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A hierarchical analysis of the impact of methodological decisions on statistical downscaling of daily precipitation and air temperatures

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet-day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for T min than T max . Furthermore, use of PCA-derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet-day threshold increases and models using PCA-derived predictors tend to outperform those based on grid cell predictors. Each model for wet-day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN-based models and those with larger predictor suites tend to have the smallest bias.

54 ENVIRONMENTAL SCIENCES↗

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005–2012

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. To discover obesity-related mixed chemical exposure patterns in the general U.S. population. Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

54 ENVIRONMENTAL SCIENCES↗

Holes in Optical Lightning Flashes: Identifying Poorly Transmissive Clouds in Lightning Imager Data

Space-based optical lightning sensors including the lightning imaging sensor (LIS) and geostationary lightning mapper (GLM) are pixelated imagers that detect lightning as transient increases in cloud top illumination. Detection requires optical emissions to escape the cloud top to space with sufficient energy to trigger a pixel on the imaging array. Through scattering and absorption, certain clouds are able to block most light from reaching the instrument, causing a reduction in detection efficiency (DE) and possibly location accuracy (LA). Radiant lightning emissions that illuminate large cloud top areas are used to examine scenarios where clouds block light from reaching orbit. In some cases, these anomalies in the spatial radiance distribution from the lightning pulse lead to “holes” in the optical lightning flash where certain pixels fail to trigger. Such holes are identified algorithmically in the Tropical Rainfall Measuring Mission satellite LIS record and the microphysical properties of the coincident storm region are queried. We find that holes primarily occur in tall (IR T b < 235 K) convection (87%) and overhanging anvil clouds (10%). The remaining 3% of holes occur in moderate-to-weak convection or in clear air breaks between stormclouds. We further demonstrate how an algorithm that assesses the spatial radiance patterns from energetic lightning pulses might be used to construct an optical transmission gridded stoplight product for GLM that could help operators identify clouds with a potentially reduced DE and LA.

54 ENVIRONMENTAL SCIENCES↗

A divalent cation-dependent variant of the glmS ribozyme with stringent Ca 2+ selectivity co-opts a preexisting nonspecific metal ion-binding site

Ribozymes use divalent cations for structural stabilization, as catalytic cofactors, or both. Because of the prominent role of Ca 2+ in intracellular signaling, engineered ribozymes with stringent Ca 2+ selectivity would be important in biotechnology. The wild-type glmS ribozyme (glmSWT) requires glucosamine-6-phosphate (GlcN6P) as a catalytic cofactor. Previously, a glmS ribozyme variant with three adenosine mutations (glm SAAA ) was identified, which dispenses with GlcN6P and instead uses, with little selectivity, divalent cations as cofactors for site-specific RNA cleavage. We now report a Ca 2+ -specific ribozyme (glmSCa) evolved from glm SAAA that is >10,000 times more active in Ca 2+ than Mg 2+ , is inactive in even 100 mM Mg2+, and is not responsive to GlcN6P. This stringent selectivity, reminiscent of the protein nuclease from Staphylococcus, allows rapid and selective ribozyme inactivation using a Ca 2+ chelator such as EGTA. Because glmSCa functions in physiologically relevant Ca 2+ concentrations, it can form the basis for intracellular sensors that couple Ca 2+ levels to RNA cleavage. Biochemical analysis of glmSCa reveals that it has co-opted for selective Ca 2+ binding a nonspecific cation-binding site responsible for structural stabilization in glmSWT and glmSAAA. Fine-tuning of the selectivity of the cation site allows repurposing of this preexisting molecular feature.

59 BASIC BIOLOGICAL SCIENCES↗

Discovering the desirable alleles contributing to the lignocellulosic biomass traits in saccharum germplasm collections for energy cane improvement

