Mission Operations of the Mars Exploration Rovers

A document describes a system of processes involved in planning, commanding, and monitoring operations of the rovers Spirit and Opportunity of the Mars Exploration Rover mission. The system is designed to minimize command turnaround time, given that inherent uncertainties in terrain conditions and in successful completion of planned landed spacecraft motions preclude planning of some spacecraft activities until the results of prior activities are known by the ground-based operations team. The processes are partitioned into those (designated as tactical) that must be tied to the Martian clock and those (designated strategic) that can, without loss, be completed in a more leisurely fashion. The tactical processes include assessment of downlinked data, refinement and validation of activity plans, sequencing of commands, and integration and validation of sequences. Strategic processes include communications planning and generation of long-term activity plans. The primary benefit of this partition is to enable the tactical portion of the team to focus solely on tasks that contribute directly to meeting the deadlines for commanding the rover s each sol (1 sol = 1 Martian day) - achieving a turnaround time of 18 hours or less, while facilitating strategic team interactions with other organizations that do not work on a Mars time schedule

Wind Streaks on Venus: Clues to Atmospheric Circulation

Magellan images reveal surface features on Venus attributed to wind processes. Sand dunes, wind-sculpted hills, and more than 5830 wind streaks have been identified. The streaks serve as local "wind vanes," representing wind direction at the time of streak formation and allowing the first global mapping of near-surface wind patterns on Venus. Wind streaks are oriented both toward the equator and toward the west. When streaks associated with local transient events, such as impact cratering, are deleted, the westward component is mostly lost but the equatorward component remains. This pattern is consistent with a Hadley circulation of the lower atmosphere

A Hybrid Dynamical Approach for Seasonal Prediction of Sea‐Level Anomalies: A Pilot Study for Charleston, South Carolina

Using Earth system models for seasonal sea‐level prediction remains challenging due to model biases and initialization shocks. Here we present a hybrid dynamical approach for seasonal sea‐level prediction to alleviate some of these issues. The approach is based on convolving atmospheric forcings with sea‐level sensitivities to these forcings. The sensitivities are pre‐computed by the adjoint model of the Estimating Circulation and Climate of the Ocean (ECCO) system. The forcings are a concatenation of ECCO forcings before prediction initialization and a 10‐member predicted atmospheric forcing ensemble from the Community Climate System Model version 4 (CCSM4) after initialization, with offline forcing bias corrections applied using the observationally‐constrained ECCO seasonal forcing climatology. As a pilot study, we conducted 12‐month hindcasts from 1995 to 2016 in Charleston (United States East Coast). Our approach avoids drifts in CCSM4 sea‐level predictions and beats seasonal climatology and damped persistence as predictors up to a 6‐month lead time. The prediction skill comes from two factors: (a) ECCO forcings prior to prediction initialization influence sea level after initialization through delayed oceanic adjustments (e.g., coastally‐trapped waves, open‐ocean Rossby waves, and advection of steric anomalies) leading to skillful predictions beyond 2 months after initialization, and (b) the 10‐member CCSM4 ensemble forcing predictions have relatively good skill at 1–2 months lead times. Our method is computationally efficient for operational sea‐level prediction at specific locations and can attribute sea‐level prediction skill and uncertainty to specific forcings or forcing from particular regions, thereby providing useful information to seasonal prediction centers for improving their prediction systems. Plain Language Summary Predicting sea level a few months ahead can help coastal communities to prepare for elevated flood risks. This is becoming more and more important because of the increase in floods due to rising sea levels. However, seasonal prediction models often struggle with predicting sea level. Here we present a new approach that combines predictions of atmospheric forcings, such as wind, heat fluxes, and precipitation with pre‐computed maps that show how sea level responds to changes in these forcings to predict sea‐level changes up to a few months ahead. We have tested this new approach for Charleston, South Carolina, and we find that our approach shows a promising prediction skill on lead times up to about 6 months. At short lead times, the relatively good skill of sea‐level prediction comes from the fidelity of the predicted atmospheric forcings. At lead times beyond 2 months, the observed forcings prior to prediction times enhance the sea‐level prediction skill because they cause delayed adjustment of sea level. Here, the ocean carries memory from past atmospheric forcing to influence future sea level. Key Points We developed a novel way to predict sea level (SL) by convolving its sensitivities to forcings with observed and coupled‐model predicted forcings A pilot project applying the method for seasonal hindcasts of Charleston SL shows positive prediction skill up to 6‐month lead time Sea‐level hindcasts using observed but not coupled‐model predicted forcings have even better skill for 2–6 months of lead time

Data Supplement for 'A hybrid dynamical approach for seasonal prediction of sea-level anomalies: a pilot study for Charleston, South Carolina'

