clemens simmer 1 and malte diederich 1 presented by alessandro battaglia 1 1 university of bonn
DESCRIPTION
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. What can MRR target?. Outlines. Focus on continental BL clouds. - PowerPoint PPT PresentationTRANSCRIPT
Spatial and temporal variability of drop size distribution from
vertically pointing micro rain radar (MRR)
Clemens Simmer1 and Malte Diederich1
Presented by Alessandro Battaglia1
1University of Bonn
AQUARadarSOP
Baltex Bridge
campaignBBC-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=150-105 m3 depending on range) so bridging between gauge and RR resolutionOriginal 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 CellMicro Rain Radar
Resolution Cell
•24.1 GHz Low power FMCW (Frequency Modulated Continuous Wave) Doppler radar;•Beamwidth 2 deg;•Range res 10-200 m (70 m);•Time res 10 s – 1 h (30 s);•Cost around 10.000 euros
Micro Rain Radar MRR-2 concept
Outputs: from MRR Doppler spectrum (after noise subtraction), rdsd grouped in 43 classes with drop diameters from 0.249 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.
http://www.meteo.uni-bonn.de/
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 1MRR 2MRR 3
2D-Video Dis.
Accumulations of drop densities in 0.2mm Accumulations of drop densities in 0.2mm drop diameter intervals in 5 rainy daysdrop 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=250R1.4
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 km3) 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 variabilityis an indicator of
how different spatial samplings
affectthe Z-based R-
estimate
In this other eventdifferent spatial
samplingare 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 inQuantitative 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
R A D A RR A D A R
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