Transcript
Aviation Weather Research Program Management Review
NCAR
Advanced Weather Radar Techniques Product Development Team Lead: Kim Elmore Distributed Leads: Cathy Kessinger – NCAR/RAP Tim Schneider – ETL David Smalley - MIT/LL
The AWRT Mission:
Develop and apply new and advanced techniques to data from various radar platforms for the benefit of the aviation community.
AWRT PDT Organization AWRP PDTs
NOAA NSSL Lead: Kim Elmore Polarization/Data Quality Multi-Sensor CONUS 4-D Mosaic
NCAR RAP Alt: Cathy Kessinger Polarization Data quality Remote Retrievals Multi-Sensor
MIT LL Alt: David Smalley Data quality FAA Wx Systems Integration ORPG Implementation
NOAA ETL Alt: Tim Schneider GRIDS Radar System
WARP Support • WSR-88D data for controllers, not meteorologists. • Different approaches to data quality needed; techniques long-range L-band radars no longer adequate. • New approaches to data quality control need to be developed so users have confidence in/don’t feel compelled to second-guess the weather data products displayed to them.
WARP: Reflectivity Reduction Problem Product 96 36
Warp: Reflectivity Reduction Problem 36 96 Product 96i
Circulation Detection Development • Violent or long-lived storms tend circulations/ mesocyclones . to possess • Current WSR-88D algorithms have a very high false alarm rate; unacceptable by controllers. • New robust and reliable circulation detection algorithms needed. • Algorithms that use circulations to diagnose storm severity or estimate storm longevity will be considerably improved by this work.
LLSD: More Accurate Shear Estimates
LLSD Location Accuracy
LLSD: 6 h Accumulated Az Shear
Technical Facilitation •Technical facilitation supports the AWRT PDT algorithm development. •The interface being developed at the NSSL, the WDSS-II, provides a way to develop, validate, verify and demonstrate algorithms developed within this PDT. •WDSS-II provides a route into the Open Radar Product Generation (ORPG) system. WDSS-II supports and incorporates the MITRE Common Operations Development Environment (CODE). •Transfer of algorithms to the MIT/LL ORPG development arm will be straightforward, as anything within WDSS-II must also conform to CODE standards.
WDSS-II Goals: Research • A system for developing new applications • • •
Common set of interfaces. Applications differ only in scientific aspects. Algorithms can colloborate with each other.
• Integration of multiple sources • Treat radars as a network – not limited to singleradar applications. • Integrate lightning, surface observations, satellite, GIS, etc.
• Support research and validation • Many tools and easy automation.
• Open system: easy to add new data sources and new applications.
WDSS-II Capabilities • Real-time Data Integration Integrate radar data with other observing systems for total view of the meteorology • Multiple radar data streams (WSR-88D, Spol, SMART-R, • Surface observations (METARs, mesonets) • • • •
Lightning (NLDN, LMA) Satellite (GOES) Model-generated output (RUC20) Graphical weather products (watches, warnings, SIGMETS, AIRMETS) All data in a common coordinate system • Earth- and time-centric, 4-Dimensional coordinates • All data sources time-synchronized
Multiple Radar • Data from adjacent radar filled the coneof-silence • Complete multiradar data used to compute VIL, for example.
Radars other than WSR -88D WSR-88D •
• • •
Terminal OKC TDWR dBZ overlaid on KTLX WSR-88D dBZ Doppler Weather Radar (TDWR) Research Dopplers-OnOther Radars (GRIDS, commercial, ASR, PAR, etc).
TDWR
KTLX
Radars other than WSR -88D WSR-88D •
• • •
Terminal Doppler Weather Radar (TDWR) Research Dopplers-OnOther Radars (commercial, ASR, PAR, etc).
KOUN
Quality Control Neural Network (QCNN)
Original dBZe
Quality Control Neural Network (QCNN)
Radar Echo Classifier (REC)
Quality Control Neural Network (QCNN)
Quality Control Neural Network (QCNN)
Quality Control Neural Network (QCNN) • Two stages: Radar-only stage (uses texture features and a neural network) Satellite and surf. Temp (optional) • The radar-only stage alone outperforms existing techniques.
