Abstract: The high frequency radars in the Super Dual Auroral Radar Network (SuperDARN) estimate the elevation angles of returned backscatter using interferometric techniques. These elevation angles allow the ground range to the scattering point to be estimated, which is crucial for the accurate geolocation of ionospheric measurements. For elevation angles to be accurately estimated, it is important to calibrate the interferometer measurements by determining the difference in the signal time delays caused by the difference in the electrical path lengths from the main array and the interferometer array to the point at which the signals are correlated. This time delay is known as tdiff. Several methods have been proposed to estimate tdiff using historical observations; these methods are summarised in this paper. Comparisons of the tdiff estimates from the different calibration methods are presented and sources of uncertainty discussed. The effect of errors in the estimated tdiff value on the accuracy of geolocation is evaluated and discussed. The paper concludes with a series of recommendations for both scientific SuperDARN data users and SuperDARN radar operators.

Abstract: This Paper Reports On Consolidated Ground-based Validation Results Of The Atmospheric No2 Data Produced Operationally Since April 2018 By The Tropospheric Monitoring Instrument (Tropomi) On Board Of The Esa/eu Copernicus Sentinel-5 Precursor (S5p) Satellite. Tropospheric, Stratospheric, And Total No2 Column Data From S5p Are Compared To Correlative Measurements Collected From, Respectively, 19 Multi-axis Differential Optical Absorption Spectroscopy (Max-doas), 26 Network For The Detection Of Atmospheric Composition Change (Ndacc) Zenith-scattered-light Doas (Zsl-doas), And 25 Pandonia Global Network (Pgn)/pandora Instruments Distributed Globally. The Validation Methodology Gives Special Care To Minimizing Mismatch Errors Due To Imperfect Spatio-temporal Co-location Of The Satellite And Correlative Data, E.g. By Using Tailored Observation Operators To Account For Differences In Smoothing And In Sampling Of Atmospheric Structures And Variability And Photochemical Modelling To Reduce Diurnal Cycle Effects. Compared To The Ground-based Measurements, S5p Data Show, On Average, (I) A Negative Bias For The Tropospheric Column Data, Of Typically ?23 % To ?37 % In Clean To Slightly Polluted Conditions But Reaching Values As High As ?51 % Over Highly Polluted Areas; (Ii) A Slight Negative Median Difference For The Stratospheric Column Data, Of About ?0.2 Pmolec Cm?2, I.e. Approx. ?2 % In Summer To ?15 % In Winter; And (Iii) A Bias Ranging From Zero To ?50 % For The Total Column Data, Found To Depend On The Amplitude Of The Total No2 Column, With Small To Slightly Positive Bias Values For Columns Below 6 Pmolec Cm?2 And Negative Values Above. The Dispersion Between S5p And Correlative Measurements Contains Mostly Random Components, Which Remain Within Mission Requirements For The Stratospheric Column Data (0.5 Pmolec Cm?2) But Exceed Those For The Tropospheric Column Data (0.7 Pmolec Cm?2). While A Part Of The Biases And Dispersion May Be Due To Representativeness Differences Such As Different Area Averaging And Measurement Times, It Is Known That Errors In The S5p Tropospheric Columns Exist Due To Shortcomings In The (Horizontally Coarse) A Priori Profile Representation In The Tm5-mp Chemical Transport Model Used In The S5p Retrieval And, To A Lesser Extent, To The Treatment Of Cloud Effects And Aerosols. Although Considerable Differences (Up To 2 Pmolec Cm?2 And More) Are Observed At Single Ground-pixel Level, The Near-real-time (Nrti) And Offline (Offl) Versions Of The S5p No2 Operational Data Processor Provide Similar No2 Column Values And Validation Results When Globally Averaged, With The Nrti Values Being On Average 0.79 % Larger Than The Offl Values.