Phenotyping Methods: The accessions (which includes 21 taxa and 1,177 accessions) in the World Collection of Sugarcane and Related Grasses (WCSRG) was evaluated for the following traits: arenchyma, internode length and diameter, pubescence, pith, Brix, stalk number and fiber. A core of 300 accessions that included each species in the World Collection was selected by using the Maximization Strategy in MStrat software. Results: The core had a higher diversity rating than random selections of 300 accessions. The Shannon–Weaver Diversity Index scores of the core and whole collection were similar indicating that the majority of the diversity was captured by the core collection. The ranges and medians between the core and WCSRG were similar; only two of the trait medians were not significant at P = 0.05 using the non-parametric Wilcoxon method and the coincidence rate (CR % = 96.2) was high (>80) indicating that extreme values were retained. Thus, the phenotypic diversity of these traits in the WCSRG was well represented by the core collection. Associations Methods: Genotypic and phenotypic data were collected for 1002 accessions of the WCSRG including 209 SSR markers. Association analysis was performed using both General Linear (GLM) and Maximum Likelihood (MLM) models. Different core collections with 300 accessions each were selected based on genotypic, phenotypic and combined data based on the Maximization Strategy in MStrat software. Results: A major portion of the genotyping involving SNPs is being conducted by Dr. Jianping Wang of the University of Florida under the DOE award DE-FG 02-11ER 65214 and the genotypic and phenotypic associations will be reported separately next year. In the current, study forty one and seventeen markers were found to be associated with traits using the GLM and MLM methods respectively including associations with arenchyma, internode length and diameter, pubescence, pith, and Sugar Cane Yellow Leaf Virus. The data indicates that each of the cores and the World Collection are similar to each other genotypically and phenotypically, but the core that was selected using only genotypic data was significantly different phenotypically. This indicates that there is not enough association between the genotypic and phenotypic diversity as to select using only genotypic diversity and get the full phenotypic diversity. Core Collection: Creation and Phenotyping Methods: To evaluate this germplasm for breeding purposes, a representative diversity panel selected from the WCSRG of approximately 300 accessions was planted at Canal Point, FL in three replications. These accessions were measured for stalk height and stalk number multiple times throughout the growing season and Brix and fresh biomass during harvest in 2013 and, stalk height, stalk number, stalk diameter, internode length, Brix and fresh and dry biomass was determined in the ratoon crop harvest in 2014. Results: In correlations of multiple measurements, there were higher correlations for early measurements of stalk number and stalk height with harvest traits like Brix and fresh weight. Hybrids had higher fresh mass and Brix while Saccharum spontaneum had higher stalk number and dry mass. The heritability of hybrid mass traits was lower in the ratoon crop. According to the principal component analysis, the diversity panel was divided into two groups. One group had accessions with high stalk number and high dry biomass like S. spontaneum and the other groups contained accessions with higher Brix and fresh biomass like S. officinarum. Mass traits correlated with each other as expected but hybrids had lower correlations between fresh and dry mass. Stalk number and the mass traits correlated with each other except in S. spontaneum and hybrids in the first ratoon. There were 110 accessions not significantly different in Brix from the commercial sugarcane checks including 10 S. spontaneum accessions. There were 27 dry and 6 fresh mass accessions significantly higher than the commercial sugarcane checks. Core Collection: Fiber analysis Methods: A biomass sample was taken from each accession then shredded and dried. Fiber analysis was then performed on each sample. The acetyl groups, acid insoluble lignin, acid soluble lignin, arabinan, glucan, holocellulose, total lignin, structural ash, and xylan were quantified on a % fiber basis and nonstructural ash on a % total basis. Results: There were significant, but not large differences between species for holocellulose, lignin, acetyl, acid soluble lignin, nonstructural ash, and glucan. For each trait, S. spontaneum had significantly more holocellulose, glucan, lignin, and nonstructural ash and less acetyl and acid soluble lignin than the other species. In all populations, glucan and was positively correlated with holocellulose were positively correlated and glucan and and holocellulose were negatively correlated with lignin. In hybrids, internode length correlated positively with holocellulose and nonstructural ash and negatively with lignin. The heritability estimates for each of the fiber component traits is low indicating that environment is an important factor in fiber composition. Principal component analysis indicated that a large amount of diversity exists within each of the species.

