Spatial and temporal variability of drop size distribution from vertically pointing micro rain radar (MRR) Clemens Simmer 1 and Malte Diederich 1 Presented by Alessandro Battaglia 1 1 University of Bonn

AQUARadar SOP Baltex Bridge campaign BBC-2 Cabauw, May 2003 Outlines MRR concept What can MRR target? Achievements in the campaign What’s next? Focus on continental BL clouds Instrument intercomparisons between vertically pointing radars, WR, disdrometer, rain gauges Investigation of drop size distributions and consequences for the relation between Z and R Study of small scale variability of prec. field. Back to short time scale rain radar-based rain retrieval

Micro Rain Radars: toward a 4D-remote sensing of the rdsd at sub-WR pixel Possible applications: Better understanding in the whole process of R-retrieval from Z measurements for WR (development of adaptive/dynamic Z- R conversion other than fixed power laws); enhanced validation method for polarimetric weather radar; comparison and validation with spectral microphysical models. Main advantage of the instrument: it avoids sampling errors thanks to its sampling volume (static= m 3 depending on range) so bridging between gauge and RR resolution Original goal: to capture the DSD variability of hydrometeors in a volume similar to a weather radar pixel (hence better 4D-understanding of in-homogeneity in clouds and precipitation), W e a t h e r R a d a r B e a m Weather Radar Resolution Cell Micro Rain Radar Resolution Cell

24.1 GHz Low power FMCW (Frequency Modulated Continuous Wave) Doppler radar; Beamwidth 2 deg; Range res m (70 m); Time res 10 s – 1 h (30 s); Cost around euros Micro Rain Radar MRR-2 concept Outputs: from MRR Doppler spectrum (after noise subtraction), rdsd grouped in 43 classes with drop diameters from to 4.6 mm are estimated. Attenuation correction are applied after computing Mie extinction from retrieved dsd. Radar reflectivity factor Z, rain rate R and mean fall velocity (first doppler moment) W are then derived.

Output layout We get the vertical profile of DSD below the cloud base

Instrument Layout during BBC-2 Disdrometer Twin net Better to use multiple instruments of the same type: if carefully calibrated, this should eliminate all instrumental biases

Intercomparisons of DSD measurements MRR 1 MRR 2 MRR 3 2D-Video Dis. Accumulations of drop densities in 0.2mm drop diameter intervals in 5 rainy days

Comparison with 3 GHz-TARA Measured Ze Z (DSD) noise level Attenuation correction Example of strongly attenuated rain event at 1800 m height

Other comparisons …

Variability of Z/R ratios and power laws from MRR and Disdrometer DSDs Observation of the evolution of Z and rain rate to form Z-R relations and „power laws“ at different altitudes Special attention is paid to track dsd height variation (possible causes & consequences for weather radar estimates) Simple characterization of precipitation by Z/R ratios: High Z/R: most reflectivity contributed from large drops Low Z/R: most reflectivity contributed from small drops

+, +, +: MRR-measurements at 200, 800 and 1500 m + disdrometer at ground level Combined disdrometer -MRR analysis of a BB event Line: Z=250R 1.4

Towards identifying different ‘‘physically homogeneous’’ parts in a raining event …..

Analysis of shallow convection event MRRs are immune to horizontal wind +, +, +: MRR-measurements at 200, 800 and 1500 m + disdrometer at ground level

MRR2 sampled volume must be reconstructed by 3-D distribution of rain drops + wind advection MRR-2 vs De-Bilt RR sampled volume Overlapping the radar grid Up to 10% of the RR volume (0.3 x 0.3 x 1 km 3 ) covered by each MRR. Despite synchronization problems with time stamp of De Bilt scan (not better than 20 s) found good correlation (0.94) with WR Z.

C(mrr1,mrr2) C(mrr1,mrr3) C(mrr2,mrr3) It seems we can!! Correlation increases where there is wind advection and spatial homogeneity (as seen by the RR) Correlation of drop-numbers in single Doppler-bins for 30-second measurements C(mrr1,mrr2) C(mrr1,mrr3) C(mrr2,mrr3) Can spatial variability be resolved with MRR?

Assessing the errors in radar R estimates within a single event caused by spatial in-homogeneity at MRR scales of DSD By averaging consecutive and spatially distributed MRR samples we can mimic a larger volume. Therefore we can compare R accumulated during each event computed at different spatial scales: directly from DSD  AP(dsd) from Z (DSD-derived) by different Z-R  AP(Z). This variability is an indicator of how different spatial samplings affect the Z-based R- estimate In this other event different spatial sampling are equivalent

Experience gained in BBC-2 Relatively new instruments (not a deus ex machina!), a lot gained in BBC-2: evaluated instrument precision/error sources in reflectivity, DSD, rain rate, noise levels (should be below 0 dB), attenuation problems in heavy rain, stability of calibration, better time synchronizations. MRR-2 can be used to study vertical evolution of DSD (thus to address dsd variations by coalescences, evaporation, break up, …) MRR-2 revealed as a useful tool for studying spatial in-homogeneity at short time scale inside RR volume. Errors introduced by using a Z- R relationship derived by a ‘‘point measurement’’ to a RR volume can be studied. To get a deeper insight we need better spatial coverage and higher time resolution.

Advances in Quantitative Areal Precipitation Estimation by Radar (proposed to the DFG) Project Cluster proposed by Clemens Simmer, Susanne Crewell, Michael Griebel Klaus Beheng, University Karlsruhe Stephan Borrmann, Subir Mitra, MPI/University Mainz Martin Hagen, DLR Gerhard Peters, University Hamburg Thomas Trautmann, Gerd Tetzlaff, DLR/University Leipzig Peter Winkler, DWD

MRR contribute to AQUAradar SOP 9-10 MRRs will provide better spatial coverage with higher time resolution (10 s): the volume distribution will no longer have to be interpolated through advection but can be measured directly Possibility of tracking rain shafts Is there any scaling behavior of rain?  Original goals seem achievable SOP to be performed in southern Germany in an overlap area of 2 polarimetric radar (POLDIRAD and DWD radar in Hohenpeissenberg) A wind profiler can measure and compensate the till now unknown error source of vertical wind