Search

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.

Advantages of using the generalized gamma (GG) model to represent raindrop size distribution (DSD) are discussed, especially when near-complete DSD spectra are considered. Measurements from three climatically different locations in the United States are used. The central approach is the use of double-moment normalization of the measured DSDs using two reference moments, together with an underlying or intrinsic shape, h(x), based on two shape parameters. Stability of h(x) is examined and illustrative examples are presented. It is shown that the modal GG-fitted h(x) representing semiarid and subtropical regimes (combined) provides equally good fits to those from outer rain bands (after landfall) of a Hurricane, a tropical storm, and a tropical depression event. We also show similarities with some recent studies which had used GG model and address the goodness-of-fit aspects of the model, especially when compared with the (much) more widely used standard gamma model. Finally, some applications of the modal GG-fitted h(x) in remote sensing and numerical modeling of microphysical processes are briefly considered.

The Global Precipitation Measurement (GPM) Mission Validation Network (VN) is a NASA software system run at Marshall Space Flight Center which geometrically matches three-dimensional precipitation retrievals from the GPM Core Observatory (CO) sensors to 118 international ground-based radars. To advance the capabilities for validation of the multi-satellite IMERG product, the GPM VN is being updated to integrate this Level-3 (gridded) product alongside GPM’s Level-2 (footprint) products (DPR, CORRA, GPROF). The updated GPM VN will enable the potential for tracing the origins of systematic and random errors back through IMERG into the source GPROF product at instances of GPM-CO overpasses. Furthermore, the GPM VN can support validation efforts to trace the origins of IMERG inaccuracies under a consistent framework across locations including North America (Eastern CONUS, Alaska, Hawaii), Brazil, and Pacific islands (e.g. Kwajalein). This first study with the updated GPM VN will assess the oceanic performance of IMERG V06B across different island sites, as well as stratify errors using the vertical profile of reflectivity and hydrometeor classification corresponding to the IMERG grid pixel. These results will help to inform improvements for future IMERG versions, as well as to aid the community in understanding the conditions under which IMERG can be aptly applied for research and societal applications.

Laboratory measurements of drop fall speeds by Gunn–Kinzer under still air conditions with pressure corrections of Beard are accepted as the “gold standard”. We present measured fall speeds of 2 and 3 mm raindrops falling in turbulent flow with 2D-video disdrometer (2DVD) and simultaneous measurements of wind velocity fluctuations using a 3D-sonic anemometer. The findings based on six rain events are, (i) the mean fall speed decreases (from the Gunn–Kinzer terminal velocity) with increasing turbulent intensity, and (ii) the standard deviation increases with increase in the rms of the air velocity fluctuations. These findings are compared with other observations reported in the literature.

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.

The Lightning Imaging Sensor (LIS) was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm‐scale horizontal resolution during both day and night. ISS LIS data are available beginning 1 March 2017. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground‐based lightning sensors. Initial comparisons with those other sensors suggest flash detection efficiency around 60% (diurnal variability of 51–75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform flash detection capability of ISS LIS from low‐Earth orbit allows assessment of spatially varying flash detection efficiency for other sensors and networks, particularly the Geostationary Lightning Mappers. ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as real‐time lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long‐term global climatology of lightning from space and is the only recent platform that extends the global record to higher latitudes (±55°). The global spatial distribution of lightning from ISS LIS is broadly similar to previous data sets, with globally averaged seasonal/annual flash rates about 5–10% lower. This difference is likely due to reduced flash detection efficiency that will be mitigated in future ISS LIS data processing, as well as the shorter ISS LIS period of record. The expected land/ocean contrast in the diurnal variability of global lightning is also observed.

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.

Diverse airborne and ground-based environmental observations are important technologies for disaster assessment and response, as well as for the validation of environmental satellite observations and atmospheric models which can improve forecasts. The VISAGE (Visualization for Integrated Satellite, Airborne and Ground-based data Exploration) project is working to provide three-dimensional visualization and basic analytics capabilities for such datasets in an interactive user interface. The use of cloud-native, serverless technologies and analysis optimized data storage will position VISAGE for integration with other technologies into a Data Analytic Center Framework.

Precipitation plays a vital role within the Earth system [...]

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.

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.

In the current era of knowledge explosion, it is becoming increasingly critical to help researchers quickly grasp the core ideas and methods used in the sea of published articles. As a first step toward the aim, this paper proposes a novel approach that simulates the cognitive process of how human beings read Earth science articles, and automatically identifies semantic entities from the articles. Among others, one major objective is to identify the datasets studied in articles. Oftentimes, however, researchers do not explicitly cite the datasets used. Thus, we propose a profile-matching method strengthened by a neural network-based method to identify implicitly cited dataset entities based on the context. Our experiments have demonstrated the effectiveness of our approaches.

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.

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.

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.

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 (

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.

