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Weather Data Analysis and QA/QC

The Photizo Project - People

  • I'm Joshua Kugler
  • My committee is Dr. Knoke, Dr. Nance, and Dr. Roth
  • Stakeholders
    • Michael Lilly: Geo-Watersheds Scientific
    • Gary Whitton: EE Internet
    • Alaska Department of Transportation and Public Facilities/UAF WERC (by extension)
    • UAF Graduate Committee

The Photizo Project - Que es?

  • Name comes from a Greek word meaning "to enlighten, render evident, to give understanding to."
  • Archiving, QA/QC, Publishing, and Data Analysis for Meteorological Sensor Networks
    • Quality Control: Making sure our data is being collected and is accurate
    • Quality Assurance: Making sure our data is meeting the customers' needs.

Problem Domain

  • Weather data is important
    • Pilots, Winter Field Operations, Traveler Safety, Winter Logistics
    • Resource Management, Climate Change
  • The data needs to be:
    • Collected
    • Imported
    • Analyzed/QA/QC
      • GIGO (Garbage in/Garbage out)
    • Published or otherwise distributed in a timely matter
  • Currently very little actual near-real-time QA/QC
  • "NOAA has some automated QA/QC"

From whence the data?

  • Primarily Campbell Scientific data loggers
  • Also other devices such as Outback power systems
  • Ends up on local servers in a CSV-like format

The solution: Photizo

Photizo is the name of a framework of modules that will facilitate the importing, analysis, and publishing of meteorological data.

  • Define stations, their sensors, and expected ranges
  • Import Data
  • Find "bad data"
  • Send alerts on pre-defined conditions
  • Publish data to web pages

Example weather page

Example weather graph

Development Methodology

  • Face to face conversation
  • E-mail
  • Extended discussion and clarification via wiki collaboration
  • Test Driven Development (TDD)
  • Probably a little "XP"/Pair due to work environment

Development Environment

  • Python
    • Dynamic
    • Easy to create a Plug-in architecture
    • Strong run-time introspection
    • Very cross platform
  • Mathplotlib
    • A strong graphing library for Python
  • ORM
    • Most likely SQLAlchemy
  • Interfaces most likely to be web based


Project Planning: September 1 - September 30

Project Requirements: October 1 - December 3

Project Design & Test Plan: December 4 - January 15

Project Implementation: January 16 - March 12

Project Testing: March 13 - April 16

Project Documentation: September 1 - May 1


  • Facility to import data
  • Facility to edit station and sensor profiles
  • Facility to define and create tests
  • Facility to produce web pages and graphs of data
    • Peripheral: requirements and design, but not my main focus
  • Facility to analyze data and send alerts on pre-defined conditions



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Page last modified on November 07, 2006, at 08:39 PM