- By
**clay** - Follow User

- 272 Views
- Uploaded on

Download Presentation
## Doppler Weather Radar Algorithms

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

**Doppler Weather Radar Algorithms**METR 4803 Kurt Hondl National Severe Storms Laboratory 28 April 2005**Basics of Radar Data**• Assumptions • Complete and uniform filling of the radar beam • Standard refraction • Observation Errors / Effects • Calibration • Number of samples / noise • Antenna rotation rate • Beamwidth / sidelobes • Other Issues • Range and velocity aliasing • AP / clutter METR 4803 - Doppler Weather Radar Algorithms**Doppler Weather Radar Observations**• What can we see/detect with weather radars? • Storm cells and features • Thunderstorm structure, supercell, hook echoes • Precipitation, hail • Rotation • Mesocyclone, tornadic vortex signature (TVS) • Wind • Wind profile, 2D wind field METR 4803 - Doppler Weather Radar Algorithms**Algorithm Basics**• What are algorithms? • Automated methods to turn vast amounts of data into useful information • Why use algorithms? • NEXRAD – 14 MB of data every 5 minutes • Humans are very good at visual image processing • But human processing capacity is limited and subject to information overload and fatigue • And human processing varies by individuals METR 4803 - Doppler Weather Radar Algorithms**The Use of Algorithms**• Algorithms are intended to aid the human decision maker • Integrate information • Provide guidance • Be a “safety net” • Identify and rank all features • Let the meteorologist make the final warning decision METR 4803 - Doppler Weather Radar Algorithms**More on Algorithms**• Algorithm capabilities • Number crunching on streaming data • Must be able to process all data in a timely manner • Feature detection through image processing • Pattern vectors, texture, filters • Artificial intelligence • Expert systems, fuzzy logic, neural network, clustering • Reliable stores of feature characteristics • Allows access to trends of information METR 4803 - Doppler Weather Radar Algorithms**More on Algorithms**• Algorithm limitations • Algorithms only as good as the technique • Based on past observations • Simple techniques become complex • Desire to remove false alarms and improve detection efficiency • Most algorithms affected by noise in the data • Adaptable parameter settings • Allows “tuning” of the algorithms to meet needs of forecasters … but this changes performance • Detection vs prediction METR 4803 - Doppler Weather Radar Algorithms**Algorithms Deal with Arrays of Data**METR 4803 - Doppler Weather Radar Algorithms**Scoring Algorithms**• How to evaluate algorithm accuracy • Probability of Detection • POD = H / (H+M) • False Alarm Ratio • FAR = F / (H+F) • Critical Success Index • CSI = H / (H+F+M) • Lead time • RMS error or RMS difference H = forecast event that occurs M = occurrence of event that wasn’t forecast F = forecast event that doesn’t occur METR 4803 - Doppler Weather Radar Algorithms**Where is the Storm? Tornado?**METR 4803 - Doppler Weather Radar Algorithms**What about now?**METR 4803 - Doppler Weather Radar Algorithms**Or now?**METR 4803 - Doppler Weather Radar Algorithms**NEXRAD Number Crunching Algorithms**• Velocity Dealiasing • Composite Reflectivity • Vertically Integrated Liquid water content • Echo Tops • Quantitative Precipitation Estimation • VAD Wind Profile METR 4803 - Doppler Weather Radar Algorithms**Velocity Dealiasing**• Radial velocity observations of velocity outside the Nyquist interval will be aliased (folded) back into the Nyquist interval • Use radial continuity and look for large changes in radial velocity (approx 2*VNyq) • Noisy or non-continuous data present problems • Other techniques being developed • Use 2D information and other data METR 4803 - Doppler Weather Radar Algorithms**Velocity Dealiasing**Aliased Velocity Dealiased Velocity METR 4803 - Doppler Weather Radar Algorithms**Velocity Dealiasing**Aliased Velocity Dealiased Velocity METR 4803 - Doppler Weather Radar Algorithms**CompRefl / VIL / ET**• CompRefl: Maximum value of reflectivity at each 2D location from any elevation angle • Obscures some signatures • Used by forecasters to obtain motion (looping of images) • VIL: An integration of reflectivity with respect to height • Using reflectivity as a substitute for liquid water content • Converted