the fraction of predicted unstable cells that failed for observed rainfall intensity can be used to specify the likely extent of hazard for a given storm. This suggests that campaigns to collect local precipitation data and detailed shallow landslide location maps after major storms could be used to calibrate models and improve their use in hazard assessment for individual storms.

California↗
USGS1990

Laboratory simulation of the effects of overburden stress on the specific storage of shallow artesian aquifers

A laboratory experiment to measure the specific storage of an aquifer material was conducted. A known dead load, simulating an overburden load, was applied to a sample of completely saturated aquifer material contained inside a cylinder. After the dead load was applied, water was withdrawn from the sample, causing the hydrostatic pressure to decrease and the effective stress to increase. The resulting compression of the sample and the amount of water withdrawn were measured after equilibrium was reached. The procedure was repeated by increasing the dead load and the hydrostatic pressure followed by withdrawing water to determine new values of effective stress and compaction. The simulated dead loads are typical of those experienced by shallow artesian aquifers. The void ratio and the effective stress of the aquifer sample, as simulated by different dead loads, determine the pore volume compressibility which, in turn, determines the values of specific storage. An analytical algorithm was used to independently determine the stress dependent profile of specific storage. These values are found to be in close agreement with laboratory results. Implications for shallow artesian aquifers, with relatively small overburden stress, are also addressed.

Conference Paper↗
USGS1998

A nowcast model for tides and tidal currents in San Francisco Bay, California

National Oceanographic and Atmospheric Administration (NOAA) installed Physical Oceanographic Real-Time System (PORTS) in San Francisco Bay, California to provide observations of tides, tidal currents, and meteorological conditions. PORTS data are used for optimizing vessel operations, increasing margin of safety for navigation, and guiding hazardous material spill prevention and response. Because tides and tidal currents in San Francisco Bay are extremely complex, limited real-time observations are insufficient to provide spatial resolution for variations of tides and tidal currents. To fill the information gaps, a highresolution, robust, semi-implicit, finite-difference nowcast numerical model has been implemented for San Francisco Bay. The model grid and water depths are defined on coordinates based on Mercator projection so the model outputs can be directly superimposed on navigation charts. A data assimilation algorithm has been established to derive the boundary conditions for model simulations. The nowcast model is executed every hour continuously for tides and tidal currents starting from 24 hours before the present time (now) covering a total of 48 hours simulation. Forty-eight hours of nowcast model results are available to the public at all times through the World Wide Web (WWW). Users can view and download the nowcast model results for tides and tidal current distributions in San Francisco Bay for their specific applications and for further analysis.

California↗
USGS2007

Climate matching as a tool for predicting potential North American spread of Brown Treesnakes

Climate matching identifies extralimital destinations that could be colonized by a potential invasive species on the basis of similarity to climates found in the species’ native range. Climate is a proxy for the factors that determine whether a population will reproduce enough to offset mortality. Previous climate matching models (e.g., Genetic Algorithm for Rule-set Prediction [GARP]) for brown treesnakes ( Boiga irregularis ) were unsatisfactory, perhaps because the models failed to allow different combinations of climate attributes to influence a species’ range limits in different parts of the range. Therefore, we explored the climate space described by bivariate parameters of native range temperature and rainfall, allowing up to two months of aestivation in the warmer portions of the range, or four months of hibernation in temperate climes. We found colonization area to be minimally sensitive to assumptions regarding hibernation temperature thresholds. Although brown treesnakes appear to be limited by dry weather in the interior of Australia, aridity rarely limits potential distribution in most of the world. Potential colonization area in North America is limited primarily by cold. Climatically suitable portions of the United States (US) mainland include the Central Valley of California, mesic patches in the Southwest, and the southeastern coastal plain from Texas to Virginia.

Conference Paper↗
USGS2016

Non-invasive genetic sampling of Southern Mule Deer ( Odocoileus hemionus fuliginatus ) reveals limited movement across California State Route 67 in San Diego County

—The Southern Mule Deer is a mobile but non-migratory large mammal found throughout southern California and is a covered species in the San Diego Multi-Species Conservation Plan. We assessed deer movement and population connectivity across California State Route 67 and two smaller roads in eastern San Diego County using non-invasive genetic sampling. We collected deer scat pellets between April and November 2015, and genotyped pellets at 15 microsatellites and a sex determination marker. We successfully genotyped 71 unique individuals from throughout the study area and detected nine recapture events. Recaptures were generally found close to original capture locations (within 1.5 km). We did not detect recaptures across roads; however, pedigree analysis detected 21 first order relative pairs, of which approximately 20% were found across State Route 67. Exact tests comparing allele frequencies between groups of individuals in pre-defined geographic clusters detected significant genetic differentiation across State Route 67. In contrast, the assignment-based algorithm of STRUCTURE supported a single genetic cluster across the study area. Our data suggest that State Route 67 may reduce, but does not preclude, movement and gene flow of Southern Mule Deer.

