OLYMPEx Precipitation B. Dolan, S. Rutledge, W. Xu and B. Fuchs

Part I: Rainfall Comparison Develop the best rainfall estimates from NPOL Assess the retrieved rainfall from NPOL and GPM during OLYMPEx using coincident overpass cases Select overpasses and coverage area from post and pre-frontal regimes Part 2: DSD Variability Use EOF analysis to examine APU data for modes of variability compared to global datasets

OLYMPEX is COMPLEX! Comparisons need to: Raining areas (no melting layer influence) Limit in range to 40 or 80 km depending on the ML height Comparisons at 1 km height No beam blockage Limit to over the ocean sample Be in Ka/Ku-band swath Grid NPOL to 1 km and degrade to 4 km for comparisons Divided into obviously prefrontal or post-frontal periods Determined due to echo coverage and agreement with classification from J. Zagrondnik 3-hour classifications We will be using the sounding data to investigate these regimes further

Pre-Frontal Overpasses DPR NPOL Comparisons are limited to below the bright band (~ 1km Nov 14, Dec 19, ~2 km for the others) 40 km in range for 14 Nov and 19 Dec 80 km in range for other cases Comparisons are limited to the ocean sector 1169 pts for NPOL 1093 for DPR 14 November 1302 UTC 17 November 2001 UTC 03 December 1522 UTC

Post-Frontal Overpasses DPR NPOL Comparisons are limited to below the bright band (~ 1km Nov 14, Dec 19, ~2 km for the others) 40 km in range for 14 Nov and 19 Dec 80 km in range for other cases Comparisons are limited to the ocean sector 416 pts for NPOL 445 pts for DPR 11 December 0508 UTC 19 December 1058 UTC 11 January 2002 UTC

NPOL Rain Algorithm Thirteen Automated Parsivel Units (APUs) were deployed during OLYMPEx Bad and snow days removed (J. Zagrodnik, A. Tokay) 296000 raining minutes Blended rain algorithm selects rain estimator based on polarimetric thresholds Zdr > 0.25 dB, Kdp > 0.3 º/km Due to the nature of the rain and drop-size distributions, R-Z and R-Z-Zdr are used most frequently R-Z: 145 R1.709 R-Z-Zdr: 0.012Z0.887Zdr-4.563 R-Kdp : 42.5 R0.723 R-Zdr-Kdp: 110.Zdr-2.171Kdp0.934 Still have some work to do here…

Comparisons: NPOL vs. DPR DPR vs. NPOL 4 km Reflectivity RR PDFs Some scatter in reflectivity comparison DPR more frequent high rain rates pre-frontal NPOL more frequent high rain rates in post-frontal

Comparisons: NPOL vs. DPR DPR vs. NPOL Ku vs. NPOL GPM DPR overestimates rain compared to NPOL (degraded) during pre-frontal overpasses GPM DPR underestimates rain compared to NPOL (degraded) during post-frontal overpasses Particularly true for rain rates > 10 mm hr-1 GPM Ku underestimates rain during pre-frontal cases compared to DPR Trends are less clear for the Ku only All results imply different Z-R relationships

Comparisons Conditional rainrate NPOL 1 km resolution > 12 dBZ Ku Ka DPR 14 November 1.18 1.17 1.22 1.43 1.20 1.01 17 November 1.39 1.37 1.46 1.78 2.01 2.98 03 December 1.51 1.48 1.64 1.76 2.44 2.0 11 December 2.73 2.71 2.8 N/A 19 December 1.41 1.29 1.6 0.74 11 January 3.4 3.33 3.74 2.1 2.39 1.36 Ka band had larger conditional rain rates than Ku, owning to the smaller sampling area. Compared to both NPOL and Ku, DPR rain rates are generally higher for pre-frontal systems but the lower for post-frontal systems, except the Nov. 14 case.  Ku and DPR are closer in light rain cases, i.e., 19 Dec. and  11 Jan.

Summary NPOL rain algorithm relies mostly on Z-R and Z-Zdr 3 overpasses from 2 different regimes show differing results GPM DPR overestimates rain compared to NPOL (degraded) during pre- frontal overpasses GPM DPR underestimates rain compared to NPOL (degraded) during post- frontal overpasses Next steps: Still have work to do to figure these differences Improve NPOL rain rates, look at variability as a function of height / location, apply to KLGX Make daily rain accumulation time series with uncertainty bars from NPOL Look at larger rain statistics Compare with iMERG

Part 2: DSD Variability Based on EOF analysis of six DSD variables (LWC, RR, Nw, Dm, µ, Nt), found the first two primary modes of variability in the DSD Using only Fish Hatchery, Amanda Park, and Bishop CRN APUs Same co-variance worldwide +PC1: High RR, LWC, Nw, Dm, low µ -Large rain rates, large sizes, lots of drops -PC1 : Low RR, LWC, Nw, Dm, high µ -Low rain rates, small sizes, fewer drops +PC2: High Nw , low Dm, high µ, high Nt -Lots of little drops, few big drops -PC2: Low Nw, high Dm, low µ -Lower number concentration, large drops

Part 2: DSD Variability Compared to SGP (mid-latitude continental) and Manus (tropical ocean), the OLYMPEx highest density resides at high log(Nw) and low D0 OLYMPEx has few points representing high +PC1, but a significant number of points in +PC2 +PC2 +PC1 Speculate +/- PC1 is convective/ stratiform Speculate +/- PC2 is warm / ice +PC2 maybe orographic enhancement in OLYMPEX?? -PC2 -PC1

Part 2: DSD Variability Future work: Put the disdrometer data in context with radar data Examine processes using model data Look at environmental influences, terrain influences, etc.

µ DSD parameters vs. rain rate densities D0 Nw