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 frequently200-km rangeFull-volume 360s, completed in 15-minIncludes rain-mapping angles (0.8,1.3,1.8-deg) & 0.0-deg

“Storm Microphysics”70-80 hours total spread over ~35 casesUsually 150-km range2-3 sector PPI volumes with 0-1 sets of RHIs in 15 minIncludes 360s @ rain-mapping angles (0.8,1.3,1.8-deg)

Insect Filtering

Courtesy of Lee Nelson

Thresholds found empiricallyMore 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 KDP 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 (KDP) calculation.

KDP 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 < 35 dBZ, then we fitted to 31 gates (4.65 km). For Z between 35 and 45 dBZ, we fitted to 21 gates (3.15 km). For Z > 45 dBZ, we fitted to 11 gates (1.65 km). This allowed for more accurate KDP 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 KDP estimation, we unfolded the differential phase field by hand using soloii.

GaseousAttenuationCorrectionBattan (1973)

Polarimetric Correction of Rain Attenuation

Find Slope of LineThis is Decrease in Z per Degree of Phase Shift Do Same Thing for ZDR

Fuzzy-Logic Based Hydrometeor ID

Follow Tessendorf et al. (2005) using mean soundingNo Mixed Precip CategoriesRun 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 RangeBehind Block

Changes to Blockage Correction

Much more stringent KDP calculation

Requirements on KDP and HID (formerly just KDP)

Using entire dataset (formerly just 1 week)

Added Giangrande et al. (2005) correction for ZDR

(formerly set to missing in blocks)

Entire Project, Significant Rain, New Methodology

Interior Ranges

ZDR in Light Rain, Entire ProjectGiangrande et al. (2005) Methodology

S-Pol Corrected Sweeps0.5, 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 Corrected0.0, 0.2, 0.3, 0.4, 0.6, 0.7, 0.9, 1.2, 1.6, 1.7, 1.9All RHIs

Not Requiring Correction2.1+

S-Pol Intercomparison with TRMM

8/18 0304 UTC

S-Pol Intercomparison with TRMM

8/10 0045 UTC

SMN QC

1. Sorted Into 15-Minute Periods2. Threshold on Z, NCP, Power - Then Despeckle3. Correct Guasave Azimuths Due To Backlash4. Clutter Filter For Guasave Developed Using Clear-Air5. Hand Edit Remaining Spurious Echo Using soloii6. Calibrated Via Intercomparison with S-Pol7. Correct Attenuation Using GATE Algorithm (Z=221R1.25)

Final Sensitivity: 10-20 dBZ Minimum Detectable

Guasave (Corrected) Intercomparison with TRMM

New ProductCourtesy of Steve Nesbitt

IR Brightness TemperatureOn 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 & TB Are Not Missing

Sea Clutter

Density of Points, TB vs. Z

Cabo Area (West of 109W, South of 25N)Suggested Filter: Ignore Z w/ TB 290K

Sea Clutter

Density of Points, TB vs. Z

TO DO LIST

1. Double-Check Z-R Via Intercomparison With Gages2. 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