Abstract:

Abstract. The lower-thermosphere–ionosphere (LTI) system consists of the upper atmosphere and the lower part of the ionosphere and as such comprises a complex system coupled to both the atmosphere below and space above. The atmospheric part of the LTI is dominated by laws of continuum fluid dynamics and chemistry, while the ionosphere is a plasma system controlled by electromagnetic forces driven by the magnetosphere, the solar wind, as well as the wind dynamo. The LTI is hence a domain controlled by many different physical processes. However, systematic in situ measurements within this region are severely lacking, although the LTI is located only 80 to 200 km above the surface of our planet. This paper reviews the current state of the art in measuring the LTI, either in situ or by several different remote-sensing methods. We begin by outlining the open questions within the LTI requiring high-quality in situ measurements, before reviewing directly observable parameters and their most important derivatives. The motivation for this review has arisen from the recent retention of the Daedalus mission as one among three competing mission candidates within the European Space Agency (ESA) Earth Explorer 10 Programme. However, this paper intends to cover the LTI parameters such that it can be used as a background scientific reference for any mission targeting in situ observations of the LTI.

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: Solar energetic particle (SEP) events are commonly separated in two categories: numerous “impulsive” events of relatively short duration, and a few “gradual” events, where SEP-intensities may stay enhanced over several days at energies up to several tens of MeV. In some gradual events the SEP spectrum extends to relativistic energies (>1 GeV), over shorter durations. The two categories are strongly related to an idea developed in the 1960s based on radio observations: Type III bursts, which were addressed in a companion chapter, outline impulsive acceleration of electrons to subrelativistic energies, while the large and the relativistic SEP events were ascribed to a second acceleration process. At radio wavelengths, typical counterparts were bursts emitted by electrons accelerated at coronal shock waves (type II bursts) and by electron populations in large-scale closed coronal structures (type IV bursts). Both burst types are related to coronal mass ejections (CMEs). Type II bursts from metric to kilometric wavelengths tend to accompany large SEP events, which is widely considered as a confirmation that CME-driven shocks accelerate the SEPs. But type II bursts, especially those related to SEP events, are most often accompanied by type IV bursts, where the electrons are rather accelerated in the wake of the CME. Individual event studies suggest that although the CME shock is the most plausible accelerator of SEPs up to some yet unknown limiting energy, the relativistic SEP events show time structure that rather points to coronal acceleration related to type IV bursts. This chapter addresses the question what type II bursts tell us about coronal shock waves and how type II and type IV radio bursts are related with relativistic proton signatures as seen by particle detectors on the Earth and by their gamma-ray emission in the solar atmosphere, focusing on two relativistic SEP events, on 2005 Jan 20 and 2017 Sep 10. The importance of radio emissions as a complement to the upcoming SEP observations from close to the Sun is underlined.

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.