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Photizo

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

Timeline

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

Deliverables

• 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

¿Preguntas?

(:notoc:)