Ionospheric Modelling and its Challenges due to Space Weather
Sandro M. RadicellaHead, Telecommunications/ICT for Development laboratory (T/ICT4D),
The Abdus salam [email protected]
The International Space Weather Initiative School onSpace Weather and Global Navigation Satellite Systems
8–12 October 2018, Baku, Azerbaijan
Modelling in Geophysics
Measurements, theory, model and prediction
MEASUREMENTS (Past data)
Theory
Model
Prediction (Current
and Future data)
Empirical models: • descriptive• based on data statistics
Main types of GeophysicalModels
Physics based models • explain and predict based on
mathematical representation of physical laws.
• are deterministic. the other.
Neither the
physics‐based nor the
empirical approach ignores
the other.
Physicists rely on observations to develop and validate physical models
Statistical or empirical models are guided by principles of physics
Because
Limits of geophysical models (1)
Impossibility to perform controlled experiments to study the Earth.
Limits of geophysical models (2)
Past data are always “uncertain guide” because of our limited data base.
Stochastic nature of many geophysical processes precludes reaching certainty.
Data errors increase the uncertainty in model specifications.
Population
Data
The limits of geophysical models (3)
The development of geophysical science had to rely entirely upon
observations of natural phenomena.
The development of geophysical science had to rely entirely upon
observations of natural phenomena.
It is impossible to draw exact conclusions from
the observations, because it is difficult to separate physical process
controlling the observation.
It is impossible to draw exact conclusions from
the observations, because it is difficult to separate physical process
controlling the observation.
Physical conclusions are uncontrolled synthesis of many mutually dependent physical
processes.
Physical conclusions are uncontrolled synthesis of many mutually dependent physical
processes.
But, things are getting better and better!
Model based Numerical Predictions in geophysics are now a “successful story”
because:
Rapid growth of the number of Earth observationsSustained and continous improvements of modelsContinous growth in computing power since the 1970s.
An outstanding example of success:
Abbe (1901) and Bjerknes (1904) proposed that
physics laws could be used to forecast weather
Today Numerical Weather prediction
Numerical Weather Predictions impact in science and society is among the greatest of any area of sciences.
Abbe, C. Mon.Weath. Rev. 29,551–561 (1901).Bjerknes, V. Meteorol. Z. 21, 1–7 (1904).
Abbe and Bjerknes ideas
“Predicting the state of the atmosphere could be treated as a problem of mathematical physics, where future weather is obtained by integrating given partial differential equations, starting from the observed current weather”
But at that time, there were:• few routine weather observations,• no computers, • little understanding of whether the weather could be predicted
More than 100 years later, systems of nonlinear differential equations are solved routinaly considering:
• dynamic, • thermodynamic, • radiative,• chemical
processes working on scales from hundreds of metres to thousands of kilometres and from seconds to weeks.
Today Numerical Weather prediction
Weather forecast skill and supercomputer power growth
P. Bauer, A. Thorpe and G. Brunet, Nature, 525, 47‐55 (2015)
Ionospheric models
We need Ionospheric Models
To understand and reproduce ionospheric
variations observed experimentally
To “predict”ionosphere conditions
To specify ionospheric
conditions needed by technological
systems
To test and improve our
knowledge about the ionosphere
Moreover
Ionospheric Models are alternatives to
direct measurements.
Models are cheaper and more
convenient to study different processes.
because
Some characteristics of ionospheric models
Main output of Ionospheric models is electron density with the possible addition of other parameters
All ionospheric models need some type of
input data
There are no all‐purpose ionospheric
models
Current ionospheric models are constantly
undergoing improvements and
validations.
Moreover
Well established databases to test and validate the existing models
are needed to generate improvements
No model is able to reproduce by itself in a satisfactory way both the “climate” and the “weather” of the
Earth ionosphere.
From “climate” to “weather”:the impact of Space Weather
• Lower atmosphere and ionosphere exhibit “climate” and a “weather” variability.
• Ionospheric “climate” is well represented by models of different types.
• Ionospheric weather variability is mostly controlled by the “Space Weather conditions”.
• A BIG challenge of ionospheric modelling is to consider the impact of varying Space Weather conditions to reproduce the observations.
Two approaches to model Ionosphere Weather
Systemic approach:coupled
physics‐based models
Data Assimilation or ingestion in
models
Ionospheric Climate Models
Different types
Empirical orProfilers Physics‐Based
Parameterized
Empirical Models or profilers
Analytical description of the ionosphere derived from experimental data.
