A multiyear (2002–2006) climatology of O/N₂ in the lower thermosphere from TIMED GUVI and ground‐based photometer observations

Abstract

[1] The Mass Spectrometer and Incoherent Scatter (MSIS) series of models has been a standard for specification and prediction of conditions in the Earth's thermosphere. One aspect of the model that has not been well validated is its predictions of the lower thermospheric compositional response to varying periodic and transient solar and geomagnetic forcings. Here we compare the predictions by the NRLMSIS model over the years 2002 to 2006 for column O/N 2 with GUVI satellite (daytime) observations at a fixed longitude of the Poker Flat Research Range in central Alaska. NRLMSIS predictions are also compared with nightside auroral O/N 2 observations obtained from ground-based photometric measurements from central Alaska. While there is often good agreement between the GUVI and NRLMSIS O/N 2 at high latitudes, there are also times during large geomagnetic storms when there is some disagreement with the largest occurring at midlatitudes. The 9-day periodicity, first described in 2007 and attributed to high-speed solar wind streams, is present in the model at all latitudes but is quite weak in the observations below 55N latitude. Away from high-latitudes, these differences may exist because the NRLMSIS O/N 2 dependence on a p forces nonperiodic contributions contained in the NRLMSIS database to give a periodic response when a p is varied periodically. While the nighttime observations show considerable agreement with NRLMSIS they are much more variable than predicted by the model. This variability suggests that there are significant local effects in the auroral zone that are not captured by the model.

Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales

“…Therefore, the red aurora occurring at higher altitudes are produced by less energetic populations colliding with atomic oxygen, whereas the green and blue aurora occurring at lower altitudes are caused by more energetic populations colliding with oxygen and molecular nitrogen, respectively. Methodologies that utilize the intensity ratios between the different auroral color wavelengths have been developed to determine the integrated energy flux and the average energy of the precipitating particle distribution (e.g., [21][22][23][24][25]); for example, more red than blue light indicates a less energetic population, whereas more blue than red light indicates the opposite.…”

Multiday thermospheric density oscillations associated with variations in solar radiation and geomagnetic activity

“…Hecht et al . [] used Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) observations of thermospheric composition (O/N 2 ) from 2002 to 2006 to show that thermospheric O/N 2 had multiday periodic oscillations. They associated these oscillations with high‐speed solar wind streams and their resultant recurrent geomagnetic activity.…”

A new technique for remote sensing of O2 density from 140 to 180 km

“…We estimate that the overall systematic error in deriving [O 2 ] from R is due primarily to a combination of the following systematic errors. These are (1) the uncertainty in the calibration of the relative response of the two photometers, which we take to be about 5% or less [Hecht et al, 2012], (2) the effect of uncertainties in the shape of the filter passbands, which is taken as about 5%, (3) the small nonlinear relation between R and [O 2 ], described above, and taken as about 3% or less, and (4) the uncertainty in the product of k, , and f in equation ( 2) that provide an additional approximate 30% uncertainty [Sander et al, 2011]. Since (4) dominates, the uncertainty in [O 2 ] due to systematic errors should be on the order of 30%.…”

“…NRLMSIS00, however, does not include wave variability, which is random, and the limited [O 2 ] database at 150 km may underestimate transport effects. A previous study that compared the variability of O/N 2 between the NRLMSIS00 model and data from the Global Ultraviolet Imager (GUVI) showed that the NRLMSIS00 model underestimates the composition variability at middle and low latitudes in the 130 to 150 km altitude region [Hecht et al, 2012]. TIMEGCM has, however, been somewhat successful in capturing the variability and transport of composition changes at middle and low latitudes [Crowley et al, 2006;Crowley and Meier, 2008].…”