Title: Robin Hogan
1A variational scheme for retrieving rainfall rate
and hail intensity
2Outline
- Rain-rate estimated by ZaRb is at best accurate
to a factor of 2 due to - Variations in drop size and number concentration
- Attenuation and hail contamination
- In principle, Zdr and fdp can overcome these
problems but tricky to implement operationally - Need to take derivative of already noisy fdp
field to get Kdp - Errors in observations mean we must cope with
negative values - Difficult to ensure attenuation-correction
algorithms are stable - The variational approach is standard in data
assimilation and satellite retrievals, but has
not yet been applied to polarization radar - It is mathematically rigorous and takes full
account of errors - Straightforward to add extra constraints
3Using Zdr and fdp for rain
- Useful at low and high R
- Differential attenuation allows accurate
attenuation correction but difficult to implement
- Need accurate calibration
- Too noisy at each gate
- Degraded by hail
Zdr
- Calibration not required
- Low sensitivity to hail
- Stable but inaccurate attenuation correction
- Need high R to use
- Must take derivative far too noisy at each gate
fdp
4Variational method
- Start with a first guess of coefficient a in
ZaR1.5 - Z/a implies a drop size use this in a forward
model to predict the observations of Zdr and fdp - Include all the relevant physics, such as
attenuation etc. - Compare observations with forward-model values,
and refine a by minimizing a cost function
Smoothness constraints
For a sensible solution at low rainrate, add an a
priori constraint on coefficient a
Observational errors are explicitly included, and
the solution is weighted accordingly
5Chilbolton example
Forward-model values at final iteration are
essentially least-squares fits to the
observations, but without instrument noise
6A ray of data
- Zdr and fdp are well fitted by the forward model
at the final iteration of the minimization of the
cost function - Retrieved coefficient a is forced to vary
smoothly - Represented by cubic spline basis functions
- Scheme also reports error in the retrieved values
7What if we only use only Zdr or fdp ?
Retrieved a
Retrieval error
Zdr and fdp
- Very similar retrievals in moderate rain rates,
much more useful information obtained from Zdr
than fdp
Zdr only
fdp only
8Response to observational errors
- Nominal Zdr error of 0.2 dB Additional random
error of 1 dB
9Heavy rain andhail
Difficult case differential attenuation of 1 dB
and differential phase shift of 80º!
10How is hail retrieved?
- Hail is nearly spherical
- High Z but much lower Zdr than would get for rain
- Forward model cannot match both Zdr and fdp
- First pass of the algorithm
- Increase error on Zdr so that rain information
comes from fdp - Hail is where Zdrfwd-Zdr gt 1.5 dB
- Second pass of algorithm
- Use original Zdr error
- At each hail gate, retrieve the fraction of the
measured Z that is due to hail, as well as a. - Now can match both Zdr and fdp
11Distribution of hail
Retrieved a
Retrieval error
Retrieved hail fraction
- Retrieved rain rate much lower in hail regions
high Z no longer attributed to rain - Can avoid false-alarm flood warnings
12Summary
- New scheme achieves a seamless transition between
the following separate algorithms - Drizzle. Zdr and fdp are both zero use a-priori
a coefficient - Light rain. Useful information in Zdr only
retrieve a smoothly varying a field (Illingworth
and Thompson 2005) - Heavy rain. Use fdp as well (e.g. Testud et al.
2000), but weight the Zdr and fdp information
according to their errors - Weak attenuation. Use fdp to estimate attenuation
(Holt 1988) - Strong attenuation. Use differential attenuation,
measured by negative Zdr at far end of ray
(Smyth and Illingworth 1998) - Hail occurrence. Identify by inconsistency
between Zdr and fdp measurements (Smyth et al.
1999) - Rain coexisting with hail. Estimate rain-rate in
hail regions from fdp alone (Sachidananda and
Zrnic 1987)