California↗
USGS2009

SToRM: A numerical model for environmental surface flows

SToRM (System for Transport and River Modeling) is a numerical model developed to simulate free surface flows in complex environmental domains. It is based on the depth-averaged St. Venant equations, which are discretized using unstructured upwind finite volume methods, and contains both steady and unsteady solution techniques. This article provides a brief description of the numerical approach selected to discretize the governing equations in space and time, including important aspects of solving natural environmental flows, such as the wetting and drying algorithm. The presentation is illustrated with several application examples, covering both laboratory and natural river flow cases, which show the model’s ability to solve complex flow phenomena.

Conference Paper↗
USGS2016

Integrated modeling approach for fate and transport of submerged oil and oil-particle aggregates in a freshwater riverine environment

The Enbridge Line 6B pipeline release of diluted bitumen into the Kalamazoo River downstream of Marshall, Michigan, U.S.A., in July 2010 was one of the largest oil spills into freshwater in North American history. A portion of the oil interacted with river sediment and submerged requiring the development and implementation of new approaches for detection and recovery of oil mixed with river sediment. Hydrodynamic and sediment transport modeling became an integral part of containment and recovery operations for decision support about the potential fate and migration of submerged oil and oiled sediment. Three models were developed for the U.S. Environmental Protection Agency to cover a range of spatial scales of interest to onsite operations. Two-dimensional (2D) hydrodynamic and sediment transport models from the Environmental Fluid Dynamics Code and the sediment bed model SEDZLJ1 were used to simulate potential resuspension, migration, and deposition of submerged oil and oiled sediment along a 38-mile reach of the Kalamazoo River affected by the oil from Marshall to Kalamazoo. An algorithm was added to SEDZLJ to represent three additional particle size classes of oilparticle aggregates (OPAs) with a range of sizes, specific gravities, and settling velocities. Field and laboratory experiments and flume tests were done to support the numerical modeling of OPAs. A three-dimensional hydrodynamic model was developed to simulate hydrodynamics and OPA tracking through Morrow Lake, the most downstream impoundment. This model incorporated wind and dam operations into high and low flow, lake drawdown, and containment simulations. Finally, a 2D unstructured grid model, HydroSed2D, was used to simulate flows and sediment transport along 1- to 2-mile segments of the Kalamazoo River around islands and through side channels and backwater areas that are particularly prone to submerged oil deposition. Integrated models could be developed quickly due to the availability of information and services combined with spill response operations that included: bathymetry and topography data, fieldbased geomorphic mapping of submerged oil, and discharge measured at U.S. Geological Survey streamflow gauges. Modeling results were included in a multiple-lines-of-evidence approach that was used by the Federal On-Scene Coordinator and operations staff for decision-making related to assessment and recovery of submerged oil, as well as net environmental benefit analysis. Similar modeling approaches will likely be useful for future oil spills in riverine environments.

Michigan↗
USGS1992

A technique for the reduction of banding in Landsat Thematic Mapper Images

The radiometric difference between forward and reverse scans in Landsat thematic mapper (TM) images, referred to as "banding," can create problems when enhancing the image for interpretation or when performing quantitative studies. Recent research has led to the development of a method that reduces the banding in Landsat TM data sets. It involves passing a one-dimensional spatial kernel over the data set. This kernel is developed from the statistics of the banding pattern and is based on the Wiener filter. It has been implemented on both a DOS-based microcomputer and several UNIX-based computer systems. The algorithm has successfully reduced the banding in several test data sets.

Photogrammetric Engineering and Remote Sensing↗
USGS1996

A digital system for surface reconstruction

A digital photogrammetric system, STEREO, was developed to determine three dimensional coordinates of points of interest (POIs) defined with a grid on a textureless and smooth-surfaced specimen. Two CCD cameras were set up with unknown orientation and recorded digital images of a reference model and a specimen. Points on the model were selected as control or check points for calibrating or assessing the system. A new algorithm for edge-detection called local maximum convolution (LMC) helped extract the POIs from the stereo image pairs. The system then matched the extracted POIs and used a least squares “bundle” adjustment procedure to solve for the camera orientation parameters and the coordinates of the POIs. An experiment with STEREO found that the standard deviation of the residuals at the check points was approximately 24%, 49% and 56% of the pixel size in the X, Y and Z directions, respectively. The average of the absolute values of the residuals at the check points was approximately 19%, 36% and 49% of the pixel size in the X, Y and Z directions, respectively. With the graphical user interface, STEREO demonstrated a high degree of automation and its operation does not require special knowledge of photogrammetry, computers or image processing.