This dataset is a supplement to the paper: 'A hybrid dynamical approach for seasonal prediction of sea-level anomalies: a pilot study for Charleston, South Carolina'. Please cite this paper when using this dataset. (c) 2022 All rights reserved. This dataset can be used in combination with the scripts that are provided in a Github '. results_tseries.zip: Contains the time series of observed, reconstructed, and predicted sea level. Provided are the estimates for tide-gauge (TG) and altimetry (Alt) observations, both detrended and non-detrended. The data are stored in the NetCDF format. results_stats.zip: Contains the statistics of all reconstructions and projections. Similar to the time series files, the estimates are provided for benchmarks against altimetry (Alt) and tide-gauge (TG) observations, both detrended and non-detrended. These data are also stored in NetCDF format

MSL's Widgets: Adding Rebustness to Martian Sample Acquisition, Handling, and Processing

Mars Science Laboratory's (MSL) Sample Acquisition Sample Processing and Handling (SA-SPaH) system is one of the most ambitious terrain interaction and manipulation systems ever built and successfully used outside of planet earth. Mars has a ruthless environment that has surprised many who have tried to explore there. The robustness widget program was implemented by the MSL project to help ensure the SA-SPaH system would be robust enough to the surprises of this ruthless Martian environment. The robustness widget program was an effort of extreme schedule pressure and responsibility, but was accomplished with resounding success. This paper will focus on a behind the scenes look at MSL's robustness widgets: the particle fun zone, the wind guards, and the portioner pokers

Exploration of Antarctic Ice Sheet 100-year contribution to sea level rise and associated model uncertainties using the ISSM framework

Estimating the future evolution of the Antarctic Ice Sheet (AIS) is critical for improving future sea level rise (SLR) projections. Numerical ice sheet models are invaluable tools for bounding Antarctic vulnerability; yet, few continental-scale projections of century-scale AIS SLR contribution exist, and those that do vary by up to an order of magnitude. This is partly because model projections of future sea level are inherently uncertain and depend largely on the model's boundary conditions and climate forcing, which themselves are unknown due to the uncertainty in the projections of future anthropogenic emissions and subsequent climate response. Here, we aim to improve the understanding of how uncertainties in model forcing and boundary conditions affect ice sheet model simulations. With use of sampling techniques embedded within the Ice Sheet System Model (ISSM) framework, we assess how uncertainties in snow accumulation, ocean-induced melting, ice viscosity, basal friction, bedrock elevation, and the presence of ice shelves impact continental-scale 100-year model simulations of AIS future sea level contribution. Overall, we find that AIS sea level contribution is strongly affected by grounding line retreat, which is driven by the magnitude of ice shelf basal melt rates and by variations in bedrock topography. In addition, we find that over 1.2 m of AIS global mean sea level contribution over the next century is achievable, but not likely, as it is tenable only in response to unrealistically large melt rates and continental ice shelf collapse. Regionally, we find that under our most extreme 100-year warming experiment generalized for the entire ice sheet, the Amundsen Sea sector is the most significant source of model uncertainty (1032 mm 6σ spread) and the region with the largest potential for future sea level contribution (297 mm). In contrast, under a more plausible forcing informed regionally by literature and model sensitivity studies, the Ronne basin has a greater potential for local increases in ice shelf basal melt rates. As a result, under this more likely realization, where warm waters reach the continental shelf under the Ronne ice shelf, it is the Ronne basin, particularly the Evans and Rutford ice streams, that are the greatest contributors to potential SLR (161 mm) and to simulation uncertainty (420 mm 6σ spread)

The ChemCam Instrument Suite on the Mars Science Laboratory (MSL) Rover: Body Unit and Combined System Tests

The ChemCam instrument suite on the Mars Science Laboratory (MSL) rover Curiosity provides remote compositional information using the first laser-induced breakdown spectrometer (LIBS) on a planetary mission, and provides sample texture and morphology data using a remote micro-imager (RMI). Overall, ChemCam supports MSL with five capabilities: remote classification of rock and soil characteristics; quantitative elemental compositions including light elements like hydrogen and some elements to which LIBS is uniquely sensitive (e.g., Li, Be, Rb, Sr, Ba); remote removal of surface dust and depth profiling through surface coatings; context imaging; and passive spectroscopy over the 240-905 nm range. ChemCam is built in two sections: The mast unit, consisting of a laser, telescope, RMI, and associated electronics, resides on the rover's mast, and is described in a companion paper. ChemCam's body unit, which is mounted in the body of the rover, comprises an optical demultiplexer, three spectrometers, detectors, their coolers, and associated electronics and data handling logic. Additional instrument components include a 6 m optical fiber which transfers the LIBS light from the telescope to the body unit, and a set of onboard calibration targets. ChemCam was integrated and tested at Los Alamos National Laboratory where it also underwent LIBS calibration with 69 geological standards prior to integration with the rover. Post-integration testing used coordinated mast and instrument commands, including LIBS line scans on rock targets during system-level thermal-vacuum tests. In this paper we describe the body unit, optical fiber, and calibration targets, and the assembly, testing, and verification of the instrument prior to launch