The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance

The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipeline keeps pace with observations to produce observable quantities, including the photospheric vector magnetic field, from sequences of filtergrams. The primary 720s observables were released in mid 2010, including Stokes polarization parameters measured at six wavelengths as well as intensity, Doppler velocity, and the line-of-sight magnetic field. More advanced products, including the full vector magnetic field, are now available. Automatically identified HMI Active Region Patches (HARPs) track the location and shape of magnetic regions throughout their lifetime. The vector field is computed using the Very Fast Inversion of the Stokes Vector (VFISV) code optimized for the HMI pipeline; the remaining 180 degree azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The Milne-Eddington inversion is performed on all full-disk HMI observations. The disambiguation, until recently run only on HARP regions, is now implemented for the full disk. Vector and scalar quantities in the patches are used to derive active region indices potentially useful for forecasting; the data maps and indices are collected in the SHARP data series, hmi.sharp_720s. Patches are provided in both CCD and heliographic coordinates. HMI provides continuous coverage of the vector field, but has modest spatial, spectral, and temporal resolution. Coupled with limitations of the analysis and interpretation techniques, effects of the orbital velocity, and instrument performance, the resulting measurements have a certain dynamic range and sensitivity and are subject to systematic errors and uncertainties that are characterized in this report.Comment: 42 pages, 19 figures, accepted to Solar Physic

Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression

Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampled across time and space. A novel machine learning method is proposed for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). Proposed hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non-parametric regression with GP is performed. The methodology is applied for modeling groundwater levels across the CV during 2015 - 2020. We demonstrate the efficacy of GP-DNN regression for modeling non-stationary features in the well data with fast and reliable uncertainty quantification. Our results indicate that the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.Comment: Submitted to Water Resources Researc

Observing System Simulation Experiment (OSSE) for the HyspIRI Spectrometer Mission

The OSSE software provides an integrated end-to-end environment to simulate an Earth observing system by iteratively running a distributed modeling workflow based on the HyspIRI Mission, including atmospheric radiative transfer, surface albedo effects, detection, and retrieval for agile exploration of the mission design space. The software enables an Observing System Simulation Experiment (OSSE) and can be used for design trade space exploration of science return for proposed instruments by modeling the whole ground truth, sensing, and retrieval chain and to assess retrieval accuracy for a particular instrument and algorithm design. The OSSE in fra struc ture is extensible to future National Research Council (NRC) Decadal Survey concept missions where integrated modeling can improve the fidelity of coupled science and engineering analyses for systematic analysis and science return studies. This software has a distributed architecture that gives it a distinct advantage over other similar efforts. The workflow modeling components are typically legacy computer programs implemented in a variety of programming languages, including MATLAB, Excel, and FORTRAN. Integration of these diverse components is difficult and time-consuming. In order to hide this complexity, each modeling component is wrapped as a Web Service, and each component is able to pass analysis parameterizations, such as reflectance or radiance spectra, on to the next component downstream in the service workflow chain. In this way, the interface to each modeling component becomes uniform and the entire end-to-end workflow can be run using any existing or custom workflow processing engine. The architecture lets users extend workflows as new modeling components become available, chain together the components using any existing or custom workflow processing engine, and distribute them across any Internet-accessible Web Service endpoints. The workflow components can be hosted on any Internet-accessible machine. This has the advantages that the computations can be distributed to make best use of the available computing resources, and each workflow component can be hosted and maintained by their respective domain experts

Recognizing Chromospheric Objects via Markov Chain Monte Carlo

The solar chromosphere consists of three classes which contribute di#erentially to ultraviolet radiation reaching the earth. We describe a data set of solar images, means of segmenting the images into the constituent classes, and a novel high-level representation for compact objects based on a triangulated spatial `membership function.' Such representations are fitted in a variable-dimension Markov chain Monte Carlo scheme. 1 Introduction The solar chromosphere, observable (see figure 1) in ultraviolet light, roughly consists of three classes: plage (bright magnetic disturbances), network (hot boundaries of convection cells), and background (cooler interiors of these cells). Plages appear as irregular groups of clumps, seldom near the solar poles. Similar to sunspots, plages experience a cycle of formation and dissipation, starting out as relatively compact regions and decaying over many days into a diffuse and broken-up cluster. The cell-structured network has little contrast with t..

