New coefficients Feature-preserving

morphing of original expansion

Expansion Mapping D/M differences to global scale

Differences

Data/Model comparison for sensor sites

Model Form

alism

Data

Small volumes of real-time

raw data

Assimilation of GIRO Data in IRI for Global Near-Real-Time Ionospheric Specification

• Accurate, prompt, and detailed global 3D ionospheric plasma nowcasting for hour-by-hour space weather applications

• The Global Ionospheric Radio Observatory (GIRO) [1] is a prime supplier of near-real-time measurements from a network of ground-based remote sensing radio instruments, digisondes

• International Reference Ionosphere (IRI) [2]: one of 2 top performers of CEDAR ETI Model Challenge [3]

• Assimilative modeling based on GIRO data: Real-Time Assimilating Mapping (RTAM) [4]

• Assimilation principle: smooth transformation of climatology maps of the main IRI ionospheric characteristics to match available observations.

• The key technological component of RTAM is the elastic transformation method that preserves integrity of the IRI’s empirical specification while modifying the model to match the sensor data

• The transformation technique keeps intact the proven IRI formalism for ionospheric specification (specific choice of expansion base function in time and space) and computes only corrections to the expansion coefficients

• Provides physics-based constraints for the difficult task of assimilating sparse and sporadic real-time data available only at a limited number of locations thus avoiding computational artifacts of unconstrained interpolation

IN33C-1558

Real-Time Data Acquisition

GIRO SENSORS

Model values at sensor sites

Expansion

Large volumes of historic

quality-ensured data

Global Ionospheric Radio Observatory

REAL-TIME NETWORK DATA

* Up to 42 real-time data feeds from worldwide locations 15 minute cadence of measurements

Neural Network Mapping Optimizer

RECURRENT HOPFIELD NEURAL NETWORK

Clip neurons to observed differences at sensor locations

Start evolving the neural network optimizer into its state of the

minimum energy

Visit http://giro.uml.edu/RTAM/

The neural network has reached its stable state of

self-consistence

Σ

Dynamic system where each neuron optimizes its state by weighing contributions from neighboring neurons

Ivan Galkin1, Bodo Reinisch1,2, Xueqin Huang1, Dieter Bilitza3, and Artem Vesnin1 1 University of Massachusetts Lowell, Center for Atmospheric Research 2 Lowell Digisonde International, LLC 3 George Mason University, Space Weather Lab

Fully connected Hopfield network

Changes to clipped neurons at sensor sites are allowed but limited

to data uncertainty

ASSIMILATION PRINCIPLE • Global 3D Ionospheric Nowcasting by “morphing” the

climatology diurnal/spatial harmonics into the best fit with sensor data at GIRO locations • Morphing is done by adjusting expansion coefficients

while keeping the function formalism intact • Smooth specification without sharp gradients and

short-lived artifacts • Compatible with existing IRI applications • Compact coefficients easily disseminated

• One update morphs 24-hr model to match 24-hr data • Robust to data noise • Captures dynamics of the ionospheric periodicity • Potential for improved forecasting

http://giro.uml.edu

Connection weights are used to model longitudinal vs latitudinal facilitation of neighboring nodes

Climatology

AGU 2012

34 digisonde stations (dots) reported data for assimilation

Climatology 2D IRI map of foF2 versus GIRO measurements RTAM 2D map of foF2

International Reference Ionosphere

degr

ee

order Spatial Expansion

Σ

Diurnal Harmonics

GLOBAL LONG-TERM CLIMATOLOGY

http://iri.gsfc.nasa.gov

3D Ne distribution

[1] Reinisch, B. W. and I. A. Galkin, Global ionospheric radio observatory (GIRO), EPS, 63, 377-381, 2011.

[2] Bilitza, D. and B.W. Reinisch (2008), International Reference Ionosphere 2007: Improvements and new parameters, J. Adv. Space Res. 42/4, 599-609, doi:10.1016/j.asr.2007.07.048.

[3] Shim, J., M. M Kuznetsova, L. Rastaetter, et al. (2010), CEDAR Electrodynamics Thermosphere Ionosphere (ETI) Challenge for Systematic Assessment of Ionospheric Models, AGU Fall Meeting 2010, Paper SM51A-1745, San Francisco, CA.

[4] Galkin, I. A., B. W. Reinisch, X. Huang, and D. Bilitza, Assimilation of GIRO Data into a Real-Time IRI, Radio Sci., 47, RS0L07, doi:10.1029/2011RS004952, 2012.

data

harmonic representation