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wradlib Documentation, Release 0.1.1
3.13 Gage adjustment
3.13.1 Concept
The objective of this module is the adjustment of radar-based rainfall estimates by rain gage observations. However,
this module could also be applied to adjust satellite rainfall by rain gage observations, remotely sensed soil moisture
patterns by ground truthing moisture sensors, or any dense spatial point pattern which could be adjusted by sparse
point measurements (ground truth).
Basically, we only need two data sources:
• point observations (e.g. rain gage observations)
• set of (potentially irregular) unadjusted point values (e.g. remotely sensed rainfall)
[GoudenhooftdandDelobbe2009] provide an excellent overview of adjustment procedures. The general idea is that
we quantify the error of the remotely sensed rainfall at the rain gage locations, assuming the rain gage observation
to be accurate. The error can be assumed to be purely additive (AdjustAdd), purely multiplicative (AdjustMultiply,
AdjustMFB) or a mixture of both (AdjustMixed). If the error is assumed to heterogeneous in space (AdjustAdd,
AdjustMultiply, AdjustMixed), the error at the rain gage locations is interpolated to the radar bin locations and then
used to adjust (correct) the raw radar rainfall estimates. In case of the AdjustMFB approach, though, the multiplicative
error is assumed to be homogenoues in space.
3.13.2 Quick start
The basic procedure consists of creating an adjustment object from the class you want to use for adjustment. After
that, you can call the object with the actual data that is to be adjusted. The following example is using the additive
error model with default settings. obs_coords and raw_coords represent arrays with coordinate pairs for the
gage observations and the radar bins, respectively. obs and raw are arrays containing the actual data.
>>> adjuster = AdjustAdd(obs_coords, raw_coords)
>>> adjusted = adjuster(obs, raw)
The user can specify the approach that should be used to interpolate the error in space, as well as the keyword arguments
which control the behaviour of the interpolation approach. For this purpose, all interpolation classes from the
wradlib.ipol module are available and can be passed by using the Ipclass argument. The default interpolation class
is Inverse Distance Weighting (wradlib.ipol.Idw). If you want to use e.g. linear barycentric interpolation:
>>> import wradlib.ipol as ipol
>>> adjuster = AdjustAdd(obs_coords, raw_coords, Ipclass=ipol.Linear)
>>> adjusted = adjuster(obs, raw)
3.13.3 Cross validation
Another helpful feature is an easy-to-use method for leave-one-out cross-validation. Cross validation is a standard
procedure for verifying rain gage adjustment or interpolation procedures. You can start the cross validation in the
same way as you start the actual adjustment, however, you call the xvalidate method instead. The result of the
cross validation are pairs of observation and the corresponding adjustment result at the observation location. Using
the wradlib.verify module, you can compute error metrics for the cross validation results.
>>> adjuster = AdjustAdd(obs_coords, raw_coords)
>>> observed, estimated = adjuster.xvalidate(obs, raw)
>>> from wradlib.verify import ErrorMetrics
>>> metrics = ErrorMetrics(observed, estimated)
>>> metrics.report()
64 Chapter 3. Library Reference