scholarly journals Recovering external contribution from the monthly mean series of a given geomagnetic observatory

2016 ◽  
Vol 59 (3) ◽  
Author(s):  
Bejo Duka ◽  
Eni Duka ◽  
Klaudio Peqini
Keyword(s):  
Time Series ◽  
Geomagnetic Field ◽  
Annual Variation ◽  
Power Spectra ◽  
Model Bias ◽  
Mean Values ◽  
Model Biases ◽  
Time Period ◽  
External Contribution ◽  
Sporadic Cases

<p>The differences between monthly mean values of the observed geomagnetic field and monthly values predicted by different models of the internal geomagnetic field (named “model biases”) for the time period 2000-2015 at several geomagnetic observatories are analyzed. We notice that increasing the maximum degree of the model is not always followed by the decrease of such “model bias”. The time series of these “model biases” reduced by their average resulted to be approximately the same for all models and should represent the external (non-modeled) contribution to the observed geomagnetic field. These time series for different observatories (close or away to each other) are compared and their power spectra are analyzed. Such spectra have common features like the annual and semi-annual variation with some possible sporadic cases of seasonal variation.</p>

scholarly journals Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

2020 ◽  
Author(s):  
Andrew Tangborn ◽  
Weijia Kuang ◽  
Terence Sabaka ◽  
Ce Ye
Keyword(s):  
Kalman Filter ◽  
Secular Variation ◽  
Ensemble Kalman Filter ◽  
Geomagnetic Field ◽  
Model Bias ◽  
Geomagnetic Secular Variation ◽  
Forecast Period ◽  
Time Period ◽  
Correction Scheme ◽  
Geomagnetic Field Models

Abstract We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020-2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590-1960), CM4 (1961-2000) and CM6 (2001-2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020-2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020-2025.

scholarly journals Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman fllter: A candidate model for IGRF 2020

2020 ◽  
Author(s):  
Andrew Tangborn ◽  
Weijia Kuang ◽  
Terence Sabaka ◽  
Ce Yi
Keyword(s):  
Secular Variation ◽  
Ensemble Kalman Filter ◽  
Geomagnetic Field ◽  
Candidate Model ◽  
Model Bias ◽  
Geomagnetic Secular Variation ◽  
Forecast Period ◽  
Time Period ◽  
Correction Scheme ◽  
Geomagnetic Field Models

Abstract We have produced a 5 year mean secular variation (SV) of the geomagnetic field for the period 2020-2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 512 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590-1960), CM4 (1961-2000) and CM6 (2001-2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecasts. The algorithm was validated on the time period 2010-2015 by comparing with the 2015 IGRF before being applied to the 2020-2025 time period. This forecast has been submitted as a candidate model for IGRF 2025.

scholarly journals Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Andrew Tangborn ◽  
Weijia Kuang ◽  
Terence J. Sabaka ◽  
Ce Yi
Keyword(s):  
Kalman Filter ◽  
Secular Variation ◽  
Ensemble Kalman Filter ◽  
Geomagnetic Field ◽  
Model Bias ◽  
Geomagnetic Secular Variation ◽  
Forecast Period ◽  
Time Period ◽  
Correction Scheme ◽  
Geomagnetic Field Models

Abstract We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020–2025. Graphical abstract

scholarly journals Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

2020 ◽  
Author(s):  
Andrew Tangborn ◽  
Weijia Kuang ◽  
Terence Sabaka ◽  
Ce Ye
Keyword(s):  
Kalman Filter ◽  
Secular Variation ◽  
Ensemble Kalman Filter ◽  
Geomagnetic Field ◽  
Model Bias ◽  
Geomagnetic Secular Variation ◽  
Forecast Period ◽  
Time Period ◽  
Correction Scheme ◽  
Geomagnetic Field Models

Abstract We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020-2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590-1960), CM4 (1961-2000) and CM6 (2001-2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020-2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020-2025.

scholarly journals Climatology and time series of surface meteorology in Ny-Ålesund, Svalbard

2013 ◽  
Vol 5 (1) ◽  
pp. 155-163 ◽  
Author(s):  
M. Maturilli ◽  
A. Herber ◽  
G. König-Langlo
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Mean Values ◽  
Time Period ◽  
Synoptic Observations ◽  
Arctic Change ◽  
Water Vapor Mixing Ratio