Technique
POD FAR CSI
REC (88D)
.44
.37
.35
QCNN
.90
.41
.55
Motion Estimation • Sophisticated technique using statistical segmentation and error analysis. • Can be used on dBZ, IR • Produces high-resolution motion field that can be used to predict hail, precipitation, rotation, lightning, etc.
Actual dBZe
Forecast dBZe
Better Velocity Dealiasing Before After ORPG
After NEW DA
Hurricane Isabel: 9/18/2003
Polarimetry • Volumetric extent of hail, freezing rain, snow, and icing conditions, as well as nonhydrometeor scatterers • Enhanced data quality • Problems associated with sea-clutter, ground
Polarimetric Hail Detection •
2003 Storm Intercept Verification
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Conventional
Provided more accurate verification data (hail/no hail) for 60 storms
Hail Detection Statistics
Conventional POD=88% FAR=39% CSI=0.56 Polarimetric POD=94% FAR=8%
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Method of Detection
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CSI=0.86
Conventional: Hail probability founded Polarimetric: Classification of hail
Location of Hail
Conventional: Probability of hail applies Polarimetric: Specific location of hail
Polarimetric
Benefits of Dual Polarization Technology Hydrometeor Classification Ice
Freezing rain
• •
Rain
Detection of bright band and delineation of rain and snow Freezing rain can be identified if polarimetric data are used in
Heavy Snow Event of 24-25 February 2003 URGENT - WINTER WEATHER MESSAGE NATIONAL WEATHER SERVICE NORMAN OK 555 PM CST MON FEB 24 2003 OKZ041-043-046>048-050>052-250550ATOKA OK-BRYAN OK-CARTER OK-COAL OK-JOHNSTON OK-LOVE OK-MARSHALL OK- MURRAY OKINCLUDING THE CITIES OF...ARDMORE AND DURANT ...HEAVY SNOW WARNING THIS EVENING... SNOWFALL...SOMETIMES HEAVY...IS EXPECTED TO ACCUMULATE 4 TO 6 INCHES BEFORE MIDNIGHT. PERIODS OF HEAVY SNOW WILL REDUCE VISIBILITY SIGNIFICANTLY. ACCUMULATIONS OF HEAVY SNOW IN THE WARNING AREA WILL CAUSE HAZARDOUS DRIVING CONDITIONS. USE EXTREME CAUTION IF TRAVEL PLANS CANNOT BE POSTPONED.
Successful Heavy Snow Warning – “Polarimetric Radar Data significantly increased forecast confidence and likely contributed to several hours additional lead time.” – Dan Miller, NWS Norman Forecaster
Benefits of Dual Polarization Technology – Data Quality Light Rain 08/24/02 (0734 UTC)
INSECTS BIRDS
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Polarimetric classification
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Doppler wind measurement
More Dual Polarization Technology Data Quality Benefits MCS 16 June 2002
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Radar reflectivity factor (Z) can be biased due to radar
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Biases can be addressed if
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Result: Improved echo classification
Dual Polarization Data Quality Benefits
SNOW ~0.85-1.00
CLUTTER ~0.5-0.85
CHAFF ~0.2-0.5 Reflectivity
Correlation Coefficient (ρHV)
Dual Polarization Data Quality Benefits
Reflectivity before quality control
Hydrometeor Reflectivityclassification after quality control algorithm
Approximately 99% of echoes with SNR > 10 dB are correctly Identified as meteorological/non-meteorological by the Hydrometeor Classification Algorithm.
Potential Benefits to Numerical Modeling DSD Retrievals Model initialization Phenomena classification
Dual Polarization: Current Results and Future Plans • Operational demo shows polarimetric radar data can be a great benefit
• With the polarimetric hydrometeor classification algorithm, “Cleaned • Gaining better understanding of polarimetric radar signatures in • Light rain and light snow polarimetric characteristics heavily overlap. • Numerous polarimetric signatures of birds have been collected and are • Still looking into microburst detection. Some promising early results. • A LOT of data left to examine!
