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NASA NTRS2012

Discrimination of Biomass Burning Smoke and Clouds in MAIAC Algorithm

The multi-angle implementation of atmospheric correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the smoke test to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 micrometers as compared to 0.47-0.67 micrometers due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements. This paper provides the algorithm detail and illustrates its performance on two examples of wildfires in US Pacific North-West and in Georgia/Florida of 2007.

Lyapustin, A.↗
NASA NTRS2012

Multi-Angle Implementation of Atmospheric Correction (MAIAC) Algorithm

Multi-Angle Implementation of Atmospheric Correction (MAIAC) is a new algorithm developed for MODIS. MAIAC uses a time series analysis and processing of groups of pixels to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance without typical assumptions about the surface. It is a generic algorithm which works over both dark and bright land surfaces, including deserts. MAIAC has an internal Cloud Mask, a dynamic land-water-snow classification and a surface change mask which allows it to flexibly choose processing path over different surfaces. A distinct feature of MAIAC is a high 1 km resolution of aerosol retrievals which is required in different applications including the air quality analysis. The novel features of MAIAC include the high quality cloud mask, discrimination of aerosol type, including biomass burning smoke and dust, and detection of surface change - all required for high quality aerosol retrievals. An overview of the algorithm, results of AERONET validation, and examples of comparison with MODIS Collection 5 aerosol product and Deep Blue algorithm for different parts of the world, will be presented.

Lyapustin, A.↗
NASA NTRS2012

Using NASA Environmental Data to Enhance Public Health Decision Making

The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗
NASA NTRS2012

Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra

The retrieval of aerosol properties from spaceborne sensors requires highly accurate and precise radiometric measurements, thus placing stringent requirements on sensor calibration and characterization. For the Terra/Moderate Resolution Imaging Spedroradiometer (MODIS), the characteristics of the detectors of certain bands, particularly band 8 [(B8); 412 nm], have changed significantly over time, leading to increased calibration uncertainty. In this paper, we explore a possibility of utilizing a cross-calibration method developed for characterizing the Terral MODIS detectors in the ocean bands by the National Aeronautics and Space Administration Ocean Biology Processing Group to improve aerosol retrieval over bright land surfaces. We found that the Terra/MODIS B8 reflectance corrected using the cross calibration method resulted in significant improvements for the retrieved aerosol optical thickness when compared with that from the Multi-angle Imaging Spectroradiometer, Aqua/MODIS, and the Aerosol Robotic Network. The method reported in this paper is implemented for the operational processing of the Terra/MODIS Deep Blue aerosol products.

Jeong, Myeong-Jae↗
NASA NTRS2012

Desert Dust Satellite Retrieval Intercomparison

This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify and understand the differences between current algorithms, and hence improve future retrieval algorithms. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. These include multi-angle approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as as20 sumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, at least as significant as these differences are sampling issues related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset.

Carboni, E.↗
NASA NTRS2012

Current Usage and Future Prospects of Multispectral (RGB) Satellite Imagery in Support of NWS Forecast Offices and National Centers

Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low-Earth orbits. The NASA Short-term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA s Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channel s available from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard METEOSAT-9. This broader suite includes products that discriminate between air mass types associated with synoptic-scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Similarly, researchers at NOAA/NESDIS and CIRA have developed air mass discrimination capabilities using channels available from the current GOES Sounders. Other applications of multispectral composites include combinations of high and low frequency, horizontal and vertically polarized passive microwave brightness temperatures to discriminate tropical cyclone structures and other synoptic-scale features. Many of these capabilities have been transitioned for evaluation and operational use at NWS Weather Forecast Offices and National Centers through collaborations with SPoRT and CIRA. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES-R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar-Orbiting Partnership (S-NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites). At the same time, new image manipulation and display capabilities are available within AWIPS II, the next generation of the NWS forecaster decision support system. This presentation will present a review of SPoRT, CIRA, and NRL collaborations regarding multispectral satellite imagery and articulate an integrated and collaborative path forward with Raytheon AWIPS II development staff for integrating current and future capabilities that support new satellite instrumentation and the AWIPS II decision support system.