09 BIOMASS FUELS↗

Multi-sensor Observations of a High-velocity Fireball over the South Atlantic on 2026 April 1

A high-velocity fireball was detected over the South Atlantic (41.9°S, 54.7°W) on 2026 April 1 at 02:13:14 UTC by U.S. Government (USG) sensors, with peak brightness at 90.5 km altitude. The event was well-observed from geostationary orbit by two civilian lightning imagers with near-orthogonal geometry, the Geostationary Operational Environmental Satellite-East Geostationary Lightning Mapper (GLM) and the Exploitation of Meteorological Satellites Meteosat Third Generation Imager 1 Lightning Imager, and it produced low-frequency acoustic signatures. The GLM measured a total radiated energy of 2.4 × 10 10 J, corresponding to a calculated impact energy of 0.086 kt TNT equivalent. Stereoscopic triangulation of the imager tracks yields an independent pre-atmospheric velocity of ~57 km s −1 , some 18% below the USG-reported value. Because orbital provenance is acutely sensitive to the entry velocity, whose reported uncertainty could be substantial, this discrepancy is notable. We document the multi-sensor record and identify the analysis required to assess provenance, which a subsequent study will present.

Silber, Elizabeth Allaryce [Sandia National Lab. (↗

Next Generation Geostationary Operational Environmental Satellite (GOES-R Series): A Space Segment Overview

The next-generation National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES-R series) is currently being developed by NOAA in cooperation with the National Aeronautics and Space Administration (NASA). The GOES-R series satellites represents a significant improvement in spatial, temporal, and spectral observations (several orders of magnitude) over the capabilities of the currently operational GOES-1 series and the about to be launched GOES-N series satellite. The GOES-R series will incorporate technically advanced third-generation instruments and spacecraft enhancements to meet evolving observational requirements of forecasting for the era 2012-2025. The GOES-R instrument complement being developed includes a Advanced Baseline Imager (ABI), a Hyperspectral Environmental Suite (HES), a GEO Lighting Mapper (GLM), a Solar Imaging Suite (SIS) and a Space Environment In-Situ Suite (SEISS). Also, candidates for a number of GOES-R Pre-Planned Product Improvements (P(sup 3)Is) includes a Geo microwave Sounder, a Coronograph, a Hyperspectral Imager, and a Solar Irradiance Sensor. Currently, the GOES-R Space Segment architecture is being evaluated as part of a GOES-R system end-to-end architecture study. The GOES-R notional baseline architecture is a constellation of two satellites (A-sat and B-sat) each nominally located at 75 degrees west longitude and at 135 degrees west longitude at geostationary altitude, 0 degrees inclination. The primary mission of the A-sat is to provide imaging from the ABI. The A-sat will also contain the SIS and the GLM. The primary mission of the B-sat is to provide sounding of the hemispherical disk of the earth from the HES. The B-sat also contains the SEISS. Both satellites have mesoscale capabilities for severe weather sounding or imaging. This paper overviews the GOES-R Space Segment development including satellite constellation trade-off, improvements and differences between the current and future instrument and spacecraft capabilities, and technology infusion.

Krimchansky, Alexander↗

Using Total Lightning Observations to Enhance Lightning Safety

Lightning is often the underrated threat faced by the public when it comes to dangerous weather phenomena. Typically, larger scale events such as floods, hurricanes, and tornadoes receive the vast majority of attention by both the general population and the media. This comes from the fact that these phenomena are large, longer lasting, can impact a large swath of society at one time, and are dangerous events. The threat of lightning is far more isolated on a case by case basis, although millions of cloud-to-ground lightning strikes hit this United States each year. While attention is given to larger meteorological events, lightning is the second leading cause of weather related deaths in the United States. This information raises the question of what steps can be taken to improve lightning safety. Already, the meteorological community s understanding of lightning has increased over the last 20 years. Lightning safety is now better addressed with the National Weather Service s access to the National Lightning Detection Network data and enhanced wording in their severe weather warnings. Also, local groups and organizations are working to improve public awareness of lightning safety with easy phrases to remember, such as "When Thunder Roars, Go Indoors." The impacts can be seen in the greater array of contingency plans, from airports to sports stadiums, addressing the threat of lightning. Improvements can still be made and newer technologies may offer new tools as we look towards the future. One of these tools is a network of sensors called a lightning mapping array (LMA). Several of these networks exist across the United States. NASA s Short-term Prediction Research and Transition Center (SPoRT), part of the Marshall Spaceflight Center, has access to three of these networks from Huntsville, Alabama, the Kennedy Space Center, and Washington D.C. The SPoRT program s mission is to help transition unique products and observations into the operational forecast environment. SPoRT has been collaborating with the Huntsville National Weather Service (NWS) Office since 2003 and has since included several other offices to better implement LMA observations into real-time applications. Much of that work has focused on the LMA s ability to detect intra-cloud lightning in addition to cloud-to-ground lightning strikes. Combined, these observations are called total lightning. With total lightning observations, NWS offices can enhance their situational awareness and improve severe weather warnings. Just as importantly, the observed intra-cloud flashes often precede the first cloud-to-ground strike by a few minutes. SPoRT and its partner NWS offices are working to develop visualizations and applications to better utilize these data. However, there is a drawback. The LMAs have a short range of no more than 200 km. This is being addressed with the next generation geostationary satellite, GOES-R, which will boast the Geostationary Lightning Mapper (GLM). SPoRT, in conjunction with NOAA s GOES-R Proving Ground, is working to prepare the end user community for the GLM era using the LMA observations as a demonstration tool. Working collaboratively with our NWS partners, SPoRT is working to determine how best to integrate these future observations to improve both severe storm warnings and lightning safety.