Abstract. A consistent meteorological dataset of the Arctic site Ny-Ålesund (11.9° E, 78.9° N) spanning the 18 yr-period 1 August 1993 to 31 July 2011 is presented. Instrumentation and data handling of temperature, humidity, wind and pressure measurements are described in detail. Monthly mean values are shown for all years to illustrate the interannual variability of the different parameters. Climatological mean values are given for temperature, humidity and pressure. From the climatological dataset, we also present the time series of annual mean temperature and humidity, revealing a temperature increase of +1.35 K per decade and an increase in water vapor mixing ratio of +0.22 g kg−1 per decade for the given time period, respectively. With the continuation of the presented measurements, the Ny-Ålesund high resolution time series will provide a reliable source to monitor Arctic change and retrieve trends in the future. The relevant data are provided in high temporal resolution as averages over 5 (1) min before (after) 14 July 1998, respectively, placed on the PANGAEA repository (doi:10.1594/PANGAEA.793046). While 6 hourly synoptic observations in Ny-Ålesund by the Norwegian Meteorological Institute reach back to 1974 (Førland et al., 2011), the meteorological data presented here cover a shorter time period, but their high temporal resolution will be of value for atmospheric process studies on shorter time scales.

scholarly journals Geomagnetic jerks characterization via spectral analysis

2012 ◽  
Vol 4 (1) ◽  
pp. 131-172
Author(s):  
B. Duka ◽  
A. De Santis ◽  
M. Mandea ◽  
A. Isac ◽  
E. Qamili
Keyword(s):  
Time Series ◽  
Geomagnetic Field ◽  
Synthetic Data ◽  
Power Spectra ◽  
Spatial Evolution ◽  
Spectral Techniques ◽  
Rapid Changes ◽  
Geomagnetic Jerks ◽  
Third Derivative ◽  
Harmonic Power

Abstract. In this study we have applied spectral techniques to analyze geomagnetic field time-series provided by observatories, and compared the results with those obtained from analogous analyses of synthetic data estimated from models. Then, an algorithm is here proposed to detect the geomagnetic jerks in time-series, mainly occurring in the Eastern component of the geomagnetic field. Applying such analysis to time-series generated from global models has allowed us to depict the most important space-time features of the geomagnetic jerks all over the globe, since the beginning of XXth century. Finally, the spherical harmonic power spectra of the third derivative of the main geomagnetic field has been computed from 1960 to 2002.5, bringing new insights to understanding the spatial evolution of these rapid changes of the geomagnetic field.

scholarly journals Climatology and time series of surface meteorology in Ny-Ålesund, Svalbard

2012 ◽  
Vol 5 (2) ◽  
pp. 1057-1076 ◽  
Author(s):  
M. Maturilli ◽  
A. Herber ◽  
G. König-Langlo
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Mean Values ◽  
Time Period ◽  
Synoptic Observations ◽  
Arctic Change ◽  
Water Vapor Mixing Ratio

Abstract. A consistent meteorological dataset of the Arctic site Ny-Ålesund (11.9° E, 78.9° N) spanning the 18-yr-period 1 August 1993 to 31 July 2011 is presented. Instrumentation and data handling of temperature, humidity, wind and pressure measurements are described in detail. Monthly mean values are shown for all years to illustrate the interannual variablity of the different parameter. Climatological mean values are given for temperature, humidity and pressure. From the climatological dataset, we also present the time series of annual mean temperature and humidity, revealing a temperature increase of +1.35 K per decade and an increase in water vapor mixing ratio of +0.22 g kg−1 per decade for the given time period, respectively. With the continuation of the presented measurements, the Ny-Ålesund high resolution time series will provide a reliable source to monitor Arctic change and retrieve trends in the future. The relevant data are provided in high temporal resolution as averages over 5 [1] min before [after] 14 July 1998, respectively, placed on the PANGAEA repository (http://doi.pangaea.de/10.1594/PANGAEA.793046). While synoptic observations by the Norwegian Meteorological Institute reach back to 1935 (Førland et al., 2011), the meteorological data presented here cover a shorter time period, but their high temporal resolution will be of value for atmospheric process studies on shorter time scales.

scholarly journals Is Call Karma Real? Resident After-Hours On-Call Imaging at the University of Saskatchewan: An Assessment of Call Volumes Including Inter-Resident Variability

