The Spire TEC Environment Assimilation Model (STEAM)

2020 ◽  
Author(s):  
Matthew Angling ◽  
Francois-Xavier Bocquet ◽  
German Olivares-Pulido ◽  
Sanita Vetra-Carvalho ◽  
Karl Nordstrom ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Grid Point ◽  
Ensemble Methods ◽  
Electron Content ◽  
Combined System ◽  
Background Error ◽  
Total Electron ◽  
Wide Range ◽  
Assimilation Model

<p>The ionosphere can affect a wide range of radio frequency (RF) systems operating below 2 GHz. One option for mitigating these effects is to produce assimilative models of the ionospheric density from which products can be derived for specific systems. Such models aim to optimally combine a background model of the ionospheric state with measurements of the ionosphere. This approach is analogous to the use of numerical weather prediction in the meteorological community, and has been evolving for ionospheric use for the last 10 to 15 years.</p><p>Published research has demonstrated the utility of this approach. However, obstacles to providing effective data products remain due to the sparseness of ionospheric data over large parts of the world and the timeliness with which data are available. Spire is working to overcome these issues through the use of its large constellation of satellites that can measure Total Electron Content (TEC) data in both zenith looking and radio occultation (RO) geometries and its large ground station network that will allow low data latency.</p><p>Spire data will be combined with an innovative data assimilation model (the Spire TEC Environment Assimilation Model, STEAM) to provide accurate and actionable ionospheric products. Data assimilation is required to overcome the limitations and assumptions of the traditional Abel Transform analysis of RO data (i.e., spherical symmetry; transmitter and receiver in free space and the same plane) and to effectively combine RO data, topside data, ground-based GNSS data, and other sources of ionospheric information (i.e., ionosondes).</p><p>STEAM uses a 4D Local ensemble transform Kalman Filter (LETKF). As with other ensemble methods, the LETKF uses an ensemble of models to approximate the background error covariance matrix. However, the LETKF provides a more efficient way to solve the ensemble equations. Furthermore, 4D operation permits the use of data with varying latency. Localisation means that grid points are only modified by data within a local volume; this restricts spurious long-range spatial correlations and means that the ensemble only has to span the space locally. The LETKF transforms the problem into ensemble space which makes each grid point independent, resulting in an algorithm that is easily parallelised.</p><p>This paper will describe the data collection and processing chain, the data assimilation model, and plans for the ongoing development of the combined system. </p>

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

scholarly journals Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

2021 ◽  
Vol 7 ◽  
Author(s):  
Juan Durazo ◽  
Eric J. Kostelich ◽  
Alex Mahalov
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Bias Correction ◽  
Geomagnetic Storms ◽  
Circulation Model ◽  
Storm Event ◽  
Electron Content ◽  
Systematic Bias ◽  
Total Electron ◽  
Mean Square Errors

The dynamics of many models of physical systems depend on the choices of key parameters. This paper describes the results of some observing system simulation experiments using a first-principles model of the Earth’s ionosphere, the Thermosphere Ionosphere Electrodynamics Global Circulation Model (TIEGCM), which is driven by parameters that describe solar activity, geomagnetic conditions, and the state of the thermosphere. Of particular interest is the response of the ionosphere (and predictions of space weather generally) during geomagnetic storms. Errors in the overall specification of driving parameters for the TIEGCM (and similar dynamical models) may be especially large during geomagnetic storms, because they represent significant perturbations away from more typical interactions of the earth-sun system. Such errors can induce systematic biases in model predictions of the ionospheric state and pose difficulties for data assimilation methods, which attempt to infer the model state vector from a collection of sparse and/or noisy measurements. Typical data assimilation schemes assume that the model produces an unbiased estimate of the truth. This paper tests one potential approach to handle the case where there is some systematic bias in the model outputs. Our focus is on the TIEGCM when it is driven with solar and magnetospheric inputs that are systematically misspecified. We report results from observing system experiments in which synthetic electron density vertical profiles are generated at locations representative of the operational FormoSat-3/COSMIC satellite observing platforms during a moderate (G2, Kp = 6) geomagnetic storm event on September 26–27, 2011. The synthetic data are assimilated into the TIEGCM using the Local Ensemble Transform Kalman Filter with a state-augmentation approach to estimate a small set of bias-correction factors. Two representative processes for the time evolution of the bias in the TIEGCM are tested: one in which the bias is constant and another in which the bias has an exponential growth and decay phase in response to strong geomagnetic forcing. We show that even simple approximations of the TIEGCM bias can reduce root-mean-square errors in 1-h forecasts of total electron content (a key ionospheric variable) by 20–45%, compared to no bias correction. These results suggest that our approach is computationally efficient and can be further refined to improve short-term predictions (∼1-h) of ionospheric dynamics during geomagnetic storms.