Model climate ionospheric variation from past data
Data sources are: ionosondes, topside sounders, incoherent scatter radars, rockets and satellites.
Empirical Models or profilers:uses and limitations
Mainly used for assessment and prediction purposes.
Easy to use.
Describe ionospheric climate. Give realistic representation of the
ionosphere in areas well covered by observations.
International Reference Ionosphere(1)
IRI is an international project sponsored by the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI).
Empirical standard model of the ionosphere, based on all available data sources.
Several improved editions of the model have been released.
Input
probability
InputYear, month, day, hour, geographic or geomagnetic coordinates, various optional input. Basic imput are the ITU‐R coeficients of foF2 and M(3000).
OutputElectron concentration, electron temperature, ion temperature, ion composition (O+, H+, He+, NO+, O+
2), ion drift, ionospheric electron content (TEC), F1 and spread‐F probability
International Reference Ionosphere(2)
International Reference Ionosphere (IRI)
D. Bilitza, International Reference Ionosphere 2000, Radio Science 36, #2, 261‐275, 2001....Bilitza, D., D. Altadill, V. Truhlik, V. Shubin, I. Galkin, B. Reinisch, and X. Huang , International Reference Ionosphere 2016: From ionospheric climate to real‐time weather predictions, Space Weather, 15, 418–429, 2017doi: 10.1002/2016SW001593.LAST VERSION OF IRI Web: https://ccmc.gsfc.nasa.gov/modelweb/models iri2016_vitmo.php
International Reference Ionosphere (3)
From: “Mitigation of Ionospheric Threats to GNSS: an Appraisal of the Scientific and TechnologicalOutputs of the TRANSMIT Project”, Chapter 3, p. 166,InTech 2014
NeQuick (1)
3D time dependent ionospheric electron density model developed at the ICTP, Trieste, Italy and at the University of Graz, Austria.
Quick‐run model particularly tailored for trans‐ionospheric applications.
InputYear, month, day, time, geographic coordinates of lower and higher endpoint , R12 or F10.7 solar flux. Basic imput are the ITU‐R coeficients of foF2 and M(3000).
OutputElectron density and TEC along any ground‐to‐satellite ray‐path.
NeQuick (2)
NeQuick
S. M. Radicella, Leitinger, R., 2001. The evolution of the DGR approach to model electron density profiles. Advances in Space Research 27 (1), 35–40.
B. Nava, P. Coïsson, and S. M. Radicella (2008), A new version of the NeQuick ionosphere electron density model, Journal of Atmospheric and Solar‐Terrestrial Physics, 70(15), 1856‐1862.
NeQuick2WEB –http://t‐ict4d.ictp.it/nequick2/nequick‐2‐web‐model
NeQuick (3)
From: “Mitigation of Ionospheric Threats to GNSS: an Appraisal of the Scientific and Technological Outputs of the TRANSMIT Project”, Chapter 3, p. 167, InTech 2014
An empirical model of Mars ionosphere: NeMars
Sánchez‐Cano, B., Radicella, S.M., Herraiz, M., Witasse, O., Rodríguez‐Caderot, G., NeMars, 2013. An empirical model of the martian dayside ionosphere based on Mars Express MARSIS data. Icarus 225 (1), 236–247. http://dx.doi.org/10.1016/j. icarus.2013.03.021.
NeMars is an empirical model for the martian dayside ionosphere (primary and secondary layers) based on MARSIS AIS data from Mars Express and on radio occultation data from Mars Express and Mars Global Surveyor.The model assumes that the martian ionosphere is in photochemical equilibrium and the two main layers can be represented by the ‐Chapman theory. Other contributions like solar activity and heliocentric distance have been considered.
Physics‐Based Models
Conservation equations (continuity, momentum, energy, etc.) are solved numerically for electrons and ions as a function of spatial and time coordinates to calculate plasma densities, temperatures and flow velocities.
Data sources are magnetospheric (convection electric field and particle precipitation) and atmospheric parameters (neutral densities, temperatures and winds)
Physics‐Based Models: uses and limitations
Are mainly used for scientific studies. Can be powerful tools to understand the
physical and chemical processes of the upper atmosphere.
Accuracy depends on the quality and quantity of input data and their error estimates.
