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  • 1
    Publication Date: 2011-08-24
    Description: A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.
    Keywords: METEOROLOGY AND CLIMATOLOGY
    Type: Journal of Atmospheric and Oceanic Technology (ISSN 0739-0572); 12; 2; p. 230-248
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  • 2
    Publication Date: 2004-12-03
    Description: The Lightning Imaging Sensor (LIS) is a NASA Earth Observing System (EOS) instrument on the Tropical Rainfall Measuring Mission (TRMM) platform designed to acquire and investigate the distribution and variability of total lightning (i.e., cloud-to-ground and intracloud) between q35' in latitude. Since lightning is one of the responses of the atmosphere to thermodynamic and dynamic forcing, the LIS data is being used to detect deep convection without land-ocean bias, estimate the precipitation mass in the mixed phased region of thunderclouds, and differentiate storms with strong updrafts from those with weak vertical motion.
    Keywords: Meteorology and Climatology
    Type: 11th International Conference on Atmospheric Electricity; 746-749; NASA/CP-1999-209261
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  • 3
    Publication Date: 2004-12-03
    Description: The mapping of the lightning optical pulses detected by the Lightning Imaging Sensor (LIS) is compared with the radiation sources by Lightning Detection and Ranging (LDAR) and the National Lightning Detection Network (NLDN) for three thunderstorms observed during and overpasses on 15 August 1998. The comparison involves 122 flashes including 42 ground and 80 cloud flashes. For ground flash, the LIS recorded the subsequent strokes and changes inside the cloud. For cloud flashes, LIS recorded those with higher sources in altitude and larger number of sources. The discrepancies between the LIS and LDAR flash locations are about 4.3 km for cloud flashes and 12.2 km for ground flashes. The reason for these differences remain a mystery.
    Keywords: Meteorology and Climatology
    Type: 11th International Conference on Atmospheric Electricity; 738-741; NASA/CP-1999-209261
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  • 4
    Publication Date: 2004-12-03
    Description: Since April 1995, lightning activity around the globe has been monitored with the Optical Transient Detector (OTD). The OTD observations acquired during the one year period from September 1995 through August 1996 have been used to statistically determine the number of flashes that occur over the Earth during each hour of the diurnal cycle, expressed both as a function of local time and universal time. The globally averaged local [il,htnina activity displays a peak in late afternoon (1500-1800 local time) and a minimum in the morning hours (0600- 1000 local time) consistent with convection associated with diurnal heating. No diurnal variation is found for oceanic storms. The diurnal lightning distribution (universal time) for the globe displays a variation of about 35% about its mean as compared to the Carnegie curve which has a variation of only 15% above and below the mean.
    Keywords: Meteorology and Climatology
    Type: 11th International Conference on Atmospheric Electricity; 742-745; NASA/CP-1999-209261
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  • 5
    Publication Date: 2004-12-03
    Description: The Optical Transient Detector (OTD) is a space-based instrument specifically designed to detect and locate lightning discharges (intracloud and cloud-to-ground) as it orbits the Earth. A statistical examination of OTD lightning data reveals that nearly 1.2 billion flashes occurred over the entire earth during the one year period from September 1995 through August 1996. This translates to an average of 37 lightning flashes occurring around the globe every second, which is well below the traditional estimate of 100 flashes per second. An average of 75% of the global lightning activity during the year occurs between 30' S and 30' N. An analysis of the annual lightning distribution reveals that an average of 82% of the lightning flashes occur over the continents and 18% over the oceans, which translates to an average land-ocean flash density ratio of nearly 11.
    Keywords: Meteorology and Climatology
    Type: 11th International Conference on Atmospheric Electricity; 726-729; NASA/CP-1999-209261
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  • 6
    Publication Date: 2019-07-18
    Description: The retrieval of vertical structure from joint passive microwave and lightning observations is demonstrated. Three years of data from the TRMM (Tropical Rainfall Measuring Mission) are used as a training dataset for regression and classification neural networks; the TMI (TRMM Microwave Imager) and LIS (Lightning Imaging Sensor) provide the inputs, the PR (Precipitation Radar) provides the training targets. Both vertical reflectivity profile categorization (into 9 convective, 7 stratiform, 2 mixed and 6 anvil types) and geophysical parameters (surface rainfall, vertically integrated liquid (VIL), ice water content (IWC) and echo tops) are retrieved. Retrievals are successful over both land and ocean surfaces. The benefit of using lightning observations as inputs to these retrievals is quantitatively demonstrated; lightning essentially provides an additional convective/stratiform discriminator, and is most important for isolation of midlevel (tops in the mixed phase region) convective profile types (this is because high frequency passive microwave observations already provide good convective/stratiform discrimination for deep convective profiles). This is highly relevant as midlevel convective profiles account for an extremely large fraction of tropical rainfall, and yet are most difficult to discriminate from comparable-depth stratiform profile types using passive microwave observations alone.
