carpe-diem 13/6/02, [email protected], slide 1german aerospace center microwaves and radar...

CARPE-DIEM 13/6/02, [email protected], slide 1German Aerospace Center Microwaves and Radar Institute

CARPE-DIEM Besprechung

Helsinki, June 2004

Ewan Archibald

German Aerospace Center

Microwaves and Radar InstitutePostfach 1116

82230 Weßling, Germanye-mail: [email protected]

Oberpfaffenhofen, 13.06.2004


WP8 Deliverable 8.2

• Objective : “Obtain estimates of the uncertainty in rainfall estimates due to variations in Z-R relationships at different spatial and temporal scales.”

• Method : Use observations from polarimetric weather radar to attempt to quantify variations in DSD and hence errors in translation from Z to R.

• Scope> Excludes errors specific to Z (e.g. calibration, clutter) but some relation to

VPR type errors through DSD development with height.

> Excludes explicit comparison with ground based instrumentation (e.g. gauges or disdrometers). Justified by difference in sampling characteristics?

> Includes attempt to development view of factors which influence DSD, and hence prospects for minimising, as well as quantifying uncertainty by varying DSD assumptions in response to wider observational matrix.

> Includes assessment of uncertainties in polarimetric variables, particularly with regard to C-band.


Drop Size Distribution

• An particular form of DSD is the key assumption in trying to relate measured Z to an estimate of R (and K, LWC, etc.).

• Variability of natural rainfall translates as uncertainty in estimated rainfall not directly, but through DSD assumptions.

• Fundamental questions– What is a good average representation of the DSD and just how

average is it?– To what extent are variations predictable in terms of identifiable physical

factors (e.g. adjust DSD in accordance with NWP data)? – What is the impact in terms of Z-R type relationships?

DSDDSD??

RRZZ


Polarimetric Measureables

ZDR

• Good spatial resolution.• Reasonably sensitive to DSD

variations.

• Differential attenuation is a significant problem.

• Affected by factors such as blockage, clutter, etc.

KDP

• Robust in presence of attenuation, blockage or partial beam filling.

• Relatively insensitive in presence of ice.

• Weak and noisy ΦDP signal.

• Poor spatial resolution.• Sensitive to ground clutter.• Relatively insensitive to DSD

variations.

In practice, classification and a combination of techniques likely to be necessary to use polarimetry effectively.


Method

– POLDIRAD has been unavailable throughout course of project due to delays in refurbishment of the radar.

– It has instead been necessary to use data collected by the S-POL radar during the MAP campaign. Two main disadvantages

• Radar operates at S-band. KDP less sensitive to rainfall rate.

• No control over scan strategy. Necessary to use spatial rather than temporal filtering.

– Data examined covers IOPs 2, 4, 7, 8 and 14. Examples shown are from IOP 2 which featured the heaviest rainfall, and where the strongest effects likely to be evident. Other cases were predominantly lower intensity stratiform rainfall.

– In both examples, radar is scanning a sector to the North-West. Terrain is mountainous, hence a relatively high elevation angle being used.

– Analysis focusses on a area 64km square. This could represent a model grid or an idealised river catchment.


S-POL, 16:49 17th September 1999Squall Line Precipitation (IOP2A)


S-POL, 06:20, 20th September 1999Heavy Frontal Precipitation (IOP2B)


Analysis 1

• ZH and KDP converted to Cartesian rainfall products at grid resolutions from 1 to 16km.

• Correspondence should improve as degree of spatial filtering increases, except where there are genuine variations due to DSD.

• But, in practice only marginal improvement in correspondence as resolution reduced.

• KDP is relatively insensitive even in heavy rainfall so noise dominates. This rather than variations in DSD explains scatter.


Analysis 2

• KDP tends to overestimate in light rain and underestimate in heavier rainfall in relation to ZH.

• Possible smearing of KDP in range may lead to location errors. Processing problem?

• Possible presence of hail in 17th September event, but ZDR consistent with rainfall.

• Contamination of KDP on 20th September where beam is above melting layer.

• Averaging in time might provide better results, but restricted by scan strategy.


Conclusions

• In practice, it will be very difficult to use polarimetry directly to improve quantitative rainfall estimates. Signals are generally weak and noisy and affected by similar problems as conventional methods (attenuation, bright band). Situation may be slightly better at C-band, but still not convincing.

• If POLDIRAD had been available, rapid scanning and filtering in time rather than space may have provided more evidence of meaningful DSD variations. However, this would be purely a research method, and does not represent how a real radar would operate.

• Polarimetry may play a secondary role in identifying problems such as attenuation, but requires operationally robust techniques for using this information.


Differential Attenuation

Fig. 8 : Zh (left) and ZDR (right) data collected by the POLDIRAD radar fora thunderstorm in southern Germany on the 10th August 1994. The sectorshown is for azimuths 10o to 50o to a maximum range of 65.1 km and with anelevation angle of 1.1o.

carpe-diem 13/6/02, [email protected], slide 1german aerospace center microwaves and radar...

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