© Copyright QinetiQ limited 2006 On the application of meteorological data assimilation techniques to radio occultation measurements of the ionosphere Matthew Angling Centre for RF Propagation and Atmospheric Research

© Copyright QinetiQ limited Ionospheric data assimilation To provide a high accuracy and timely specification of the ionosphere for use in RF systems Increased accuracy of ground and space based trans-ionospheric sensors −EWR, SAR, AMTI/GMTI, satellite geolocation systems Improved accuracy of single frequency navigation systems −GPS, Galileo Improved LPI/LPJ characteristics of HF communications Significant reduction in the errors for HF position finding systems

© Copyright QinetiQ limited Data assimilation models Model Empirical IRI RIBG PIM Physical Ionospheric Coupled LT persistence Forecast Physical forecast Representation Shells Single Multiple 3D basis functions Horiz harmonics Vertical EOFs 3D grid Geographic Geomagnetic Estimation Non-optimal Profile adjustment Tomography ART, MART, etc Optimal DIT GMKF Approx Kalman Full Kalman Variational methods No covariances

© Copyright QinetiQ limited JPL GIM Model Empirical IRI RIBG PIM Physical Ionospheric Coupled LT persistence Forecast Physical forecast Representation Shells Single Multiple 3D basis functions Horiz harmonics Vertical EOFs 3D grid Geographic Geomagnetic Estimation Non-optimal Profile adjustment Tomography ART, MART, etc Optimal DIT GMKF Approx Kalman Full Kalman Variational methods No covariances

© Copyright QinetiQ limited IonoNumerics Model Empirical IRI RIBG PIM Physical Ionospheric Coupled LT persistence Forecast Physical forecast Representation Shells Single Multiple 3D basis functions Horiz harmonics Vertical EOFs 3D grid Geographic Geomagnetic Estimation Non-optimal Profile adjustment Tomography ART, MART, etc Optimal DIT GMKF Approx Kalman Full Kalman Variational methods No covariances

© Copyright QinetiQ limited USU GAIM Model Empirical IRI RIBG PIM Physical Ionospheric Coupled LT persistence Forecast Physical forecast Representation Shells Single Multiple 3D basis functions Horiz harmonics Vertical EOFs 3D grid Geographic Geomagnetic Estimation Non-optimal Profile adjustment Tomography ART, MART, etc Optimal DIT GMKF Approx Kalman Full Kalman Variational methods No covariances

© Copyright QinetiQ limited Electron Density Assimilative Model Model Empirical IRI RIBG PIM Physical Ionospheric Coupled LT persistence Forecast Physical forecast Representation Shells Single Multiple 3D basis functions Horiz harmonics Vertical EOFs 3D grid Geographic Geomagnetic Estimation Non-optimal Profile adjustment Tomography ART, MART, etc Optimal DIT GMKF Approx Kalman Full Kalman Variational methods No covariances

© Copyright QinetiQ limited Electron Density Assimilative Model PIM used for background model −Electrons only Designed to be scalable −Can assimilate single or multiple measurements Low demands on computer resources Simple evolution −Exponential decay of electron density grid differences Uses sun-fixed geomagnetic coordinate system Model Variances are propagated, covariance are estimated as required

© Copyright QinetiQ limited Best Linear Unbiased Estimator Most probable atmospheric state ( x a ) is obtained by modifying background state ( x b ) with differences between the observation vector ( y ) and the background state x a = most probable atmospheric state x b = a priori (background) atmospheric model y = observations B = background error covariance matrix H = observation operator R = observation error covariance matrix K = weight matrix

© Copyright QinetiQ limited

© Copyright QinetiQ limited Radio Occultation GPS transmitter, LEO receiver Global coverage with high vertical, low horizontal resolution In the ionosphere bending angles are small Estimating slant TEC from L1/L2 phase difference removes clock and POD errors Assimilation of sTEC has potential to overcome limitations of Abel Transform RO provides important height information

© Copyright QinetiQ limited foF2/hmF2 testing Previous study has shown that EDAM can improved foF2 performance But hmF2 performance is relatively poor Can RO data improve representation of vertical structure in EDAM?

© Copyright QinetiQ limited Assimilation tests Assimilations run for August and 4, 10 September 2006 −Disturbed, moderate and quiet conditions Assimilate COSMIC podTEC data −Calibrated slant TEC −Reduced sampling rate at high elevations −Constellation is not yet fully deployed Runs with just RO data, just IGS data and with RO + IGS data Test using vertical profiles −ionPRF files from UCAR-CDAAC −Abel Transform vertical profiles −True height profiles from AFRL vertical ionosonde network

© Copyright QinetiQ limited IGS and DISS stations

© Copyright QinetiQ limited Example ionPRF vertical profile RMS error in electron density calculated at 4 km intervals Little quality control of ionPRF −Values must be positive Not comparing similar measurements −ionPRF is a distributed measurement

© Copyright QinetiQ limited IonPRF RMS errors

© Copyright QinetiQ limited IonPRF RMS errors

© Copyright QinetiQ limited Example VI vertical profile RMS error in electron density calculated at 4 km intervals Little quality control of VI profile −Autoscaled data −Values must be positive No attempt to limit VI data to that close to RO measurements

© Copyright QinetiQ limited VI RMS errors

© Copyright QinetiQ limited VI RMS errors

© Copyright QinetiQ limited Conclusions COSMIC podTEC data has a positive effect on EDAM analysis For moderate and disturbed conditions, assimilation of podTEC improves the electron density RMS error at all heights from 200 to 500 km The interaction between podTEC and ground based TEC requires further investigation Modest improvements, limited by −Difficult test −COSMIC constellation −Autoscaled vertical ionosonde data