Tuesday, March 16, 2010

Where you can't code...

Please don't have the feeling that a research aircraft isn't one of the places where you can't code comfortably :) This blog is more to do with post-philosophical investigations of the title.

This is one of the times that my programming literacy is to no avail to perform the research task ahead of me.

The problem is simply defined: Find the time-ranges where the research aircraft was sampling at cloud-bases.

What about the data-set?
About 37 hours of airborne data-set from the Saudi Spring 09 atmospheric measurement campaign. It contains aerosol and cloud micro-physical data and atmospheric state parameters from at least 10 different probes, listing over 100 different measured variables (e.g. pressure altitude, cloud condensation nuclei concentration, 2D images of ice-cloud particles.) We also recorded some of the flights from take-off to landing for nostalgia purposes and mainly aiding us while performing post-flight studies.

I get these ideas half-way through my manual exploration of the data-set. Many measurements are helpful in this analysis (e.g. air-dew point temperatures to determine where clouds are forming, the state of the aircraft in a given time-series plot, amount of liquid water content to distinguish in and out of cloud conditions, and most valuably in my opinion is visual observation from the recorded videos) helping me to infer when actually the aircraft was sampling right underneath a cloud in a level path.

This is part of my job and at the core of my thesis work. Even tough I complain little about the situation I get paid for what I am doing right now. My complaints are mostly for, in spite of all that rich data-set I am the one eventually making that final informative decision after manually and cautiously going through the data at hand. Far from being generic or universal. Good luck to myself if I need to extend the analysis for another airborne data-set :) I wish I had taken much wiser notes instead of trying to spot the most interesting occurring cloud of the day.

To complicate the analysis to a bit further level: not only find the cloud-base measurements also find the consecutive vertical passes of that same cloud in an automated fashion. No, not the one on its left nor the one on the right.

I am counting months backward for my graduation. Probably I won't have much time to see a breakthrough in AI research till then as mentioned in this article: How Long Till Human-Level AI?


  1. You mean you need to know when the aircraft was right below a cloud ?

    Can't you use images from geostationary satellites to infer cloudiness and cloud top along the aircraft trajectory, and then use the in-situ measurements of particle concentrations to check if the aircraft was actually inside a cloud?

    Of course it might be tricky to check 1) if the aircraft is not actually above the clouds seen by the satellite and 2) how far the aircraft is from the cloud... But apart from using extra (non in-situ) information, I don't see how you can check the existence of a cloud above the aircraft...

    Good luck, analysis of in-situ measurements is a hairy problem :-) Colocation is even worse.

  2. Satellite data is really sparse for my case since there are only very rare AQUA and TERRA passes for those days. Even if I had abundant satellite imagery and data it still would be hard to distinguish the aircraft below the cloud-base level.

    Only segmentation part is time-consuming and exhausting for me :) Once I get passed this step I should be able to easily move on. I know people who deals with co-location problem. What is your field?