The ILRS Contribution to ITRF2020

2021 ◽  
Author(s):  
Vincenza Luceri ◽  
Erricos C. Pavlis ◽  
Antonio Basoni ◽  
David Sarrocco ◽  
Magdalena Kuzmicz-Cieslak ◽  
...  
Keyword(s):  
Time Series ◽  
Tracking System ◽  
A Priori ◽  
Systematic Errors ◽  
Novel Approach ◽  
Priori Information ◽  
The Stability ◽  
The Individual ◽  
Stability And Consistency

<p>The International Laser Ranging Service (ILRS) contribution to ITRF2020 has been prepared after the re-analysis of the data from 1993 to 2020, based on an improved modeling of the data and a novel approach that ensures the results are free of systematic errors in the underlying data. This reanalysis incorporates an improved “target signature” model (CoM) that allows better separation of true systematic error of each tracking system from the errors in the model describing the target’s signature. The new approach was developed after the completion of ITRF2014, the ILRS Analysis Standing Committee (ASC) devoting almost entirely its efforts on this task. The robust estimation of persistent systematic errors at the millimeter level permitted the adoption of a consistent set of long-term mean corrections for data collected in past years, which are now applied a priori (information provided by the stations from their own engineering investigations are still taken into consideration). The reanalysis used these corrections, leading to improved results for the TRF attributes, reflected in the resulting new time series of the TRF origin and especially in the scale. Seven official ILRS Analysis Centers computed time series of weekly solutions, according to the guidelines defined by the ILRS ASC. These series were combined by the ILRS Combination Center to obtain the official ILRS product contribution to ITRF2020.</p><p>The presentation will provide an overview of the analysis procedures and models, and it will demonstrate the level of improvement with respect to the previous ILRS product series; the stability and consistency of the solution are discussed for the individual AC contributions and the combined SLR time series.</p>

scholarly journals Monitoring distributed computing beyond the traditional time-series histogram

2020 ◽  
Vol 245 ◽  
pp. 03036
Author(s):  
M S Doidge ◽  
P. A. Love ◽  
J Thornton
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Real Time ◽  
A Priori ◽  
Time Data ◽  
Use Of Time ◽  
Computing Services ◽  
Monitoring Tools ◽  
Novel Approach ◽  
Current Monitoring

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.

Using Deconvolution to Improve PET Spatial Resolution in OSEM Iterative Reconstruction

2007 ◽  
Vol 46 (02) ◽  
pp. 231-235 ◽  
Author(s):  
I. Castiglioni ◽  
G. Russo ◽  
M. Tana ◽  
F. Dell'Acqua ◽  
M. Gilardi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Spatial Resolution ◽  
Penalty Function ◽  
Iterative Reconstruction ◽  
A Priori ◽  
Image Deconvolution ◽  
A Priori Information ◽  
Novel Approach ◽  
Priori Information ◽  
Pet Image Reconstruction

Summary Objectives : A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as “a priori" information to guide OSEM toward an improved solution. Methods : Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included i in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this “recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. Results : Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. Conclusions : Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.

Impacts of A Priori Databases Using Six WRF Microphysics Schemes on Passive Microwave Rainfall Retrievals

2013 ◽  
Vol 30 (10) ◽  
pp. 2367-2381 ◽  
Author(s):  
Ju-Hye Kim ◽  
Dong-Bin Shin ◽  
Christian Kummerow
Keyword(s):  
A Priori ◽  
Wrf Model ◽  
Tropical Rainfall Measuring Mission ◽  
Cloud Microphysics ◽  
Passive Microwave ◽  
A Priori Information ◽  
Physically Based ◽  
Double Moment ◽  
Priori Information ◽  
The Individual

Abstract Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.

A Status Report on the ILRS Contribution to ITRF2020

2020 ◽  
Author(s):  
Vincenza Luceri ◽  
Erricos C. Pavlis ◽  
Antonio Basoni ◽  
David Sarrocco ◽  
Magdalena Kuzmicz-Cieslak ◽  
...  
Keyword(s):  
A Priori ◽  
Pilot Project ◽  
Systematic Errors ◽  
Status Report ◽  
New Approach ◽  
Novel Approach ◽  
Ground System ◽  
Space Geodetic Techniques ◽  
Preliminary Version ◽  
And Performance

<p>The International Laser Ranging Service (ILRS) Analysis Standing Committee (ASC) plans to complete the re-analysis of the SLR data since 1983 to end of this year by early 2021. This will ensure that the ILRS contribution to ITRF2020 will be available to ITRS by February 2021, as agreed by all space geodetic techniques answering its call. In preparation for the development of this contribution, the ILRS completed the re-analysis of all data (1983 to present), based on an improved modeling of the data and a novel approach that ensures the results are free of systematic errors in the underlying data. The new approach was developed after the completion of ITRF2014, the ILRS ASC devoting almost entirely its efforts on this task. A Pilot Project initially demonstrated the robust estimation of persistent systematic errors at the millimeter level, leading us to adopt a consistent set of a priori corrections for data collected in past years. The initial reanalysis used these corrections, leading to improved results for the TRF attributes, reflected in the resulting new time series of the TRF origin and scale. The ILRS ASC will now use the new approach in the development of its operational products and as a tool to monitor station performance, extending the history of systematics for each system that will be used in future re-analysis. The new operational products form a seamless extension of the re-analysis series, providing a continuous product based on our best knowledge of the ground system behavior and performance, without any dependence whatsoever on a priori knowledge of systematic errors (although information provided by the stations from their own engineering investigations are always welcome and taken into consideration). The presentation will demonstrate the level of improvement with respect to the previous ILRS product series and give a glimpse of what is to be expected from the development of a preliminary version of the ITRF2020.</p>