Accuracy depends also on possible missing physics (not enough knowledge of all the physical processes involved)
SAMI3 (1)
SAMI3 is three‐dimensional global ionospheric model. It calculates the chemical evolution of seven ion species (H+, He+, N+, O+, NO+, N2
+ and O2+) in the altitude
range 85 km to 20,000 km. An offset, tilted dipole geomagnetic field is used, and the plasma is modeled from hemisphere to hemisphere. The neutral composition and temperature are provided and the neutral winds are obtained from models. SAMI3 uses a unique, nonorthogonal, nonuniform, fixed grid.
SAMI3 (2)
SAMI3 Huba, J. D., G. Joyce, and J. Krall (2008), Three‐dimensional equatorial spread F modeling, Geophys. Res. Lett., 35, L10102, doi:10.1029/2008GL033509.InputValue of F10.7 (1 day and 3 months average), AP IndexExB Drift velocityOutpution density, ion temperature, ion velocity, electron temperature, NmF2, hmF2,TEC (Total Electron Content)SAMI3 Request a run:https://ccmc.gsfc.nasa.gov/requests/IT/SAMI3/sami3_user_registration.php
GITM (1)
Global Ionosphere Thermosphere Model (GITM) is a 3‐dimensional spherical code that models the Earth's thermosphere and ionosphere system using a grid in latitude and altitude. The number of grid points in each direction can be specified, so the resolution is extremely flexible. GITM explicitly solves for the neutral densities of O, O2, N(2D), N(2P), N(4S), N2, and NO; and ion species O+(4S), O+(2D), O+(2P), O2+, N+, N2+, and NO+.It uses an altitude grid instead of a pressure grid. The vertical grid spacing is less than 3 km in the lower thermosphere, and over 10 km in the upper thermosphere. GITM allows a more realistic dynamics in the auroral zone. GITM covers all latitudes and a vertical range from about 90 km to 600 km. The latitude resolution is 2.5º, and longitude resolution is 5º.
GITM (2)
Global Ionosphere Thermosphere Model GITM)
php
Global Ionosphere Thermosphere Model (GITM)Ridley, A. J., Y. Deng, and G. Toth., 2006, The Global Ionosphere‐Thermosphere Model (GITM). J. Atmos. Solar‐Terrestr. Phys. 68, 839‐864.InputF10.7, Hemispheric Power Index (HPI), Interplanetary Magnetic FieldSolar wind velocity, Solar irradiance (for event runs)OutputTemperatures: neutral, ion, electron (K), Neutral winds: zonal, meridional, vertical (m/s), Plasma velocities: zonal, meridional, vertical (m/s), Neutral mass density (kg/m3), Number densities: neutral (O, O2, N(2D), N(2P), N(4S), N2, and NO), ion (O+(4S), O+(2D), O+(2P), O2+, N+, N2+, and NO+), and electron (m‐3)GITM Request a run: https://ccmc.gsfc.nasa.gov/requests/IT/GITM/gitm_user_registration.php
Parameterized Models
They are based on orthogonal function fits to data that are output of physics‐based models.
Theoretical model runs are performed for various solar‐geophysical conditions and parameterization is usually done in terms of solar activity, geomagnetic activity and season.
Parameterized Models:uses and limitations
Describe the climatology of the ionosphere.
Are computationally fast still retaining physics of theoretical models.
Cannot accurately reproduce specific situations.
They are suitable only for well‐specified geophysical problems.
PIM (1)
The Parameterized Ionospheric Model (PIM) is a climatology model built from combining model output from the Global Theoretical Ionospheric Model (GTIM) model for low and middle latitude with output from the TDIM model for high latitudes and from an empirical model for plasmaspheric altitudes Gallagher et al. (1988).
TEC
September 22
UT=1:39
F10.7= 200.
PIM (2)
Parameterized Ionospheric Model (PIM)Daniell, R. E., Jr., L. D. Brown, D. N. Anderson, M. W. Fox, P. H. Doherty, D. T. Decker, J. J. Sojka, and R. W. Schunk (1995), Parameterized ionospheric model: A global ionospheric parameterization based on first principles models, Radio Sci., 30(5), 1499–1510,Gallagher, D. L., P. D. Craven, R. H. Comfort (1988), An empirical model of the earth's plasmasphere, Adv. Space Res., 8(8), 15‐24.InputSolar‐Geophysical conditions (UT,DOY,F10.7,Kp) and geographical coordinates.OutputElectron density profile from 90 to 25000 Km plus foF2, foE, hmF2, hmE, TEC Info: https://ccmc.gsfc.nasa.gov/modelweb/ionos/pim.html
From “climate” to “weather”
FORCING FROM SUN AND
MAGNETOSPHERE
FORCING FROM SUN AND
MAGNETOSPHERE
IONOSPHEREIONOSPHERE
FORCING FROM LOW
ATMOSPHERE
FORCING FROM LOW
ATMOSPHERE
From Climate
toWeather
Systemic approach:coupled physics‐based models
TIEGCM
The Thermospheric General Circulation Model of the High Altitude Observatory at the National Center for Atmospheric Research:• 3D, • time‐dependent ,• numeric simulation of the Earth's upper atmosphere,
including the upper Stratosphere, Mesosphere, and Thermosphere.
