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NASA NTRS2016

Retrieval of Aerosol Optical Properties Using MERIS Observations: Algorithm and Some First Results

The MEdium Resolution Imaging Spectrometer (MERIS) instrument on board ESA Envisat made measurements from 2002 to 2012. Although MERIS was limited in spectral coverage, accurate Aerosol Optical Thickness (AOT) from MERIS data are retrieved by using appropriate additional information. We introduce a new AOT retrieval algorithm for MERIS over land surfaces, referred to as eXtensible Bremen AErosol Retrieval (XBAER). XBAER is similar to the dark-target (DT) retrieval algorithm used for Moderate-resolution Imaging Spectroradiometer (MODIS), in that it uses a lookup table (LUT) to match to satellite-observed reflectance and derive the AOT. Instead of a global parameterization of surface spectral reflectance, XBAER uses a set of spectral coefficients to prescribe surface properties. In this manner, XBAER is not limited to dark surfaces (vegetation) and retrieves AOT over bright surface (desert, semiarid, and urban areas). Preliminary validation of the MERIS-derived AOT and the ground-based Aerosol Robotic Network (AERONET) measurements yield good agreement, the resulting regression equation is y (0.92 x +/- 0.07) + (0.05 +/- 0.01) and Pearson correlation coefficient of R 0.78. Global monthly means of AOT have been compared from XBAER, MODIS and other satellite-derived datasets.

satellite retrievals↗
NASA NTRSDate not supplied

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, to estimate the broadband fluxes between CERES observations. This requires stable imager visible channel calibration, which the CERES project verifies by utilizing deep convective clouds (DCC) as an invariant Earth target. GSICS, which is an international collaboration, is also evaluating the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the Aqua-MODIS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical DCC-identified pixel-level reflectances, which are histogrammed to find the mode reflectance of the probability density function (PDF). The imager stability is monitored by tracking the monthly DCC PDF mode reflectance over time. Radiometric scaling is accomplished by ratioing the GEO and VIIRS DCC mode reflectance values. The PDF shape and mode dependency on sensor pixel resolution, which varies among sensors, is unknown. This study will characterize the impact of pixel resolution on the DCC PDFs by aggregating Landsat 30-m pixel reflectances into various coarser pixel resolutions ranging from 100-m to 4-km, and comparing the corresponding PDF statistics. This analysis will assist in improving the uncertainty in a DCC-based intercalibration between instruments with different pixel resolutions.

Conor Haney↗
NASA NTRS1995

Uncertainty in cloud optical depth estimates made from satellite radiance measurements

The uncertainty in optical depths retrieved from satellite measurements of visible wavelength radiance at the top of the atmosphere is quantified. Techniques are briefly reviewed for the estimation of optical depth from measurements of radiance, and it is noted that these estimates are always more uncertain at greater optical depths and larger solar zenith angles. The lack of radiometric calibration for visible wavelength imagers on operational satellites dominates the uncertainty retrievals of optical depth. This is true for both single-pixel retrievals and for statistics calculated from a population of individual retrievals. For individual estimates or small samples, sensor discretization can also be significant, but the sensitivity of the retrieval to the specification of the model atmosphere is less important. The relative uncertainty in calibration affects the accuracy with which optical depth distributions measured by different sensors may be quantitatively compared, while the absolute calibration uncertainty, acting through the nonlinear mapping of radiance to optical depth, limits the degree to which distributions measured by the same sensor may be distinguished.

