Figure 4: Temporal and spatial evolution of horizontal wind field on 11 February 2010 estimated by SDI (monostatic (blue)) and FPI bistatic (without vertical wind correction (red) and with vertical wind correction (green)). Arrow in circle represents the azimuthal location of sun at Poker Optical remote sensing is a very powerful tool for studying thermospheric neutral dynamics, and many variants of it have been developed. Because the techniques are somewhat indirect, it is important for the various methods to be cross validated. In Alaska, two different ground based Doppler spectrometers namely Scanning Doppler imagers (SDI) and Fabry-Perot Interferometers (FPI) are working independently. SDI and FPI are monitoring common volumes of the thermosphere at the same wavelength (630nm). Both are based on Doppler spectroscopy of naturally occurring optical emissions from the thermosphere, and both use Fabry-Perot etalons to obtain the required spectral resolution. They are entirely different in modes of operation, data collection, and data analysis. Ishii et al. [2001] compared vertical wind inferred by these two techniques, but horizontal neutral wind inferred by SDI has never been cross-compared before. In this study, we present the first ever detailed cross-comparison of F-region horizontal neutral winds inferred by SDI and FPI. Horizontal neutral winds inferred by monostatic SDI, bistatic SDI, and bistatic FPI (with and without vertical wind) were cross compared for total 7 nights from 2010, collecting data from four observatories (two SDI and two FPI) in Alaska. The results of this study show a high degree of correlation between SDI and FPI measured diurnal behavior of line-of-sight (LOS) wind. The variability recorded in FPI LOS wind was stronger than SDI. Every night there were many instances when temporal characteristics of the high frequency fluctuations in time series recorded by SDI and FPI were very similar, which suggests that those fluctuation recorded by both instruments are geophysical. SDI produces high resolution maps of thermospheric neutral wind over a wide geographic region of thermosphere. Angular size of the smallest zone of SDI is much larger than for FPI. The suppression in fluctuations in SDI LOS wind compared to FPI suggests the presence of local scale structures with size smaller than roughly 40 km. Zonal and meridional wind estimated by monostatic SDI, bistatic SDI, and bistatic FPI wind fits were in close agreement. Mapped wind fields showed that most of the discrepancies between estimated winds by two instruments occurred when neutral wind speed was slow. High neutral wind speed presumably suppressed structures in neutral wind field. In Alaska, two different variants of ground based optical Doppler spectroscopic instruments are mapping neutral winds independently. Both are Fabry-perot Doppler spectrometers, but one is a narrow field of view Fabry-Perot interferometer (FPI) with fixed gap etalon; another is an All-sky scanning Doppler imager Fabry-Perot interferometer (SDI) with scanning etalon. Horizontal winds inferred by SDI has never been cross-compared. The major goal of this study is to compare geophysical results derived using these two related but nonetheless very different optical remote sensing instruments utilizing 630 nm atomic oxygen emission, which occurs naturally in a broad layer centered around 240 km altitude and spanning many tens of km in height. First ever cross-comparison of thermospheric wind measured by narrow and wide field Doppler spectroscopy Manbharat Singh Dhadly 1, John W. Meriwether 2, Mark Conde 1, Don Hampton 1 [ 1 Geophysical Institute (UAF) 2 Clemson University] 2. Objective 3. Instrumentation and Data  We presented first ever cross-comparison between horizontal neutral wind fields mapped by two entirely different and completely independent ground based optical Doppler spectrometers (SDI and FPI) working at auroral latitudes.  Diurnal behavior of the LOS wind inferred by two entirely independent instruments have demonstrated a very good agreement with high correlation.  LOS wind measured by both instruments on all the seven nights showed high frequency oscillatory structures. Variability present in FPI LOS wind measurements was stronger than SDI.  Observations of similar oscillations by two independent instruments at same temporal location suggest that these high frequency oscillations have thermospheric origin.  However, monostatic SDI wind fit is based on substantial assumptions compared to the bistatic wind fit (which involved only an assumption about the vertical wind) but they were in good agreement.  SDI LOS wind and mapped horizontal wind field was smoother compared to FPI. This is presumably due to the large geographic area coverage of even smallest zone of SDI compared to FPI CV. Measured SDI LOS wind is the spatial average of the LOS wind samples from a range of azimuths and zeniths.  Most of the discrepancies between the mapped wind fields by two instruments occurred when the neutral wind speed was slow.  Small variability measured in larger SDI zone area compared to FPI suggests the presence of small scale structures with scale size smaller than the size of SDI zones. Thermospheric winds may be more complex than previously thought. 5. Conclusions Figure 1. Location of SDI (blue), FPI (red circles) observatories on the map of Alaska. Red crosses indicate FPI common volume locations (CV’s). CV1, CV2, CV3, and CV4 are in clockwise sequence with CV1 as westernmost vertex of CV polygon and closest to Poker Flat. Large blue circle on the map of Alaska represents 70 0 field of view of an all-sky SDI when projected at an altitude of 240km. Figure 2: Line-of-sight wind speed (m/sec) measured by SDI and FPI observatories from common volume regions on selected nights of Data were obtained from SDI and FPI located at Poker Flat. Figure 5: Line-of-sight wind speed measured by SDI (blue) and FPI (red) observatories in CV regions on 11 February Data was obtained from SDI and FPI located at Poker Flat. Note that CV3 Y-axis scale is different from other panels Figure 6: Zonal and meridional wind computed from SDI and FPI data sets. Blue, black, red, and green represent the zonal and meridional wind estimated by SDI monostatic fit, SDI bistatic fit, FPI bistatic fit without vertical wind correction, and FPI bistatic fit with vertical wind correction respectively. Figure 3: Blue, black, red, and green represent the zonal and meridional wind estimated by SDI monostatic fit, SDI bistatic fit, FPI bistatic fit without vertical wind correction, and FPI bistatic fit with vertical wind correction respectively. 4. Results Data sets generated by SDI and FPI utilizing 630nm red line emission on total for seven nights of 2010 (10, 11, and 24 Jan; 03, 11,12, and 16 Feb) were selected for cross-comparison. FPI data sets reported here were taken from Poker Flat and Fort Yukon sites. Selection of FPI look directions was achieved in such a way that the LOS by one FPI instrument intersects the LOS by another FPI instrument through the centroid of 630nm emission layer. For each exposure, FPI’s at Poker Flat and Fort Yukon were pointed at a the common location, and repointed the observing sequence between exposures. Locations of intersections are referred to here as ‘”common volume (CV) locations”. FPI bistatic wind vectors were derived using LOS wind from these two stations with and without vertical wind correction. SDI data was taken from Poker Flat and Gakona. Geophysical information was obtained from only Poker Flat LOS wind measurements by applying a “monostatic” wind fit with substantial assumptions. A location (65.075N, W) that was inside the CV polygon, was chosen for bistatic wind analysis by combining observations of Poker Flat and Gakona SDI’s. Vertical winds were considered negligible in bistatic wind analysis. 1. Abstract Figure 7: Relation between wind speed and difference in wind directions estimated by SDI and FPI. Wind speed and wind direction computed from SDI monostatic wind fit were used. “FPI(with/without)” represents wind speed inferred from FPI bistatic wind with/without vertical wind correction in wind fit analysis