GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 4. Quality Control of Hourly Data.

Slides:



Advertisements
Similar presentations
Calculation of Sampling Errors MICS3 Regional Workshop on Data Archiving and Dissemination Alexandria, Egypt 3-7 March, 2007.
Advertisements

MICS Data Processing Workshop Overview. Data Processing Design Data processing is organized around clusters There is one set of data files for each cluster.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Overview of Data Processing System.
Credit hours: 4 Contact hours: 50 (30 Theory, 20 Lab) Prerequisite: TB143 Introduction to Personal Computers.
Structured ASIC Xcellence Framescript A case study demonstrating the power of Framescript to automate the generation of a Data Book or Catalogue.
Management Unit of the North Sea Mathematical Models MUMM | BMM | UGMM OURS Manual, November 2008 [1][1] OURS – Onboard Underway.
Chapter 3 Loaders and Linkers
SRI International Bioinformatics 1 The consistency Checker, or Overhauling a PGDB By Ron Caspi.
Lab 2 – Tidal Analysis GGE5013 Ian Church. Tidal Analysis Lab Perform Harmonic Analysis on 7 Tide Stations Extract the Harmonic Constituents ( using t_tide)
CS115 HOW TO INSTALL THE JAVA DEVELOPMENT KIT (JDK)
An introduction to systems programming
1 Using Editors Editors let you create and edit ASCII files UNIX normally includes two editors: vi and Emacs Vi and Emacs are screen editors: they display.
Ostende GLOSS Course 2006 Characteristics of Sea Level Records Philip L. Woodworth Permanent Service for Mean Sea Level With thanks to Simon Williams.
Quality Assurance in the clinical laboratory
Plex Training. 2 Course Objectives Learn how to Log on and Change Passwords in Plex Learn the Common Functions on the Control Panel Learn how to Log into.
GLOSS - The Global Sea Level Observing System Global Level of the Sea Surface 1. Monitoring and Measuring.
Editing Java programs with the BlueJ IDE. Working environments to develop (= write) programs There are 2 ways to develop (write) computer programs: 1.Using.
Model Assessment and Selection Florian Markowetz & Rainer Spang Courses in Practical DNA Microarray Analysis.
GDP Keyboarding “Nothing Even Comes Close” Presented by Dr. Jack E. Johnson.
1 Team Leader TKS Job Aid. 2 Viewing the On-line Presentation If you are viewing this presentation via Internet Explorer for best results resize the “Notes”
Fakebook Nature of Work Working Conditions Training/Qualifications Employment Job Outlook Earnings Images Why I... Works Cited Your Name English 10 Mrs.
Mass & Volume Lab: Measuring Volume
Plan My Move & MilitaryINSTALLATIONS May, 2008 Relocation Personnel Roles and Responsibilities MC&FP.
Paul Mundy Editing step by step How an expert does it.
Introduction to Computers and Windows. Overview  What is a computer?  What is an operating system?  Starting and Shutting Down a computer  The mouse.
1 The EDIT Program The Edit program is a full screen text editor that allows you to: Create text files Create text files Edit an existing text files Edit.
Microsoft Word ITE115 Trisha Cummings. MsWord - Word Processing Program Allows you to create Letters, Envelopes, Mailing Labels, Memo’s , Fax’s.
Lab C 2007 Tidal Analysis Lab Ian Church
Oracle Data Integrator Procedures, Advanced Workflows.
Indexed and Relative File Processing
The consistency Checker, or Overhauling a PGDB By Ron Caspi.
Tidal Constituent and Residual Interpolation Survey Planning and Weighting Function Computation.
Mass & Volume Lab: Measuring Volume We are making new groups We are making new groups Take out your lab notebooks. Write the DQ: How does the volume of.
6 th Annual Focus Users’ Conference 6 th Annual Focus Users’ Conference Import Testing Data Presented by: Adrian Ruiz Presented by: Adrian Ruiz.
11/25/2015Slide 1 Scripts are short programs that repeat sequences of SPSS commands. SPSS includes a computer language called Sax Basic for the creation.
This document gives one example of how one might be able to “fix” a meteorological file, if one finds that there may be problems with the file. There are.
1 James N. Bellinger University of Wisconsin-Madison 27-November-2009 Status of Transfer Line Reconstruction James N. Bellinger 27-November-2009.
MnSGC Ballooning Team Techniques: APRS tracking-data processing James Flaten Summer 2010.
Analysis Introduction Data files, SPSS, and Survey Statistics.
Subtractive Manufacturing Exercise #1 Part 3 Key Fob Project Using Velocity CNC Software for the CNC Milling Machine Note: In order to use this set of.
Stats Midterm Chapters: 4, 5, 8, 9, Vocab Questions / 31 terms / 23 terms are used / 8 are not used. 6 pages / 40 questions / 43 points.
Data Management Seminar, 8-11th July 2008, Hamburg WinDEM- Verification Checks Part I.
1 Project 3 String Methods. Project 3: String Methods Write a program to do the following string manipulations: Prompt the user to enter a phrase and.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. A Concise Introduction to MATLAB ® William J. Palm III.
GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 3. Tidal Analysis and Prediction.
GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 1. Introduction.
Introduction to Programming on MATLAB Ecological Modeling Course Sep 11th, 2006.
 Are two random variables related to each other ?  What does it mean if the data are independent?  What is meant by the term covariance?  What does.
GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 1. Calibration.
Introduction to Eviews Eviews Workshop September 6, :30 p.m.-3:30 p.m.
NOAA National Climatic Data Center Dr. Karsten Shein Climatologist NOAA/NESDIS/NCDC 151 Patton Ave. Asheville, NC
GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 5. Filtering of Hourly Data.
Performance Evaluations
Chapter 7 Process Control.
Introduction to Programming
Quality Assurance in the clinical laboratory
Workflow of Tsunami Inversion
Guide To UNIX Using Linux Third Edition
Practical clinical chemistry
Tuning AUTCLN for editing
Replace this image with a picture of you, then delete this. Your Name
Loaders and Linkers.
Eviews Tutorial for Labor Economics Lei Lei
3.1 Basic Concept of Directory and Sub-directory
GGE 5013 Lab II – Tidal Analysis November 15th, 2006
Running a Java Program using Blue Jay.
An introduction to systems programming
WinSLAMM Batch Editor Module 23
Compile and run c files.
Presentation transcript:

