Precipitation Estimates by Radars

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Precipitation Estimates by Radars

• V. Chandrasekar, Colorado State University
• R. Meneghini, NASA Goddard Space Flight Center
• I. Zawadzki, McGill University
• Presented at the 2003 Radar Meteorology Conf

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Quantitative Precipitation Estimation

• Radar have come along way in contributing to
  quantitative precipitation estimation.

• One of the major advance is we are starting to
  get a much clearer picture of the error structure
  of QPE.

• This talk will be restricted to few selected
  issues to focus the discussions in this otherwise
  large subject matter.

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We will start with the structure of errors and in
particular, will focus on errors due to beam
broadening and height of measurements
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Ground clutter of the McGill radar on a 0.5
elevation PPI
Laurentians
shadows
Tall building
Adirondacks
120 km
The curvature of the earth and the topography
forces the measurements to be taken well above
ground over a good part of the area
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Computation of the errors due to the range
effect
The effects taken into account are beam
smoothing, post detection integration and height
of measurement
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For a benign stratiform case with a relatively
weak bright band
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Comparison of the values at the measurement
height to actual values at ground as obtained by
projecting non-contaminated measurements at the
20-40 km range to the lowest non-contaminated
position of the measurement over the entire radar
coverage (one hour of data)
Bias due to the on the average change in
reflectivity with height
From Gyu Won Lee (PhD thesis)
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Effect of area averaging
Error structure in height-range coordinates.
BIAS expressed as factors in R Residual RMS
error in
3x3 km2
13x13 km2
From Gyu Won Lee (PhD thesis)
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AREAL RAINFALL ESTIMATE
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Introduction

• The concept of using path integrated measurements
  to Areal Rainfall is discussed in
• Raghavan and Chandrasekar(1994) JAM
• Ryzhkov et al(2000) JAM
• Bringi et al(2001) J. Tech.

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• The areal rainfall is defined as
• R(x,y) is the rain rate field
• Converting this to polar coordinates
• This can be reduced to(with assumptions)

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• In the previous formula for a given beam
  (constant q) AR depends on its boundary values at
  r1 and r2 as well as area under the Fdp range
  profile.

• As azimuth angle changes from q1 to q2, an areal
  sweep of Fdp over rain occurs naturally
  performing spatial integration.

• Advantage No need to estimate Kdp which is a
  difficult estimator

• Good for estimating precipitation over small
  basins

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How well does this work?

• Illustrates the dense gage network near
  Darwin, and the boundaries of the polar area used
  for estimating the area rainfall

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• Time series of mean areal rain rate (Rcsu)
  from using the AR estimator and from the gage
  network (Rg) versus time for the storm event of
  18 February 1999. The radar sampling interval is
  10 min. Standard error bars on Rcsu reflect both
  the parameterization error as well as the
  measurement error

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The storm total rain accumulation from radar
versus gage network accumulation for 12 storm
events. The normalized error is 14.1 and the
normalized bias is 5.6 for the AR estimator.
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• Similar studies have been done with data from
  Florida and Brazil and obtained similar results

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POLARIMETRICALLY TUNED Z-R RELATIONS
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Concept

• Using the normalization of DSD as described in
  Tested et al(2001), the Z-R relation can be
  expressed as

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• The following example shows application of
  such a Z-R relation over time over a profiler and
  gage network

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• Global estimates of rainfall rate
• global rainfall requires a satellite-based
  instrument
• PR alone does not define space-based weather
  radar
• GPM dual-wavelength radar (w/ bore-sighted
  radiom)
• CloudSat (94 GHz cloud radar)
• near-nadir Doppler radar (polarimetry, adaptive
  scan)
• conical-scanning
• geostationary
• common features of PR and ground-based radars
• restricted viewing of rain near surface
• dependence on Z-R relations (at least in part)
• sensitivity to radar calibration errors

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• Global estimates of rainfall rate
• differences of PR with ground-based radars
• attenuation correction required
• near-nadir viewing geometry
• poor temporal resolution/ but w/ global coverage
• polarimetric/ Doppler methods not available
• common features among spaceborne radars (LEO)
• poor temporal resolution
• restricted swath
• use of frequencies at X-band or higher

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• Rain Estimation Methods from Space
• single wavelength
• Hitschfeld-Bordan (initial-value)/ iterative
• Hitschfeld-Bordan with constraints
• ?-, C-adjustment
• final value
• hybrid
• mirror-image/ stereo-radar
• dual-wavelength (w/ or w/o radiometers)
• difference of differences
• Hitschfeld-Bordan with constraints
• integral eqs. for DSD parameters
• constraints
• surface reference method
• radiometer

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• Rain Estimation Methods from Space
• statistical (over large space-time regions)
• probability matching
• fractional-area methods
• log-normal based retrievals

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• Calibration Validation
• Calibration of PR is stable and accurate
• active radar calibrator (CRL)
• ?0 comparisons with TOPEX
• comparisons with WSR-88D dBZ above BB
• Attenuation correction must be checked
• compare on instant. statistical basis dBZ
• above melting layer (cal check)
• near surface (attenuation correction check)
• So too rain rate and rain classification
  accuracy
• compare near-surf rain rates (instant.
  statistically)
• compare rain classification (convective/
  stratiform)

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• Summary
• the TRMM PR has demonstrated the utility of
  space radar
• improved global rain estimation
• more frequent monitoring of severe storms/
  typhoons
• provides rain classification/ improved latent
  heating est.
• increasing use in modeling, data assimilation

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Future Prospects

• Global Precipitation Missions goals
• better temporal resolution (3 hr revisit time)
• use of dual-wavelength radar for
• DSD estimation (rain snow), more accurate RR,
• improved rain classification, phase state
  detection
• Research is needed, however
• better/ more uniform validation methodologies
• dual-wavelength radar-radiometer techniques
• lowered-cost, improved reliability of spaceborne
  radars
• goal is to enable technologies to make radar an
  integral
• part of future satellite precipitation/cloud
  missions

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Summary and future prospects

• It may rightly appear that we are still chasing
  the precipitation estimation problem, but we have
  made lot of progress and have made our goals more
  stringent as we go along.

• Absolute calibration is very critical for QPE

• We are more aware of the error structure of
  precipitation than before, but still lot of
  progress is needed.

• Data quality is emerging as the most important
  issue because research radars have demonstrated
  fairly accurate precipitation estimates with good
  quality data.

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Summary Continued.

• Polarimetric radars will play a significant role
  in
• a) improving data quality
• b) hydrometeor identification and
• c) precipitation measurements in extreme events
  such as flash floods.

• Polarimetric radars have contributed
  significantly to understanding of precipitation
  microphysics and will continue to do so in the
  future.

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Summary Continued ..

• Spaceborne weather radar is still in its
  infancy and many challenges and opportunities lie
  ahead

• The desire to improve space-time sampling from
  space borne observations will drive the science
  and technology

• Rainrate estimates from space will be improved
  from current levels of accuracy

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Summary

• In both global and local scale validation of
  radar rainfall estimates should be emphasized
  (without taking rain gage measurements as eternal
  truth).

• Validation(including coupling of radar estimates
  with hydrological models) is very important for
  making progress.