Radar Data Quality Control Warn-on-Forecast & High Impact Weather Workshop 8 February 2012 Kevin L. Manross OU/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 Examples Dealiasing (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) KMHX 21:50Z, 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 Raw Obs Anomalous Radial Removed 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 at Gates Marked Uncertain Calculate Mean Wind Profile Model Data or Sounding …Continued Create perturbation Vr Field Mean Wind Profile Continue to Remapping

KTLX 10 May ° Scan Raw Obs Mean Wind Shear Check Quad Fit

DART RADAR QC

Radar-Data Quality Control in Data 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 before final DART dealiasing

Clutter Residue Editing Map (CREM) Lakshmanan et al Ground-clutter map for a specific radar / radar deployment computed from multi-hour radar-data statistics –Frequency of occurrence of observations with Doppler velocity close to zero and reflectivity above a specified threshold –Function of range, azimuth angle, and elevation angle Clutter-map computation and data removal recently implemented in two software packages used by Warn-on-Forecast project –Observation Processing and Wind Synthesis (OPAWS) Tools for quality control and objective analysis by D. Dowell and L. Wicker –DORADE Radar Editing Algorithms, Detection, Extraction, and Retrieval (DREADER) Tools for editing radar data by Curtis Alexander

CREM: Doppler on Wheels Example frequency of occurrence of observations with relatively high reflectivity and near-zero velocity DOW7 deployment near Dumas, TX 18 May 2010 statistics computed from 1.5-hour dataset statistics computed for each elevation angle (1.0 deg shown here) N S E W 10 km

ReflectivityDoppler Velocity Raw Data Data after CREM Quality Control N S E W 20 km

Clutter Map: WSR-88D Examples 0600 UTC 27 April – 0600 UTC 28 April 2011 locations where more than 50% of observations have Vr ≤ 1.0 m s-1 and ZdB ≥ -20 dBZ at elevation angles 1.8° – 3.1° KLZK (Little Rock, AR)KBMX (Birmingham, AL) Note: no persistent ground clutter detected at elevation angles 0.5° – 1.3° (ground clutter already removed by operational WSR-88D algorithms) 50 km N S E W KLZKKBMX

Clutter Map: WSR-88D Examples 0600 UTC 27 April – 0600 UTC 28 April 2011 locations where more than 50% of observations have Vr ≤ 1.0 m s-1 and ZdB ≥ -20 dBZ at elevation angle 15.6° KLZK (Little Rock, AR)KBMX (Birmingham, AL) ground-clutter contamination from vertical sidelobes? 50 km N S E W KLZKKBMX

NSSL’S MR/MS

NMQ “Bloom/AP Removal” Flowchart Reference: Tang et al /2/1222For Kevin Manross

NMQ Bloom/AP QC example: KCRP & KBRO 06:50Z, 10/13/2011 QCNN RAWBloomAP_QC RAWBloomAP_QC 2/2/1223For Kevin Manross

NMQ Remaining challenges: bloom/AP mixed with rain 2/2/12For Kevin Manross24

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 Implemented LabelConceptZ/VStrengthsWeaknesses AP-Remove Use 3D structure to determine precipitating echoes from AP Z Works well in precip, and sea clutter Struggles 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 maps Clutter 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 dealiasing Rejects data in sharp inversions; requires adequate data coverage for VAD (fails at long range isolated cells)

Cases Using SOLOii to manually edit Students trained (VORTEX2 – strongly tornadic) – KCYS (~21-00z; 40 vol. scans; 558 elev. scans) – KFTG (~21-00z; 36 vol. scans; 502 elev. scans) (VORTEX2 – weakly tornadic) – KPUX (~22-01z; 39 vol. scans; 544 elev. scans) – KGLD (~23-01z; 26 vol. scans; 362 elev. scans) (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 DIFFERENCENhitmissfach Truth-Raw102,881,77841,719, ,161,67614 Truth-QCNN45,372,20537,428,0764,198,2573,745,85814 Truth-CREM44,881,26833,740,7085,439,6183,321,6132,379,329 QCNN-Raw102,615,37141,173,948061,441,4230 CREM-Raw102,434,66336,697,101062,993,0132,744,549

Velocity QC 2D Legacy0.5 deg 4.0 deg 2D Dealiasing Legacy vs 2D

Velocity QC DIFFERENCENhitmissfa 2D-Raw62,898,61661,036,3001,697,813164,503 Legacy-Raw62,898,61661,039,0781,696,103163,435 Truth-2D62,899,32362,881,3536,7548,587 Truth-Legacy62,899,32362,873,00310,98212,675 Truth-Raw62,899,32361,033,7261,698,514167,063

Future Work Xu’s AR-VAD method

Variational Dealiasing Method Alias operator: vr o = Z[vr t +  o, vN] First guess b from combined AR-VAD analysis. Analysis a minimizes J = (a-b) T B -1 (a-b) + ∑i{Z[Hia - vr o i, vN]} 2 /  o 2 with vr o i = vr o (  i) filtered by Z[Hib – vr o i, vN] ≤ (1 -  )vN, where  = ¾, ½, ¼ in iteration 1, 2, 3. ( Xu et al. 2009a,b Tellus ) vrovro vr+ovr+o vNvN -vN-vN b a vrovro -vN-vN vNvN I ce storm case at 04:36UTC on 1/29/09 vr o at 1.5 o 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 vr o at 0.5 o from KTLX with vN = 28 m/s Dealiased in step 3 Dealiased in step 1 Norman raw obs

Vr Birds QC using Dual-Pol Obs Fuzzy logical technique ZDR ρHV ΦDP Birds at night Insects at day Z from KVNX Product Rain Bird & insect AP ΦDPΦDP ZDR Parameter ranges for birds, insects & hydrometeors ΦDPΦDP

Vr Birds QC using Dual-Pol Obs VAD winds vs RUC winds Daytime Nighttime V RUC β RUC V obs β obs VAD wind speed V obs & direction β obs are well correlated to RUC V RUC & β RUC, respectively, in the absence of bird during daytime. Obs from KVNX on 10/28/11 V obs (or β obs ) is not well correlated to RUC V RUC (or β RUC ) in the presence of bird during nighttime: V obs is larger than V RUC ; β obs is divided from β RUC. Daytime Nighttime (Xu & Jiang 2012 ) V RUC V obs β RUC β obs

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

Questions