spacecraft landed successfully on Mars and imaged the surface to characterize the surficial geology. Here we report on the geology and subsurface structure of the landing site to aid in situ geophysical investigations. InSight landed in a degraded impact crater in Elysium Planitia on a smooth sandy, granule-and pebble-rich surface with few rocks. Superposed impact craters are common and eolian bedforms are sparse. During landing, pulsed retrorockets modified the surface to reveal a near surface stratigraphy of surficial dust, over thin unconsolidated sand, underlain by a variable thickness duricrust, with poorly sorted, unconsolidated sand with rocks beneath. Impact, eolian, and mass wasting processes have dominantly modified the surface. Surface observations are consistent with expectations made from remote sensing data prior to landing indicating a surface composed of an impactfragmented regolith overlying basaltic lava flows.

Forecasting the in situ properties of coronal mass ejections (CMEs) from remote images is expected to strongly enhance predictions of space weather and is of general interest for studying the interaction of CMEs with planetary environments. We study the feasibility of using a single heliospheric imager (HI) instrument, imaging the solar wind density from the Sun to 1 AU, for connecting remote images to in situ observations of CMEs. We compare the predictions of speed and arrival time for 22 CMEs (in 2008CMEs (in -2012 to the corresponding interplanetary coronal mass ejection (ICME) parameters at in situ observatories (STEREO PLASTIC/IMPACT, Wind SWE/MFI). The list consists of front-and backsided, slow and fast CMEs (up to 2700 km s −1 ). We track the CMEs to 34.9 ± 7.1 deg elongation from the Sun with J maps constructed using the SATPLOT tool, resulting in prediction lead times of −26.4 ± 15.3 hr. The geometrical models we use assume different CME front shapes (fixed-Φ, harmonic mean, self-similar expansion) and constant CME speed and direction. We find no significant superiority in the predictive capability of any of the three methods. The absolute difference between predicted and observed ICME arrival times is 8.1 ± 6.3 hr (rms value of 10.9 hr). Speeds are consistent to within 284 ± 288 km s −1 . Empirical corrections to the predictions enhance their performance for the arrival times to 6.1 ± 5.0 hr (rms value of 7.9 hr), and for the speeds to 53 ± 50 km s −1 . These results are important for Solar Orbiter and a space weather mission positioned away from the Sun-Earth line.

mission designed to orbit as close as 7 million km (9.86 solar radii) from Sun center. WISPR employs a 95 • radial by 58 • transverse field of view to image the fine-scale structure of the solar corona, derive the 3D structure of the large-scale corona, and determine whether a dust-free zone exists near the Sun. WISPR is the smallest heliospheric imager to date yet it comprises two nested wide-field telescopes with large-format (2 K × 2 K) APS CMOS detectors to optimize the performance for their respective fields of view and to minimize the risk of dust damage, which may be considerable close to the Sun. The WISPR electronics are very flexible allowing the collection of individual images at cadences up to 1 second at perihelion or the summing of multiple images to increase the signal-to-noise when the spacecraft is further from the Sun. The dependency of the Thomson scattering emission of the corona on the imaging geometry dictates that WISPR will be very sensitive to the emission from plasma close to the spacecraft in contrast to the situation for imaging from Earth orbit. WISPR will be the first 'local' imager providing a crucial link between the large-scale corona and the in-situ measurements.

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Author contributions D.J.M. is IS☉IS Principal Investigator (PI) and led the data analysis and writing of the study. E.R.C. is IS☉IS Deputy PI, helped develop EPI-Hi, and participated in the data analysis. C.M.S.C. helped develop EPI-Hi and participated in the data analysis. A.C.C. helped develop EPI-Hi and participated in the data analysis. A.J.D. helped develop EPI-Hi and participated in the data analysis. M.I.D. participated in the data analysis. J.G. participated in the data analysis. M.E.H. helped develop EPI-Lo and participated in the data analysis. C.J.J. produced Figs. 3, 4 and participated in the data analysis. S.M.K. participated in the data analysis. A.W.L. helped develop EPI-Hi and participated in the data analysis. R.A.L. helped develop EPI-Hi and participated in the data analysis. O.M. participated in the data analysis. W.H.M. participated in the data analysis. R.L.M. led the development of EPI-Lo and participated in the data analysis. R.A.M. helped develop EPI-Hi and participated in the data analysis. D.G.M. helped develop EPI-Lo and participated in the data analysis. A.P. participated in the data analysis. J.S.R. helped develop EPI-Hi and participated in the data analysis. E.C.R. participated in the data analysis. N.A.S. led the development of the IS☉IS Science Operations Center and participated in the data analysis. E.C.S. helped develop EPI-Hi and participated in the data analysis. J.R.S. led the development of the analysis tool, produced Figs. 1, 2, and participated in the data analysis. M.E.W. led the development of EPI-Hi and participated in the data analysis. S.D.B. is FIELDS PI and participated in the data analysis. J.C.K. is SWEAP PI and participated in the data analysis. A.W.C. helped develop SWEAP and participated in the data analysis. K.E.K. helped develop SWEAP and participated in the data analysis. R.J.M. helped develop FIELDS and participated in the data analysis. M.P. helped develop FIELDS and participated in the data analysis. M.L.S. helped develop SWEAP and participated in the data analysis. A.P.R. led the CME simulation work and participated in the data analysis.