Pincus, Robert↗
NASA NTRS2006

Multilayered Clouds Identification and Retrieval for CERES Using MODIS

Traditionally, analyses of satellite data have been limited to interpreting the radiances in terms of single layer clouds. Generally, this results in significant errors in the retrieved properties for multilayered cloud systems. Two techniques for detecting overlapped clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. The first technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other method uses microwave (MWR) data. The use of BTD, the 11-12 micrometer brightness temperature difference, in conjunction with tau, the retrieved visible optical depth, was suggested by Kawamoto et al. (2001) and used by Pavlonis et al. (2004) as a means to detect multilayered clouds. Combining visible (VIS; 0.65 micrometer) and infrared (IR) retrievals of cloud properties with microwave (MW) retrievals of cloud water temperature Tw and liquid water path LWP retrieved from satellite microwave imagers appears to be a fruitful approach for detecting and retrieving overlapped clouds (Lin et al., 1998, Ho et al., 2003, Huang et al., 2005). The BTD method is limited to optically thin cirrus over low clouds, while the MWR method is limited to ocean areas only. With the availability of VIS and IR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and MW data from the Advanced Microwave Scanning Radiometer EOS (AMSR-E), both on Aqua, it is now possible to examine both approaches simultaneously. This paper explores the use of the BTD method as applied to MODIS and AMSR-E data taken from the Aqua satellite over non-polar ocean surfaces.

Sun-Mack, Sunny↗
NASA NTRS2016

Utilization of O4 Slant Column Density to Derive Aerosol Layer Height from a Space-Borne UV-Visible Hyperspectral Sensor: Sensitivity and Case Study

The sensitivities of oxygen-dimer (O4) slant column densities (SCDs) to changes in aerosol layer height are investigated using the simulated radiances by a radiative transfer model, the linearized pseudo-spherical vector discrete ordinate radiative transfer (VLIDORT), and the differential optical absorption spectroscopy (DOAS) technique. The sensitivities of the O4 index (O4I), which is defined as dividing O4 SCD by 10(sup 40) molecules (sup 2) per centimeters(sup -5), to aerosol types and optical properties are also evaluated and compared. Among the O4 absorption bands at 340, 360, 380, and 477 nanometers, the O4 absorption band at 477 nanometers is found to be the most suitable to retrieve the aerosol effective height. However, the O4I at 477 nanometers is significantly influenced not only by the aerosol layer effective height but also by aerosol vertical profiles, optical properties including single scattering albedo (SSA), aerosol optical depth (AOD), particle size, and surface albedo. Overall, the error of the retrieved aerosol effective height is estimated to be 1276, 846, and 739 meters for dust, non-absorbing, and absorbing aerosol, respectively, assuming knowledge on the aerosol vertical distribution shape. Using radiance data from the Ozone Monitoring Instrument (OMI), a new algorithm is developed to derive the aerosol effective height over East Asia after the determination of the aerosol type and AOD from the MODerate resolution Imaging Spectroradiometer (MODIS). About 80 percent of retrieved aerosol effective heights are within the error range of 1 kilometer compared to those obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements on thick aerosol layer cases.

Air Pollution↗
NASA NTRS2005

Testing the MODIS Satellite Retrieval of Aerosol Fine-Mode Fraction

Satellite retrievals of the fine-mode fraction (FMF) of midvisible aerosol optical depth, tau, are potentially valuable for constraining chemical transport models and for assessing the global distribution of anthropogenic aerosols. Here we compare satellite retrievals of FMF from the Moderate Resolution Imaging Spectroradiometer (MODIS) to suborbital data on the submicrometer fraction (SMF) of tau. SMF is a closely related parameter that is directly measurable by in situ techniques. The primary suborbital method uses in situ profiling of SMF combined with airborne Sun photometry both to validate the in situ estimate of ambient extinction and to take into account the aerosol above the highest flight level. This method is independent of the satellite retrieval and has well-known accuracy but entails considerable logistical and technical difficulties. An alternate method uses Sun photometer measurements near the surface and an empirical relation between SMF and the Angstrom exponent, A, a measure of the wavelength dependence of optical depth or extinction. Eleven primary and fifteen alternate comparisons are examined involving varying mixtures of dust, sea salt, and pollution in the vicinity of Korea and Japan. MODIS ocean retrievals of FMF are shown to be systematically higher than suborbital estimates of SMF by about 0.2. The most significant cause of this discrepancy involves the relationship between 5 and fine-mode partitioning; in situ measurements indicate a systematically different relationship from what is assumed in the satellite retrievals. Based on these findings, we recommend: (1) satellite programs should concentrate on retrieving and validating since an excellent validation program is in place for doing this, and (2) suborbital measurements should be used to derive relationships between A and fine-mode partitioning to allow interpretation of the satellite data in terms of fine-mode aerosol optical depth.