Molthan, Andrew L.↗
NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

This project has dual goals in decision ]making activities .. Providing information to decision makers about associations between environmental exposures and health conditions in a large national cohort study. Enriching the CDC Wide ]ranging Online Data for Epidemiologic Research (WONDER) system by integrating environmental exposure data. .. Develop daily high ]quality spatial data sets of environmental variables for the conterminous U.S. for the years 2003-2008 utilizing NASA data (Objective 1). Fine Particulates (PM2.5) (NASA MODIS and EPA AQS). Land Surface Temperature (NASA MODIS). Solar Insolation and Heat ]related Products (Reanalysis Data). Link these environmental variables with public health data from a national cohort study and examine environmental health relationships (Objective 2). Cognitive Function. Hypertension. Make the environmental datasets available to public health professionals, researchers and the general public via the CDC WONDER system (Objective 3).

Al-Hamdan, Mohammad↗
NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

The overall goal of this study is to address issues of environmental health and enhance public health decision making by using NASA remotely sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology and Laboratory Services. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the environmental data sets and associated public health analyses to local, state and federal end ]user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) on a 10-km grid using US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of MODIS Land Surface Temperature (LST); and (3) a 12-km grid of daily incoming solar radiation and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county-level, state-level, or regional-level as per users f need and downloaded in tabular, graphical, and map formats. This provides a significant addition to the CDC WONDER online system, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER. It also substantially expands public access to NASA data, making their use by a wide range of decisionmakers feasible.

Al-Hamdan, Mohammad↗
NASA NTRS2013

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

Gladkova, Irina↗
NASA NTRS2013

A-Train Observations of Deep Convective Storm Tops

The paper highlights simultaneous observations of tops of deep convective clouds from several space-borne instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Aqua satellite, Cloud Profiling Radar (CPR) of the CloudSat satellite, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) flown on the CALIPSO satellite. These satellites share very close orbits, thus together with several other satellites they are referred to as the "A-Train" constellation. Though the primary responsibility of these satellites and their instrumentation is much broader than observations of fine-scale processes atop convective storms, in this study we document how data from the A-Train can contribute to a better understanding and interpretation of various storm-top features, such as overshooting tops, cold-U/V and cold ring features with their coupled embedded warm areas, above anvil ice plumes and jumping cirrus. The relationships between MODIS multi-spectral brightness temperature difference (BTD) fields and cloud top signatures observed by the CPR and CALIOP are also examined in detail to highlight the variability in BTD signals across convective storm events.

Setvak, Martin↗
NASA NTRS2013

Surface Irradiances Consistent With CERES-Derived Top-of-Atmosphere Shortwave and Longwave Irradiances

The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth's Radiant Energy System (CERES). This paper presents amethod to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, andModerate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthlymean irradiances calculated with 10 years of data is 4.7 (13.3) W/sq m for downward shortwave and 22.5 (7.1) W/sq m for downward longwave irradiances over ocean and 21.7 (7.8) W m22 for downward shortwave and 21.0 (7.6) W/sq m for downward longwave irradiances over land. The bias andRMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES,MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.

Kato, Seiji↗
NASA NTRS2013

Public Health Applications of Remotely-sensed Environmental Datasets for the Conterminous United States

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision-making using NASA remotely-sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid using the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Incoming Solar Radiation (Insolation) and heat-related products using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets were linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental datasets and the results of the public health linkage analyses will be disseminated to end-users for decision-making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer-reviewed publications respectively. The linkage of these data with the CDC WONDER system substantially expands public access to NASA data, making their use by a wide range of decision makers feasible. By successful completion of this research, decision-making activities, including policy-making and clinical decision-making, can be positively affected through utilization of the data products and analyses provided on the CDC WONDER system.

Al-Hamdan, Mohammad↗
NASA NTRS2013

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters : Synthetic Sensor Radiance Formulation - Pt. 1

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Simulations↗
NASA NTRS2013

You Can Run, But You Can't Hide Juniper Pollen Phenology and Dispersal

Pollen can be transported great distances. Van de Water et. al., 2003 reported Juniperus spp. pollen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. The DREAM (Dust REgional Atmospheric Model, Nickovic et al. 2001) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust. We are modified the DREAM model to incorporate pollen transport. Pollen release is estimated based on MODIS derived phenology of Juniperus spp. communities. Ground based observational records of pollen release timing and quantities are used as verification. This information will be used to support the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program and the State of New Mexico environmental public health decision support for asthma and allergies alerts.