to kg/m2 using a fudge factor • May be contaminated by hail • Echo Top: Altitude of the top of the 18 dBZ echo • Or 10 dBZ, or 0 dBZ • Assumes standard propagation • Height calculated from center of beam • Elevation angles dependent on scan strategy METR 4803 - Doppler Weather Radar Algorithms**QPE**• R = 200 Z 1.6 (Marshall-Palmer formula) • Many Z/R relationships used for different environments • Convective, stratiform, tropical • Accumulates/integrates rainfall over a period of time • Observations may be different than actual rainfall amounts in rain gages • Large areal estimate vs point value METR 4803 - Doppler Weather Radar Algorithms**VAD Wind Profile**• Radial velocity at constant range & elevation varies azimuthally like a sine wave • Phase & amplitude of sine wave used to estimate wind direction and speed • Assumes linearity of the wind field • Estimates at different ranges/elevations provides wind values at different altitudes METR 4803 - Doppler Weather Radar Algorithms**VAD Wind Profile**METR 4803 - Doppler Weather Radar Algorithms**NEXRAD Feature Detection Algorithms**• Storm Cell Identification and Tracking • Hail Detection Algorithm • Mesocyclone Detection Algorithm • TVS Detection Algorithm METR 4803 - Doppler Weather Radar Algorithms**NEXRAD Feature Detection Algorithms**METR 4803 - Doppler Weather Radar Algorithms**Storm Cell Identification & Tracking**• Identifies cell centroids using pattern vectors • Searches for relative maxima in reflectivity data • Works better with filtered data • Correlates centroids across time to determine past locations of the same feature • Uses past locations and linear regression to estimate speed and direction of motion (and to forecast locations) METR 4803 - Doppler Weather Radar Algorithms**Storm Cell Identification and Tracking (SCIT)**• Searches for “gate runs” (segments) using multiple reflectivity thresholds (30, 35, 40,...60 dBZ) on each elevation scan. • Correlates “gate runs” into 2D “features” and extracts cores from multiple reflectivity threshold information. METR 4803 - Doppler Weather Radar Algorithms**Uses reflectivity structure to detect hail**Uses empirical formula obtained from hundreds of reported hail events and associated radar signatures Vertical integration of reflectivity Uses altitude of 0o and -20o C temperature levels Detects hail aloft … before it falls to the ground Estimates produced Probability of any size hail Probability of severe hail (>0.75 inches) Maximum expected size of hail Hail Detection Algorithm METR 4803 - Doppler Weather Radar Algorithms**Uses pattern vectors to detect radial velocity differences**across radials (shear) at a constant range Groups 1D shear vectors into 2D and 3D sets Expert system then classifies detected signatures Circ, CPLT, MESO Neural network to calculate the probability of a tornado associated with the mesocyclone Only cyclonic signatures are detected What is a shear vector? Mesocyclone Detection Algorithm (MDA) METR 4803 - Doppler Weather Radar Algorithms**Find Shear Segments and construct**Vertically associate 2D circulations. 2D circulations. Reflectivity Storm-relative Velocity o 2.4 330o 330o o Storm cloud 1.5 Mesocyclone Mesocyclone 100 km 100 km o 0.5 WSR-88D 99.75 -4.5 -20.0 -20.5 22.0 21.5 19.5 17.5 Mesocyclone Cloud base 99.50 -9.0 -22.0 -22.5 23.0 23.5 22.5 20.0 99.25 -12.0 -22.0 -25.5 24.0 24.5 24.0 20.5 99.00 -20.5 -25.0 -26.5 26.0 27.0 27.0 21.0 Shear Segments 98.75 -15.0 -25.0 -27.0 24.5 28.0 28.5 20.5 Range (km) 98.50 -7.5 -18.5 -23.5 21.5 29.5 30.5 21.0 98.25 -8.5 -19.5 -23.5 19.5 28.0 29.0 20.5 98.00 -5.5 -19.5 -23.0 14.5 27.5 28.5 20.0 97.75 -5.5 -11.0 -20.5 15.5 26.5 27.5 20.0 329.5o 330.5o 331.5o 332.5o 333.5o 334.5o 335.5o Azimuth Track and display output MDA Details Classify and Diagnose 4 Rule Bases (MESO, LOWTOP, WKCIRC, SHALLO) 4 Strength Rank, MSI 4 Neural Network Probabilities METR 4803 - Doppler Weather Radar Algorithms**Similar technique to MDA**Shear must be from adjacent azimuths Shear must be at lowest elevation angle to be a TVS Classifies signatures as Elevated TVS or TVS TVS Detection Algorithm (TDA) METR 4803 - Doppler Weather Radar Algorithms**Reflectivity**SRM Velocity o 2.4 30 30 o o 50 km 50 km o Storm cloud 1.5 60 o 60 o TVS o 0.5 WSR-88D Tornado Cloud base Shear segments 24.5 Range (km) 28.5 -6.5 8.0 53.5 54.5 o o 