Photogrammetric Engineering and Remote Sensing↗
USGS1992

Destriping of Landsat MSS images by filtering techniques

The removal of striping noise encountered in the Landsat Multispectral Scanner (MSS) images can be generally done by using frequency filtering techniques. Frequency domain filtering has, however, several problems, such as storage limitation of data required for fast Fourier transforms, ringing artifacts appearing at high-intensity discontinuities, and edge effects between adjacent filtered data sets. One way for circumventing the above difficulties is to design a spatial filter to convolve with the images. Because it is known that the striping always appears at frequencies of 1/6, 1/3, and 1/2 cycles per line, it is possible to design a simple one-dimensional spatial filter to take advantage of this a priori knowledge to cope with the above problems. The desired filter is the type of finite impulse response which can be designed by a linear programming and Remez's exchange algorithm coupled with an adaptive technique. In addition, a four-step spatial filtering technique with an appropriate adaptive approach is also presented which may be particularly useful for geometrically rectified MSS images.

Photogrammetric Engineering and Remote Sensing↗
USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗
USGS2015

Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry

Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features through cartographic generalization (Regnauld and McMaster 2008, Stanislawski et al. 2014). A variety of algorithms exist to simplify linear cartographic features, and all of the methods affect the positional accuracy of the features (Shahriari and Tao 2002, Regnauld and McMaster 2008, Stanislawski et al. 2012). In general, simplification operations are controlled by one or more tolerance parameters that limit the amount of positional change the operation can make to features. Using a single tolerance value can have varying levels of positional change on features; depending on local shape, texture, or geometric characteristics of the original features (McMaster and Shea 1992, Shahriari and Tao 2002, Buttenfield et al. 2010). Consequently, numerous researchers have advocated calibration of simplification parameters to control quantifiable properties of resulting changes to the features (Li and Openshaw 1990, Raposo 2013, Tobler 1988, Veregin 2000, and Buttenfield, 1986, 1989). This research identifies relations between local topographic conditions and geometric characteristics of linear features that are available in the National Hydrography Dataset (NHD). The NHD is a comprehensive vector dataset of surface 18 th ICA Workshop on Generalisation and Multiple Representation, Rio de Janiero, Brazil 2015 2 water features within the United States that is maintained by the U.S. Geological Survey (USGS). In this paper, geometric characteristics of cartographic representations for natural stream and river features are summarized for subbasin watersheds within entire regions of the conterminous United States and compared to topographic metrics. A concurrent processing workflow is implemented using a Linux high-performance computing cluster to simultaneously process multiple subbasins, and thereby complete the work in a fraction of the time required for a single-process environment. In addition, similar metrics are generated for several levels of simplification of the hydrographic features to quantify the effects of simplification over the various landscape conditions. Objectives of this exploratory investigation are to quantify geometric characteristics of linear hydrographic features over the various terrain conditions within the conterminous United States and thereby illuminate relations between stream geomorphological conditions and cartographic representation. The synoptic view of these characteristics over regional watersheds that is afforded through concurrent processing, in conjunction with terrain conditions, may reveal patterns for classifying cartographic stream features into stream geomorphological classes. Furthermore, the synoptic measurement of the amount of change in geometric characteristics caused by the several levels of simplification can enable estimation of tolerance values that appropriately control simplification-induced geometric change of the cartographic features within the various geomorphological classes in the country. Hence, these empirically derived rules or relations could help generate multiscale-representations of features through automated generalization that adequately maintain surface drainage variations and patterns reflective of the natural stream geomorphological conditions across the country.

Conference Paper↗
USGS2017

Remote measurement of surface-water velocity using infrared videography and PIV: a proof-of-concept for Alaskan rivers

Thermal cameras with high sensitivity to medium and long wavelengths can resolve features at the surface of flowing water arising from turbulent mixing. Images acquired by these cameras can be processed with particle image velocimetry (PIV) to compute surface velocities based on the displacement of thermal features as they advect with the flow. We conducted a series of field measurements to test this methodology for remote sensing of surface velocities in rivers. We positioned an infrared video camera at multiple stations across bridges that spanned five rivers in Alaska. Simultaneous non-contact measurements of surface velocity were collected with a radar gun. In situ velocity profiles were collected with Acoustic Doppler Current Profilers (ADCP). Infrared image time series were collected at a frequency of 10Hz for a one-minute duration at a number of stations spaced across each bridge. Commercial PIV software used a cross-correlation algorithm to calculate pixel displacements between successive frames, which were then scaled to produce surface velocities. A blanking distance below the ADCP prevents a direct measurement of the surface velocity. However, we estimated surface velocity from the ADCP measurements using a program that normalizes each ADCP transect and combines those normalized transects to compute a mean measurement profile. The program can fit a power law to the profile and in so doing provides a velocity index, the ratio between the depth-averaged and surface velocity. For the rivers in this study, the velocity index ranged from 0.82 – 0.92. Average radar and extrapolated ADCP surface velocities were in good agreement with average infrared PIV calculations.

