We reconstruct the temporal evolution of the source distribution for the four major gas species H 2 O, CO 2 , CO, and O 2 on the surface of comet 67P/Churyumov-Gerasimenko during its 2015 apparition. The analysis applies an inverse coma model and fits to data between August 6th 2014 and September 5th 2016 measured with the Double Focusing Mass Spectrometer (DFMS) of the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) and the COmet Pressure Sensor (COPS). The spatial distribution of gas sources with their temporal variation allows one to construct surface maps for gas emissions and to evaluate integrated productions rates. For all species peak production rates and integrated productions rates per orbit are evaluated separately for the northern and the southern hemisphere. The nine most active emitting areas on the comet's surface are defined and their correlation to emissions for each of the species is discussed.

The coma of comet 67P/Churyumov-Gerasimenko has been probed by the Rosetta spacecraft and shows a variety of different molecules. The ROSINA COmet Pressure Sensor and the Double Focusing Mass Spectrometer provide in-situ densities for many volatile compounds including the 14 gas species H2O, CO2, CO, H2S, O2, C2H6, CH3OH, H2CO, CH4, NH3, HCN, C2H5OH, OCS, and CS2. We fit the observed densities during the entire comet mission between August 2014 and September 2016 to an inverse coma model. We retrieve surface emissions on a cometary shape with 3996 triangular elements for 50 separated time intervals. For each gas we derive systematic error bounds and report the temporal evolution of the production, peak production, and the time-integrated total production. We discuss the production for the two lobes of the nucleus and for the northern and southern hemispheres. Moreover we provide a comparison of the gas production with the seasonal illumination.

Abstract. We present semi-implicit (IMEX) formulations of the compressible Navier-Stokes equations (NSE) for applications in nonhydrostatic atmospheric modeling. The compressible NSE in nonhydrostatic atmospheric modeling include buoyancy terms that require special handling if one wishes to extract the Schur complement form of the linear implicit problem. We present results for five different forms of the compressible NSE and describe in detail how to formulate the semi-implicit time-integration method for these equations. Finally, we compare all five equations and compare the semi-implicit formulations of these equations both using the Schur and No Schur forms against an explicit Runge-Kutta method. Our simulations show that, if efficiency is the main criterion, it matters which form of the governing equations you choose. Furthermore, the semi-implicit formulations are faster than the explicit RungeKutta method for all the tests studied especially if the Schur form is used. While we have used the spectral element method for discretizing the spatial operators, the semi-implicit formulations that we derive are directly applicable to all other numerical methods. We show results for our five semi-implicit models for a variety of problems of interest in nonhydrostatic atmospheric modeling, including: inertia gravity waves, rising thermal bubbles (i.e., Rayleigh-Taylor instabilities), density current (i.e., Kelvin-Helmholtz instabilities), and mountain test cases; the latter test case requires the implementation of non-reflecting boundary conditions. Therefore, we show results for all five semi-implicit models using the appropriate boundary conditions required in nonhydrostatic atmospheric modeling: no-flux (reflecting) and non-reflecting boundary conditions. It is shown that the non-reflecting boundary conditions exert a strong impact on the accuracy and efficiency of the models.Key words. compressible flow; element-based Galerkin methods; Euler; IMEX; Lagrange; Legendre; NavierStokes; nonhydrostatic; spectral elements; time-integration.AMS subject classifications. 65M60, 65M70, 35L65, 86A101. Introduction. It can be argued that the single most important property of an operational nonhydrostatic mesoscale atmospheric is efficiency. Clearly, this efficiency should not come at the cost of accuracy but if a weather center has the choice between a very accurate model and one that is efficient, they will probably pick the efficient one; however, as numerical analysts, we would like to build models that are both accurate and efficient. One way to achieve this goal is to construct numerical models based on high-order methods: this class of methods offers exponential (spectral) convergence for smooth problems and achieves excellent scalability on modern multi-core systems if they are used in an element-based approach (i.e., if the approximating polynomials have compact/local support). This is the idea behind element-based Galerkin methods such as spectral element (SE) and discontinuous Galerkin (DG) methods (see [14] and [2...

Starting from several monthly data sets of Rosetta's COmetary Pressure Sensor we reconstruct the gas density in the coma around comet 67P/Churyumov-Gerasimenko. The underlying inverse gas model is constructed by fitting ten thousands of measurements to thousands of potential gas sources distributed across the entire nucleus surface. The ensuing self-consistent solution for the entire coma density and surface activity reproduces the temporal and spatial variations seen in the data for monthly periods with Pearson correlation coefficients of 0.93 and higher. For different seasonal illumination conditions before and after perihelion we observe a systematic shift of gas sources on the nucleus.

A new class of unsteady analytical solutions of the spherical shallow water equations (SSWE) is presented. Analytical solutions of the SSWE are fundamental for the validation of barotropic atmospheric models. To date, only steady state analytical solutions are known from the literature. The unsteady analytical solutions of the SSWE are derived by applying the transformation method to the transition from a fixed cartesian to a rotating coordinate system. Fundamental examples of the new unsteady analytical solutions are presented for specific wind profiles. With the presented unsteady analytical solutions one can provide a measure of the numerical convergence in the case of a temporally evolving system. An application to the atmospheric model PLASMA shows the benefit of unsteady analytical solutions for the quantification of convergence properties.