Non-parametric Methodology to Improve the GPM Combined

Precipitation EstimatesMircea Grecu1,2, Kwo-Sen Kuo3,2 and W.S. Olson4,2

(1) Morgan State University, Baltimore, Maryland, USA

(2)NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

(3) University of Maryland, College Park, Maryland, USA

(4) University of Maryland, Baltimore County, Maryland, USA

Objectives

vDevelop methodology to directly estimate Path Integrated Attenuation (PIA) from observed brightness temperaturesØ Direct estimates are defined as PIA=f(Tb) where f is a

statistical function independent of the radar observations

vInvestigate (qualitatively at first) impact of such PIA estimates on combined GPM retrievalsØ The current GPM combined already incorporates Tb

information into retrievals, but in a more indirect way

11/8/18 IPWG 9th Workshop, Seoul, S Korea 2

Methodology

vUse reliable differential surface technique (dSRT) PIA estimates to create a large database of collocated PIA and associated brightness temperatures

vDevelop statistical procedures (i.e. k-NN and tensorFlowbased) to estimate PIA from collocated brightness temperatures

v Investigate performance using a cross-validation approach

vQualitatively analyze the impact on combined retrievals

11/8/18 IPWG 9th Workshop, Seoul, S Korea 3

Cross validation results

vAbout 200,000 of records derived from GPM over North Atlantic during October and November 2014 are used for training

vTwo months of data (i.e. October and November 2014) over South Atlantic are used for evaluation

11/8/18 IPWG 9th Workshop, Seoul, S Korea 4

Machine-learned vs. dSRT– PIA(Ku)

11/8/18 IPWG 9th Workshop, Seoul, S Korea 5

Implications on GPM combined retrievals

v Insight may be derived by investigating the agreement between the dSRT PIA and the current combined PIA estimatesØ Current combined IPA algorithm: Hitschfeld-Bordan (HB) adjusted

vDisagreement between dSRT and combined PIA could be caused by:1. Large random errors in the dSRT estimates2. Sub-optimal use of the dSRT information in the combined

algorithm

vCombined estimates in the 2nd category can be improved through tighter adjustments of the radar-only retrievals

11/8/18 IPWG 9th Workshop, Seoul, S Korea 6

Combined PIA vs. dSRT– PIA(Ku)

11/8/18 IPWG 9th Workshop, Seoul, S Korea 7

Example DPR observations

11/8/18 IPWG 9th Workshop, Seoul, S Korea 8

Ka-reflectivity and PIA estimates

11/8/18 IPWG 9th Workshop, Seoul, S Korea 9

Cluster analysis of the systematic dSRT combined PIA differences

vWhile the visual inspection of cases suggests that the combined estimates can be adjusted to better fit the dSRT PIA estimates, only a rigorous statistical analysis can determine the optimal approach (i.e. parameterizations, modeling and observations uncertainties, etc.)

vA K-Means cluster analysis can be applied to quantify systematic differences between the dSRT and combined PIAs as a function of the vertical reflectivity distributionØ The K-Means procedure assigns observed Ku-band profiles to

one of 20 classes based on their similarityØ The clustering procedure filters out random differences but

captures the systematic differences

11/8/18 IPWG 9th Workshop, Seoul, S Korea 10

K-Means Cluster Analysis – Mean profiles and class PIA biases

11/8/18 IPWG 9th Workshop, Seoul, S Korea 11

K-Means Cluster Analysis – Class “biases” and average PIA

11/8/18 IPWG 9th Workshop, Seoul, S Korea 12

Findings of the K-Means cluster analysis

vThe cluster analysis confirms that the combined PIA to dSRT PIA ratio increases with the PIA, i.e. Ø It is significantly lower than 1.0 for low PIAs and greater than

1.0 for large PIAsv Some of the systematic differences are not necessarily

signs of biases in the combined algorithm:Ø Non-uniform beam filling (NUBF) tends to decrease the

apparent dSRT PIA relative to the HB PIA, while cloud water tends to increase the dSRT relative to the HB PIA.

vNevertheless, additional work is needed to determine whether NUBF and cloud water alone can explain the differences between dSRT PIA and combined PIA

11/8/18 IPWG 9th Workshop, Seoul, S Korea 13

Summary and conclusions

vStatistical relationships have been derived to estimate dSRT PIA directly from observations

vWhile performance is satisfactory, its impact on GPM combined retrieval is difficult to anticipate as systematic differences between the combined PIA and the dSRT PIA estimates exist even when the dSRT PIA is reliable

vAdditional work is needed to reconcile these differences

11/8/18 IPWG 9th Workshop, Seoul, S Korea 14

11/8/18 IPWG 9th Workshop, Seoul, S Korea 15