Description and Status of the DC Lightning Mapping Array

The DC Lightning Mapping Array (DC LMA) centered on the Washington, DC metro region has been in operation since 2006. During that time the DC LMA has provided real time data to regional National Weather Service (NSF) Sterling, VA forecast office for operations support and the NOAA Meteorological Development Laboratory (MDL) for new product development and assessment. Data from this network (as well as other from other LMA systems) are now being used to create proxy Geostationary Lightning Mapper (GLM) data sets for GOES-R risk reduction and algorithm development activities. In addition, since spring 2009 data are provided to the Storm Prediction Center in support of Hazardous Weather Testbed and GOES-R Proving Ground activities during the Spring Program. Description, status and plans will be discussed

Forecasting Lightning Threat using Cloud-Resolving Model Simulations

Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models,the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of forecasts become available

The GOES-R Series Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithm

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

Key points that this analysis will begin to address are: 1)What physically is going on in the cloud when there is a jump in lightning? - Updraft variations, ice fluxes. 2)How do these processes fit in with severe storm conceptual models? 3)What would this information provide an end user (i.e., the forecaster)? - Relate LJA to radar observations, like changes in reflectivity, MESH, VIL, etc. based multi-Doppler derived physical relationships 4) How do we best transistionthis algorithm into the warning decision process. The known relationship between lightning updraft strength/volume and precipitation ice mass production can be extended to the concept of the lightning jump. Examination of the first lightning jump times from 329 storms in Schultz et al. shows an increase in the mean reflectivity profile and mixed phase echo volume during the 10 minutes prior to the lightning jump. Limited dual-Doppler results show that the largest lightning jumps are well correlated in time with increases in updraft strength/volume and precipitation ice mass production; however, the smaller magnitude lightning jumps appear to have more subtle relationships to updraft and ice mass characteristics

Characterizing the GOES-R (GOES-16) Geostationary Lightning Mapper (GLM) On-Orbit Performance

Two overlapping efforts help to characterize the GLM performance, the Post Launch Test (PLT) phase to validate the predicted pre-launch instrument performance and the Post Launch Product Test (PLPT) phase to validate the lightning detection product used in forecast and warning decision-making. This paper documents the calibration and validation plans and activities for the first 6 months of GLM on-orbit testing and validation commencing with first light on 4 January 2017. The PLT phase addresses image quality, on-orbit calibration, RTEP threshold tuning, image navigation, noise filtering, and solar intrusion assessment, resulting in a GLM calibration parameter file. The PLPT includes four main activities, the Reference Data Comparisons (RDC), Algorithm Testing (AT), Instrument Navigation and Registration Testing (INRT), and Long Term Baseline Testing (LTBT). Field campaigns are also designed to contribute valuable insights into the GLM performance capabilities. The PLPT tests each contribute to the beta, provisional, and fully validated GLM data

Diversity, host specialization, and geographic structure of filarial nematodes infecting Malagasy bats

We investigated filarial infection in Malagasy bats to gain insights into the diversity of these parasites and explore the factors shaping their distribution. Samples were obtained from 947 individual bats collected from 52 sites on Madagascar and representing 31 of the 44 species currently recognized on the island. Samples were screened for the presence of micro-and macro-parasites through both molecular and morphological approaches. Phylogenetic analyses showed that filarial diversity in Malagasy bats formed three main groups, the most common represented by Litomosa spp. infecting Miniopterus spp. (Miniopteridae); a second group infecting Pipistrellus cf. hesperidus (Vespertilionidae) embedded within the Litomosoides cluster, which is recognized herein for the first time from Madagascar; and a third group composed of lineages with no clear genetic relationship to both previously described filarial nematodes and found in M. griveaudi, Myotis goudoti, Neoromicia matroka (Vespertilionidae), Otomops madagascariensis (Molossidae), and Paratriaenops furculus (Hipposideridae). We further analyzed the infection rates and distribution pattern of Litomosa spp., which was the most diverse and prevalent filarial taxon in our sample. Filarial infection was disproportionally more common in males than females in Miniopterus spp., which might be explained by some aspect of roosting behavior of these cave-dwelling bats. We also found marked geographic structure in the three Litomosa clades, mainly linked to bioclimatic conditions rather than host-parasite associations. While this study demonstrates distinct patterns of filarial nematode infection in Malagasy bats and highlights potential drivers of associated geographic distributions, future work should focus on their alpha taxonomy and characterize arthropod vectors

Precision Farming by Cotton Producers in Six Southern States: Results from the 2001 Southern Precision Farming Survey

Precision Farming by Cotton Producers in Six Southern States: Results from the 2001 Southern Precision Farming Surveycotton, precision farming, survey, Agribusiness, Farm Management, Production Economics, Research and Development/Tech Change/Emerging Technologies,

