Radar Data Quality Control

Warn-on-Forecast & High Impact Weather Workshop8 February 2012

Kevin L. ManrossOU/CIMMS/NOAA/NSSL

Importance of Radar QC

• Radar data assimilation: “Garbage in – garbage out… on steroids”– “Any source of data bias will cause bias in the

resulting analysis, even if it is localized in space. For example, poorly removed clutter causes problems when assimilating radial velocity, as these cause errors in the obtained winds that persist in time and may ultimately falsely trigger instabilities…” Fabry (2011 Radar Conference)

2½ Radar QC Techniques

• Multiple ways to skin a cat– There are a number of ways to QC Radar data

• Manually• Automated

• RDA vs RPG– Signal Processing– After products Generated

• Control– End user– Data Collector (radar operator)

• Combination

1) RDA QC

Operate on spectral data at the Radar Data Acquisition (RDA) step

(timeseries” or “Level I”)

Examples• Notch Filter• Clutter Mitigation Decision (CMD)

Algorithm• Gaussian Model Adaptive Processing

(GMAP)• Staggered Pulse Repetition Time

(SPRT)• Phase coding• Many others• Controlled by radar operator*

*If implemented, If operator considers need, If operator trained to do so, If…

2) Product Algorithms (After the RPG)

• Operate on “products” (Reflectivity, Velocity, Correlation Coefficient, etc.) after the Radar Product Generation (RPG) step

• End user enhancements (Control)

• Operates on gridded data

• ExamplesDealiasing (Legacy, Other)Clutter removal (AP-Remove, QCNN, CREM)

• This is where our focus will be

2.5) Dual-Pol

• New RDA• New Products• Great at discriminating

non-hydrometeors / hydrometeors– IDs ground clutter and

biologicals very well• Game Changer (moving

forward)

KMHX21:50Z, 10/19/2011

Current Radar QC on Products

• Efforts at CAPS• Efforts in DART• NSSL MRMS

– Comparison

RADAR QC AT CAPS

Reflectivity Quality Control Flowchart

Read Tilt

Anomalous Radial Removal

Despeckle &Median Filter (opt)

Assemble Volume

Ground Clutter Removal

Despeckle

Continue to Remapping

For All Elev < 1.0

All Tilts

KDDC Sunset & Clutter

RawObs

AnomalousRadialRemoved

Clutter Removal

Radial Velocity Quality Control Flowchart

Read Tilt

Spectrum Width Filter

Assemble Volume

Ground Clutter Removal

Despeckle

Continue to Unfolding

For All Elev < 1.0

Despeckle &Median Filter (opt)

All Tilts

Radial Velocity Quality Control Flowchart

Compare to mean wind

Gate-to-Gate Shear Check

Quadratic Check atGates Marked

Uncertain

Calculate MeanWind Profile

Model Data or

Sounding …Continued

Create perturbation Vr Field

Mean Wind

Profile

Continue to Remapping

KTLX 10 May 2010 6.5° Scan

Raw Obs

Mean Wind

ShearCheck

Quad Fit

DART RADAR QC

Radar-Data Quality Control inData Assimilation Research Testbed (DART) System

• Observation rejection– Observation likelihoods more than a specified number of ensemble standard

deviations away from the prior ensemble mean are not assimilated.• Factors: observation, observation error, ensemble mean, ensemble standard deviation

• Doppler-velocity dealiasing (Miller et al. 1986; Dowell et al. 2010)– Velocities are locally dealiased during preprocessing (e.g., objective analysis).– Final dealiasing occurs within DART immediately before the observation is

assimilated (i.e., the observation is unfolded into the Nyquist-velocity bin closest to the prior ensemble mean).

locally-unfolded,objectively-analyzed

Doppler velocity beforefinal DART dealiasing

NSSL’S MR/MS

For Kevin Manross 22

NMQ “Bloom/AP Removal” Flowchart

Reference: Tang et al. 2011 http://ams.confex.com/ams/35Radar/webprogram/Paper191296.html 2/2/12

For Kevin Manross 23

NMQ Bloom/AP QC example: KCRP & KBRO 06:50Z, 10/13/2011

QCNNRAW BloomAP_QC

RAW BloomAP_QC

2/2/12

For Kevin Manross 24

NMQ Remaining challenges: bloom/AP mixed with rain

2/2/12

Implementation and Comparison of Techniques

• Several techniques identified and implemented to be run in realtime

• Manually cleaned cases• Comparison method:

– Compare • algorithm to raw (unedited)• algorithm to truth (manually edited)• Do gate-by-gate for every elevation scan available• Track gates removed/added/changed