Stano, Geoffrey T.↗

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011in the vicinity of São Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. The SP-LMA data also will be intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment and analyses for intercomparison studies and GOES-R proxy activities

Blakeslee, R. J.↗

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011 in the vicinity of Sao Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012 during the Vale do Paraiba campaign. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to 150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. As the CHUVA Vale do Paraiba campaign opportunity was formulated, a broad community-based interest developed for a comprehensive Lightning Location System (LLS) intercomparison and assessment study, leading to the participation and/or deployment of eight other ground-based networks and the space-based Lightning Imaging Sensor (LIS). The SP-LMA data is being intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment including noise reduction criteria, detection efficiency estimates, and statistical and climatological (both temporal and spatially) analyses for intercomparison studies and GOES-R proxy activities.

Bailey, J. C.↗

Further Research on the Electrification of Pyrocumulus Clouds

Past research on pyrocumulus electrification has demonstrated that a variety of lightning types can occur, including cloud‐to‐ground (CG) flashes, sometimes of dominant positive polarity, as well as small intra‐cloud (IC) discharges in the upper levels of the pyro‐cloud. In Colorado during summer 2012, the first combined polarimetric radar, multi‐Doppler radar, and three‐dimensional lightning mapping array (LMA) observations of lightning‐producing pyrocumulus were obtained. These observations suggested that the National Lightning Detection Network (NLDN) was not sensitive enough to detect the small IC flashes that appear to be the dominant mode of lightning in these clouds. However, after an upgrade to the network in late 2012, the NLDN began detecting some of this pyrocumulus lightning. Multiple pyrocumulus clouds documented by the University of Wisconsin for various fires in 2013 and 2014 (including over the Rim, West Fork Complex, Yarnell Hill, Hardluck, and several other incidents) are examined and reported on here. This study exploits the increased‐sensitivity NLDN as well as the new nationwide U.S. network of polarimetric Next‐generation Radars (NEXRADs). These observations document the common occurrence of a polarimetric "dirty ice" signature ‐ modest reflectivities (20‐40+ dBZ), near‐zero differential reflectivity, and reduced correlation coefficient (less than 0.9) ‐ prior to the production of lightning. This signature is indicative of a mixture of ash and ice particles in the upper levels of the pyro‐cloud (less than ‐20 C), with the ice interpreted as being necessary for pyro‐cloud electrification. Pseudo‐Geostationary Lightning Mapper (GLM) data will be produced from the 2012 LMA observations, and the ability of GLM to detect small pyrocumulus ICs will be assessed. The utility of lightning and polarimetric radar for documenting rapid wildfire growth, as well as for documenting pyrocumulus impacts on the composition of the upper troposphere/lower stratosphere (UTLS), will be discussed.

