Statistical Averages of F-Layer Electron Density, Electron Temperature and Ion Temperature Over Millstone Hill

Details Statistical Averages of F-Layer Electron Density, Electron Temperature and Ion Temperature Over Millstone Hill Statistical Averages of F-Layer Electron Density, Electron Temperature and Ion Temperature Over Millstone Hill

Dec 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001agufmsa32a0685z&link_type=abstract

American Geophysical Union, Fall Meeting 2001, abstract #SA32A-0685

Physics

2443 Midlatitude Ionosphere, 2447 Modeling And Forecasting, 2467 Plasma Temperature And Density, 6952 Radar Atmospheric Physics

Scientific paper

All Millstone Hill incoherent scatter radar data collected since 1978 are available through the Madrigal Database at MIT Haystack Observatory. A set of empirical models for basic and derived incoherent scatter parameters, including electron density Ne, electron and ion temperatures Te and Ti, electric field and parallel ion drift is being developed from this extensive dataset. Such models of the average behavior of key ionosphere-thermosphere (IT) parameters, based on long term accumulated data, are important for space weather studies not only in terms of quantitative descriptions of the IT system but also in terms of clarifying several outstanding scientific problems. This paper presents Ne, Te and Ti averages in the ionospheric F-layer from which local empirical models can be generated. We sort every parameter measured locally into bins. The binning parameters are local time (0000-2400 LT), the day of year (season), and altitude (150-1000 km). Each data point belongs to a certain bin and has corresponding solar flux index F107 and geomagnetic index Ap. For each bin, a multiple regression is performed for a function including (1) the constant term, (2) linear effect terms of F107 and (3) Ap, and (4) the F107 and Ap cross effect term, to give a set of fitting coefficients, such that our model of bin averages is keyed to F107 and Ap. The deviations of actual data from the model represent the remaining day-to-day variability. We will present the data distribution of each bin and discuss the main features of our averages and models.

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