Noise

Reflectivity measurements are disturbed by noise from internal electrical circuits used in the receiver chain and by atmospheric noise from outside the system [Lengfeld et al., 2014]. To be able to detect also weak weather signals, an accurate background noise estimation is crucial.

Beam Expansion

In contrast to the received signal, the signal of background noise is independent of the distance to the radar. To estimate the noise level we need to apply the beam expansion effect, more precisely we have to invert the preprocessed \(r^2\)-dependency of the background noise. For this purpose, the pylawr package provides the class BeamExpansion. This filter applies or removes the beam expansion effect to or from a reflectivity field. The linear reflectivity to operate on has to be tagged with TAG_BEAM_EXPANSION_CORR or TAG_BEAM_EXPANSION_UNCORR accordingly. Keep in mind to remove the beam expansion effect after noise removal, otherwise your radar reflectivity decreases with \(r^2\).

The example below shows, some simple application of the filter BeamExpansion.

if not array_has_tag(reflectivity, TAG_BEAM_EXPANSION_UNCORR):
    refl_filtered = beam_expansion_filter.transform(
        reflectivity.lawr.to_z(), inverse=True
)

Remove Noise

The NoiseRemover determines dynamically an appropriate spatially independent noise threshold to subtract from the reflectivity field. In a first step, an initial guess of noise level from a rain-free field is used to separate assumed meteorological signals from noise background. The initial first guess overestimates the expected noise level by approximately a factor of 10, so that no noise will remain in the radar image after noise removal. If more than 10 % radar range gates detect no rain after subtracting the initial guess from the original reflectivity field, the 10th percentile of the original reflectivity field is chosen as the next noise level. If this condition is not fulfilled, the noise level from the last time step is kept. The estimated noise level is used as the initial guess for the noise estimation in the next time step. To reduce influence of radar artefacts on the algorithm, the average of the recent 10 estimates are used to correct the reflectivity. Note the beam expansion before and after noise removal (Beam Expansion). For additional information, refer to [Lengfeld et al., 2014].

Two tags come with the class NoiseRemover. The tag TAG_NEG_LIN_REFL_REPL indicates that negative linear reflectivities were replaced with something. The tag TAG_NOISE_FILTER indicate that a noise filter was applied.

The application of the filter is exemplary shown below.

# Treat `NoiseRemover` as dynamic object, so do not instantiate this class
# inside a for-loop processing multiple time steps.
noise_remover = NoiseRemover()

beam_expansion_filter = BeamExpansion()

# The array `reflectivity` should be tagged with `TAG_BEAM_EXPANSION_CORR`
if not array_has_tag(reflectivity, TAG_BEAM_EXPANSION_UNCORR):
    refl_filtered = beam_expansion_filter.transform(
        reflectivity.lawr.to_z(), inverse=True
    )
else:
    refl_filtered = reflectivity.lawr.to_z()
noise_remover.fit(refl_filtered)
refl_filtered = noise_remover.transform(refl_filtered)
refl_filtered = beam_expansion_filter.transform(refl_filtered.lawr.to_z())

# Keep in mind, the filtered reflectivity is linear, so for later usage
# you might convert it to logarithmic reflectivity.
refl_filtered = refl_filtered.lawr.to_dbz()

pylawr.transform.spatial.BeamExpansion()	Filter to apply/remove the beam expansion effect to/from a reflectivity field.
pylawr.transform.temporal.NoiseRemover([...])	Noise filter that dynamically determines an appropriate spatially independent noise threshold to subtract from the reflectivity field.

Functional API

For functional-api usage, please note the method remove_noise(). The method removes background noise by taking an instance of a NoiseRemover and the radar field. The advantage of this method is, you do not need to consider beam expansion, if the tag for beam expansion exists.

pylawr.functions.transform.remove_noise(...)

Remove background noise from given reflectivity field.