Lang, Timothy J.↗

The Intra-Cloud Lightning Fraction in the Contiguous United States

Lightning is dangerous and destructive; cloud-to-ground (CG) lightning flashes can start fires, interrupt power delivery, destroy property and cause fatalities. Its rate-of-occurrence reflects storm kinematics and microphysics. For decades lightning research has been an important focus, and advances in lightning detection technology have been essential contributors to our increasing knowledge of lightning. A significant step in detection technology is the Geostationary Lightning Mapper (GLM) to be onboard the Geostationary Operational Environment Satellite R-Series (GOES-R) to be launched in early 2016. GLM will provide continuous "Total Lightning" observations [CG and intra-cloud lightning (IC)] with near-uniform spatial resolution over the Americas by measuring radiance at the cloud tops from the different types of lightning. These Total Lightning observations are expected to significantly improve our ability to nowcast severe weather. It may be important to understand the long-term regional differences in the relative occurrence of IC and CG lightning in order to understand and properly use the short-term changes in Total Lightning flash rate for evaluating individual storms.

lightning↗

Uncertainty Estimates of Psychoacoustic Thresholds Obtained from Group Tests

Adaptive psychoacoustic test methods, in which the next signal level depends on the response to the previous signal, are the most efficient for determining psychoacoustic thresholds of individual subjects. In many tests conducted in the NASA psychoacoustic labs, the goal is to determine thresholds representative of the general population. To do this economically, non-adaptive testing methods are used in which three or four subjects are tested at the same time with predetermined signal levels. This approach requires us to identify techniques for assessing the uncertainty in resulting group-average psychoacoustic thresholds. In this presentation we examine the Delta Method of frequentist statistics, the Generalized Linear Model (GLM), the Nonparametric Bootstrap, a frequentist method, and Markov Chain Monte Carlo Posterior Estimation and a Bayesian approach. Each technique is exercised on a manufactured, theoretical dataset and then on datasets from two psychoacoustics facilities at NASA. The Delta Method is the simplest to implement and accurate for the cases studied. The GLM is found to be the least robust, and the Bootstrap takes the longest to calculate. The Bayesian Posterior Estimate is the most versatile technique examined because it allows the inclusion of prior information.

Rathsam, Jonathan↗

Initial Navigation Alignment of Optical Instruments on GOES-R

Post-launch alignment errors for the Advanced Baseline Imager (ABI) and Geospatial Lightning Mapper (GLM) on GOES-R may be too large for the image navigation and registration (INR) processing algorithms to function without an initial adjustment to calibration parameters. We present an approach that leverages a combination of user-selected image-to-image tie points and image correlation algorithms to estimate this initial launch-induced offset and calculate adjustments to the Line of Sight Motion Compensation (LMC) parameters. We also present an approach to generate synthetic test images, to which shifts and rotations of known magnitude are applied. Results of applying the initial alignment tools to a subset of these synthetic test images are presented. The results for both ABI and GLM are within the specifications established for these tools, and indicate that application of these tools during the post-launch test (PLT) phase of GOES-R operations will enable the automated INR algorithms for both instruments to function as intended.

geolocation↗

RELAMPAGO Lightning Mapping Array Update

GLM (Geostationary Lightning Mapper) Science Hypothesis for RELAMPAGO (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations): Argentinian storms produce some of the highest flash rates ever recorded from space; Due to similarity in geographic and atmospheric environments between Argentina and the U.S. High Plains, we hypothesize that many of these storms have anomalous charge structures, with predominantly positive charge in the mid-levels; Thus, most intracloud lightning flashes are smaller, occur at lower altitudes, and occur in more optically thick portions of the cloud; Therefore, we hypothesize that GLM (and LIS (Lightning Imaging Sensor)) flash rates are biased low in many Argentinian storms, due to reduced optical detection efficiency.

Lang, Timothy↗

Changes in Lightning Characteristics as Hurricane Dorian (2019) Reached Peak Intensity