Techniques ImplementedLabel Concept Z/V Strengths Weaknesses

AP-Remove Use 3D structure to determine precipitating echoes from AP Z Works well in precip, and sea

clutterStruggles with widespread/strong clear air echo; removes fine features such as gust fronts

QCNN Neural-net to distinguish between good and bad echoes Z Robust and , and can

incorporate neighboring radars; holistic approach for spatial contiguity

Takes a while to train neural net; multi-radar errors are additive; may need to be retrained for climatology

CREM Realtime clutter map collected during non-clear air sensing Z Adaptable; rerun periodically to

update clutter mapsClutter map needs to probably be run frequently in transition seasons

Legacy Dealias Check against neighboring (previous) radials V Simple and fast; can effectively

incorporate wind profile for improved accuracy

Failures compound; struggles in sparse data areas, particularly near echo tops

2D-Dealias 2D least mean squares run on entire elevation scan V Simple; removes “noisy” velocity

fields (seen in upper tilts); being implemented by NEXRAD

Assumption of smooth field; fails in strong shear (though upgraded improvement)

AR-VAD hi-res VAD, essentially performed at each gate V Good correction without false

dealiasingRejects data in sharp inversions; requires adequate data coverage for VAD (fails at long range isolated cells)

Cases• Using SOLOii to manually edit• Students trained

• 20090605 (VORTEX2 – strongly tornadic)– KCYS (~21-00z; 40 vol. scans; 558 elev. scans)– KFTG (~21-00z; 36 vol. scans; 502 elev. scans)

• 20090611 (VORTEX2 – weakly tornadic)– KPUX (~22-01z; 39 vol. scans; 544 elev. scans)– KGLD (~23-01z; 26 vol. scans; 362 elev. scans)

• 20110524 (strongly tornadic)– KTLX✪ (~20-22z; X vol. scans; Y elev. scans)– KFDR✪(~20-22z; X vol. scans; Y elev. scans)– MPAR (~20-22z; 108 vol. scans*; 1512 elev. Scans)

✪ In progress* Up to 19.5 deg elevation

Reflectivity QC

QCNN

CREM/QCNN

Algo-Raw Truth-Algo

Reflectivity QC

DIFFERENCE N hit miss fa ch

Truth-Raw 102,881,778 41,719,382 706 61,161,676 14

Truth-QCNN 45,372,205 37,428,076 4,198,257 3,745,858 14

Truth-CREM 44,881,268 33,740,708 5,439,618 3,321,613 2,379,329

QCNN-Raw 102,615,371 41,173,948 0 61,441,423 0

CREM-Raw 102,434,663 36,697,101 0 62,993,013 2,744,549

Velocity QC

2D

Legacy0.5 deg

4.0 deg

2D Dealiasing Legacy vs 2D

Velocity QC

DIFFERENCE N hit miss fa

2D-Raw62,898,61

6 61,036,300 1,697,813 164,503

Legacy-Raw62,898,61

6 61,039,078 1,696,103 163,435

Truth-2D62,899,32

3 62,881,353 6,754 8,587

Truth-Legacy62,899,32

3 62,873,003 10,982 12,675

Truth-Raw62,899,32

3 61,033,726 1,698,514 167,063

Future Work

• Xu’s AR-VAD method

Variational Dealiasing Method

Alias operator: vro = Z[vrt + o, vN]

First guess b from combined AR-VAD analysis.

Analysis a minimizes J = (a-b)TB-1(a-b) + ∑i{Z[Hia - vroi, vN]}2/o2

with vroi = vro(fi) filtered by Z[Hib – vroi, vN] ≤ (1 - a)vN,

where a = ¾, ½, ¼ in iteration 1, 2, 3.

(Xu et al. 2009a,b Tellus)

vro

vr+ovN

-vN

b

a

vro

-vN

vN

Ice storm case at 04:36UTC on 1/29/09vro at 1.5o from KTLX with vN = 11.5 m/s

raw obs dealiased

Xu et al. 2011, 2012 JTech (X11, X12 hereafter)

Illustrative example:

Multi-Step Hybrid Dealiasing Method for fine-scale vortices

Basic idea

Use different techniques for different scales and structures as listed below:

1. Variational dealiasing of X11 for broad areas, but flag local misfit on each tilt;

2. Block-to-point continuity check of X12 for local misfit, but flag discontinuities;

3. Beam-to-beam discontinuity check for small areas with discontinuities.

Tornadic case at 22:41UTC on 5/24/2011 vro at 0.5o from KTLX with vN = 28 m/s

Dealiased in step 3Dealiased in step 1

Norman

raw obs

Future Work

• Dual-Pol• SPRT• If/When implemented, future is bright!

Questions