Abstract. The use of the noble gas radon (222Rn) as a tracer for different research studies, for example observation-based estimation of greenhouse gas (GHG) fluxes, has led to the need of high-quality 222Rn activity concentration observations with high spatial and temporal resolution. So far a robust metrology chain for these measurements is not yet available. A portable direct atmospheric radon monitor (ARMON), based on electrostatic collection of 218Po, is now running at Spanish stations. This monitor has not yet been compared with other 222Rn and 222Rn progeny monitors commonly used at atmospheric stations. A 3-month intercomparison campaign of atmospheric 222Rn and 222Rn progeny monitors based on different measurement techniques was realized during the fall and winter of 2016–2017 to evaluate (i) calibration and correction factors between monitors necessary to harmonize the atmospheric radon observations and (ii) the dependence of each monitor's response in relation to the sampling height and meteorological and atmospheric aerosol conditions. Results of this study have shown the following. (i) All monitors were able to reproduce the atmospheric radon variability on a daily basis. (ii) Linear regression fits between the monitors exhibited slopes, representing the correction factors, between 0.62 and 1.17 and offsets ranging between −0.85 and −0.23 Bq m−3 when sampling 2 m above ground level (a.g.l.). Corresponding results at 100 m a.g.l. exhibited slopes of 0.94 and 1.03 with offsets of −0.13 and 0.01 Bq m−3, respectively. (iii) No influence of atmospheric temperature and relative humidity on monitor responses was observed for unsaturated conditions at 100 m a.g.l., whereas slight influences (order of 10−2) of ambient temperature were observed at 2 m a.g.l. (iv) Changes in the ratio between 222Rn progeny and 222Rn monitor responses were observed under very low atmospheric aerosol concentrations. Results also show that the new ARMON could be useful at atmospheric radon monitoring stations with space restrictions or as a mobile reference instrument to calibrate in situ 222Rn progeny monitors and fixed radon monitors. In the near future a long-term comparison study between ARMON, HRM, and ANSTO monitors would be useful to better evaluate (i) the uncertainties of radon measurements in the range of a few hundred millibecquerels per cubic meter to a few becquerels per cubic meter and (ii) the response time correction of the ANSTO monitor for representing fast changes in the ambient radon concentrations.

CAGIRE is the near infrared camera of the Colibrí robotic telescope, designed for the follow-up of SVOM alerts, mainly Gamma Ray Bursts (GRBs), and the quick imaging of sky regions where transient sources are detected by the SVOM satellite. CAGIRE is based on the Astronomical Large Format Array (ALFA) 2k x 2k SWIR sensor from the French consortium CEA-LYNRED. In the context of CAGIRE the sensor is operated in "Up the Ramp" mode to observe the sky in a square field of view of 21.7 arcmin on a side, in the range of wavelengths from 1.1 to 1.8 µm. An observation with CAGIRE consists of a series of short (1-2 minutes) exposures during which the pixels are read out every 1.3 second, continuously accumulating charges proportionally to the received flux, building a ramp.The main challenge is to quickly process and analyse these ramps, in order to identify and study the near infrared counterparts of the bursts, within 5 minutes of the reception of an alert. Our preprocessing, which is under development, aims at providing reliable flux maps for the astronomy pipeline. It is based on a sequence of operations. First, calibration maps are used to identify saturated pixels, and for each pixel, the usable (non saturated) range of the ramp. Then, the ramps are corrected for the electronic common mode noise, and differential ramps are constructed. Finally, the flux is calculated from the differential ramps, using a previously calibrated map of pixel non-linearities. We present here the sequence of operations performed by the preprocessing, which are based on previous calibrations of the sensor response. These operations lead to the production of a flux map corrected from cosmic-rays hits, a map depicting the quality of the fit, a map of saturated pixels and a map of pixels hit by cosmic-rays, before the acquisition of the next ramp. These maps will be used by the astronomy pipeline to quickly extract the scientific results of the observations, like the identification of uncatalogued or quickly variable sources that could be GRB afterglows.

Abstract. The use of the noble gas radon (222Rn) as tracer for different research studies, for example observation-based estimation of greenhouse gas (GHG) fluxes, has led to the need of high-quality 222Rn activity concentration observations with high spatial and temporal resolution. So far a robust metrology chain for these measurements is not yet available. A 3-month inter-comparison campaign of atmospheric 222Rn and 222Rn progeny monitors based on different measurement techniques was realized during the fall and winter of 2016-2017 to evaluate: i) calibration and correction factors between monitors necessary to harmonize the atmospheric radon observations; and ii) the dependence of each monitor’s response in relation to the sampling height, meteorological and atmospheric aerosol conditions. Results of this study have shown that: i) all monitors were able to reproduce the atmospheric radon variability on daily basis; ii) linear regression fits between the monitors exhibited slopes between 0.62 and 1.17 and offsets ranging between −0.85 Bq m−3 and −0.23 Bq m−3 when sampling 2 m above ground level (a.g.l.). Corresponding results at 100 m a.g.l. exhibited slopes of 0.94 and 1.03 with offsets of −0.13 Bq m−3 and 0.01 Bq m−3, respectively; iii) no influence of atmospheric temperature and relative humidity on monitor responses was observed for unsaturated conditions; and iv) changes of the ratio between radon progeny and radon monitor responses were observed under very high atmospheric humidity and under very low atmospheric aerosol concentrations. However, a more statistically robust evaluation of these last influences based on a longer dataset should be conducted to improve the harmonization of the data.