GLOSS Training Workshop Course Japan Meteorological Agency May 15-26, 2006 Sea Level Data Processing with SLPR2 4. Quality Control of Hourly Data

Manual Section Residuals = predicted tides minus observed data Use \slpr2\QC\RESID.EXE Plot with \slpr2\plot\HOURYR.EXE Three types of correctable errors 1.Timing shifts (exact increments of an hour) 2.Short gaps 3. Data spikes, glitches SLPR2 Quality Control Procedures

Manual Section Interpretation of Residual Plots Foreman Tidal Analysis can not resolve all tidal constituents in some cases Complex shallow water tides SLPR2 Quality Control Procedures

Example: Possible Timing Shift and Data Spikes

SLPR2 Quality Control Procedures Look at Plot of Hourly Data for Clues

Balboa 1995 (based on 12/91-12/92)Balboa 1995 (based on 1/95 – 1/96) SLPR2 Quality Control Procedures Same year of data, yet predicted tides based on different years of tidal analysis

SLPR2 Quality Control Procedures Manual Section 5.3 Timing Errors Causes: -Incorrect setting of initial gauge time -Tide gauge clock error -Error during digitization -Error during transfer of data -Programmer error in data file management

SLPR2 Quality Control Procedures Manual Section 5.3 Timing Errors SLPR2 Correction Method Only possible for shifts of exact increments of an hour in hourly data files Step 1. Identify timing error in residual plot Step 2. Review original gauge data files to search for error (digitization table) Step 3. Estimate the magnitude and direction of shift -Use predicted tides file: See Appendix I Step 4. Determine the hour/day of the start/end (use residual data file) Step 5. run \slpr2\util\TSALL.EXE Step 6. Copy original file to a backup area, cut out bad segment in data file, paste in corrected segment from output of TSALL.EXE Step 7. Verify correction (make and plot residuals)

SLPR2 Quality Control Procedures Manual Section 5.4 Short Gaps (24 hours long or less) Causes: -Instrument malfunction -Gauge clock malfunction - Data transmission glitch Correction: Step 1. Use \slpr2\util\GAPCOU.EXE to get count of missing hours Step 2. Use \slpr2\qc\GAPFALL.BAT p1 (p1 is station number) Must be ran from MS DOS Prompt window Step 3. Look at output file, GAPsss.TXT (sss: station number) Step 4. Make and plot residuals for interpolated file to verify correction Interpolation Method: Predicted Tide Method

SLPR2 Quality Control Procedures Manual Section 5.4 Spikes and Glitches (24 hours long or less) Causes: -Instrument malfunction -Data transmission glitch -Digitizing error Correction: Step 1. Identify spikes and glitches in residual plots Step 2. Using text editor (split screen with data file on top and residual file on bottom), to replace hour of spike (or consecutive hours with glitch) with 9999 (missing data flag) Step 4. run GAPFALL.BAT (as for short gaps) Step 3. Make and plot residuals to verify correction

SLPR2 Quality Control Procedures Data File Management Place finalized, quality controlled, calibrated data in separate directory (up to individual agencies on how to define these) At least have the original (pre-corrections) file safely saved Evaluate whether to save intermediate processing step files (usually these are deleted)

HOTS ASSIGNMENT 1.Make and plot residuals file for available years of data (at least one year) 2.If no timing errors are present, use the sample file under \slpr2\SAMP 3.Go through the steps for Timing Erorr Correction 4.Identify any spikes or glitches 5.Correct and verify 6. Perform mock finalized data management