Anderson, Theodore L.↗
NASA NTRS2021

First retrieval of AOD at fine-resolution over shallow and turbid coastal waters from MODIS

The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Dark-Target (DT) aerosol product fails to accurately retrieve Aerosol Optical Depth (AOD) over shallow and turbid Coastal Waters (CWs). To fill in gaps, and to improve land to ocean AOD continuity, we developed a coastal water retrieval algorithm at a spatial resolution of 1 km (CW-1km). CW-1km relies on observed top-of-atmosphere reflectance at 2.1 μm (ρ2.1), both to derive AOD and to perform a spatial variation test that enhances the existing DT masks for clouds and land. We show that the CW-1km improves spatial continuity of AOD between land, coast, and open ocean, while also increasing AOD product availability by 47.0%. Comparing with 15 years of marine aerosol network measurements, CW-1km AODs are validated to have a normalized mean bias of 1.0%, which is much smaller than 17.6% for the original DT product.

MODIS AOD↗
NASA NTRS2007

Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain Rate Estimates at Different Spatial Scales

This study provides a comprehensive inter-comparison of instantaneous rain estimates from the two rain sensors aboard the TRMM satellite with ground data from thee designated Ground Validation Sites: Kwajalein Atoll, Melbourne, Florida and Houston, Texas. The satellite rain retrievals utilize rain observations collected by the TRMM microwave imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard instantaneous rain products are the generated from the rain information retrieved from the satellite using the TMI, PR and Combined (COM) rain algorithms. The validation data set used in this study was obtained from instantaneous rain rates inferred from ground radars at each GV site. The first comparison used 0.5(sup 0) x 0.5(sup 0) gridded data obtained from the TRMM 3668 product, and similarly gridded GV data obtained from ground-based radars. The comparisons were made at the same spatial and temporal scales in order to eliminate sampling biases in our comparisons. An additional comparison was made by averaging rain rates for the PR, COM and GV estimates within each TMI footprint (approx. 150 square kilometers). For this analysis, unconditional mean rain rates from PR, COM and GV estimates were calculated within each TMI footprint that was observed within 100 km from the respective GV site (and also observed by the PR). This analysis used all the available matching data from the period 1999-2004, representing a sample size of over 50,000 footprints for each site. In the first analysis our results showed that all of the respective rain rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm per hour over the ocean. This mode was noted over ocean areas of Melbourne, Florida and Kwajalein, Republic of the Marshall Islands, and is shown to exist in TRMM tropical-global ocean areas as well. Further research by algorithm developers is needed to explain or justify the seemingly errant observed probability distributions.

Wolff, David B.↗
NASA NTRS2000

MODIS Retrievals of Cloud Optical Thickness and Particle Radius

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides an unprecedented opportunity for global cloud studies with 36 spectral bands from the visible through the infrared, and spatial resolution from 250 m to 1 km at nadir. In particular, all solar window bands useful for simultaneous retrievals of cloud optical thickness and particle size (0.67, 0.86, 1.2, 1.6, 2.1, and 3.7 micron bands) are now available on a single satellite instrument/platform for the first time. An operational algorithm for the retrieval of these optical and cloud physical properties (including water path) have been developed for both liquid and ice phase clouds. The product is archived into two categories: pixel-level retrievals at 1 km spatial resolution (referred to as a Level-2 product) and global gridded statistics (Level-3 product). An overview of the MODIS cloud retrieval algorithm and early level-2 and -3 results will be presented. A number of MODIS cloud validation activities are being planned, including the recent Southern Africa Regional Science Initiative 2000 (SAFARI-2000) dry season campaign conducted in August/September 2000. The later part of the experiment concentrated on MODIS validation in the Namibian stratocumulus regime off the southwest coast of Africa. Early retrieval results from this regime will be discussed.

Platnick, S.↗
NASA NTRS1984

Storage, retrieval, and analysis of ST data

Space Telescope can generate multidimensional image data, very similar in nature to data produced with microdensitometers. An overview is presented of the ST science ground system between carrying out the observations and the interactive analysis of preprocessed data. The ground system elements used in data archival and retrieval are described and operational procedures are discussed. Emphasis is given to aspects of the ground system that are relevant to the science user and to general principles of system software development in a production environment. While the system being developed uses relatively conservative concepts for the launch baseline, concepts were developed to enhance the ground system. This includes networking, remote access, and the utilization of alternate data storage technologies.