Precision Farming by Cotton Producers in Eleven Southern States: Results from the 2005 Southern Precision Farming Survey

Precision Farming by Cotton Producers in Eleven Southern States: Results from the 2005 Southern Precision Farming Surveycotton, precision farming, survey, Agribusiness, Farm Management, Production Economics, Research and Development/Tech Change/Emerging Technologies,

Failure Modeling: A Basis for Strength Prediction of Lumber

Failure modeling of knot-containing wood members was investigated as a means to predict member tensile strength. A finite element/fracture mechanics model was developed to model the progressive fracture process observed during failure of wood members. The failure modeling process yields predicted tensile strengths for members that contain knots in the wide face. Predicted strengths compared favorably with tensile strength data measured in initial experimental tests. Predicted strengths are generated from basic engineering computation and are not derived or adjusted by any empirical factors. With further research and verification, the concepts presented hold promise for use in lumber grading and quality assurance

Aperçu sur la fragmentation de la forêt naturelle dans la Réserve Spéciale d’Ambohitantely et ses alentours entre 1949 et 2017, Hautes Terres Centrales

Cette étude vise à caractériser la dynamique de la couverture forestière et la fragmentation de la forêt naturelle de la Réserve Spéciale d’Ambohitantely et les zones périphériques dans un rayon de 10 km de la limite de l’aire protégée entre 1949 et 2017. Au total, cinq images satellitaires pour les années 1989, 1995, 2002, 2010 et 2017, et 59 clichés de photographies aériennes prises en 1949 ont été utilisées. La télédétection et le système d’information géographique ont été utilisés pour la cartographie de l’occupation du sol pour les six périodes d’études, ainsi que pour l’analyse de la dynamique de la couverture forestière et l’estimation de la perte de surface forestière. Six métriques disponibles sur le logiciel FRAGSTATS ont été sélectionnées pour l’analyse de la fragmentation à l’échelle du paysage à savoir, le nombre de parcelles (NP), la densité de parcelles (PD), la variabilité de la taille des parcelles (AREA_SD), l’indice de la dimension fractale (FRAC_MN), l’indice de contiguïté (CONTIG_MN) et l’indice d’agrégation (AI). Après une classification supervisée, les classes d’occupation du sol ont été reclassées en forêt ou non-forêt. La dynamique de la couverture forestière dans la zone étudiée a montré qu’une vaste zone forestière a été convertie en zone non forestière. L’estimation de la perte de forêt indique que le taux annuel dans la réserve varie, et la plus importante estimée à 586,4 ha soit 4,05% par an a été enregistrée entre 1995 et 2002, et la plus faible est de 473,4 ha soit 0,41% par an, entre 1949 et 1989. Les résultats ont montré la diminution du nombre de fragments ainsi que la densité des fragments depuis 1989 à 2017, ce qui indique la disparition de fragments forestiers. En parallèle, la réduction de l’indice de la dimension fractale et de la variabilité de la taille des parcelles révèlent la simplification de la forme des fragments et la faible diversification de la superficie des différents fragments. L’augmentation de l’indice d’agrégation contre la diminution de l’indice de contiguïté confirme l’isolement des fragments.  AbstractThis study aims to characterize the dynamics of forest cover and fragmentation of the natural forest of the Ambohitantely Special Reserve between 1949 and 2017 and within a radius of 10 km of the boundary limit. Five different periods of satellite images were employed, specifically the years 1989, 1995, 2002, 2010, and 2017, as well as aerial photographs taken in 1949. Remote sensing and geographic information systems were used for land cover mapping for the six study periods, as well as for analyzing forest cover dynamics and estimating forest cover loss. Using the software FRAGSTATS, six different metrics were selected for the analysis of forest fragmentation at the landscape level: number of patches (NP), patch density (PD), patch size standard deviation (AREA_SD), mean patch fractal dimension (FRAC_MN), contiguity index (CONTIG_MN), and aggregation index (AI). Following a supervised classification, land cover classes were reclassified as forest or non-forest. The dynamics of forest cover at the site and over the study period indicated that considerable zones of forest were transformed to non-forested areas. The estimate of forest loss indicates that the annual rate in the reserve varies, and the largest estimated at 586.4 ha or 4.05% per year was recorded between 1995 and 2002, and the lowest is 473.4 ha or 0.41% between 1949 and 1989. The results indicate a decrease in the number of fragments as well as the density of fragments from 1989 to 2017 associated with the disappearance of forest. In parallel, the reduction of the mean patch fractal dimension and variability of the patch size denotes the simplification of the fragments’ shapes and the slight diversification of the areas of the different fragments. An increase in the aggregation index as compared to a decrease in the contiguity index confirms the isolation of the fragments