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

2. 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

3. 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

4. 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

5. 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

6. NPOL Rain Algorithm
Thirteen Automated Parsivel Units (APUs) were deployed during OLYMPEx
Bad and snow days removed (J. Zagrodnik, A. Tokay)
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: 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…

7. 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

8. 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

9. 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.

10. 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

11. 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

12. 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

13. 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.

14. DSD parameters vs. rain rate densities D0 Nw