Multi Radar Composites • The area for which any arbitrary ARTCC has responsibility likely encompasses the coverage area of several WSR88D installations. • Neither the ROC nor the NWS has in place plans to treat the various WSR-88D installations as a single network, so there are no existing algorithms that use data from more than one radar. • Serious limitation: treating each radar separately leads to ambiguities when the radar data overlap. Currently, the users must independently mitigate these ambiguities, which requires significant knowledge about meteorological radar data and the nature of the algorithms that are run on these data. • Algorithms and techniques aimed specifically at multiple radar composites must be developed so that WSR-88Ds may be treated as a network.
Intensity Bias: Where Does the Damping Occur?
CREF-Raw
CREF - Mosaic
CREF - diff
Most damping occurs within 15km range of radar site
Storm Intensity Reduction •
Results: The 3D mosaic produces smooth and physically realistic 3D reflectivity analysis No significant reduction (3 dB or more) to storm intensity beyond 15km range >=5 dB damping only occurs within 5-10km of radar range
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A new composite reflectivity product derived from raw radar reflectivity (after QC) is generated to show full intensity of storms
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Difference field between raw composite refl and mosaic composite refl is
4D Dynamic Grid: No time weighting vs. exponential time weighting Note smoother reflectivity field with time-weighting No weighting
Exponential weighting
3D National Mosaic Products • Available in NetCDF format currently to CWPDT available to other PDTs by request
• 3D reflectivity mosaic • Composite Reflectivity • 2D reflectivity on 21 constant height levels
1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17km MSL
• 2D reflectivity on 9 constant temperature levels
20, 10, 0, -10, -20, -30, -40, -50, and -60°C
But Wait! There’s More… • CONUS domain 1km × 1km × 500m spatial resolution 5 minute update rate (run time CPU < 150s)
• High Resolution National QPE Products Precipitation Rate and Type Rain/Snow line delineation 1, 3, 6, 12, 24, 72-h accumulations
• National Radar Calibration Monitoring
National Mosaic Domain - 19 Tiles
National Mosaic - Domain Specifications Tile ID
ctrlat (ºN)
ctrlon (ºW)
dx (ºlon)
dy (ºlat)
nx
ny
nz
nradars
P1 N1 N2 N3 N4 N5 N6 A1
45 45 45 45 45 45 45 45
127.5 120 110 100 90 80 70 62.5
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
501 1001 1001 1001 1001 1001 1001 501
1001 1001 1001 1001 1001 1001 1001 1001
21 21 21 21 21 21 21 21
9 18 24 31 41 36 16 2
P2 S1 S2 S3 S4 S5 A2 G1 G2 G3 G3
35 35 35 35 35 35 35 27.5 25 25 25
127.5 120 110 100 90 80 70 110 100 90 80
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
501 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001
1001 1001 1001 1001 1001 1001 1001 501 1001 1001 1001
21 21 21 21 21 21 21 21 21 21 21
6 23 35 48 70 54 15 11 16 30 21
dx≈ 0.715km -- 1.045km dy ≈1.112km
Benchmark Test for National Implementation • Computer LINUX cluster of 10 nodes Each node: 2.8 GHZ CPU, 3GB RAM, 512MB Cache
• Grid -Tile S3 1001x1001x21 47 radars
• Case “Worst Scenario” Simulation
• Mosaic Performance 135.9s CPU 694MB RAM
A fake case: 47 radars, wide spread precip
3D Mosaic Grid
Precipitation Rate and Type
Precipitation Rate
Precipitation Type
Radar Calibration Diagnostic
FY04 Work • TDWR data study and integration into the 3D mosaic • Radar data QC improvements • 3D mosaic grid support for other PDTs • Continued development of the 4D dynamic grid
The AWRT Takes Off! • • • • •
The NEPDT has a broadened venue and is now the AWRT. New, distributed leadership across four major institutions. Bonds forged across several PDTs. Plenty of work to do; lots of improvements to make. Exciting times ahead!