Luvall, Jeffrey C.↗
NASA NTRS2013

A 4-D Climatology (1979-2009) of the Monthly Tropospheric Aerosol Optical Depth Distribution over the Mediterranean Region from a Comparative Evaluation and Blending of Remote Sensing and Model Products

Since the 1980s several spaceborne sensors have been used to retrieve the aerosol optical depth (AOD) over the Mediterranean region. In parallel, AOD climatologies coming from different numerical model simulations are now also available, permitting to distinguish the contribution of several aerosol types to the total AOD. In this work, we perform a comparative analysis of this unique multiyear database in terms of total AOD and of its apportionment by the five main aerosol types (soil dust, seasalt, sulfate, black and organic carbon). We use 9 different satellite-derived monthly AOD products: NOAA/AVHRR, SeaWiFS (2 products), TERRA/MISR, TERRA/MODIS, AQUA/MODIS, ENVISAT/MERIS, PARASOL/POLDER and MSG/SEVIRI, as well as 3 more historical datasets: NIMBUS7/CZCS, TOMS (onboard NIMBUS7 and Earth- Probe) and METEOSAT/MVIRI. Monthly model datasets include the aerosol climatology from Tegen et al. (1997), the climate-chemistry models LMDz-OR-INCA and RegCM-4, the multi-model mean coming from the ACCMIP exercise, and the reanalyses GEMS and MACC. Ground-based Level- 2 AERONET AOD observations from 47 stations around the basin are used here to evaluate the model and satellite data. The sensor MODIS (on AQUA and TERRA) has the best average AOD scores over this region, showing a relevant spatio-temporal variability and highlighting high dust loads over Northern Africa and the sea (spring and summer), and sulfate aerosols over continental Europe (summer). The comparison also shows limitations of certain datasets (especially MERIS and SeaWiFS standard products). Models reproduce the main patterns of the AOD variability over the basin. The MACC reanalysis is the closest to AERONET data, but appears to underestimate dust over Northern Africa, where RegCM-4 is found closer to MODIS thanks to its interactive scheme for dust emissions. The vertical dimension is also investigated using the CALIOP instrument. This study confirms differences of vertical distribution between dust aerosols showing a large vertical spread, and other continental and marine aerosols which are confined in the boundary layer. From this compilation, we propose a 4-D blended product from model and satellite data, consisting in monthly time series of 3-D aerosol distribution at a 50 km horizontal resolution over the Euro-Mediterranean marine and continental region for the 2003-2009 period. The product is based on the total AOD from AQUA/MODIS, apportioned into sulfates, black and organic carbon from the MACC reanalysis, and into dust and sea-salt aerosols from RegCM-4 simulations, which are distributed vertically based on CALIOP climatology.We extend the 2003-2009 reconstruction to the past up to 1979 using the 2003-2009 average and applying the decreasing trend in sulfate aerosols from LMDz-OR-INCA, whose AOD trends over Europe and the Mediterranean are median among the ACCMIP models. Finally optical properties of the different aerosol types in this region are proposed from Mie calculations so that this reconstruction can be included in regional climate models for aerosol radiative forcing and aerosol-climate studies.

