CARPE-DIEM 13/6/02, slide 1German Aerospace Center Microwaves and Radar Institute CARPE-DIEM Besprechung Helsinki, June 2004 Ewan Archibald German Aerospace Center Microwaves and Radar Institute Postfach Weßling, Germany Oberpfaffenhofen,

CARPE-DIEM 13/6/02, slide 2German Aerospace Center Microwaves and Radar Institute 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.

CARPE-DIEM 13/6/02, slide 3German Aerospace Center Microwaves and Radar Institute 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? DSD? RZ

CARPE-DIEM 13/6/02, slide 4German Aerospace Center Microwaves and Radar Institute Polarimetric Measureables Z DR Good spatial resolution. Reasonably sensitive to DSD variations. Differential attenuation is a significant problem. Affected by factors such as blockage, clutter, etc. K DP Robust in presence of attenuation, blockage or partial beam filling. Relatively insensitive in presence of ice. Weak and noisy Φ DP 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.

CARPE-DIEM 13/6/02, slide 5German Aerospace Center Microwaves and Radar Institute 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. K DP 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.

CARPE-DIEM 13/6/02, slide 6German Aerospace Center Microwaves and Radar Institute S-POL, 16:49 17 th September 1999 Squall Line Precipitation (IOP2A)

CARPE-DIEM 13/6/02, slide 7German Aerospace Center Microwaves and Radar Institute S-POL, 06:20, 20 th September 1999 Heavy Frontal Precipitation (IOP2B)

CARPE-DIEM 13/6/02, slide 8German Aerospace Center Microwaves and Radar Institute Analysis 1 Z H and K DP 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. K DP is relatively insensitive even in heavy rainfall so noise dominates. This rather than variations in DSD explains scatter.

CARPE-DIEM 13/6/02, slide 9German Aerospace Center Microwaves and Radar Institute Analysis 2 K DP tends to overestimate in light rain and underestimate in heavier rainfall in relation to Z H. Possible smearing of K DP in range may lead to location errors. Processing problem? Possible presence of hail in 17 th September event, but ZDR consistent with rainfall. Contamination of K DP on 20th September where beam is above melting layer. Averaging in time might provide better results, but restricted by scan strategy.

CARPE-DIEM 13/6/02, slide 10German Aerospace Center Microwaves and Radar Institute 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.

CARPE-DIEM 13/6/02, slide 11German Aerospace Center Microwaves and Radar Institute Differential Attenuation