VieVS User Workshop 14 – 16 September, 2011 Vienna Processing Intensive Sessions with VieVS Johannes Böhm.

Slides:

Advertisements

Similar presentations

Tutorial 3 Refractor assignment, Analysis, Modeling and Statics

Advertisements

For(int i = 1; i

Extrinsic and Depth Calibration of TOF-cameras Reporter ：鄒嘉恆 Date ： 2009/12/22.

Neural Networks and SVM Stat 600. Neural Networks History: started in the 50s and peaked in the 90s Idea: learning the way the brain does. Numerous applications.

VieVS User Workshop 7 – 9 September, 2010 Vienna Creating special files: Process lists, parameter files, TRF and EOP files Tobias Nilsson.

Simultaneous inference Estimating (or testing) more than one thing at a time (such as β 0 and β 1 ) and feeling confident about it …

VieVS User Workshop 7 – 9 September, 2010 Vienna Vie_LSM Kamil Teke and Johannes Böhm.

Module 2: Introduction to ERP Statistical Concepts and Tools Common Measures Training Chelmsford, MA September 28, 2006.

VieVS User Workshop 7 – 9 September, 2010 Vienna Hana Spicakova Results obtained using VieVS: Love numbers and FCN period presented at IVS General Meeting.

Visual Tracking: Case Study CMPUT 615 Nilanjan Ray.

Observation. Defining Behavior page 192 Topography Function Characteristics Duration Latency Frequency Amplitude.

VieVS User Workshop 14 – 16 September, 2011 Vienna VIE_MOD Lucia Plank.

VieVS User Workshop 14 – 21 September, 2011 Vienna Reprocessing the complete history of VLBI observations with VieVS Sigrid Böhm.

VieVS User Workshop 14 – 16 September, 2011 Vienna VIE_GLOB Hana Spicakova.

VieVS User Workshop 7 – 9 September, 2010 Vienna Introduction Johannes Böhm.

VieVS User Workshop 14 – 16 September, 2011 Vienna External Ionospheric Files Claudia Tierno Ros.

VieVS User Workshop 7 – 9 September, 2010 Vienna VIE_INIT Tobias Nilsson.

VieVS User Workshop 7 – 9 September, 2010 Vienna High frequency EOP estimated from the CONT campaigns Tobias Nilsson.

VieVS User Workshop 7 – 9 September, 2010 Vienna Automatic download and processing of VLBI data using VieVS Tobias Nilsson.

VieVS User Workshop 7 – 9 September, 2010 Vienna VIE_SETUP Tobias Nilsson.

VieVS User Workshop 7 – 9 September, 2010 Vienna VIE_GLOB Hana Spicakova.

VieVS User Workshop 14 – 16 September, 2011 Vienna VIE_SIM Jing SUN, Andrea PANY.

VieVS User Workshop 14 – 16 September, 2011 Vienna VIE_SCHED Jing SUN.

VieVS User Workshop 7 – 9 September, 2010 Vienna Creating OPT-files Detecting and solving problems Tobias Nilsson.

VieVS User Workshop 14 – 16 September, 2011 Vienna VIE_LSM Kamil Teke and Johannes Böhm.

VieVS User Workshop 14 – 16 September, 2011 Vienna External tropospheric files in VieVS Matthias Madzak.

VieVS User Workshop 7 – 9 September, 2010 Vienna Overwiev of VieVS: Installation, files, structure of the program Tobias Nilsson.

Simple Linear Regression Analysis

Principles of the Global Positioning System Lecture 10 Prof. Thomas Herring Room A;

Principles of the Global Positioning System Lecture 11 Prof. Thomas Herring Room A;

Introduction In previous lessons, linear functions were compared to linear functions and exponential functions to exponential. In this lesson, the properties.

IGS Analysis Center Workshop, Miami Beach, June 2008 Comparison of GMF/GPT with VMF1/ECMWF and Implications for Atmospheric Loading Peter Steigenberger.

 We cannot use a two-sample t-test for paired data because paired data come from samples that are not independently chosen. If we know the data are paired,

Prediction concerning Y variable. Three different research questions What is the mean response, E(Y h ), for a given level, X h, of the predictor variable?

Different options for the assimilation of GPS Radio Occultation data within GSI Lidia Cucurull NOAA/NWS/NCEP/EMC GSI workshop, Boulder CO, 28 June 2011.

Tropospheric parameters from DORIS in comparison to other techniques during CONT campaigns Johannes Böhm (1), Kamil Teke (1, 2), Pascal Willis (3, 4),

Chapter 12 Multiple Linear Regression Doing it with more variables! More is better. Chapter 12A.

