Patrick Gatlin

Comparison of Raindrop Size Distribution Measurements by Collocated Disdrometers

AbstractAn impact-type Joss–Waldvogel disdrometer (JWD), a two-dimensional video disdrometer (2DVD), and a laser optical OTT Particle Size and Velocity (PARSIVEL) disdrometer (PD) were used to measure the raindrop size distribution (DSD) over a 6-month period in Huntsville, Alabama. Comparisons indicate event rain totals for all three disdrometers that were in reasonable agreement with a reference rain gauge. In a relative sense, hourly composite DSDs revealed that the JWD was more sensitive to small drops (<1 mm), while the PD appeared to severely underestimate small drops less than 0.76 mm in diameter. The JWD and 2DVD measured comparable number concentrations of midsize drops (1–3 mm) and large drops (3–5 mm), while the PD tended to measure relatively higher drop concentrations at sizes larger than 2.44 mm in diameter. This concentration disparity tended to occur when hourly rain rates and drop counts exceeded 2.5 mm h−1 and 400 min−1, respectively. Based on interactions with the PD manufacturer, the p...

A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstor...

Describing the Shape of Raindrop Size Distributions Using Uncorrelated Raindrop Mass Spectrum Parameters

AbstractRainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters Nw, Dm, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of Dm. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/Dm], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter j...

Drop Shapes and Fall Speeds in Rain: Two Contrasting Examples

AbstractTwo rain events are analyzed using two collocated 2D-video disdrometers (2DVD) and a C-band polarimetric radar at 15-km distance. Both events had moderate-to-intense rainfall rates, but the second event had an embedded convective line. For the first event, the fall speed distribution for a given drop diameter interval showed a narrow and symmetric distribution with a mode at the expected value; the second event produced a wider distribution with a significant skewness toward lower fall speeds. The “slower” drops in the second event were detected while the convective line was directly over the 2DVD site. Drop shape information from the two 2DVD instruments showed that, during the passage of the convection line, around 30%–40% of the drops did not have an axis of rotational symmetry, whereas for event 1, it was only 5%. The implications are that for event 1 the dominant mode of drop oscillation is the axisymmetric mode, and that within the convective line of event 2 other fundamental modes were freq...

Estimating the Accuracy of Polarimetric Radar–Based Retrievals of Drop-Size Distribution Parameters and Rain Rate: An Application of Error Variance Separation Using Radar-Derived Spatial Correlations

AbstractThe accuracy of retrieving the two drop size distribution (DSD) parameters, median volume diameter (D0), and normalized intercept parameter (NW), as well as rain rate (R), from polarimetric C-band radar data obtained during a cool-season, long-duration precipitation event in Huntsville, Alabama, is examined. The radar was operated in a special “near-dwelling” mode over two video disdrometers (2DVD) located 15 km away. The polarimetric radar–based retrieval algorithms for the DSD parameters and rain rate were obtained from simulations using the 2DVD measurements of the DSD. A unique feature of this paper is the radar-based estimation of the spatial correlation functions of the two DSD parameters and rain rate that are used to estimate the “point-to-area” variance. A detailed error variance separation is performed, including the aforementioned point-to-area variance, along with variance components due to the retrieval algorithm error, radar measurement error, and disdrometer sampling error. The spat...

Searching for Large Raindrops: A Global Summary of Two-Dimensional Video Disdrometer Observations

AbstractA dataset containing 9637 h of two-dimensional video disdrometer observations consisting of more than 240 million raindrops measured at diverse climatological locations was compiled to help characterize underlying drop size distribution (DSD) assumptions that are essential to make precise retrievals of rainfall using remote sensing platforms. This study concentrates on the tail of the DSD, which largely impacts rainfall retrieval algorithms that utilize radar reflectivity. The maximum raindrop diameter was a median factor of 1.8 larger than the mass-weighted mean diameter and increased with rainfall rate. Only 0.4% of the 1-min DSD spectra were found to contain large raindrops exceeding 5 mm in diameter. Large raindrops were most abundant at the tropical locations, especially in Puerto Rico, and were largely concentrated during the spring, especially at subtropical locations. Giant raindrops exceeding 8 mm in diameter occurred at tropical, subtropical, and high-latitude continental locations. The ...

Toward Completing the Raindrop Size Spectrum: Case Studies Involving 2D-Video Disdrometer, Droplet Spectrometer, and Polarimetric Radar Measurements

AbstractAnalysis of drop size distributions (DSD) measured by collocated Meteorological Particle Spectrometer (MPS) and a third-generation, low-profile, 2D-video disdrometer (2DVD) are presented. Two events from two different regions (Greeley, Colorado, and Huntsville, Alabama) are analyzed. While the MPS, with its 50-μm resolution, enabled measurements of small drops, typically for drop diameters below about 1.1 mm, the 2DVD provided accurate measurements for drop diameters above 0.7 mm. Drop concentrations in the 0.7–1.1-mm overlap region were found to be in excellent agreement between the two instruments. Examination of the combined spectra clearly reveals a drizzle mode and a precipitation mode. The combined spectra were analyzed in terms of the DSD parameters, namely, the normalized intercept parameter NW, the mass-weighted mean diameter Dm, and the standard deviation of mass spectrum σM. The inclusion of small drops significantly affected the NW and the ratio σM/Dm toward higher values relative to u...

The System for Integrating Multi-platform data to Build the Atmospheric column (SIMBA) precipitation observation fusion framework

AbstractResearchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s...

The art and science of data curation: Lessons learned from constructing a virtual collection

Abstract A digital, or virtual, collection is a value added service developed by libraries that curates information and resources around a topic, theme or organization. Adoption of the virtual collection concept as an Earth science data service improves the discoverability, accessibility and usability of data both within individual data centers but also across data centers and disciplines. In this paper, we introduce a methodology for systematically and rigorously curating Earth science data and information into a cohesive virtual collection. This methodology builds on the geocuration model of searching, selecting and synthesizing Earth science data, metadata and other information into a single and useful collection. We present our experiences curating a virtual collection for one of NASAs twelve Distributed Active Archive Centers (DAACs), the Global Hydrology Resource Center (GHRC), and describe lessons learned as a result of this curation effort. We also provide recommendations and best practices for data centers and data providers who wish to curate virtual collections for the Earth sciences.

A Fine-Grained API Link Prediction Approach Supporting Mashup Recommendation

Service (API) discovery and recommendation is key to the wide spread of service oriented architecture and service oriented software engineering. Service recommendation typically relies on service linkage prediction calculated by the semantic distances (or similarities) among services based on their collection of inherent attributes. Given a specific context (mashup goal), however, different attributes may contribute differently to a service linkage. In this paper, instead of training a model for all attributes as a whole, a novel approach is presented to simultaneously train separate models for individual attributes. Meanwhile, a latent attribute modeling method is developed to reveal context-aware attribute distribution. Experiments over real-world datasets have demonstrated that this fine-grained method yields higher link prediction accuracy.