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- Author or Editor: Jeremy Solbrig x
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Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of two-dimensional, cloud-top information and derived environmental products. In addition, the A-Train constellation of satellites presents an opportunity to combine MODIS data with coincident vertical-profile data collected from sensors on CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Approximating the vertical structure of clouds in data-sparse regions can be accomplished through a two-step process that consists of cluster analysis of MODIS data and quantitative analysis of coincident vertical-profile data. Daytime data over the eastern North Pacific Ocean are used in this study for both the summer (June–August) and winter (December–February) seasons in separate cluster analyses. A-Train data from 2006 to 2009 are collected, and a K-means cluster analysis is applied to selected MODIS data that are coincident with single-layer clouds found in the CloudSat/CALIPSO (“GEOPROF-lidar”) data. The resultant clusters, 16 in both summer and winter, are quantified in terms of average cloud-base height, cloud-top height, and normalized cloud water content profile. A cluster and its quantified characteristics can then be assigned to a given pixel in near real-time MODIS data, regardless of its proximity to the observed vertical-profile data. When applied to a two-dimensional MODIS dataset, these assigned clusters can provide an approximate three-dimensional representation of the cloud scene.
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of two-dimensional, cloud-top information and derived environmental products. In addition, the A-Train constellation of satellites presents an opportunity to combine MODIS data with coincident vertical-profile data collected from sensors on CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Approximating the vertical structure of clouds in data-sparse regions can be accomplished through a two-step process that consists of cluster analysis of MODIS data and quantitative analysis of coincident vertical-profile data. Daytime data over the eastern North Pacific Ocean are used in this study for both the summer (June–August) and winter (December–February) seasons in separate cluster analyses. A-Train data from 2006 to 2009 are collected, and a K-means cluster analysis is applied to selected MODIS data that are coincident with single-layer clouds found in the CloudSat/CALIPSO (“GEOPROF-lidar”) data. The resultant clusters, 16 in both summer and winter, are quantified in terms of average cloud-base height, cloud-top height, and normalized cloud water content profile. A cluster and its quantified characteristics can then be assigned to a given pixel in near real-time MODIS data, regardless of its proximity to the observed vertical-profile data. When applied to a two-dimensional MODIS dataset, these assigned clusters can provide an approximate three-dimensional representation of the cloud scene.
GeoColor: A Blending Technique for Satellite ImageryAbstract
Value-added imagery is a useful means of communicating multispectral environmental satellite radiometer data to the human analyst. The most effective techniques strike a balance between science and art. The science side requires engineering physical algorithms capable of distilling the complex scene into a reduced set of key parameters. The artistic side involves design and construction of visually intuitive displays that maximize information content within the product image. The utility of such imagery to human analysts depends on the extent to which parameters or features of interest are conveyed unambiguously. Here, we detail and demonstrate a dynamic blended imagery technique, based on spatially variant transparency factors whose values are tied to algorithmically isolated parameters. The technique enables seamless display of multivariate information, and is applicable to any imaging system based on red–green–blue composites. We illustrate this technique in the context of GeoColor—an application of the Geostationary Operational Environmental Satellite R (GOES-R) series Advanced Baseline Imager (ABI) supporting operational forecasting and used widely in public communication of weather information.
Abstract
Value-added imagery is a useful means of communicating multispectral environmental satellite radiometer data to the human analyst. The most effective techniques strike a balance between science and art. The science side requires engineering physical algorithms capable of distilling the complex scene into a reduced set of key parameters. The artistic side involves design and construction of visually intuitive displays that maximize information content within the product image. The utility of such imagery to human analysts depends on the extent to which parameters or features of interest are conveyed unambiguously. Here, we detail and demonstrate a dynamic blended imagery technique, based on spatially variant transparency factors whose values are tied to algorithmically isolated parameters. The technique enables seamless display of multivariate information, and is applicable to any imaging system based on red–green–blue composites. We illustrate this technique in the context of GeoColor—an application of the Geostationary Operational Environmental Satellite R (GOES-R) series Advanced Baseline Imager (ABI) supporting operational forecasting and used widely in public communication of weather information.