scholarly journals Thermal Infrared and Ionospheric Anomalies of the 2017 Mw6.5 Jiuzhaigou Earthquake

2020 ◽  
Vol 12 (17) ◽  
pp. 2843
Author(s):  
Meijiao Zhong ◽  
Xinjian Shan ◽  
Xuemin Zhang ◽  
Chunyan Qu ◽  
Xiao Guo ◽  
...  
Keyword(s):  
Time Series Data ◽  
Longwave Radiation ◽  
Thermal Infrared ◽  
Geological Structure ◽  
Series Data ◽  
Electron Content ◽  
Jiuzhaigou Earthquake ◽  
Total Electron ◽  
Wide Range ◽  
First Time

Taking the 2017 Mw6.5 Jiuzhaigou earthquake as a case study, ionospheric disturbances (i.e., total electron content and TEC) and thermal infrared (TIR) anomalies were simultaneously investigated. The characteristics of the temperature of brightness blackbody (TBB), medium-wave infrared brightness (MIB), and outgoing longwave radiation (OLR) were extracted and compared with the characteristics of ionospheric TEC. We observed different relationships among the three types of TIR radiation according to seismic or aseismic conditions. A wide range of positive TEC anomalies occurred southern to the epicenter. The area to the south of the Huarong mountain fracture, which contained the maximum TEC anomaly amplitudes, overlapped one of the regions with notable TIR anomalies. We observed three stages of increasing TIR radiation, with ionospheric TEC anomalies appearing after each stage, for the first time. There was also high spatial correspondence between both TIR and TEC anomalies and the regional geological structure. Together with the time series data, these results suggest that TEC anomaly genesis might be related to increasing TIR.

scholarly journals Derivation of a planetary ionospheric storm index

2008 ◽  
Vol 26 (9) ◽  
pp. 2645-2648 ◽  
Author(s):  
T. L. Gulyaeva ◽  
I. Stanislawska
Keyword(s):  
Solar Cycle ◽  
Extreme Values ◽  
Grid Point ◽  
Radiant Energy ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Ionospheric Storm ◽  
Total Electron ◽  
Grid Points ◽  
Storm Index

Abstract. The planetary ionospheric storm index, Wp, is deduced from the numerical global ionospheric GPS-IONEX maps of the vertical total electron content, TEC, for more than half a solar cycle, 1999–2008. The TEC values are extracted from the 600 grid points of the map at latitudes 60° N to 60° S with a step of 5° and longitudes 0° to 345° E with a step of 15° providing the data for 00:00 to 23:00 h of local time. The local effects of the solar radiant energy are filtered out by normalizing of the TEC in terms of the solar zenith angle χ at a particular time and the local noon value χ0. The degree of perturbation, DTEC, is computed as log of TEC relative to quiet reference median for 27 days prior to the day of observation. The W-index map is generated by segmentation of DTEC with the relevant thresholds specified earlier for foF2 so that 1 or −1 stands for the quiet state, 2 or −2 for the moderate disturbance, 3 or −3 for the moderate ionospheric storm, and 4 or −4 for intense ionospheric storm at each grid point of the map. The planetary ionospheric storm Wp index is obtained from the W-index map as a latitudinal average of the distance between maximum positive and minimum negative W-index weighted by the latitude/longitude extent of the extreme values on the map. The threshold Wp exceeding 4.0 index units and the peak value Wpmax≥6.0 specify the duration and the power of the planetary ionosphere-plasmasphere storm. It is shown that the occurrence of the Wp storms is growing with the phase of the solar cycle being twice as much as the number of the magnetospheric storms with Dst≤−100 nT and Ap≥100 nT.

scholarly journals Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 125 ◽  
Author(s):  
Sarah Dance ◽  
Susan Ballard ◽  
Ross Bannister ◽  
Peter Clark ◽  
Hannah Cloke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Observation Error ◽  
Error Statistics ◽  
Convective Rainfall ◽  
Background Error ◽  
Spatially Correlated ◽  
Operational Forecasts ◽  
The Impact

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.

