Dependencies in Dataset #10680
Entries (observed variables)
| No. | Name |
|---|---|
| A | virtual entry: time [unspecified unit] given by 1650340 constant values |
| 01 | latitude [unspecified unit] |
| 02 | longitude [unspecified unit] |
| 03 | corrected target return estimate online |
| 04 | corrected target return estimate offline |
| 05 | differential atmospheric optical thickness |
| 06 | error of differential atmospheric optical thickness |
| 07 | speckle error of differential atmospheric optical thickness |
| 08 | averaged differential atmospheric optical thickness |
| 09 | averaged differential atmospheric optical thickness excluding cloud targets |
| 10 | averaged differential atmospheric optical thickness only for cloud targets |
| 11 | data is good |
| 12 | data is from cloud |
| 13 | attitude correction |
| 14 | doppler shift (cm^-1) |
| 15 | scattering surface elevation (m) |
| 16 | aircraft altitude (m) |
| 17 | target altitude according to a DEM (m) |
| 18 | integrated weighting function from platform to target |
| 19 | integrated weighting function to target |
| 20 | weighted mixing ratio |
| 21 | error of weighted mixing ratio |
| 22 | weighted mixing ratio average |
| 23 | error of weighted mixing ratio average |
| 24 | xghg bias correction |
| 25 | weighted mixing ratio average excluding cloud targets |
| 26 | error of weighted mixing ratio average excluding cloud targets |
| 27 | xghg bias correction excluding cloud targets |
| 28 | weighted mixing ratio average for cloud targets |
| 29 | error of weighted mixing ratio average for cloud targets |
| 30 | xghg bias correction for cloud targets |
| 31 | xghg of other trace gases |
| 32 | online wave number (cm^-1) |
| 33 | atmospheric pressure at target (Pa) |
| 34 | atmospheric pressure at flight altitude (Pa) |
| 35 | atmospheric pressure at DEM surface (Pa) |
| 36 | bitmask for individual quality checks |
Dependencies of the observed variables
The interactive table provides a visualization of the dependencies of this dataset's entries (variables) which will hopefully assist you to understand the datasets structure.
Most datasets are simple, e.g. like 10 entries depending on 1 entry. But there are other datasets which are far more complex; with relationships between various entries and even reversed dependencies.
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Direction of relationships
| Entry A | Entry B | |
|---|---|---|
| (=depends on) |
Explanation
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