Maximum-Likelihood Estimation of Complex Sinusoids and Toeplitz Covariances

In an extension of previous methods for maximumlikelihood (ML) Toeplitz covariance estimation, new iterative algorithms for computing joint ML estimates of complex sinusoids in unknown stationary Gaussian noise are proposed. The number of sinusoids is assumed known, but their frequencies and amplitudes are not. The iterative algorithm, an adaptation of the expectation-maximization (EM) technique, proceeds from an initial estimate of the mean and Toeplitz covariance, and iterates between estimating the mean given the current covariance and vice versa, with likelihood increasing at each step. The resulting ML covariance estimates are compared to conventional estimators and Cram'er-Rao bounds. An analysis of the Kay and Marple data set is also presented. The effectiveness of the new algorithm for estimating means in unknown noise is investigated, and the usefulness of simultaneously estimating the covariance and the mean is demonstrated. Keywords--- spectrum estimation, covariance estima..

Glacier Ice Surface Properties in South-West Greenland Ice Sheet: First Estimates From PRISMA Imaging Spectroscopy Data

[EN] Snow and ice melt processes on the Greenland Ice Sheet are a key in Earth's energy balance and are acutely sensitive to climate change. Melting dynamics are directly related to a decrease in surface albedo, amongst others caused by the accumulation of light-absorbing particles (LAPs). Featuring unique spectral patterns, these accumulations can be mapped and quantified by imaging spectroscopy. We present first results for the retrieval of glacier ice properties from the spaceborne PRISMA imaging spectrometer by applying a recently developed simultaneous inversion of atmospheric and surface state using optimal estimation. The image analyzed in this study was acquired over the South-West margin of the Greenland Ice Sheet in late August 2020. The area is characterized by patterns of both clean and dark ice associated with a high amount of LAPs deposited on the surface. We present retrieval maps and uncertainties for grain size, liquid water, and algae concentration, as well as estimated reflectance spectra for different surface properties. We then show the feasibility of using imaging spectroscopy to interpret multiband sensor data to achieve high accuracy, frequently repeated observations of changing snow and ice conditions. For example, the impurity index calculated from multiband Sentinel-3 Ocean and Land Colour Instrument measurements is dependent on dust particles, but we show that algae concentration alone can be predicted from this data with less than 20% uncertainty. Our study evidences that present and upcoming orbital imaging spectroscopy missions such as PRISMA, Environmental Mapping and Analysis Program, Copernicus Hyperspectral Imaging Mission, and the Surface Biology and Geology designated observable, can significantly support research of melting ice sheets.This work has been done in the frame of EnMAP, which is funded under the DLR Space Administration with resources from the German Federal Ministry of Economic Affairs and Energy (Grant No. 50 EE 0850) and contributions from DLR, GFZ, and OHB System AG. A portion of this research took place at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). US Government Support acknowledged. Open access funding enabled and organized by Projekt DEAL.Bohn, N.; Di Mauro, B.; Colombo, R.; Thompson, DR.; Susiluoto, J.; Carmon, N.; Turmon, MJ.... (2022). Glacier Ice Surface Properties in South-West Greenland Ice Sheet: First Estimates From PRISMA Imaging Spectroscopy Data. Journal of Geophysical Research: Biogeosciences. 127(3):1-21. https://doi.org/10.1029/2021JG006718121127

Dawn: A Simulation Model for Evaluating Costs and Tradeoffs of Big Data Science Architectures

No abstract availabl