E4DVar: Coupling an Ensemble Kalman Filter with Four-Dimensional Variational Data Assimilation in a Limited-Area Weather Prediction Model

2012 ◽  
Vol 140 (2) ◽  
pp. 587-600 ◽  
Author(s):  
Meng Zhang ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Prediction Model ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Coupled System ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Hybrid Data Assimilation

A hybrid data assimilation approach that couples the ensemble Kalman filter (EnKF) and four-dimensional variational (4DVar) methods is implemented for the first time in a limited-area weather prediction model. In this coupled system, denoted E4DVar, the EnKF and 4DVar systems run in parallel while feeding into each other. The multivariate, flow-dependent background error covariance estimated from the EnKF ensemble is used in the 4DVar minimization and the ensemble mean in the EnKF analysis is replaced by the 4DVar analysis, while updating the analysis perturbations for the next cycle of ensemble forecasts with the EnKF. Therefore, the E4DVar can obtain flow-dependent information from both the explicit covariance matrix derived from ensemble forecasts, as well as implicitly from the 4DVar trajectory. The performance of an E4DVar system is compared with the uncoupled 4DVar and EnKF for a limited-area model by assimilating various conventional observations over the contiguous United States for June 2003. After verifying the forecasts from each analysis against standard sounding observations, it is found that the E4DVar substantially outperforms both the EnKF and 4DVar during this active summer month, which featured several episodes of severe convective weather. On average, the forecasts produced from E4DVar analyses have considerably smaller errors than both of the stand-alone EnKF and 4DVar systems for forecast lead times up to 60 h.

Improving Total Electron Content (TEC) Models for Geodetic Applications

2020 ◽  
Author(s):  
Mona Kosary ◽  
Saeed Farzaneh ◽  
Maike Schumacher ◽  
Ehsan Forootan
Keyword(s):  
Data Assimilation ◽  
Total Electron Content ◽  
Precise Point Positioning ◽  
Main Tool ◽  
Model Parameters ◽  
Electron Content ◽  
Total Electron ◽  
Radio Science ◽  
Imperfect Knowledge ◽  
Point Positioning

<p>Increasing the quality of ionosphere modeling is crucial and remains a challenge for many geodetic applications such as GNSS Precise Point Positioning (PPP) and navigation. Ionosphere models are the main tool to provide an estimation of Total Electron Content (TEC) to be corrected from GNSS career phase and pseudorange measurements. Skills of these models are however limited due to the simplifications in model equations and the imperfect knowledge of model parameters. In this study, an ionosphere reconstruction approach is presented, where global estimations of geodetic-based TEC measurements are combined with an ionospheric background model. This is achieved here through a novel simultaneous Calibration and Data Assimilation (C/DA) technique that works based on the sequential Ensemble Kalman Filter (EnKF). The C/DA method ingests the actual ionospheric measurements (derived from global GNSS measurements) into the IRI (International Reference Ionosphere) model. It also calibrates those parameters that control the F2 layer’s characteristics such as selected important CCIR (Comité Consultatif International des Radiocommunicationsand) URSI (International Union of Radio Science) coefficients.  The calibrated parameters derived from the C/DA are then replaced in the IRI to simulate TEC values in locations, where less GNSS ground-station infrastructure exists, as well as to enhance the prediction of TEC when the observations are not available or their usage is cautious due to low quality. Our numerical assessments indicate the advantage of the C/DA to improve the IRI’s performance. Values of the TEC-Root Mean Square of Error (RMSE) are found to be decreased by up to 30% globally, compared to the original IRI simulations. The importance of the new TEC estimations is demonstrated for PPP applications, whose results show improvements in navigation applications.</p><p><strong>Keywords: </strong>Ionosphere, Calibration and Data Assimilation (C/DA), IRI, Total Electron Content (TEC), Precise Point Positioning (PPP), GNSS</p>

scholarly journals Data Assimilation as Synchronization of Truth and Model: Experiments with the Three-Variable Lorenz System*

2006 ◽  
Vol 63 (9) ◽  
pp. 2340-2354 ◽  
Author(s):  
Shu-Chih Yang ◽  
Debra Baker ◽  
Hong Li ◽  
Katy Cordes ◽  
Morgan Huff ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Chaos Synchronization ◽  
Prediction Models ◽  
Partial Information ◽  
Coupling Parameter ◽  
Weather Prediction ◽  
Adaptive Synchronization ◽  
Accurate Analysis ◽  
Wide Range ◽  
Bred Vectors