Albrecht, R.↗
NASA NTRS2013

Validation and Uncertainty Estimates for MODIS Collection 6 "Deep Blue" Aerosol Data

The "Deep Blue" aerosol optical depth (AOD) retrieval algorithm was introduced in Collection 5 of the Moderate Resolution Imaging Spectroradiometer (MODIS) product suite, and complemented the existing "Dark Target" land and ocean algorithms by retrieving AOD over bright arid land surfaces, such as deserts. The forthcoming Collection 6 of MODIS products will include a "second generation" Deep Blue algorithm, expanding coverage to all cloud-free and snow-free land surfaces. The Deep Blue dataset will also provide an estimate of the absolute uncertainty on AOD at 550 nm for each retrieval. This study describes the validation of Deep Blue Collection 6 AOD at 550 nm (Tau(sub M)) from MODIS Aqua against Aerosol Robotic Network (AERONET) data from 60 sites to quantify these uncertainties. The highest quality (denoted quality assurance flag value 3) data are shown to have an absolute uncertainty of approximately (0.086+0.56Tau(sub M))/AMF, where AMF is the geometric air mass factor. For a typical AMF of 2.8, this is approximately 0.03+0.20Tau(sub M), comparable in quality to other satellite AOD datasets. Regional variability of retrieval performance and comparisons against Collection 5 results are also discussed.

MODIS collection 6↗
NASA NTRS2016

The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations

In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar-radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice-air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraft-borne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

Ice particles; Radiances; Radiative transfer; Airc↗
NASA NTRS2006

Consistency of Global Modis Aerosol Optical Depths over Ocean on Terra and Aqua Ceres SSF Datasets

Aerosol retrievals over ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua platforms are available from the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) datasets generated at NASA Langley Research Center (LaRC). Two aerosol products are reported side-by-side. The primary M product is generated by sub-setting and remapping the multi-spectral (0.47-2.1 micrometer) MODIS produced oceanic aerosol (MOD04/MYD04 for Terra/Aqua) onto CERES footprints. M*D04 processing uses cloud screening and aerosol algorithms developed by the MODIS science team. The secondary AVHRR-like A product is generated in only two MODIS bands 1 and 6 (on Aqua, bands 1 and 7). The A processing uses the CERES cloud screening algorithm, and NOAA/NESDIS glint identification, and single-channel aerosol retrieval algorithms. The M and A products have been documented elsewhere and preliminarily compared using 2 weeks of global Terra CERES SSF Edition 1A data in which the M product was based on MOD04 collection 3. In this study, the comparisons between the M and A aerosol optical depths (AOD) in MODIS band 1 (0.64 micrometers), tau(sub 1M) and tau(sub 1A) are re-examined using 9 days of global CERES SSF Terra Edition 2A and Aqua Edition 1B data from 13 - 21 October 2002, and extended to include cross-platform comparisons. The M and A products on the new CERES SSF release are generated using the same aerosol algorithms as before, but with different preprocessing and sampling procedures, lending themselves to a simple sensitivity check to non-aerosol factors. Both tau(sub 1M) and tau(sub 1A) generally compare well across platforms. However, the M product shows some differences, which increase with ambient cloud amount and towards the solar side of the orbit. Three types of comparisons conducted in this study - cross-platform, cross-product, and cross-release confirm the previously made observation that the major area for improvement in the current aerosol processing lies in a more formalized and standardized sampling (and most importantly, cloud screening) whereas optimization of the aerosol algorithm is deemed to be an important yet less critical element.