Experimental field campaigns of rain precipitation usually require the coexistence of several ground and satellite based observations in order to guarantee a more complete analysis of the collected case studies at the various spa- tial and temporal scales of interest. In the framework of the Ground Validation programme of the NASAA/JAXA Global Precipitation Measurement (GPM) mission, several climate regions of the Earth have been interested by various field campaigns involving experimental setup which include one or more ground based disdrometers and profilers. In such situation a typical implementation of the measurement scheme consists of a pair of K-band verti- cally pointing micro rain radar (MRR) and a 2D video disdrometer (2DVD) installed close each other. Since 2DVD estimates are referred to the ground level, the co-located MRR is supposed to provide complementary vertical pro- files of drop size distribution (DSD) measurements. However, if not properly processed MRR and 2DVD raw data can lead to erroneous interpretations of the underlying microphysics. In this work, we investigate some typical issues occurring when dealing with MRR and 2DVD observations proposing techniques to ensure the adequate data quality required in typical field validation campaigns. More in detail, MRR is an affordable continuous wave frequency-modulated radar (CWFM) typically used at ver- tical incidence. In the MMR configuration used, DSD profiles are estimated from Doppler spectra determined by drops falling at different velocities and at different heights from 1000 meters almost up to the ground level with a vertical resolution of 35 meters and time resolution up to 10 seconds. The importance of the microphysical mea- surements from MRR are related to the effects of the vertical gradients of rain precipitation at the sub-resolution scale of the measurements based remote sensing instruments such as those provided by the dual frequency radar of GPM as well as by ground based weather radars. However, MRR standard processing is based on several assump- tions that, along with some characteristics of the implementation of the CWFM scheme such as spectra aliasing and height-Doppler ambiguity, limit its usefulness for heavy precipitations or in convection. This is particularly true when the influence of air motion on DSD retrieval is not negligible. In this work some of these limitations are investigated through simulations and through a comparison with ground based disdrometers. It is shown that the synergy between 2DVD and MRR measurements, when properly reprocessed, can be exploited to reduce the uncertainty on MRR profiles estimates.

Synergy between micro rain radar and disdrometer to improve raindrop size distribution vertical profiles

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

Measurements from a 2-D video disdrometer (2DVD) have been used for drop size distribution (DSD) comparisons with co-located Parsivel measurements in Huntsville, Alabama. The comparisons were made in terms of the mass-weighted mean diameter, Dm, the standard deviation of the mass-spectrum, σm, and the rainfall rate, R, all based on 1-min DSD from the two instruments. Time series comparisons show close agreement in all three parameters for cases where R was less than 20 mm h−1. In four cases, discrepancies in all three parameters were seen for "heavy" events, with the Parsivel showing higher Dm, σm and R, when R reached high values (particularly above 30 mm h−1). Possible causes for the discrepancies include the presence of a small percentage of non-fully melted hydrometers, with higher than expected fall velocity and with very different axis ratios as compared with rain, indicating small hail or ice pellets or graupel. We also present here Parsivel-to-Parsivel comparisons as well as comparisons between two 2DVD instruments, namely a low-profile unit and the latest generation, "compact unit" which was installed at the same site in November 2009. The comparisons are included to assess the variability between the same types of instrument. Correlation coefficients and the fractional standard errors are compared.

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.

An experimental setup composed of one or more disdrometers at ground and the profilers has been adopted in several field experiments conducted within the Ground Validation programme of the NASAA/JAXA Global Precipitation Measurement (GPM) mission in several climate regions of the Earth. A typical implementation of this scheme consists of a pair of K-band vertically pointing micro rain radar (MRR) and a 2D video disdrometer installed close each other [1]. Among DSD measurements collected by ground based disdrometers with different measurement principles, those form 2DVD are considered to be more accurate within all the typical sizes of drops and are frequently exploited for DSD studies. Since 2DVD estimates are referred to the ground level, the co-located profiling instrument is supposed to provide complementary DSD measurements referred at different heights of the rain “column” above. MRR is an affordable continuous wave frequency-modulated radar (CWFM) typically used at vertical incidence. In the configuration used, DSD profiles are estimated from Doppler spectra determined by drops falling at different velocities and at different heights from almost ground level to 1000 meters with a height resolution of 35 meters and time resolution up to 10 seconds [2]. Importance of these measurements is related to the influence of vertical gradients within measurements cells of rain and microphysics retrievals based remote sensing instruments such as the dual frequency radar of GPM and ground based weather radars [3]. However, MRR standard processing is based on several assumptions that, along with some characteristics of the implementation of the Continuous Wave Frequency modulated scheme such as spectra aliasing and height-Doppler ambiguity, limit the usefulness of this instrument for heavy precipitations or in convection [4]. Using disdrometer measurements as a reference, a critical assessment of the microphysics retrieval in different rain regimes will be illustrated. In particular, the synergy between 2DVD and MRR, properly reprocessed, will be exploited to reduce uncertainty on MRR profiles estimates.

The North Alabama Lightning Mapping Array became operational in November 2001 as a principal component of a severe weather test bed to infuse new science and technology into the short-term forecasting of severe and hazardous weather, principally within nearby National Weather Service forecast offices. Since the installation of the LMA, it has measured the total lightning activity of a large number of severe weather events, including three supercell tornado outbreaks, two supercell hailstorm events, and numerous microburst-producing storms and ordinary non-severe thunderstorms. The key components of evolving storm morphology examined are the time rate-of-change (temporal trending) of storm convective and precipitation characteristics that can be diagnosed in real-time using NEXRAD WSR-88D Doppler radar (echo growth and decay, precipitation structures and velocity features, outflow boundaries), LMA (total lightning flash rate and its trend) and National Lightning Detection Network (cloud-to- ground lightning, its polarity and trends). For example, in a transitional season supercell tornado outbreak, peak total flash rates for typical supercells in Tennessee reached 70-100/min, and increases in the total flash rate occurred during storm intensification as much as 20-25 min prior to at least some of the tornadoes. The most intense total flash rate measured during this outbreak (over 800 flashes/min) occurred in a storm in Alabama. In the case of a severe summertime pulse thunderstorm in North Alabama, the peak total flash rate reached 300/min, with a strong increase in total lightning evident some 9 min before damaging winds were observed at the surface. In this paper we provide a sampling of LMA observations and products during severe weather events to illustrate the capability of the system, and discuss the prospects for improving the short-term forecasting of convective weather using total lightning data.