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Significant changes to the global observing network are expected in the coming decades including the launch of a global ring of geostationary satellites and a reduction in the number of low earth orbit (LEO) platforms. It is anticipated that there may be a gap in the LEO coverage between the planned mid-morning and early afternoon orbits. Here, the utility of an early morning LEO orbit for numerical weather prediction is considered using an observing system simulation experiment (OSSE). A global observing network with two LEO platforms including microwave and hyperspectral infrared instruments and three geostationary hyperspectral infrared platforms is considered for a future baseline scenario. Two instruments, a microwave radiometer modeled on the Advanced Technology Microwave Sounder (ATMS) and a hyperspectral infrared radiometer modeled on the Cross-track Infrared Sounder (CrIS), are tested both individually and in conjunction on a new early morning orbit in addition to the future baseline scenario. The microwave instrument is found to have beneficial impacts for up to 4–7 days in the medium range forecast period with beneficial impacts for the infrared instrument for up to 3–5 days. Short-range forecast impacts estimated with Forecast Sensitivity Observation Impacts (FSOI) over the conterminous United States for the early morning orbit are somewhat weaker than for the same instruments in the early afternoon orbit due to the orbital passage being coincident with rawinsondes while the afternoon orbit is coincident with the minima of both rawinsondes and aircraft.
A globally distributed network of dual-frequency GPS receivers currently exists and enables the monitoring of ionospheric total electron content (TEC) on global scales.
The GPS Network Operations center at JPL Provides resources for the collection, distribution and maintenance of GPS data from global network of GPS receivers. This includes the ability to remotely diagnosis receiver problems, communicate with receivers and operators of receivers, to automatically collect, process and distribute data for 14 stations in the IGS network. This amounts to 35 megabytes of raw, finished data and products daily.
Following a brief overview of the mission concepts for a Mars Global Network Mission as of the time of the workshop, we present the principal scientific objectives to be achieved by a Mars seismic network. We review the lessons for extraterrestrial seismology gained from experience to date on the Moon and on Mars. An important unknown on Mars is the expected rate of seismicity, but theoretical expectations and extrapolation from lunar experience both support the view that seismicity rates, wave propagation characteristics, and signal-to-noise ratios are favorable to the collection of a scientifically rich dataset during the multiyear operation of a global seismic experiment. We discuss how particular types of seismic waves will provide the most useful information to address each of the scientific objectives, and this discussion provides the basis for a strategy for station siting. Finally, we define the necessary technical requirements for the seismic stations.
The Global Oscillation Network Group (GONG) Project will place a network of instruments around the world to observe solar oscillations as continuously as possible for three years. The Project has now chosen the six network sites based on analysis of survey data from fifteen sites around the world. The chosen sites are: Big Bear Solar Observatory, California; Mauna Loa Solar Observatory, Hawaii; Learmonth Solar Observatory, Australia; Udaipur Solar Observatory, India; Observatorio del Teide, Tenerife; and Cerro Tololo Interamerican Observatory, Chile. Total solar intensity at each site yields information on local cloud cover, extinction coefficient, and transparency fluctuations. In addition, the performance of 192 reasonable networks assembled from the individual site records is compared using a statistical principal components analysis. An accompanying paper descibes the analysis methods in detail; here we present the results of both the network and individual site analyses. The selected network has a duty cycle of 93.3%, in good agreement with numerical simulations. The power spectrum of the network observing window shows a first diurnal sidelobe height of 3 x 10(exp -4) with respect to the central component, an improvement of a factor of 1300 over a single site. The background level of the network spectrum is lower by a factor of 50 compared to a single-site spectrum.
An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.
Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.
Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.
A globally distributed network of dual-frequency global positioning system (GPS) receivers is the primary source of data used to measure ionospheric total electron content (TEC) on global scales. Maps of TEC useful for calibrating propagation delays, or monitoring the solar-terrestrial environment, can be produced using this continuously operating network. The maps can also form the basis of a TEC calibration service for users around the world. Potential users may include single-frequency satellite altimetry missions, satellite tracking stations, and astronomical observatories.
The Global Oscillation Network Group (GONG) Project is planning to place a set of instruments around the world to observe solar oscillations as continuously as possible for at least three years. The Project has now chosen the sites that will comprise the network. This paper describes the methods of data collection and analysis that were used to make this decision. Solar irradiance data were collected with a one-minute cadence at fifteen sites around the world and analyzed to produce statistics of cloud cover, atmospheric extinction, and transparency power spectra at the individual sites. Nearly 200 reasonable six-site networks were assembled from the individual stations, and a set of statistical measures of the performance of the networks was analyzed using a principal component analysis. An accompanying paper presents the results of the survey.
An overview of the VEGA probe to Venus is given, including a detailed examination of the balloon experiment to study the atmosphere of Venus. The areas of study include the ground network, the global network of radiotelescopes, meteorological measurements, the thermal structure of the Venus atmosphere in the middle cloud layer, atmospheric dynamics, and other results of the VEGA 1 and 2 experiments.
The accuracy of satellite-based single-frequency radar ocean altimeters benefits from calibration of the total electron content (TEC) of the ionosphere below the satellite. Data from the global network of Global Positioning System (GPS) receivers provides timely, continuous, and globally well-distributed measurements of ionospheric electron content. We have created a daily automated process called Daily Global Ionospheric Map (Daily-GIM) whose primary purpose is to use global GPS data to provide ionospheric calibration data for the Geosat Follow-On (GFO) ocean altimeter. This process also produces an hourly time-series of global maps of the electron content of the ionosphere. This system is designed to deliver "quick-look" ionospheric calibrations within 24 hours with 90+% reliability and with a root-mean-square accuracy of 2 cm at 13.6 GHz. In addition we produce a second product within 72 hours which takes advantage of additional GPS data which were not available in time for the first process. The diagram shows an example of a comparison between TEC data from the Topographic Experiment (TOPEX) ocean altimeter and Daily-GIM. TEC are displayed in TEC units, TECU, where 5 TECU is 1 cm at 13.6 GHz. Data from a single TOPEX track is shown. Also shown is the Bent climatological model TEC for the track. Although the GFO satellite is not yet in its operational mode, we have been running Daily-GIM reliably (much better than 90%) with better than 2-cm accuracy (based on comparisons against TOPEX) for several months. When timely ephemeris files for the European Remote Sensing Satellite 2 (ERS-2) are available, daily ERS-2 altimeter ionospheric calibration files are produced. When GFO ephemeris files are made available to us, we produce GFO ionosphere calibration files. Users of these GFO ionosphere calibration files find they are a great improvement over the alternative International Reference Ionosphere 1995 (IRI-95) climatological model. In addition, the TOPEX orbit determination team at JPL has been using the global ionospheric maps to calibrate the single frequency GPS data from the TOPEX receiver, and report highly significant improvements in the ephemeris. The global ionospheric maps are delivered daily to the International GPS Service (IGS), making them available to the scientific community. Additional information is contained in the original.
Convolutional neural networks (CNNs) are becoming an increasingly prevalent machine learning algorithm due to their high accuracy and lack of reliance on heuristic processes. One of the major drawbacks of convolutional neural networks is their reliance on large amounts of training data in order to generate sensible results. This talk will cover how our team has utilized the strengths and overcome the weaknesses of convolutional neural networks as they apply to seagrass habitat mapping. We will share our technical CNN results over time, detail the requirements and challenges that our team overcame and explore how other teams can better incorporate a stronger seagrass component into their machine learning projects.