Ignatov, Alexander↗
NASA NTRS2002

Multispectral Cloud Retrievals from MODIS on Terra and Aqua

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched onboard the Terra spacecraft on December 18, 1999 and the Aqua spacecraft on April 26, 2002. MODIS scans a swath width sufficient to provide nearly complete global coverage every two days from each polar-orbiting, sun-synchronous, platform at an altitude of 705 km, and provides images in 36 spectral bands between 0.415 and 14.235 microns with spatial resolutions of 250 m (2 bands), 500 m (5 bands) and 1000 m (29 bands). In this paper we will describe the various methods being used for the remote sensing of cloud properties using MODIS data, focusing primarily on the MODIS cloud mask used to distinguish clouds, clear sky, heavy aerosol, and shadows on the ground, and on the remote sensing of cloud optical properties, especially cloud optical thickness and effective radius of water drops and ice crystals. Additional properties of clouds derived from multispectral thermal infrared measurements, especially cloud top pressure and emissivity, will also be described. Results will be presented of MODIS cloud properties both over the land and over the ocean, showing the consistency in cloud retrievals over various ecosystems used in the retrievals. The implications of this new observing system on global analysis of the Earth's environment will be discussed.

King, Michael D.↗
NASA NTRS2013

The Collection 6 'dark-target' MODIS Aerosol Products

Aerosol retrieval algorithms are applied to Moderate resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua, creating two streams of decade-plus aerosol information. Products of aerosol optical depth (AOD) and aerosol size are used for many applications, but the primary concern is that these global products are comprehensive and consistent enough for use in climate studies. One of our major customers is the international modeling comparison study known as AEROCOM, which relies on the MODIS data as a benchmark. In order to keep up with the needs of AEROCOM and other MODIS data users, while utilizing new science and tools, we have improved the algorithms and products. The code, and the associated products, will be known as Collection 6 (C6). While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. In its entirety, the C6 algorithm is comprised of three sub-algorithms for retrieving aerosol properties over different surfaces: These include the dark-target DT algorithms to retrieve over (1) ocean and (2) vegetated-dark-soiled land, plus the (3) Deep Blue (DB) algorithm, originally developed to retrieve over desert-arid land. Focusing on the two DT algorithms, we have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to 84) to increase pole-ward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such as topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence in the retrieval, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time as we have introduced algorithm changes, we have also accounted for upstream changes including: new instrument calibration, revised land-sea masking, and changed cloud masking. Upstream changes also impact the coverage and global statistics of the retrieved AOD. Although our responsibility is to the DT code and products, we have also added a product that merges DT and DB product over semi-arid land surfaces to provide a more gap-free dataset, primarily for visualization purposes. Preliminary validation shows that compared to surface-based sunphotometer data, the C6, Level 2 (along swath) DT-products compare at least as well as those from C5. C6 will include new diagnostic information about clouds in the aerosol field, including an aerosol cloud mask at 500 m resolution, and calculations of the distance to the nearest cloud from clear pixels. Finally, we have revised the strategy for aggregating and averaging the Level 2 (swath) data to become Level 3 (gridded) data. All together, the changes to the DT algorithms will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. Changes in calibration will have more impact to Terras time series, especially over land. This will result in a significant reduction in artificial differences in the Terra and Aqua datasets, and will stabilize the MODIS data as a target for AEROCOM studie

Aerosol retrieval algorithms↗
NASA NTRS2007

Coadding Techniques for Image-based Wavefront Sensing for Segmented-mirror Telescopes

Image-based wavefront sensing algorithms are being used to characterize optical performance for a variety of current and planned astronomical telescopes. Phase retrieval recovers the optical wavefront that correlates to a series of diversity-defocused point-spread functions (PSFs), where multiple frames can be acquired at each defocus setting. Multiple frames of data can be coadded in different ways; two extremes are in "image-plane space," to average the frames for each defocused PSF and use phase retrieval once on the averaged images, or in "pupil-plane space," to use phase retrieval on every set of PSFs individually and average the resulting wavefronts. The choice of coadd methodology is particularly noteworthy for segmented-mirror telescopes that are subject to noise that causes uncorrelated motions between groups of segments. Using data collected on and simulations of the James Webb Space Telescope Testbed Telescope (TBT) commissioned at Ball Aerospace, we show how different sources of noise (uncorrelated segment jitter, turbulence, and common-mode noise) and different parts of the optical wavefront, segment and global aberrations, contribute to choosing the coadd method. Of particular interest, segment piston is more accurately recovered in "image-plane space" coadding, while segment tip/tilt is recovered in "pupil-plane space" coadding.

Smith, Scott↗
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