2021 ◽  
pp. 084653712110263
Author(s):  
James Huynh ◽  
David Horne ◽  
Rhonda Bryce ◽  
David A Leswick
Keyword(s):  
Mean Shift ◽  
Mean Values ◽  
Body Regions ◽  
Time Period ◽  
Relative Value ◽  
Number Of Patients ◽  
After Hours ◽  
Disproportionate Number ◽  
The University ◽  
Changes Over Time

Purpose: Quantify resident caseload during call and determine if there are consistent differences in call volumes for individuals or resident subgroups. Methods: Accession codes for after-hours computed tomography (CT) cases dictated by residents between July 1, 2012 and January 9, 2017 were reviewed. Case volumes by patient visits and body regions scanned were determined and categorized according to time period, year, and individual resident. Mean shift Relative Value Units (RVUs) were calculated by year. Descriptive statistics, linear mixed modeling, and linear regression determined mean values, differences between residents, associations between independent variables and outcomes, and changes over time. Consistent differences between residents were assessed as a measure of good or bad luck / karma on call. Results: During this time there were 23,032 patients and 30,766 anatomic regions scanned during 1,652 call shifts among 32 residents. Over the whole period, there were on average 10.6 patients and 14.3 body regions scanned on weekday shifts and 22.3 patients and 29.4 body regions scanned during weekend shifts. Annually, the mean number of patients, body regions, and RVUs scanned per shift increased by an average of 0.2 (1%), 0.4 (2%), and 1.2 (5%) (all p < 0.05) respectively in regression models. There was variability in call experiences, but only 1 resident had a disproportionate number of higher volume calls and fewer lower volume shifts than expected. Conclusions: Annual increases in scan volumes were modest. Although residents’ experiences varied, little of this was attributable to consistent personal differences, including luck or call karma.

scholarly journals Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Magnus D. Hammer ◽  
Grace A. Cox ◽  
William J. Brown ◽  
Ciarán D. Beggan ◽  
Christopher C. Finlay
Keyword(s):  
Time Series ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Time Windows ◽  
Low Earth Orbit ◽  
Data Selection ◽  
Spherical Harmonic Analysis ◽  
Core Field ◽  
The Core ◽  
Swarm Satellites

AbstractWe present geomagnetic main field and secular variation time series, at 300 equal-area distributed locations and at 490 km altitude, derived from magnetic field measurements collected by the three Swarm satellites. These Geomagnetic Virtual Observatory (GVO) series provide a convenient means to globally monitor and analyze long-term variations of the geomagnetic field from low-Earth orbit. The series are obtained by robust fits of local Cartesian potential field models to along-track and East–West sums and differences of Swarm satellite data collected within a radius of 700 km of the GVO locations during either 1-monthly or 4-monthly time windows. We describe two GVO data products: (1) ‘Observed Field’ GVO time series, where all observed sources contribute to the estimated values, without any data selection or correction, and (2) ‘Core Field’ GVO time series, where additional data selection is carried out, then de-noising schemes and epoch-by-epoch spherical harmonic analysis are applied to reduce contamination by magnetospheric and ionospheric signals. Secular variation series are provided as annual differences of the Core Field GVOs. We present examples of the resulting Swarm GVO series, assessing their quality through comparisons with ground observatories and geomagnetic field models. In benchmark comparisons with six high-quality mid-to-low latitude ground observatories we find the secular variation of the Core Field GVO field intensities, calculated using annual differences, agrees to an rms of 1.8 nT/yr and 1.2 nT/yr for the 1-monthly and 4-monthly versions, respectively. Regular sampling in space and time, and the availability of data error estimates, makes the GVO series well suited for users wishing to perform data assimilation studies of core dynamics, or to study long-period magnetospheric and ionospheric signals and their induced counterparts. The Swarm GVO time series will be regularly updated, approximately every four months, allowing ready access to the latest secular variation data from the Swarm satellites.

scholarly journals Complexity traits and synchrony of cryptocurrencies price dynamics

2021 ◽  
Author(s):  
Davide Provenzano ◽  
Rodolfo Baggio
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Complex Structures ◽  
Slope Parameter ◽  
Exponential Distributions ◽  
Horizontal Visibility ◽  
Time Period ◽  
Before And After ◽  
Visibility Algorithm ◽  
Similar Complex

AbstractIn this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.

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