Inversion of inductive electromagnetic data in highly conductive terrains

Geophysics ◽  
2005 ◽  
Vol 70 (1) ◽  
pp. G16-G28 ◽  
Author(s):  
G. Schultz ◽  
C. Ruppel
Keyword(s):  
A Priori ◽  
Two Dimensions ◽  
Forward Model ◽  
Practical Applications ◽  
Shallow Subsurface ◽  
Subsurface Structures ◽  
Conductivity Structure ◽  
Actual Field ◽  
Priori Information ◽  
The Stability

Despite the increasing use of controlled-source frequency-domain EM data to characterize shallow subsurface structures, relatively few inversion algorithms have been widely applied to data from real-world settings, particularly in high-conductivity terrains. In this study, we develop robust and convergent regularized, least-squares inversion algorithms based on both linear and nonlinear formulations of mutual dipole induction for the forward problem. A modified version of the discrepancy principle based on a priori information is implemented to select optimal smoothing parameters that simultaneously guarantee the stability and best-fit criteria. To investigate the problems of resolution and equivalence, we consider typical layered-earth models in one and two dimensions using both synthetic and observed data. Synthetic examples show that inversions based on the nonlinear forward model more accurately resolve subsurface structure, and that inversions based on the linear forward model tend to drastically underpredict high conductivities at depth. Inversions of actual field data from well-characterized sites (e.g., National Geotechnical Experimentation Site; sand-dominated coastal aquifer in the Georgia Bight) are used to test the applicability of the model to terrains with different characteristic conductivity structure. A comparison of our inversion results with existing cone-penetrometer and downhole-conductivity data from these field sites demonstrates the ability of the inversions to constrain conductivity variations in practical applications.

Link Between Long-Term Variability in Upwelling and Fish Production in the Northeast Pacific Ocean

1991 ◽  
Vol 48 (12) ◽  
pp. 2296-2306 ◽  
Author(s):  
Daniel M. Ware ◽  
Richard E. Thomson
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Southern California ◽  
Coastal Upwelling ◽  
Pelagic Fish ◽  
Biological Time ◽  
Long Period ◽  
Pacific Sardine ◽  
The Individual

The biomass of pelagic fish in the Coastal Upwelling Domain off the west coast of North America decreased by a factor of 5 in the first half of this century. We assemble several physical and biological time series spanning this period to determine what may have caused this decline in productivity. Based on an observed link between time series of the coastal wind and primary production, we conclude that there was a strong relaxation in wind-induced upwelling and primary production between 1916 and 1942 off southern California. The fact that the individual biomasses of the dominant pelagic fish species tend to rise and fall in phase through the sediment record off southern California is consistent with our belief that these species are responding to a long-period (40–60 yr) oscillation in primary and secondary production, which, in turn, is being forced by a long-period oscillation in wind-induced upwelling. Our extended sardine recruitment time series indicates that there is a nonlinear relationship between Pacific sardine (Sardinops sagax) recruitment and upwelling and suggests that optimal recruitment occurs when the wind speed during the first few months of life averages 7–8 m/s.

A Priori Information and Time Series Analysis. Essays in Economic Theory and Measurement

1963 ◽  
Vol 14 (2) ◽  
pp. 305
Author(s):  
Henri Guitton ◽  
F. M. Fisher ◽  
C. Kaysen
Keyword(s):  
Economic Theory ◽  
Time Series ◽  
Time Series Analysis ◽  
A Priori ◽  
A Priori Information ◽  
Series Analysis ◽  
Priori Information

scholarly journals Retrieval of Canopy component temperatures through Bayesian inversion of directional thermal measurements

2009 ◽  
Vol 6 (2) ◽  
pp. 3007-3040 ◽  
Author(s):  
J. Timmermans ◽  
W. Verhoef ◽  
C. van der Tol ◽  
Z. Su
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
A Priori ◽  
Inversion Method ◽  
Success Rates ◽  
Brightness Temperatures ◽  
Ill Posed ◽  
Priori Information ◽  
The Individual ◽  
High Level

Abstract. In remote sensing evapotranspiration is estimated using a single surface temperature. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (ℜspectra≈0.3, ℜgonio≈0.3, and ℜAATSR≈0.5), and no improvement using mono-angular sensors (ℜ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K), but for low LAI values the measurement setup provides extra disturbances in the directional brightness temperatures, RMSEyoung maize=2.85 K, RMSEmature maize=2.85 K. As these disturbances, were only present for two crops and can be eliminated using masked thermal images the method is considered successful.

scholarly journals MODEL FOR DETERMINING CLASSIFICATION OF FILLING MATERIALS HARDENING

2020 ◽  
Vol 5 (443) ◽  
pp. 6-12
Author(s):  
Arslanov M.Z., ◽  
◽  
Mustafin S.A., ◽  
Zeinullin A.A., ◽  
Kulpeshov B.S., ◽  
...  
Keyword(s):  
Time Series ◽  
A Priori ◽  
The State ◽  
Filling Material ◽  
Filling Materials ◽  
Fundamental Possibility ◽  
Heterogeneous Features ◽  
Priori Information ◽  
Dynamic Time

This paper presents the model for solving the problem of classification of trajectories of development of states of filling material in the presence of a priori information on the trajectories of processes that have already passed the development of states of the processes. Consideration of the hardening process of stowage material as a chemical-technological process, which can be considered as a multi-parameter dynamic (time) series, allows us to determine the development class of the state of the material based on the classification of the state of the stowage. The proposed approach has established the fundamental possibility of using the proposed methodology to solve the problem of dividing given trajectories represented by time series into classes. It allows us to obtain a model that, according to formal rules, determines the classification of trajectories by sets of heterogeneous features of its state at certain time and improves the reliability of the classification.

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