Abstract The potential use of chaos synchronization techniques in data assimilation for numerical weather prediction models is explored by coupling a Lorenz three-variable system that represents “truth” to another that represents “the model.” By adding realistic “noise” to observations of the master system, an optimal value of the coupling strength was clearly identifiable. Coupling only the y variable yielded the best results for a wide range of higher coupling strengths. Coupling along dynamically chosen directions identified by either singular or bred vectors could improve upon simpler chaos synchronization schemes. Generalized synchronization (with the parameter r of the slave system different from that of the master) could be easily achieved, as indicated by the synchronization of two identical slave systems coupled to the same master, but the slaves only provided partial information about regime changes in the master. A comparison with a standard data assimilation technique, three-dimensional variational analysis (3DVAR), demonstrated that this scheme is slightly more effective in producing an accurate analysis than the simpler synchronization scheme. Higher growth rates of bred vectors from both the master and the slave anticipated the location and size of error spikes in both 3DVAR and synchronization. With less frequent observations, synchronization using time-interpolated observational increments was competitive with 3DVAR. Adaptive synchronization, with a coupling parameter proportional to the bred vector growth rate, was successful in reducing episodes of large error growth. These results suggest that a hybrid chaos synchronization–data assimilation approach may provide an avenue to improve and extend the period for accurate weather prediction.

scholarly journals On the extraction of wind information from the assimilation of ozone profiles in Météo–France 4-D-Var operational NWP suite

2009 ◽  
Vol 9 (14) ◽  
pp. 4855-4867 ◽  
Author(s):  
N. Semane ◽  
V.-H. Peuch ◽  
S. Pradier ◽  
G. Desroziers ◽  
L. El Amraoui ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Degrees Of Freedom ◽  
Variance Reduction ◽  
Lower Stratosphere ◽  
Transport Model ◽  
Weather Prediction ◽  
Error Variance ◽  
Numerical Weather Prediction Model ◽  
Background Error ◽  
Better Than

Abstract. By applying four-dimensional variational data-assimilation (4-D-Var) to a combined ozone and dynamics Numerical Weather Prediction model (NWP), ozone observations generate wind increments through the ozone-dynamics coupling. The dynamical impact of Aura/MLS satellite ozone profiles is investigated using Météo-France operational ARPEGE NWP 4-D-Var assimilation system for a period of 3 months. A data-assimilation procedure has been designed and run on 6-h windows. The procedure includes: (1) 4-D-Var assimilating both ozone and operational NWP standard observations, (2) ARPEGE transporting ozone as a passive-tracer, (3) MOCAGE, the Météo–France chemistry and transport model re-initializing the ARPEGE ozone background at the beginning time of the assimilation window. Using observation minus forecast statistics, it is found that the ozone assimilation reduces the wind bias in the lower stratosphere. Moreover, the Degrees of Freedom for Signal diagnostics show that the MLS data covering the 68.1–31.6 hPa vertical pressure range are the most informative and their information content is nearly of the same order as tropospheric humidity-sensitive radiances. Furthermore, with the help of error variance reduction diagnostics, the ozone contribution to the reduction of the horizontal divergence background-error variance is shown to be better than tropospheric humidity-sensitive radiances.

scholarly journals Using the local ensemble Transform Kalman Filter for upper atmospheric modelling

2019 ◽  
Vol 9 ◽  
pp. A30 ◽  
Author(s):  
Sean Elvidge ◽  
Matthew J. Angling
Keyword(s):  
Kalman Filter ◽  
General Circulation ◽  
Computational Cost ◽  
Circulation Model ◽  
Electron Content ◽  
Total Electron ◽  
Rms Error ◽  
Ensemble Transform Kalman Filter ◽  
Assimilation Model ◽  
Ensemble Transform

The Advanced Ensemble electron density (Ne) Assimilation System (AENeAS) is a new data assimilation model of the ionosphere/thermosphere. The background model is provided by the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) and the assimilation uses the local ensemble transform Kalman filter (LETKF). An outline derivation of the LETKF is provided and the equations are presented in a form analogous to the classic Kalman filter. An enhancement to the efficient LETKF implementation to reduce computational cost is also described. In a 3 day test in June 2017, AENeAS exhibits a total electron content (TEC) RMS error of 2.1 TECU compared with 5.5 TECU for NeQuick and 6.8 for TIE-GCM (with an NeQuick topside).

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