Modern Navigation Thomas Herring

10.1: Confidence Intervals – The Basics. Introduction Is caffeine dependence real? What proportion of college students engage in binge drinking? How do.

Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.

A new Ad Hoc Positioning System 컴퓨터 공학과 오영준.

What are the times on these clocks?5-minute intervals

Workshop 6: Thermal Analysis of a Plate with a Hole University of Puerto Rico at Mayagüez Department of Mechanical Engineering Modified by (2008): Dr.

Unified Analysis Workshop, December 5-7, 2007, Beach Resort Monterey, CA GG S Proposals for Extended Parameterization in SINEX Markus Rothacher GeoForschungsZentrum.

Combination of long time series of tropospheric parameters observed by VLBI R. Heinkelmann, J. Boehm, H. Schuh Institute of Geodesy and Geophysics, TU.

Principles of the Global Positioning System Lecture 12 Prof. Thomas Herring Room ;

Sampling Distributions Chapter 18. Sampling Distributions A parameter is a measure of the population. This value is typically unknown. (µ, σ, and now.

Global VLBI Solution IGG05R01 1 Institute of Geodesy and Geophysics (IGG), Vienna, Austria 2 German Geodetic Research Institute (DGFI), Munich, Germany.

COMPARING LINEAR TO EXPONENTIAL FUNCTIONS ~adapted from Walch Education.

Lecture 5: Statistical Methods for Classification CAP 5415: Computer Vision Fall 2006.

Introduction Previously, we calculated the rate of change of linear functions as well as intervals of exponential functions. We observed that the rate.

Do we need WVRs for geodetic VLBI? Joerg Wresnik (1), Johannes Boehm (1), Harald Schuh (1), Arthur Niell (2) Workshop on Measurement of Atmospheric Water.

The accuracy of averages We learned how to make inference from the sample to the population: Counting the percentages. Here we begin to learn how to make.

Science is a process, or method, that usually starts with an observation.

Sampling Distributions Chapter 18. Sampling Distributions A parameter is a number that describes the population. In statistical practice, the value of.

The average velocity of an object during a time interval t is The acceleration, assumed constant, is 2-5 Motion at Constant Acceleration.

Euler’s method and so on… Euler’s method of approximation of a function y = f(x) is approximations. constructed from repeated applications of linear It.

A Brief Introduction of RANSAC

CHAPTER 29: Multiple Regression*

Assessing the Compatibility of Microwave Geodetic Systems

Chapter 25: Paired Samples and Blocks

CHAPTER 22: Inference about a Population Proportion

Kamil Teke and Johannes Böhm

Troposphere and Clock Parameterization During Continuous VLBI Campaigns Kamil Teke1, 2, Johannes Boehm1, Hana Spicakova1, Andrea Pany1, Harald Schuh1 1.

IDS workshop, Venice, Italy, September 2012

VIE_INIT_V30 Daniel Landskron.

Correlation coefficients

Introduction In previous lessons, linear functions were compared to linear functions and exponential functions to exponential. In this lesson, the properties.

Objectives 6.1 Estimating with confidence Statistical confidence

Presentation transcript:

VieVS User Workshop 14 – 16 September, 2011 Vienna Processing Intensive Sessions with VieVS Johannes Böhm

Introduction 2 INT1 (2-3 days latency) Kokee-Wettzell Mo-Fr 18:30 INT2 (e-transfer) Tsukuba-Wettzell Sa-So 7:30 INT3 (e-transfer) Tsukuba-Wettzell-NyAlesund Mo 7:00 Luzum and Nothnagel (2010)

VieVS User Workshop Typically, we need version 4, but in case of INT2 sessions, it is N003.

VieVS User Workshop Remember that.

VieVS User Workshop At least not more than a linear function between the two clocks.

VieVS User Workshop Choose this option. This is important. The time interval should cover the whole session.

VieVS User Workshop Make one constant zenith wet delay per station and do not estimate gradients.

VieVS User Workshop Do not estimate station coordinates.

VieVS User Workshop Only estimate one constant dUT1 per session. (1e-4)

VieVS User Workshop Do not estimate source coordinates.

VieVS User Workshop

VieVS User Workshop I would say ok. I would not treat this as outlier if there are only 15 observations.

VieVS User Workshop This is not too much.

VieVS User Workshop Could be better but ok.

VieVS User Workshop Actually, we only have 5 parameters: 2 for a linear clock between the stations, 2 for constant zenith wet delays at the stations, 1 dUT1.

VieVS User Workshop Here it is dUT1 in msec. It has been estimated in addition to the apriori values.