    Keywords: Meteorology and Climatology
    Type: American Geophysical Union Fall Meeting 2004; Dec 13, 2004 - Dec 17, 2004; San Francisco, CA; United States
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  • 7
    Publication Date: 2019-07-18
    Description: Joint observations from the TRMM mission are used to examine the contribution which lightning observations can make towards estimation of vertical structure and rainfall from passive microwave data. Passive microwave (TMI) observations are binned into distinct vertical structure categories based on cluster analysis of radar (PR) vertical profiles and convective/stratiform (C/S) classifiers. TMI rain estimates are high-biased relative to radar for stratiform and mixed convective/stratiform vertical structures and low-biased relative to radar for convective structures. Significant ambiguity exists in the TMI brightness temperature space between midlevel stratiform and convective profile types which are primary contributors to tropical rainfall. The ambiguity is worst for convective profile types with radar echo tops within and just above the mixed-phase region. The ability of TMI data (including all low and high frequency polarized brightness temperatures; 19V, 19H, 21V, 37V, 37H, 85V, 85H) to predict vertical structure is assessed. The incremental benefit of including TMI convective/stratiform (C/S) classifiers (both 85 GHz polarization and 19, 37 and 85 GHz texture-based) and LZS lightning observations is then considered. The use of all parameters (brightness temperatures, TMI C/S classifiers, lightning data, predicted vertical structure) to reduce TMI/PR rainfall estimate scatter is demonstrated. For both vertical structure and rainfall estimation, inclusion of C/S classifiers and lightning observations provide statistical skill improvements but do not completely alleviate critical midlevel profile C/S ambiguity and related rain estimate errors.
    Keywords: Meteorology and Climatology
    Type: Paper-86040 , American Meteorological Society 85th Annual Meeting/Conference; Jan 09, 2005 - Jan 13, 2005; San Diego, CA; United States
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  • 8
    Publication Date: 2019-07-18
    Description: Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.
    Keywords: Meteorology and Climatology
    Type: International Lightning Detection Conference; Jun 07, 2004 - Jun 09, 2004; Helsinki; Finland
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  • 9
    Publication Date: 2019-07-18
    Description: The problems of managing and searching large archives of scientific journal articles can potentially be addressed through data mining and statistical techniques matured primarily for quantitative scientific data analysis. A journal paper could be represented by a multivariate descriptor, e.g., the occurrence counts of a number key technical terms or phrases (keywords), perhaps derived from a controlled vocabulary ( e . g . , the American Meteorological Society's Glossary of Meteorology) or bootstrapped from the journal archive itself. With this technique, conventional statistical classification tools can be leveraged to address challenges faced by both scientists and professional societies in knowledge management. For example, cluster analyses can be used to find bundles of "most-related" papers, and address the issue of journal bifurcation (when is a new journal necessary, and what topics should it encompass). Similarly, neural networks can be trained to predict the optimal journal (within a society's collection) in which a newly submitted paper should be published. Comparable techniques could enable very powerful end-user tools for journal searches, all premised on the view of a paper as a data point in a multidimensional descriptor space, e.g.: "find papers most similar to the one I am reading", "build a personalized subscription service, based on the content of the papers I am interested in, rather than preselected keywords", "find suitable reviewers, based on the content of their own published works", etc. Such services may represent the next "quantum leap" beyond the rudimentary search interfaces currently provided to end-users, as well as a compelling value-added component needed to bridge the print-to-digital-medium gap, and help stabilize professional societies' revenue stream during the print-to-digital transition.
    Keywords: Documentation and Information Science
    Type: Fall American Geophysical Union Conference; Dec 08, 2003 - Dec 12, 2003; San Francisco, CA; United States
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  • 10
    Publication Date: 2019-07-19
    Description: Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).
    Keywords: Earth Resources and Remote Sensing
    Type: 27th Conference on Hurricanes and Tropical Meteorology; Apr 24, 2006 - Apr 28, 2006; Monterey, CA; United States
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