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Radar Quality Control and Quantitative Precipitation Estimation Intercomparison Project Status. Paul Joe Environment Canada Commission of Instruments, Methods and Observations (CIMO) Upper Air and Remote Sensing Technologies (UA&RST). Outline. Project Concept The Problem - PowerPoint PPT Presentation
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Radar Quality Control and Quantitative Precipitation Estimation
Intercomparison ProjectStatus
Paul JoeEnvironment Canada
Commission of Instruments, Methods and Observations (CIMO)Upper Air and Remote Sensing Technologies (UA&RST)
Outline
• Project Concept
• The Problem
• Overview of Data Quality Techniques
• Pre-RQQI Results
• Status
External Factors
Segmenting the DQ Process for Quantitative Precipitation Estimation
Remove Artifacts- Cleaned Up 3D volume
Estimating Surface/3D Reflectivity
Estimating Surface/3DPrecipitation
MosaicingSpace-Time Estimation
Focus on Reflectivity
NowcastingClear Air Echo as Information
Segmenting the DQ Process
Remove Artifacts- Cleaned Up 3D volume
Estimating Surface/3D Radar Moments
Estimating Surface3D Precipitation (Classification)
MosaicingSpace-Time Estimation in 3D
Reflectivity
Radial Velocity
Dual-Polarization
Every radar has clutter due to environment!
Sea Clutter and Ducting
Electromagnetic Interference
Techniques
CAPPI is a classic technique to overcome ground clutter
VVO
5o
4
3
2
1
0
Lines are elevation angles at 1o spacing, orange is every 5o.
Canada Australia
U.S./China VCP21 Whistler Valley Radar
3.0 CAPPI
1.5 CAPPI
There are a variety of Scan Strategies(CAPPI Profiles)
Make better or drop
The elevation angles but nature of weather important for CAPPI
2.5o
1.5o
0.5o
PPI’s1.5km CAPPI
Doppler Zero Velocity Notch
1. Doppler Velocity Spectrum• Pulse pair (time domain)• FFT (frequency domain)
2. Reflectivity statistics
Before
After
Doppler Filtering
SNOWRAIN
Too much echo removed! However, better than without filtering?
Data Processing plus Signal Processing
Texture + Fuzzy Logic + Spectral
Dixon, Kessinger, Hubbert
Data Processing plus Signal Processing
FUZZY LOGIC
Removal of Anomalous Propagation
NONQC QC
Liping Liu, CMA
The Metric of Success
Iso-range “Variance” as an intercomparison Metric
Daniel Michelson, SMHI
Accumulation – a winter season log (Raingauge-Radar Difference)
No blockageRings of decreasing value
Difference increases range!
almost
Convection
Stratiform
Snow
Vertical Profile of Reflectivity is smoothed as the beam spreads in range
Due to Earth curvature and beam propagating above the weather.
Variance Metric
Similar to before except area of partial blockage contributes to lots of scatterAlgorithms that are able to infill data should reduce the variance in the scatter!
Michelson
Proposed Metric
Alternate MetricsAccumulation of Radial Velocity should produce the mean wind for the site.
Both look believable, maybe difference is due to different data set length
nonQC QC
Modality
• Need a variety of techniques
• Need a variety of scan strategies
• Need a variety of data sets that integrate to a uniform pattern
• Need weather with a variety of artifacts
Pilot Study
Purpose is to test the assumptions of the project modality-Short data sets for uniformity-Check the interpretation of the metric-Variety of scan strategies, algorithms, etc-Evaluate feasibility
Uniform Fields
Sample Cases
• Uniform with local clutter (XLA)• Uniform with partial blocking (WVY)• Urban Clutter/Niagara Escarpment (WKR)• Strong Anomalous Propagation Echo (TJ 2006)• Strong AP with Weather (TJ 2007)• Sea Clutter (Sydney AU, Kurnell)• Sea Clutter / Multi-path AP (Saudi 2002)• Convective Weather with Airplane Tracks - One
season (TJ Radar 2007)
XLA
The data accumulates to uniform pattern. Widespread snow. A baseline case. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.
WVY
The data accumulates to uniform pattern with an area of blockage. Widespread snow. A baseline case. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.
WKR
The data accumulates to uniform pattern with an area of blockage. Widespread snow. Urban (skyscrapers) and small terrain clutter. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.
BSCAN of Z accumulation with no filtering, Doppler and CAPPI
CAPPI
Doppler
No Filtering
Azimuth
Ran
ge [
km]
0
1
00
Probability Density Function of Reflectivity as a function of range
Raw Doppler CAPPI
What length of data sets are needed?
Highly Variable More uniform, smoother, more continuous
The Techniques
• Doppler Notching
• CAPPI 1.5km
• CAPPI 3.0km
• Mixed of Doppler Notching and CAPPI
• Radar Echo Classifier (REC)– Anomalous Propagation– Sea Clutter
• REC-CMA
The Statistic
Spread of PDF (at constant range) for various cases and techniques…
Status
Status and Acknowledgements
•Kimata, Japan•Liu, China•Seed, Australia•Michelson, Sweden•Sempere-Torres, Spain•Howard, USA•Hubbert, USA•Calhieros, Brazil•Levizzani, Italy/IPWG•Gaussiat, UK/OPERA HUB•Donaldson, Canada
Data Providers
Algorithm Providers
Evaluation Team
Reviewers
Summary
• On-going• Data Providers, Processors
identified• ODIM_H5 format identified• BOM will host and convert
data for Data Processors• Initial Metric identified• Review
– Variety of techniques– Variety of scan strategies– Variety of data sets – Weather (e.g. convective, snow)
with a variety of artifacts
Alternate radial velocity metric