Cabo Guasave S-Pol NAME Radar Data - Product Description & Quality Control

NAME Radar Network Planned ● S-Pol ● 4 SMN Radars ● SMN radars run in full- volume 360s ● 15-min resolution Actual ● S-Pol (7/8-8/21) ● Cabo (7/15-Fall) ● Guasave (6/10-Fall) ● SMN radars single low-level sweep (high temporal resolution)

S-Pol Operations 24-h Ops started 7/8, continued through 8/21 Occasional downtime for Ka-band work in preparation for RICO – Usually mid-morning precipitation minimum Two Modes of Scanning: “Climatology” Used most frequently 200-km range Full-volume 360s, completed in 15-min Includes rain-mapping angles (0.8,1.3,1.8-deg) & 0.0-deg “Storm Microphysics” hours total spread over ~35 cases Usually 150-km range 2-3 sector PPI volumes with 0-1 sets of RHIs in 15 min Includes rain-mapping angles (0.8,1.3,1.8-deg)

Insect Filtering Courtesy of Lee Nelson Thresholds found empirically More stringent than TRMM-LBA

Other Thresholds Standard Deviation of Differential Phase (Z-dependent) [Std Dev of Phase determined over 11 gates; used to be 21] Correlation Coefficient (range-dependent) LDR & Differential Phase (Second Trip) Test pulse removed via range filter, sometimes hand edit [Did not check all sweeps, only 0.8, 1.3, 1.8 SUR]

S-Pol Differential Phase Filtering and K DP Calculation Differential phase was filtered using a 21-gate (3.15-km) finite impulse response filter developed by John Hubbert of NCAR and V. N. Bringi of Colorado State University. Small data gaps (less than 20%; used to be 50%) within this moving window were filled using linear interpolation, in order to increase the amount of usuable windows for subsequent specific differential phase (K DP ) calculation. K DP was calculated from the slope of a line fitted to the filtered differential phase field. The window over which this line was fitted changed depending on the Z of the central gate. If Z 45 dBZ, we fitted to 11 gates (1.65 km). This allowed for more accurate K DP estimates at both high and low Z. For a handful of sweeps during a major storm on 8/3, we found that differential phase became folded due to the large areas of intense rain. Prior to filtering and K DP estimation, we unfolded the differential phase field by hand using soloii.

Gaseous Attenuation Correction Battan (1973)

Polarimetric Correction of Rain Attenuation Find Slope of Line This is Decrease in Z per Degree of Phase Shift Do Same Thing for Z DR

Fuzzy-Logic Based Hydrometeor ID Follow Tessendorf et al. (2005) using mean sounding No Mixed Precip Categories Run ID in polar coordinates Lt Green - Snow, Dk Green - Rain, Yellows - Graupel

Partial Beam Blockage Correction

One Week of Data in Significant Rain - Old Methodology

S-Pol Compositing - Old Methodology Only correct up to 5 dBZ before moving to next angle

Mean Reflectivity (Avg’d in Log Space) Old Methodology, Entire Project

Low Bias At Long Range Behind Block

Changes to Blockage Correction Much more stringent K DP calculation Requirements on K DP and HID (formerly just K DP ) Using entire dataset (formerly just 1 week) Added Giangrande et al. (2005) correction for Z DR (formerly set to missing in blocks)

Entire Project, Significant Rain, New Methodology

Interior Ranges

Z DR in Light Rain, Entire Project Giangrande et al. (2005) Methodology

S-Pol Corrected Sweeps 0.5, 0.8, 1.0, 1.3, 1.4, 1.5, 1.8, 2.0 (PPIs and SURs) (0.8, 1.3, 1.8 have best confidence for Z) Not Corrected 0.0, 0.2, 0.3, 0.4, 0.6, 0.7, 0.9, 1.2, 1.6, 1.7, 1.9 All RHIs Not Requiring Correction 2.1+

S-Pol Intercomparison with TRMM 8/ UTC

S-Pol Intercomparison with TRMM 8/ UTC

SMN QC 1. Sorted Into 15-Minute Periods 2. Threshold on Z, NCP, Power - Then Despeckle 3. Correct Guasave Azimuths Due To Backlash 4. Clutter Filter For Guasave Developed Using Clear-Air 5. Hand Edit Remaining Spurious Echo Using soloii 6. Calibrated Via Intercomparison with S-Pol 7. Correct Attenuation Using GATE Algorithm (Z=221R 1.25 ) Final Sensitivity: dBZ Minimum Detectable

Guasave (Corrected) Intercomparison with TRMM

New Product Courtesy of Steve Nesbitt IR Brightness Temperature On Same Grid As Radar Data 2 & 5 km Resolutions Available

Sea Clutter Near Cabo Easily Identified, If We Had Higher Angle Sweeps!

Entire Project, All Points Where Z & T B Are Not Missing Sea Clutter Density of Points, T B vs. Z

Cabo Area (West of 109W, South of 25N) Suggested Filter: Ignore Z w/ T B  290K Sea Clutter Density of Points, T B vs. Z

TO DO LIST 1. Double-Check Z-R Via Intercomparison With Gages 2. Check Capping of Z-R (Ice Contamination) 3. Create S-Pol Rain Map Composites Using Higher Angles (4. Quantify rainfall errors) Then Send Data to NCAR to Create v2 Regional Composites 3-D S-Pol Data Ready for Research Now! (PPIs, SURs, RHIs; watch out for test pulse) v1 composites in /net/andes/data2/tlang/name

Guasave /net/andes/data2/tlang/smn/guasave/qc (DZA & RR) Cabo /net/andes/data2/tlang/smn/cabo/qc (DZA & RR) S-Pol /net/cook/data/name/spol/NAME_sweeps (thru 8/17) /net/shasta/data/tlang/name (8/18-8/21) (DZC & DRC) USE NCSWP FILES!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Version 3? Use FHC-based data filtering