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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.

A unique capability of the Global Precipitation Measurement (GPM) mission is its ability to better estimate the raindrop size distribution (DSD) on a global scale. To validate the GPM DSD retrievals, a network of more than 100 ground-based polarimetric radars from across the globe are utilized within the broader context of the GPM Validation Network (VN) processing architecture. The GPM VN ensures quality controlled dual-polarimetric radar moments for use in providing reference estimates of the DSD. The VN DSD estimates are carefully geometrically matched with the GPM core satellite measurements for evaluation of the GPM algorithms. We use the GPM VN to compare the DSD retrievals from the GPM&rsquo, s Dual-frequency Precipitation Radar (DPR) and combined DPR&ndash, GPM Microwave Imager (GMI) Level-2 algorithms. Results suggested that the Version 06A GPM core satellite algorithms provide estimates of the mass-weighted mean diameter (Dm) that are biased 0.2 mm too large when considered across all precipitation types. In convective precipitation, the algorithms tend to overestimate Dm by 0.5&ndash, 0.6 mm, leading the DPR algorithm to underestimate the normalized DSD intercept parameter (Nw) by a factor of two, and introduce a significant bias to the DPR retrievals of rainfall rate for DSDs with large Dm. The GPM Combined algorithm performs better than the DPR algorithm in convection but provides a severely limited range of Nw estimates, highlighting the need to broaden its a priori database in convective precipitation.

Earth science data are being collected for various science needs and applications, processed using different algorithms at multiple resolutions and coverages, and then archived at different archiving centers for distribution and stewardship causing difficulty in data discovery. Curation, which typically occurs in museums, art galleries, and libraries, is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest. Curating data sets around topics or areas of interest addresses some of the data discovery needs in the field of Earth science, especially for unanticipated users of data. This paper describes a methodology to automate search and selection of data around specific phenomena. Different components of the methodology including the assumptions, the process, and the relevancy ranking algorithm are described. The paper makes two unique contributions to improving data search and discovery capabilities. First, the paper describes a novel methodology developed for automatically curating data around a topic using Earth science metadata records. Second, the methodology has been implemented as a stand-alone web service that is utilized to augment search and usability of data in a variety of tools.

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.

In order to help Earth scientists share knowledge and expedite scientific exploration, NASA is developing NASA Earth Science Enterprise (ESE) whose underlying basis is NASA Science Knowledge Graph (SKG). This paper focuses on the information model design of the SKG, where entity-and relationship-encapsulated features are extracted as first-class citizens in SKG. The rationale is that typological structural analysis can thus be exploited for runtime knowledge extraction, discovery, and prediction, in addition to semantic analysis. Based on the information model, a knowledge discovery technique is equipped to answer queries and provide personalized recommendation based upon the SKG. Deep learning techniques, i.e., Stacked Denoising Auto-Encoders (SDAE) and Translating Embeddings (TransE), are applied to detect structural similarity and explore paths at runtime, respectively, supporting higher scalability and performance. Experiments on a science-oriented testbed serves as a proof of concept and demonstrates the feasibility and effectiveness of the techniques.

Vertical variability in the raindrop size distribution (RSD) can disrupt the basic assumption of a constant rain profile that is customarily parameterized in radar-based quantitative precipitation estimation (QPE) techniques. This study investigates the utility of melting layer (ML) characteristics to help prescribe the RSD, in particular the mass-weighted mean diameter (Dm), of stratiform rainfall. We utilize ground-based polarimetric radar to map the ML and compare it with Dm observations from the ground upwards to the bottom of the ML. The results show definitive proof that a thickening, and to a lesser extent a lowering, of the ML causes an increase in raindrop diameter below the ML that extends to the surface. The connection between rainfall at the ground and the overlying microphysics in the column provide a means for improving radar QPE at far distances from a ground-based radar or close to the ground where satellite-based radar rainfall retrievals can be ill-defined.

A technique for separating stratiform and convective rain types using the characteristics of two of the main drop size distribution (DSD) parameters is presented. The method was originally developed based on observations from dual-frequency profiler and dual-polarization radar observations in Darwin, Australia. In this paper, we will present the testing of the method using data from 2D video disdrometers (2DVD) from two very different locations, namely, Ontario, Canada, and Huntsville, Alabama, USA. One-minute DSDs from 2DVD are used as input to a gamma-fitting procedure and our separation technique uses the fitted values of log 10 ( N W ) and D 0 (where N W is the scaling parameter and D 0 is the median volume diameter) and an “index” to quantify where the points lie in the log 10 ( N W ) versus D 0 domain. For the Ontario location, the output of the classification is compared with simultaneous observations from a collocated, vertically pointing, X-band Doppler radar. A “bright-band” detection algorithm is used to classify each height profile as either stratiform or convective, depending on whether or not a clearly defined melting layer is present at an expected height. If present, the maximum reflectivity within the melting layer and the corresponding height are determined. Similar testing is carried out for two events in Huntsville and compared with observations from a collocated UHF profiler (with Doppler capability). Additional case studies are required, but these results indicate our separation technique seems to be applicable to many different locations and climatologies based on previously published data.

Accurate measurements of rain drop size distributions (DSD), with particular emphasis on small and tiny drops, are presented. Measurements were conducted in two very different climate regions, namely Northern Colorado and Northern Alabama. Both datasets reveal a combination of (i) a drizzle mode for drop diameters less than 0.7 mm and (ii) a precipitation mode for larger diameters. Scattering calculations using the DSDs are performed at S and X bands and compared with radar observations for the first location. Our accurate DSDs will improve radar-based rain rate estimates as well as propagation predictions.

A measurement scheme aimed at investigating precipitation properties based on collocated disdrometer and profiling instruments is used in many experimental campaigns. Raindrop size distribution (RSD) estimated by disdrometer is referred to the ground level; the collocated profiling instrument is supposed to provide complementary estimation at different heights of the precipitation column above the instruments. As part of the Special Observation Period 1 of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, conducted between 5 September and 6 November 2012, a K-band vertically pointing micro rain radar (MRR) and a 2D video disdrometer (2DVD) were installed close to each other at a site in the historic center of Rome (Italy). The raindrop size distributions collected by 2D video disdrometer are considered to be fairly accurate within the typical sizes of drops. Vertical profiles of raindrop sizes up to 1085 m are estimated from the Doppler spectra measured by the micro rain radar with a height resolution of 35 m. Several issues related to vertical winds, attenuation correction, Doppler spectra aliasing, and range-Doppler ambiguity limit the performance of MRR in heavy precipitation or in convection, conditions that frequently occur in late summer or in autumn in Mediterranean regions. In this paper, MRR Doppler spectra are reprocessed, exploiting the 2DVD measurements at ground to estimate the effects of vertical winds at 105 m (the most reliable MRR lower height), in order to provide a better estimation of vertical profiles of raindrop size distribution from MRR spectra. Results show that the reprocessing procedure leads to a better agreement between the reflectivity computed at 105 m from the reprocessed MRR spectra and that obtained from the 2DVD data. Finally, vertical profiles of MRR-estimated RSDs and their relevant moments (namely median volume diameter and reflectivity) are presented and discussed in order to investigate the microstructure of rain both in stratiform and convective conditions.