28.0 -18.0 21.0 Azimuth 27.5 -32.5 27.0 -31.0 23.5 26.5 -15.5 14.0 26.0 -10.5 6.5 Check size/strength 4 Base Height: 0.5 or o < .6 km AGL 4 Depth: >/= 1.5 km 4 Max. Vel. Diff.: Base and 3D TDA Details Find shear segments and construct 2D circulation features Vertically associate 2D circulation signatures Track and display output METR 4803 - Doppler Weather Radar Algorithms**NetRad TDA/MDA**METR 4803 - Doppler Weather Radar Algorithms**Advanced Algorithms**• Multi-Radar, Multi-Sensor Algorithms • Take advantage of increases in computational capacity • Forecast techniques are using inputs from multiple sensors • Algorithms also making use of multiple radars and other sensors to provide a more complete look at the storm and to fill in data gaps METR 4803 - Doppler Weather Radar Algorithms**Data from adjacent radars are available to fill in the**cone-of-silence Complete multi-radar data used for: VIL, POSH, MEHS Multiple Radar SSAP METR 4803 - Doppler Weather Radar Algorithms**KJAN**KLIX KMOB Multiple radars provide one answer METR 4803 - Doppler Weather Radar Algorithms**Combining Data from Multiple Radars**• Mosaic data from multiple radars to create a 3D Cartesian lat/lon/ht grid. • Uses time-weighting and inverse distance weighting schemes. • Can also advect older data when running motion estimator (later slide). • Run algorithms on continuously-updating 3D grids: • 3D reflectivity field for VIL, echo top, LRM, hail • 3D velocity derivative fields for vortex (rotation) and wind shift (convergence) detection • Easy to integrate other sensor information (NSE, satellite, lightning, etc.). METR 4803 - Doppler Weather Radar Algorithms**Multi-Radar VIL Example**METR 4803 - Doppler Weather Radar Algorithms**Reflectivity**Velocity Rotational shear Rotation tracks METR 4803 - Doppler Weather Radar Algorithms**Multi-Doppler Wind Analysis**• View of the same vortex from multiple radars • Simulated radar data from a storm-scale numerical model METR 4803 - Doppler Weather Radar Algorithms**Multi-Doppler Wind Analysis**• Multi-Doppler analysis provides 2D wind vectors in real-time • Wind vectors computed from simulated radar data METR 4803 - Doppler Weather Radar Algorithms**Gridded Hail Products**• A new paradigm in hail information delivery • Improves public service by giving them geo-spatial information on hail size versus a simple yes/no. • Geospatial info also facilitates improved verification. • Coupled with NSSL motion estimation algorithm, capability exists to predict short-term hail swaths. METR 4803 - Doppler Weather Radar Algorithms**Reflectivity (dBZ)**Probability of Severe Hail (>19 mm dia) Two Hour Path of Max Hail Size (mm) Maximum Expected Hail Size (mm) Gridded Hail Products METR 4803 - Doppler Weather Radar Algorithms**Sophisticated technique using statistical segmentation and**error analysis. Can be used on dBZ, IR satellite, VIL, lightning density, etc. Produces high-resolution motion field that can be used to predict hail, precipitation, rotation, lightning, etc. Motion Estimation 00 min Observed Reflectivity at T0 METR 4803 - Doppler Weather Radar Algorithms**Motion Estimation**30 min 30 min Observed Reflectivity at T30 Forecast Reflectivity at T0+30 METR 4803 - Doppler Weather Radar Algorithms**Motion Estimation**60 min 60 min Observed Reflectivity at T60 Forecast Reflectivity at T0+60 METR 4803 - Doppler Weather Radar Algorithms**Quality Control Neural Network**• QCNN uses multiple-sensor information to segregate precipitation echoes from non-precipitation echoes: • Non-precipitating clear-air return • Ground Clutter • Anomalous Propagation (AP) • Chaff • Resulting clean “precipitation” field used as input to other applications (MDA, TDA, QPE) • Lowers the number of False Alarms • Two stages: • Radar-only (texture statistics from all three moments, vertical profiles) • Radar, satellite, and surface temperature (for additional “cloud cover” product). METR 4803 - Doppler Weather Radar Algorithms**Quality Control Neural Network**Radar-only QCNN Original dBZ Cloud Cover (Tsfc – Tsat) Multi-sensor QCNN METR 4803 - Doppler Weather Radar Algorithms**Dual Polarization Hydrometeor Classification Algorithm**• Fuzzy logic algorithm to classify hydrometeor types based on polarimetric data METR 4803 - Doppler Weather Radar Algorithms

Download Presentation

Connecting to Server..