Miguel Angel Rico-Ramirez - hydrology · Miguel Angel Rico-Ramirez 26 th Feb 2014, University of...
Transcript of Miguel Angel Rico-Ramirez - hydrology · Miguel Angel Rico-Ramirez 26 th Feb 2014, University of...
Improving data quality with dual-
polarisation weather radars
Miguel Angel Rico-Ramirez26th Feb 2014, University of Bristol
Outline
• Precipitation measurement
• Errors in radar rainfall measurements
• Identification of non-meteorological echoes with
DP radar
• Attenuation correction with DP radar
• Hydrometeor classification and BB identification
• Concluding comments
Precipitation measurementThere are three ways to measure precipitation:
1. Point measurements (raingauges, snow pillows, disdrometers)
• Traditional, long established ⇒ long time series
• Point measurement ⇒ prone to sampling error because the
spatial variability in precipitation is not known
• Raingauges prone to instrumental errors and siting bias
2. Ground-based radar measurements
• Spatially-distributed measurements
• Real-time
• Measurements prone to error
• Difficult to measure rates of snowfall
• New, recently established ⇒ no long time series
3. Satellite-based measurements
• Experimental
• Potential for global coverage including inaccessible regions
and oceans
• Currently no snow fall products available
• Temporal sampling problem (snapshot available when a
satellite is overhead)
Increasingly, merged products are developed that combine a number
of these data sources to develop a best-estimate precipitation
product.
>1km2
5min
~200cm2
0.2mm tips
Global Precipitation Measurement
>25km2
3hr
Precipitation measurement with weather radars
RADAR – Radio Detection and Ranging
Principle:
• Transmit pulse of electromagnetic
radiation in a known, changing direction
with moving antenna.
• Some of the radiation is reflected back by
hydrometeors (e.g. rain, snow, hail, etc)
• Measure the strength of the return as a
function of time to find the distance of the
reflection from the radar system.
The relationship between the rainfall rate (R in
mm/hr) and the radar reflectivity (Z in mm6 m-3) is
given by an empirical Z-R relationship Z=aRb,
where the coefficients a and b depend on the
storm characteristics and can vary within the storm
(Z=200R1.6 is used in the UK and Z=300R1.4 is
used in the US).
Quantitative rain estimation with radars is dubious
without ground-based rain gauges to provide
absolute calibration.
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But radar measurements are prone to error …
1. Radar beam overshooting the shallow precipitation at long ranges. Radar beam is at
several kilometers above the ground at long ranges.
2. Low level evaporation of precipitation beneath the radar beam
3. Orographic enhancement above hills which goes undetected beneath
4. Vertical profile of reflectivity. There is a variation of reflectivity in the vertical.
Uncertainty in the extrapolation of the reflectivity measured aloft to the ground.
5. Overestimation of precipitation in the melting layer (bright band). If the radar beam
intercepts the ML, the result is an increase of power reflected back to the radar.
Errors can be up to a factor of 5 in the bright band.
6. Changes in the Drop Size Distribution N(D). This affects the Z-R relationship.
7. Partial beam blocking. Hills close to the radar block the beam path. This blocking can
be total or partial.
8. Representativeness errors. Radar scans with a given spatial and temporal resolution
9. Attenuation by hydrometeors and atmospheric gases. Affects higher frequencies
(e.g. C-band=5GHz or X-band = 10GHz).
10.Ground clutter. Ground echoes because radar scans at low elevation angles.
11.Anomalous propagation of the radar beam due to changes in the atmospheric
conditions. The path of the beam departs from standard propagation and in some
cases it is bent towards the earth surface producing ground echoes.
12.Radar miscalibration. This can bias the rainfall estimations.
Moist
Dry
But radar measurements are prone to error …
Areas where dual-polarisation radar can help
9. Attenuation by hydrometeors and atmospheric gases. Affects higher frequencies
(e.g. C-band=5GHz or X-band = 10GHz).
10.Ground clutter Ground echoes because radar scans at low elevation angles.
11.Anomalous propagation of the radar beam due to changes in the atmospheric
conditions. The path of the beam departs from standard propagation and in some
cases it is bent towards the earth surface producing ground echoes.
12.Radar miscalibration This can bias the rainfall estimations.
13.Hydrometeor Classification To classify rain, snow, melting snow, hail.
14.Rainfall estimation – Use of phase measurements in heavy rain and to correct for
attenuation.
Background on dual-polarisation radars
[ ]r
rdDDNDfDf
kK vvhhdp
d
)(d5.0)()()(Re
2 dp
0
Φ=−= ∫
π
hhσ
∫∫ ≈= dDDNDdDDND
K
Zhhhh
)()()( 6
25
4
σ
π
λ
∫≈ dDDNDR )(67.3
vvσ
Raindrops falling to the ground are
distorted into oblate spheroids due to
aerodynamic forces, being in average their
larger dimensions horizontally oriented
b
hhaRZ =
vv
hh
Z
ZZDR log10=
Size of raindrops
hvρ LDR V W
Clutter signals in weather radars
Clutter are those unwanted echoes on
weather radar scans:
• Fixed ground echoes (predictable)
• Anomalous propagation echoes
(unpredictable)
• Sea echoes (unpredictable)
• Birds, airplanes, ships (unpredictable)
• Wind farms
Predictable Ground Clutter
Anomalous Propagation (anaprop)
Non-meteorological echoes in radar rainfall measurements
Rico-Ramirez & Cluckie (2008) Classification of ground clutter and anomalous propagation
using dual-polarization weather radar, IEEE Trans. On Geoscience and Remote Sensing, 46,
1892-1904.