Conference Paper↗
USGS2018

Probabilistic models of seafloor composition using multispectral acoustic backscatter: The benthic detectorists

We describe and compare two probabilistic models for task-specific seafloor characterization based on multispectral backscatter. We examine whether generative or discriminative approaches to supervised seafloor characterization do better at harnessing the greatly increased information about seafloor substrate composition that is encoded in the backscattering response across multiple frequencies. A Gaussian mixture model (GMM) is proposed as a generative model, and a fully-connected conditional random field (CRF) is proposed as a discriminative model. Either model uses input data derived from monospectral or multispectral backscatter without modification. The CRF approach considers both the relative backscatter magnitudes of different substrates as well as their relative proximity, and can be optimized using parameters. The GMM model, in contrast, includes no spatial information in its estimates, being based solely on relative backscatter magnitudes. Both GMM and CRF modeling approaches perform better with multispectral backscatter compared to monospectral, significantly outperforming all three monospectral frequencies. With multispectral backscatter inputs, based on average classification accuracies alone, there was little to choose between the two modeling approaches (classification accuracy of 81% and 83% for GMM and CRF models, respectively, evaluated using 50% of available bed observations to train and 50% to test the models). However, a CRF model that has been optimized with respect to its tunable parameters tends to produce higher posterior probabilities (i.e. greater certainty) for its classifications. Using monospectral backscatter inputs, the CRF model significantly outperformed the GMM model in terms of average classification accuracy. On balance, therefore, based on the evidence presented here, the CRF is suggested to be the superior approach for task-specific seafloor classification. Although further work using additional data is required to further examine this conclusion, the work presented here will guide and focus subsequent research efforts as more areas of the seafloor are mapped with the new technology. In order to facilitate these efforts, the algorithms presented here are encoded in a freely available python toolbox for Probabilistic acoustic Sediment Mapping, called PriSM , that can be used for both monospectral and multispectral backscatter. Finally, we show that application of the CRF model to the outputs of a geoacoustical model of seafloor scattering results in realistic substrate classification boundaries. This hybrid CRF and physics-based approach can predict the physical properties of the seafloor at a finer spatial resolution than is possible using the geoacoustical model alone.

Conference Paper↗
USGS2009

Earth's magnetic field complex: U.S. National activities during the Decade of Geopotential Field Research

The US geomagnetism community is supported by NASA, NOAA, USGS, NSF, DOD, and US universities. During the Decade of Geopotential Field Research, inaugurated in 1999 with the launch of the Danish satellite Ørsted on a US rocket, the US community has been involved in satellite mission development and analysis, instrument development, model development, and in the discovery and understanding of new processes with satellite magnetic signatures. The ESA Swarm mission has been a primary focus of the US community, with three US scientists on Swarm's Mission Advisory Group. Swarm will measure, for the first time, the E-W gradient of the magnetic field. One of us (T. Sabaka) is involved with the development of a Comprehensive inversion scheme as part of the SMART consortium. This effort is an outgrowth of the Comprehensive Model [1]. Swarm will also provide valuable observations for ionospheric specification and forecast. The geomagnetism group at NOAA (S. Maus, P. Alken and C. Manoj) has developed algorithms to estimate the strength of the eastward electric field (EEF). As the driver of the equatorial plasma fountain, the EEF is an important space weather parameter. ESA is considering the implementation of the EEF as a dedicated inversion chain in the Level-2 Facility. In 2006, NASA launched a minisatellite magnetometer constellation mission (ST-5) to test technologies and software. The ST-5 constellation featured the first along-track gradient measurements. NASA has also initiated efforts to study geomagnetism mission concepts after Swarm. One of the ideas under consideration is the systematic measurement of radial field gradients. Instrument development, and geomagnetic observatories, are also an integral part of the US effort. The past decade has seen significant advances in the development of a self-calibrating vector helium magnetometer, and in the automation of the US observatory network. Working in coordination with Intermagnet, the USGS Geomagnetism Program has made operational 1-second data acquisition at 13 of its magnetic observatories. The Program is also developing a realtime 1-minute and 1-hour Dst service. Within the past decade, US scientists have been leaders in the development of models that describe the global geomagnetic environment, including comprehensive models (the CM series), maps of the lithospheric field from satellite (MF-series), near surface maps of the lithospheric field (WDMAM-series), models of the thickness of the magnetic crust, the IGRF and World Magnetic Model series, ionospheric models such as the EEJM1, JVDM1, and the IRI, and data assimilation-based models (MoSST-series) that predict the future state of the geomagneic field.

Conference Paper↗