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Langley Calibration. Web interface a) Limiting the Lamp Calibration dates b) Creating a Langley Calibration factor from Mauna Loa data c) Calculating the Langley Factor on the fly New Langley Calibration method. Web Interface. Limiting the Lamp Calibration

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langley calibration
Langley Calibration
  • Web interface

a) Limiting the Lamp Calibration dates

b) Creating a Langley Calibration factor from

Mauna Loa data

c) Calculating the Langley Factor on the fly

  • New Langley Calibration method
web interface
Web Interface

Limiting the Lamp Calibration

Done, needs debugging

Creating a Langley Calibration factor from Mauna Loa data (table: langley_calibrations)

Need to add login, needs debugging

Calculating Langley on the fly

Needs to be done.


First 8 weeks use langley calibration factor from Mauna Loa data

Otherwise use daily vnaught created by new langley method

java code already in place.

new langley calibration method
New Langley Calibration method
  • All data has been reloaded using new method

(1997 – May 2010)

  • Things to do

Needs to be verified/examined (1997,2007 done)

Holes need to be filled in when possible

So far, all software bugs have been fixed but will continue when examining data

Better error trapping

Determination of good data/ qc codes have changed

Steps in New Langley Calibration Method

Step 1:

Run la for every day

store data in table optical_depths_la

air mass is 2 -6 for VIS and 1.2/1.5 – 2.2/3 for UV

AM data only

New field added to table date_excluded (yes or no)

Two sources: (previous data) (nightly load)

Previous loads:

Reads tracking table for every site to get time period to run so will not span a serial number

Get cosine corrected voltages for every day during time period determine by tracking table

Creates la config file

Checks qc codes/ sunny days to throw out data is needed

Runs la and stores in table optical_depths_la

Nightly Run:

Runs every night after cosine corrected voltages have been loaded. Currently 7:20 am.

Things still to do:

What if site did not poll, when to run it? During maybe?

Serial Number logic. What if serial number changed during the day? Need to run twice one for old serial number and one for new serial number.

Qc code usage, what is thrown out? Need to verify since qc codes have been updated.

Better error trapping with descriptions and less debug statements

Step 2: Trimming out extraneous la values

Table: optical_depths_mean



Source: data)

Logic: At a serial number start:

Take first 3 values and create a mean/standard deviation

Check the next value against the mean/standard deviation derived from first 3

If (new value – mean value)/mean value > .5


else if (new value – mean value) > std dev * 2

just a possibility to throw out


keep value

Continue updating the mean and standard deviation for 12 values only.

If the second criteria above was selected then keep track for the next value. If you have 3 of those values you then want to keep the values. If you do not get three of those values you throw it out.

If you threw value out, update column

date_excluded in table optical_depths_la

Store the values you kept with mean/standard

deviation in table optical_depths_mean

Still to do:

Nightly logic for this. Deciding the 3 in a row logic. And also a new serial number logic. Need a table to store the 12 running mean values.

When to run it? What about a site that was missed at night?

What if no vnaughts for yesterday. Just keep previous running mean? And tag it along?

Who is going to keep track if there is no vnaughts. What was the reason/ instrument failure, cloudy day etc.

Email message/ what if discovered a problem and a flag is added/ need to rerun?

Step 3: Creating the daily vnaught

Table: daily_vnaughts_mean


Logic: PASS 1: Input start date and end data(one day)

gets voltage intercept from optical_depths_mean table. Goes back 3 months and forward 3 months in previous data. Trims out 2 standard deviations from least squares regression. Must have a minimum of 5 to continue.

If none then use yesterdays prediction and carry it along for 2 months.... More than 2 months -9998....

If more than 5 then get a linear regression(dividing by 1000) to make values more reasonable. At end multiply by 1000.

Get predictedValue

PASS TWO: Use a weighted sum of values to try and eleviate the noise.

Num of days = date wanted - optical depth start

sum = value/(num of days*num of days)

valsDaySum = 1/(numofdays*numofdays) + sum

predictedValue= sum/valsDaysSum

Store value in daily_vnaughts table.

To do: Implementing nightly version.

When to run. Serial number logic.. Problems when fails.

Cannot go forward 3 months. Go back 6 instead?