• Includes also a self‐consistent aeronomic scheme for the coupled Thermosphere/Ionosphere system, Thermosphere Ionosphere Electrodynamic General Circulation Model
• Extension of the lower boundary from 97 to 30 km, including the physical and chemical processes appropriate for the Mesosphere and upper Stratosphere.
An important result using this model
From the paper:“Day‐to‐day ionospheric variability due to lower atmosphere perturbations” by H.‐L. Liu, V. A. Yudin, and R. G. Roble; GEOPHYSICAL RESEARCH LETTERS, VOL. 40, 665–670.This study demonstrates that TIEGCM constrained in the stratosphere and mesosphere by the hourly Whole Atmosphere Community Climate Model (WACCM) is capable of reproducing observed features of day‐to‐day variability in the thermosphere‐ionosphere.
Day‐to‐day variability of NmF2
Under constant solar minimum and geomagnetically quiet conditions the meteorological driving may contribute comparably with geomagnetic forcing to the ionospheric day‐to‐day variability.
From Climate
toWeather
Data Assimilation or ingestion in models
increasing availability of data:solar data, ionospheric ground and space‐based TEC, ionosonde and radar data
Development of data assimilation/data ingestion techniques to specify ionospheric weather variability mostly induced by Space Weather events
During the last 20 years
due also to
USU‐GAIM
This was possible also because of the increasing availability of experimental data even in real time (solar data, ground and space‐based GNSS ionospheric, ionosonde and radar data).
These models and schemes are of different complexity and rely on different kinds of data.
One of the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) models uses a physics‐based model of the ionosphere and a Kalman filter as a basis for assimilating real‐time (or near real‐time) bottom‐side Ne profiles, slant TEC ground GPS/TEC stations, in situ Ne from four DMSP satellites, and line‐of‐sight solar UV emissions measured by satellites.
The main output of the model is a 3‐dimensional electron density distribution at user specified times. In addition, auxiliary parameters are also provided, including NmE, hmE, NmF2, hmF2, slant and vertical TEC.
Request a run:https://ccmc.gsfc.nasa.gov/models/modelinfo.php?model=USU‐GAIM
IRTAM
IRI Real‐Time Assimilative Modelling (IRTAM) system assimilates digisonde data from the Global Ionospheric Radio Observatory (GIRO) network into the IRI model.
The IRTAM approach is based on the ITU‐R models for the F2 peak plasma frequency foF2 and the propagation factor M(3000)F2 that are being used in IRI.
IRTAM uses the CCIR set of functions to describe the global and spatial variation of the difference between the digisonde measurement and the IRI prediction of foF2.
Galkin, I. A., B. W. Reinisch, X. Huang, and D. Bilitza (2012), Assimilation of GIRO data into a real‐time IRI, Radio Sci., 47, RS0L07, doi:10.1029/2011RS004952.
A T/ICT4D technique for global specification at a given epoch
From: Nava, B., S. M. Radicella, and F. Azpilicueta (2011), Data ingestion into NeQuick 2, Radio
Sci., 46, RS0D17, doi:10.1029/2010RS004635.).
The real challenge faced by the models is their ability to forecast and nowcast local and global
ionospheric effects of Space Weather events.
The models performance show an evident dependence on geomagnetic activity with RMS errors increasing with increasing geomagnetic activity.
The models performance show also a complex dependence with latitude.
Are these types of models
now able to specify
ionospheric weather
conditions?
Nigussie, M., S. M. Radicella, B. Damtie, E. Yizengaw, B. Nava, and L. Roininen (2016), Validation of NeQuick TEC data ingestion technique against C/NOFS and EISCAT electron density measure‐ ments, Radio Sci., 51, doi:10.1002/ 2015RS005930.