Training of the classifiersClutter Precipitation
Zh
ZDR
Φdp
ρhv
The noisier the dual-pol radar measurement for a
particular pixel, the more likely to be clutter
Identification of non-meteorological echoes in radar rainfall measurements
Rico-Ramirez & Cluckie (2008) Classification of ground clutter and anomalous
propagation using dual-polarization weather radar, IEEE Trans. On Geoscience and
Remote Sensing, 46, 1892-1904.
clutter
precipitation
Radar measurement
),...,(
)|()(
),...,|(1
1
1
n
n
i
i
n
xxP
cxPcP
xxcP
∏=
=
Raw reflectivity Clutter Corrected Reflectivity
Identification of non-meteorological echoes in radar rainfall measurements – Thurnham Radar
Raw reflectivity Clutter Corrected Reflectivity
Identification of non-meteorological echoes in radar rainfall measurements – Wardon Hill Radar
Using the classifier developed for Thurnham!
Uniform rain rate of 20 mm/h
10 km path
20 mm/h
PIA=0.1 dB
1 dB
5 dB
(3 GHz)
(5.5 GHz)
(10 GHz)
Radar measurements are affected by attenuation at higher frequencies
Chenies radar Thurnham radar
Radar signal attenuation in heavy rain at C-band
Polarimetric Measurements
dp∆Φ
Zh
ZDR
Φdp
ρhv
Attenuation is a function of Φdp
Raw reflectivity Attenuation-corrected reflectivity
Attenuation Correction at C-band
Rico-Ramirez (2012) Adaptive attenuation correction techniques for C-band
Polarimetric Weather Radars, IEEE Trans. On Geoscience and Remote Sensing,
50, 5061-5071.
Attenuation correction – Does it work?
Bringi, Rico-Ramirez & Thurai (2011) Rainfall Estimation with an Operational Polarimetric C-Band Radar in the
United Kingdom: Comparison with a Gauge Network and Error Analysis, J. Hydrometeorology, 12, 935-954.
Total rain in mm
19-24 Jul 2007
Radar rain
accumulations
shown in colour;
Gauge
accumulations
shown with
numbers
Errors:
Attenuation correction – Does it work?
Rico-Ramirez (2012) Adaptive attenuation correction techniques for
C-band Polarimetric Weather Radars, IEEE Trans. On Geoscience
and Remote Sensing, 50, 5061-5071.
Polarimetric Radar Rainfall Estimation
Bringi, Rico-Ramirez and Thurai (2011), J. Hydrometeorology
R2≡ Z-R, Z attenuation uncorrected
R1≡ Z-R, Z attenuation corrected
RC≡ f(Zh, Zdr and Kdp)
RK ≡ f(Kdp)
Bright band (melting ice)
Raindrops(below the bright band)
Snowflakes(above the bright band)
Range
He
igh
t (
km
)
Z R
Variation of the Vertical Profile of Reflectivity
Rico-Ramirez et al (2007), A high-resolution experiment in
the Island of Jersey, Meteorological Applications, 14, 117-
129.
Variation of the Vertical Profile of Reflectivity
Snow
Rain
Zh
Zdr
Melting Snow
Rico-Ramirez et al. (2005), Atmos. Science Letters, 6, 40-46.
Classification of rain, snow and melting snow @ S-band frequencies
Zh
Ldr
Real-time bright band correction
Kitchen et al (1994)
Rico-Ramirez et al (2005), Atmos Letters
The bright band was identified using
Zh, ZDR and LDR in a fuzzy classifier.
Snow
Melting
Snow
Rain
Concluding comments
• Weather radar has the advantage of providing rainfall measurements with high- spatial and temporal resolution.
• The use of DP radar for automatic removal of clutter echoes improves data quality. This is particularly important during anomalous propagation conditions.
• The use of ΦDP to correct Zh for attenuation improves the estimation of precipitation.
• There is still a lot of work to do to fully exploit the potential of DP radar to improve rain estimation in real-time operation (e.g. BB identification/correction, auto-calibration, dynamic compositing)
Thanks!
References
• Rico-Ramirez, MA. 'Adaptive attenuation correction techniques for C-band
polarimetric weather radars', IEEE Transactions on Geoscience and Remote
Sensing, 50, (5061-5071), 2012. DOI: 10.1109/TGRS.2012.2195228
• Islam, T, Rico-Ramirez, MA, Han, D & Srivastava, PK. 'Artificial Intelligence
Techniques for Clutter Identification with Polarimetric Radar Signatures', Atmospheric
Research, 109-110, (pp. 95-113), 2012. DOI: 10.1016/j.atmosres.2012.02.007
• Bringi, VN, Rico-Ramirez, MA & Thurai, M. 'Rainfall Estimation with an Operational
Polarimetric C-band Radar in the UK: Comparison with a Gauge Network and Error
Analysis', Journal of Hydrometeorology, 12, (pp. 935-954), 2011. ISSN: 1525-755X
DOI: 10.1175/JHM-D-10-05013.1
• Rico-Ramirez, MA & Cluckie, ID. 'Classification of ground clutter and anomalous
propagation using dual-polarization weather radar', IEEE Transactions on
Geoscience and Remote Sensing, 46 (7), (pp. 1892-1904), 2008. ISSN: 0196-2892
DOI: 10.1109/TGRS.2008.916979
• Rico-Ramirez, MA, Cluckie, ID & Han, D. 'Correction of the bright band using dual-
polarisation radar', Atmospheric Science Letters, 6 (1), (pp. 40-46), 2005. ISSN:
1530-261X DOI: 10.1002/asl.89