Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO 2 ) with the accuracy, resolution, and coverage needed to quantify CO 2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO 2 dry air mole fraction, X CO 2 , that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of X CO 2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X CO 2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south X CO 2 gradient is small. Enhanced X CO 2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south X CO 2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in X CO 2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO 2 . As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X CO 2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in earth science. Satellite observations can provide information on the atmospheric state at fine spatial and temporal resolution while providing substantial coverage across the globe. For example, this capability can greatly enhance the understanding of the space-time variation of the greenhouse gas, carbon dioxide (CO2), since ground-based measurements are limited. NASA's Orbiting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric CO2. The retrieval is an inverse problem and consists of a physical forward model for the transfer of radiation through the atmosphere that includes absorption and scattering by gases, aerosols, and the surface. The model and other algorithm inputs introduce key sources of uncertainty into the retrieval problem. This article develops a computationally efficient surrogate model that is embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution of the retrieval error. Abstract. Remote sensing of the atmosphere has provided a wealth of data for analyses and 5 inferences in Earth science. Satellite observations can provide information on the atmospheric state 6 at fine spatial and temporal resolution while providing substantial coverage across the globe. For 7 example, this capability can greatly enhance the understanding of the space-time variation of the 8 greenhouse gas, carbon dioxide (CO 2 ), since ground-based measurements are limited. NASA's Or-9 biting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight 10 daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of 11 atmospheric CO 2 . The retrieval is an inverse problem and consists of a physical forward model for 12 the transfer of radiation through the atmosphere that includes absorption and scattering by gases, 13 aerosols, and the surface. The model and other algorithm inputs introduce key sources of uncertainty 14 into the retrieval problem. This article develops a computationally efficient surrogate model that is 15 embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution 16 of the retrieval error. 17

Real-time location of earthquakes can be achieved by using direct imaging of the recorded wave field based on a Kirchhoff reconstruction method similar to that used in the migration of seismic reflection data. The standard method of event location requires the wave arrival at each sensor to be picked and associated with an event. By using direct imaging, the event is identified once in the imaged wave field. The computation is independent of the level of seismic activity and can be carried out on a typical desktop computer. The procedure has been successfully demonstrated in two and three dimensions using data from the Southern California Seismic Network (Trinet). At higher resolutions, the reconstruction method can identify finite source effects. Further work considers extending the method by implementing full elastic theory and solving for moment tensors at all locations in the mesh.Background

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014, and joined the 705 km Afternoon Constellation on 3 August 2014. On monthly time scales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the northern hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high resolution, global data set.

Abstract. A novel variant of the parallel QR algorithm for solving dense nonsymmetric eigenvalue problems on hybrid distributed high performance computing (HPC) systems is presented. For this purpose, we introduce the concept of multi-window bulge chain chasing and parallelize aggressive early deflation. The multi-window approach ensures that most computations when chasing chains of bulges are performed in level 3 BLAS operations, while the aim of aggressive early deflation is to speed up the convergence of the QR algorithm. Mixed MPI-OpenMP coding techniques are utilized for porting the codes to distributed memory platforms with multithreaded nodes, such as multicore processors. Numerous numerical experiments confirm the superior performance of our parallel QR algorithm in comparison with the existing ScaLAPACK code, leading to an implementation that is one to two orders of magnitude faster for sufficiently large problems, including a number of examples from applications.Key words. Eigenvalue problem, nonsymmetric QR algorithm, multishift, bulge chasing, parallel computations, level 3 performance, aggressive early deflation, parallel algorithms, hybrid distributed memory systems. AMS subject classifications. 65F15, 15A181. Introduction. Computing the eigenvalues of a matrix A ∈ R n×n is at the very heart of numerical linear algebra, with applications coming from a broad range of science and engineering. With the increased complexity of mathematical models and availability of HPC systems, there is a growing demand to solve large-scale eigenvalue problems.While iterative eigensolvers, such as Krylov subspace and Jacobi-Davidson methods [8], may quite successfully deal with large-scale sparse eigenvalue problems in most situations, classical factorization-based methods, such as the QR algorithm discussed in this paper, still play an important role. This is already evident from the fact that most iterative methods rely on the QR algorithm for solving (smaller) subproblems. In certain situations, factorization-based methods may be the preferred choice even for directly addressing a large-scale problem. For example, it might be difficult or impossible to guarantee that an iterative method returns all eigenvalues in a specified region of the complex plane. Even the slightest chance of having an eigenvalue missed may have perilous consequences, e.g., in a stability analysis. Moreover, by their nature, standard iterative eigensolvers are ineffective in situations where a large fraction of eigenvalues and eigenvectors needs to be computed, as in some algorithms for linear-quadratic optimal control [48] and density functional theory [50]. In contrast, factorization-based methods based on similarity transformations, such as the QR algorithm, compute all eigenvalues anyway and there is consequently no danger to miss an eigenvalue. We conclude that an urgent need for high performance parallel variants of factorization-based eigensolvers can be expected to persist in the future.Often motivated by applications in computational...