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The Lightning Forecasting Algorithm (LFA), a simple empirical procedure that transforms kinematic and microphysical fields from explicit-convection numerical models into mapped fields of estimated total lightning flash origin density, has been incorporated into operational forecast models in recent years. While several changes designed to improve LFA accuracy and reliability have been implemented, the basic linear relationship between model proxy amplitudes and diagnosed total lightning flash rate densities remains unchanged. The LFA has also been added to many models configured with microphysics and boundary layer parameterizations different from those used in the original study, suggesting the need for checks of the LFA calibration factors. To assist users, quantitative comparisons of LFA output for some commonly used model physics choices are performed. Results are reported here from a 12-member ensemble that combines four microphysics with three boundary layer schemes, to provide insight into the extent of LFA output variability. Data from spring 2018 in Nepal–Bangladesh–India show that across the ensemble of forecasts in the entire three-month period, the LFA peak flash rate densities all fell within a factor of 1.21 of well-calibrated LFA-equipped codes, with most schemes failing to show differences that are statistically significant. Sensitivities of threat areal coverage are, however, larger, suggesting substantial variation in the amounts of ice species produced in storm anvils by the various microphysics schemes. Current explicit-convection operational models in the United States employ schemes that are among those exhibiting the larger biases. For users seeking optimum performance, we present recommended methods for recalibrating the LFA.

Researchers 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 Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.

Analysis 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 using the 2DVD-based spectra alone. For each of the two events, polarimetric radar data were used to characterize the variation of radar-measured reflectivity Zh and differential reflectivity Zdr with Dm from the combined spectra. In the Greeley event, this variation at S band was well captured for small values of Dm (Zdr tended to 0 dB but Zh showed a noticeable decrease with decreasing Dm. For the Huntsville event, an overpass of the Global Precipitation Measurement mission Core Observatory satellite enabled comparison of satellite-based dual-frequency radar retrievals of Dm with ground-based DSD measurements. Small differences were found between the satellite-based radar retrievals and disdrometers.

CHRISTOPHER R. WILLIAMS, V. N. BRINGI, LAWRENCE D. CAREY, V. CHANDRASEKAR, PATRICK N. GATLIN, ZIAD S. HADDAD, ROBERT MENEGHINI, S. JOSEPH MUNCHAK, STEPHEN W. NESBITT, WALTER A. PETERSEN, SIMONE TANELLI, ALI TOKAY, ANNA WILSON, AND DAVID B. WOLFF Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado NOAA/ESRL Physical Sciences Division, Boulder, Colorado Colorado State University, Fort Collins, Colorado University of Alabama in Huntsville, Huntsville, Alabama NASA Marshall Space Flight Center, Huntsville, Alabama f Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California NASA Goddard Space Flight Center, Greenbelt, Maryland University of Maryland, College Park, College Park, Maryland University of Illinois at Urbana–Champaign, Urbana, Illinois NASA Goddard Space Flight Center Wallops Flight Facility, Wallops Island, Virginia University of Maryland, Baltimore County, Greenbelt, Maryland Duke University, Durham, North Carolina

A 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 greatest numbers of giant raindrops were found in the subtropical locations, with the largest being a 9.7-mm raindrop that occurred in northern Oklahoma during the passage of a hail-producing thunderstorm. These results suggest large raindrops are more likely to fall from clouds that contain hail, especially those raindrops exceeding 8 mm in diameter.

Rainfall 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 joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter Dm and mass spectrum standard deviation σm. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation is constructed to be statistically independent of Dm, with representing the most likely value and std representing its dispersion. Joint PDFs of Dm and μ are created from Dm and . A simple algorithm shows that rain-rate estimates had smaller biases when assuming the DSD breadth of than when assuming a constant μ.

An 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 (

The 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 spatial decorrelation distance was found to be smallest for the R (4.5 km) and largest for D0 (8.24 km). For log10(NW), it was 7.22 km. The proportion of the variance of the difference between radar-based estimates and 2DVD measurements that could be explained by the aforementioned errors was 100%, 57%, and 73% for D0, log10(NW), and R, respectively. The overall accuracy of the radar-based retrievals for the particular precipitation event quantified in terms of the fractional standard deviation were estimated to be 6.8%, 6%, and 21% for D0, log10(NW), and R, respectively. The normalized bias was

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 thunderstorm potential.