Previous work using land-based lightning detection networks and lightning imagers in low Earth orbit has established that lighting is a useful predictor of TC intensification. Increases in the number of lightning flashes in both the inner core and outer rainbands of hurricanes are linked to hurricane intensification. Sometimes, however, lightning outbreaks can occur in storms that weaken during or after the outbreak. Environmental conditions such as vertical wind shear and land interaction or inner-core structural changes such as secondary eyewall formation can account for some of this discrepancy. Hurricane Dorian (2019), however, exhibited a distinct inner-core lightning outbreak in a low-shear environment with minimal land interaction. The lightning flashes during this outbreak were located on the inner edge of the eyewall, which is not the expected location of enhanced convection during secondary eyewall formation. Regardless, Dorian weakened during and after this lightning outbreak. This presentation will analyze Dorian’s lightning evolution using data from the Geostationary Lightning Mapper (GLM) aboard the GOES-16 satellite. GLM provides continuous observations of not only the number and polarity of lightning flashes, but also the size and optical energy of the flashes. Here we show marked differences in the distributions of average flash area and total optical energy between the two most pronounced lightning outbreaks in Dorian’s lifetime: one while the storm was rapidly intensifying and another while the storm began to weaken. During intensification, lightning flashes were fewer but larger and more energetic, whereas during weakening, lightning flashes were much more numerous, but were smaller and less energetic. We present evidence that barotropic mixing between the eye and eyewall could have contributed to both the increase in inner-core lightning and the weakening of Dorian’s storm-scale maximum wind speed. These results suggest that average flash area and total optical energy could help to distinguish between lightning outbreaks that correspond to intensification and outbreaks that correspond to weakening.

Hurricanes↗

NASA SPoRT Suite of Legacy and Current Satellite Products in Support of Tropical Analysis and Forecasting

The NASA Short-term Prediction, Research, and Transition (SPoRT) Program works closely with NOAA/NWS weather forecasters to transition unique satellite data and capabilities into operations in order to assist with nowcasting and short-term forecasting issues. SPoRT has applied data and capabilities from a variety of research-oriented missions to improve the operational analysis and short-term forecasting of the tropical environment and tropical cyclones(TC).Toward researching new products, SPoRT is examining the diurnal cycle of TC intensity by observing changes in precipitation, winds, and midlevel moisture, including the use of NUCAPS soundings to assess the moisture and temperature environment around TCs, as well as examining changes in sounding profiles associated with the TC diurnal cycle. SPoRT has developed capabilities to analyze the evolution and diurnal cycle of GPM/IMERG rain rates and GLM lightning characteristics associated with TC, by compass and up-/down-shear quadrants relative to the cyclone center. As Early Adopters in the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, SPoRT is also in the process of developing new experimental products utilizing TROPICS data. Furthermore, SPoRT is investigating the relationship between TC intensity and lightning flash size and optical energy. MSFC hosts a GOES ground rebroadcast station which enables the potential for very low latency GLM products. These products provide the ability to monitor tropical regions/systems in data-void oceanic regions and to fill the gaps in traditional observational systems. SPoRT seeks to expand our collaborations with operational centers and other stakeholders to provide new tools to aid in tropical analysis and forecasting.

satellite remote sensing↗

Assessment Overview: Regional Impacts of Using the Day Cloud Phase Distinction RGB for Lightning IDSS

The new generation of GOES satellites has presented a wealth of novel observations and data products to the National Weather Service forecasters throughout the country. While high quality, low latency data is always welcome in operational environments, it is valuable to understand how new information is interpreted, and how it complements and supplements existing datasets. To that end, NASA SPoRT, in partnership with the NWS, hosted a formal assessment of the use of the Day Cloud Phase Distinction RGB to anticipate lightning initiation as indicated by either GLM, NLDN, or both, using an approach outlined by Elsenheimer and Gravelle (2019). As is typical for SPoRT assessments, we focused on a targeted time period (about 6 weeks in June and July 2021) and provided a brief training module to help forecasters become familiar with the application we were targeting and the objectives of our assessment. Unlike typical SPoRT assessments, we focused on a broad geographic region, coordinating with NWS Regional offices to garner participation from several different WFOs throughout the country. Given the field of view and observation angles of GOES-East and GOES-West, which anecdotally impacts GLM lightning observations in particular, having a geographically diverse participant list helps us interpret how these viewing differences may impact interpretation of the RGB and its value in anticipating lightning. Not unexpectedly, we found that the participating forecasters expressed that they could readily interpret the RGB to recognize glaciation and, in the absence of lightning data, use the RGB to anticipate that lightning was possible, for the purpose of impact-based decision support services (IDSS). Lead times from observations of glaciation to first flash were 19 minutes on average, and -10C isothermal reflectivities ranged from 34.5 to 52.5 dBZ. Herein, we present the detailed results of the formal assessment period and provide next steps and recommendations based on forecaster feedback for using the DCPD RGB in the context of anticipating lightning initiation and for IDSS, including regional differences in observations.

satellite remote sensing↗