Improved forecasting of thermospheric densities using multi-model ensembles

Abstract. The estimation of the ionospheric electron density by kriging is based on the optimization of a parametric measurement covariance model. First, the extension of kriging with slant total electron content (STEC) measurements based on a spatial covariance to kriging with a spatialtemporal covariance model, assimilating STEC data of a sliding window, is presented. Secondly, a novel tomography approach by gradient-enhanced kriging (GEK) is developed. Beyond the ingestion of STEC measurements, GEK assimilates ionosonde characteristics, providing peak electron density measurements as well as gradient information. Both approaches deploy the 3-D electron density model NeQuick as a priori information and estimate the covariance parameter vector within a maximum likelihood estimation for the dedicated tomography time stamp. The methods are validated in the European region for two periods covering quiet and active ionospheric conditions. The kriging with spatial and spatial-temporal covariance model is analysed regarding its capability to reproduce STEC, differential STEC and foF2. Therefore, the estimates are compared to the NeQuick model results, the 2-D TEC maps of the International GNSS Service and the DLR's Ionospheric Monitoring and Prediction Center, and in the case of foF2 to two independent ionosonde stations. Moreover, simulated STEC and ionosonde measurements are used to investigate the electron density profiles estimated by the GEK in comparison to a kriging with STEC only. The results indicate a crucial improvement in the initial guess by the developed methods and point out the potential compensation for a bias in the peak height hmF2 by means of GEK.

show abstract

“…Nava et al, 2006;Brunini et al, 2011) or the combination of different, independent background models (e.g. Elvidge et al, 2016).…”

Section: Simulation Resultsmentioning

confidence: 99%

Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics

et al. 2016

View full text Add to dashboard Cite

show abstract

“…None of the models perform perfectly for all cases. In such cases, the uncertainty in thermospheric neutral density in an event can be represented well by using an ensemble of models and iterating the results (Elvidge et al, 2016). In an operational scenario, the ensemble method and baseline shifts using the previous, quiet-day estimations can be used together to tune the models and their output, so that the storm time variations can be better estimated.…”

Section: Discussionmentioning

confidence: 99%

“…Several metrics are employed to assess the model performances. For the neutral density studies, the most used metrics are the mean absolute error (MAE), bias (B), correlation (R), root mean square error (RMSE), standard deviation (Std), prediction efficiency (PE), ratio of maximum and ratio of average (Bruinsma, 2015;Elvidge et al, 2014Elvidge et al, , 2016Emmert et al, 2017;Kodikara et al, 2018;Pardini et al, 2012;Shim et al, 2012), and the version of the metrics in log space (Bruinsma et al, 2018;Picone et al, 2002;Sutton, 2018). Each of these metrics has advantages and disadvantages (Hyndman et al, 2006;Shcherbakov et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Bias may have different definitions based on the study. Bias is sometimes calculated as the difference between the model and observation in percentage (Pardini et al, 2012) and sometimes as the mean difference between the model and observation (Elvidge et al, 2016). In our work, we define model bias as the quiet time mean difference between the model and observation (mean of model minus mean of observation).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations

et al. 2019

View full text Add to dashboard Cite

Accurate determination of thermospheric neutral density holds crucial importance for satellite drag calculations. The problem is twofold and involves the correct estimation of the quiet time climatology and storm time variations. In this work, neutral density estimations from two empirical and three physics‐based models of the ionosphere‐thermosphere are compared with the neutral densities along the Challenging Micro‐Satellite Payload satellite track for six geomagnetic storms. Storm time variations are extracted from neutral density by (1) subtracting the mean difference between model and observation (bias), (2) setting climatological variations to zero, and (3) multiplying model data with the quiet time ratio between the model and observation. Several metrics are employed to evaluate the model performances. We find that the removal of bias or climatology reveals actual performance of the model in simulating the storm time variations. When bias is removed, depending on event and model, storm time errors in neutral density can decrease by an amount of 113% or can increase by an amount of 12% with respect to error in models with quiet time bias. It is shown that using only average and maximum values of neutral density to determine the model performances can be misleading since a model can estimate the averages fairly well but may not capture the maximum value or vice versa. Since each of the metrics used for determining model performances provides different aspects of the error, among these, we suggest employing mean absolute error, prediction efficiency, and normalized root mean square error together as a standard set of metrics for the neutral density.

show abstract

“…Several reasons for this difference are identified by Lang et al (2017), a major one being data availability. Translating the uncertainty from initialization into probabilistic forecasts using different ensemble techniques (Schunk et al, 2014;Elvidge et al, 2016;Knipp, 2016;Owens et al, 2017) is getting standard. Dependence on initial conditions can be chaotic (as in NWP, e. g., magnetosphere/ionosphere/thermosphere Horton et al, 2001;Mannucci et al, 2016;Wang et al, 2016) or non-chaotic (e.g., CME propagation toward Earth Cash et al, 2015; Lee et al, 2013Lee et al, , 2015Pizzo et al, 2015).…”

Section: Modeling Aspectsmentioning

confidence: 99%

Climate, weather, space weather: model development in an operational context

Folini

2018

J. Space Weather Space Clim.

View full text Add to dashboard Cite

-Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must À within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

show abstract

Improved forecasting of thermospheric densities using multi-model ensembles

Cited by 21 publications

References 40 publications

Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics

Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics

Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations

Climate, weather, space weather: model development in an operational context

Contact Info

Product

Resources

About