Context. Radio continuum surveys of the Galactic plane are an excellent way to identify different source populations such as planetary nebulae, H ii regions, and radio stars and characterize their statistical properties. The GLOSTAR survey will study the star formation in the Galactic plane between −2 • < < 85 • and |b| < 1 • with unprecedented sensitivity in both, flux density (∼40 µJy beam −1 ) and range of angular scales (∼1. 5 to the largest radio structures in the Galaxy). Aims. In this paper we present the first results obtained from a radio continuum map of a 16 square degree sized region of the Galactic plane centered on = 32 • and b = 0 • (28 • < < 36 • and |b| < 1 • ). This map has a resolution of 18 and a sensitivity of ∼60-150 µJy beam −1 . Methods. We present data acquired in 40 hours of observations with the VLA in D-configuration. Two 1 GHz wide sub-bands were observed simultaneously and they were centred at 4.7 and 6.9 GHz. These data were calibrated and imaged using the Obit software package. The source extraction has been performed using the BLOBCAT software package and verified through a combination of visual inspection and cross-matching with other radio and mid-infrared surveys. Results. The final catalog consists of 1575 discrete radio sources and 27 large scale structures (including W43 and W44). By crossmatching with other catalogs and calculating the spectral indices (S (ν) ∝ ν α ), we have classified 231 continuum sources as H ii regions, 37 as ionization fronts, and 46 as planetary nebulae. The longitude and latitude distribution and negative spectral indices are all consistent with the vast majority of the unclassified sources being extragalactic background sources. Conclusions. We present a catalog of 1575 radio continuum sources and discuss their physical properties, emission nature and relation with previously reported. These first GLOSTAR results have increased the number of reliable H ii regions in this part of the Galaxy by a factor of four.

&lt;p&gt;Sodankyl&amp;#228;&#039;s high latitude location serves an ideal ground for testing more comprehensive physics theory related to radio meteor data. The atmospheric scale height shows significant variation at this latitude.&amp;#160; Also, the observational geometry towards the plane of&amp;#160; ecliptic plane changes drastically with seasons. On theory, the reflected radio signal from the ablating meteor train can be used to continuously monitor atmospheric temperature at the 90 km altitudes. In practice, complication arises due to the selection effects in the system as well as the persistent effect of natural variability (size, mass, velocity, entry angle) in meteoroids property. The long-standing hypothesis that needs to be debated: Is the assumed equality between atmospheric scale height (H_KT) and the effective diffusion scale height (H_D) of meteor trails valid for these data? In this study, we argue that such an hypotheis can not be experimentally validated, and hence the need for subsequent calibration. Furthermore, long-term trend analysis showed that the discrepancy between H_KT and H_D&amp;#160; has non-linear seasonal trends. Alternatively, we demonstrate an alternative method of&amp;#160; scale-height measurement based on meteor height distribution. The technical and theoretical limits of this methodology are discussed and validated using 10 years of observational data.&lt;/p&gt;

Abstract. For 2 decades, meteor radars have been routinely used to monitor atmospheric temperature around 90 km altitude. A common method, based on a temperature gradient model, is to use the height dependence of meteor decay time to obtain a height-averaged temperature in the peak meteor region. Traditionally this is done by fitting a linear regression model in the scattered plot of log⁡10(1/τ) and height, where τ is the half-amplitude decay time of the received signal. However, this method was found to be consistently biasing the slope estimate. The consequence of such a bias is that it produces a systematic offset in the estimated temperature, thus requiring calibration with other co-located measurements. The main reason for such a biasing effect is thought to be due to the failure of the classical regression model to take into account the measurement error in τ and the observed height. This is further complicated by the presence of various geophysical effects in the data, as well as observational limitation in the measuring instruments. To incorporate various error terms in the statistical model, an appropriate regression analysis for these data is the errors-in-variables model. An initial estimate of the slope parameter is obtained by assuming symmetric error variances in normalised height and log⁡10(1/τ). This solution is found to be a good prior estimate for the core of this bivariate distribution. Further improvement is achieved by defining density contours of this bivariate distribution and restricting the data selection process within higher contour levels. With this solution, meteor radar temperatures can be obtained independently without needing any external calibration procedure. When compared with co-located lidar measurements, the systematic offset in the estimated temperature is shown to have reduced to 5 % or better on average.

Abstract. For two decades meteor radars have been routinely used to monitor temperatures around the 90 km altitude. A common method, based on a temperature-gradient model, is to use the height dependence of meteor decay time to obtain a height-averaged temperature in the peak meteor region. Traditionally this is done by fitting a linear regression model in the scattered plot of log10(1 / τ) and height, where τ is the half-amplitude decay time of the received signal. However, this method was found to be consistently biasing the slope estimate. The consequence of such bias is that it produces a systematic offset in the estimated temperature, and thus requiring calibration with other colocated measurements. The main reason for such a biasing effect is thought to be due to the failure of the classical regression model to take into account the measurement error in τ or the observed height. This is further complicated by the presence of various geophysical effects in the data, which are not taken into account in the physical model. The effect of such biasing is discussed on both theoretical and experimental grounds. An alternative regression method that incorporates various error terms in the statistical model is used for line fitting. This model is used to construct an analytic solution for the bias-corrected slope coefficient for this data. With this solution, meteor radar temperatures can be obtained independently without using any external calibration procedure. When compared with colocated lidar measurements, the temperature estimated using this method is found to be accurate within 7 % or better and without any systematic offset.

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