troposphere↗
NASA NTRS2014

Using the SPoRT POES/GOES Hybrid Product in OCONUS Forecasting

The SPoRT (Short-term Prediction and Research Transition) Program at the NASA/Marshall Space Flight Center has been providing unique NASA and NOAA data and techniques to partner Weather Forecast Offices (WFOs) for ten years. Data are provided in the Decision Support System used by WFO forecasters: AWIPS. For the last couple of years, SPoRT has been producing the POES/GOES Hybrid. This suite of products combines the strength ofl5- minute animations of GOES imagery - providing temporal continuity, with the higher resolution, relatively random availability, of polar orbiting (POES) imagery data. The product was first introduced with only MODIS data from NASA's Terra and Aqua satellites, but recently the VIIRS instrument onboard the Suomi-NPP satellite was added, providing better high-resolution coverage. These products represent SPoRT's efforts to prepare for higher resolution, higher frequency GOES-R imagery - as well as helping to move VIIRS (JPSS) data into the mainstream of weather forecasting. SPoRT generates 5 products for this dataset: Visible, Longwave Infrared (11 micrometers), Shortwave IR (3.7 micrometers), Water Vapor (6.7 micrometers), and Fog (Difference of 11 micrometer and 3.7 micrometer channels). The Water Vapor hybrid product has a Red-Blue-Green image from MODIS inlaid, since it provides even more qualitative information than water vapor alone. Animated examples of the products will be shown in this presentation. While the resolution at nadir of GOES imagery is nominally Han (4km for IR channels), the inlaid polar orbiter imagery has a resolution of 250m (lkm for IR channels). This has tremendous application in the continental US. However, in high latitudes, since the usefulness of GOES degrades poleward rapidly, the contrast of GOES and POES data is stark. The consistent temporal nature of GOES, even though at a reduced resolution at high latitudes, provides basic situational awareness, but the introduction of polar data is very helpful in seeing the big picture with clarity - even if only briefly. This presentation will offer real situations where these products helped forecasters make better informed decisions quickly. Plans to augment the product further with the addition of data from several A VHRR instruments will be described.

Smith, Matt↗
NASA NTRS2014

Identifying Hail Signatures in Satellite Imagery from the 9-10 August 2011 Severe Weather Event

Severe thunderstorms can produce large hail that causes property damage, livestock fatalities, and crop failure. However, detailed storm surveys of hail damage conducted by the National Weather Service (NWS) are not required. Current gaps also exist between Storm Prediction Center (SPC) hail damage estimates and crop‐insurance payouts. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites can be used to support NWS damage assessments, particularly to crops during the growing season. The two‐day severe weather event across western Nebraska and central Kansas during 9‐10 August 2011 offers a case study for investigating hail damage signatures by examining changes in Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery. By analyzing hail damage swaths in satellite imagery, potential economic losses due to crop damage can be quantified and further improve the estimation of weather impacts on agriculture without significantly increasing manpower requirements.

Dryden, Rachel L.↗
NASA NTRS2014

Sport Transition of JPSS VIIRS Imagery for Night-time Applications

The NASA/Short‐term Prediction, Research, and Transition (SPoRT) Program and NOAA/Cooperative Institute for Research in the Atmosphere (CIRA) work within the NOAA/Joint Polar Satellite System (JPSS) Proving Ground to demonstrate the unique capabilities of the VIIRS instrument. Very similar to MODIS, the VIIRS instrument provides many high‐resolution visible and infrared channels in a broad spectrum. In addition, VIIRS is equipped with a low‐light sensor that is able to detect light emissions from the land and atmosphere as well as reflected sunlight by the lunar surface. This band is referred to as the Day‐Night Band due to the sunlight being used at night to see cloud and topographic features just as one would typically see in day‐time visible imagery. NWS forecast offices that collaborate with SPoRT and CIRA have utilized MODIS imagery in operations, but have longed for more frequent passes of polar‐orbiting data. The VIIRS instrument enhances SPoRT collaborations with WFOs by providing another day and night‐time pass, and at times two additional passes due to its large swath width. This means that multi‐spectral, RGB imagery composites are more readily available to prepare users for their use in GOES‐R era and high‐resolution imagery for use in high‐latitudes is more frequently able to supplement standard GOES imagery within the SPoRT Hybrid GEO‐LEO product. The transition of VIIRS also introduces the new Day‐Night Band capability to forecast operations. An Intensive Evaluation Period (IEP) was conducted in Summer 2013 with a group of "Front Range" NWS offices related to VIIRS night‐time imagery. VIIRS single‐channel imagery is able to better analyze the specific location of fire hotspots and other land features, as well as provide a more true measurement of various cloud and aerosol properties than geostationary measurements, especially at night. Viewed within the SPoRT Hybrid imagery, the VIIRS data allows forecasters to better interpret the more frequent, but coarse GOES Imagery. Night‐time Microphysics and Dust RGB Imagery provides cloud analysis of cloud height, thickness, and composition in order for operational applications such as separating fog from low clouds, dust plume detection, and determining precipitating clouds in radar-void/ blocked regions. The Day‐Night Band has a particular benefit to seeing light from cities, fires, or other emissions as well as the reflection of moonlight off of clouds and smoke plumes, given the right lunar phase and angle. Examples from the VIIRS transition and IEP will be presented.

Fuell, Kevin↗