Abstract
The first observationally based conceptual model for intense pyrocumulonimbus (pyroCb) development is described by applying reanalyzed meteorological model output to an inventory of 26 intense pyroCb events from June to August 2013 and a control inventory of intense fire activity without pyroCb. Results are based on 88 intense wildfires observed within the western United States and Canada. While surface-based fire weather indices are a useful indicator of intense fire activity, they are not a skillful predictor of intense pyroCb. Development occurs when a layer of increased moisture content and instability is advected over a dry, deep, and unstable mixed layer, typically along the leading edge of an approaching disturbance or under the influence of a monsoonal anticyclone. Upper-tropospheric dynamics are conducive to rising motion and vertical convective development. Mid- and upper-tropospheric conditions therefore resemble those that produce traditional dry thunderstorms. The specific quantity of midlevel moisture and instability required is shown to be strongly dependent on the surface elevation of the contributing fire. Increased thermal buoyancy from large and intense wildfires can serve as a potential trigger, implying that pyroCb occasionally develop in the absence of traditional meteorological triggering mechanisms. This conceptual model suggests that meteorological conditions favorable for pyroCb are observed regularly in western North America. PyroCb and ensuing stratospheric smoke injection are therefore likely to be significant and endemic features of summer climate. Results from this study provide a major step toward improved detection, monitoring, and prediction of pyroCb, which will ultimately enable improved understanding of the role of this phenomenon in the climate system.
Abstract
The first observationally based conceptual model for intense pyrocumulonimbus (pyroCb) development is described by applying reanalyzed meteorological model output to an inventory of 26 intense pyroCb events from June to August 2013 and a control inventory of intense fire activity without pyroCb. Results are based on 88 intense wildfires observed within the western United States and Canada. While surface-based fire weather indices are a useful indicator of intense fire activity, they are not a skillful predictor of intense pyroCb. Development occurs when a layer of increased moisture content and instability is advected over a dry, deep, and unstable mixed layer, typically along the leading edge of an approaching disturbance or under the influence of a monsoonal anticyclone. Upper-tropospheric dynamics are conducive to rising motion and vertical convective development. Mid- and upper-tropospheric conditions therefore resemble those that produce traditional dry thunderstorms. The specific quantity of midlevel moisture and instability required is shown to be strongly dependent on the surface elevation of the contributing fire. Increased thermal buoyancy from large and intense wildfires can serve as a potential trigger, implying that pyroCb occasionally develop in the absence of traditional meteorological triggering mechanisms. This conceptual model suggests that meteorological conditions favorable for pyroCb are observed regularly in western North America. PyroCb and ensuing stratospheric smoke injection are therefore likely to be significant and endemic features of summer climate. Results from this study provide a major step toward improved detection, monitoring, and prediction of pyroCb, which will ultimately enable improved understanding of the role of this phenomenon in the climate system.
Abstract
The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) nighttime visible channel was designed to detect earth–atmosphere features under conditions of low illumination (e.g., near the solar terminator or via moonlight reflection). However, this sensor also detects visible light emissions from various terrestrial sources (both natural and anthropogenic), including lightning-illuminated thunderstorm tops. This research presents an automated technique for objectively identifying and enhancing the bright steaks associated with lightning flashes, even in the presence of lunar illumination, derived from OLS imagery. A line-directional filter is applied to the data in order to identify lightning strike features and an associated false color imagery product enhances this information while minimizing false alarms. Comparisons of this satellite product to U.S. National Lightning Detection Network (NLDN) data in one case as well as to a lightning mapping array (LMA) in another case demonstrate general consistency to within the expected limits of detection. This algorithm is potentially useful in either finding or confirming electrically active storms anywhere on the globe, particularly those occurring in remote areas where surface-based observations are not available. Additionally, the OLS nighttime visible sensor provides heritage data for examining the potential usefulness of the Visible-Infrared Imager-Radiometer Suite (VIIRS) Day/Night Band (DNB) on future satellites including the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). The VIIRS DNB will offer several improvements to the legacy OLS nighttime visible channel, including full calibration and collocation with 21 narrowband spectral channels.
Abstract
The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) nighttime visible channel was designed to detect earth–atmosphere features under conditions of low illumination (e.g., near the solar terminator or via moonlight reflection). However, this sensor also detects visible light emissions from various terrestrial sources (both natural and anthropogenic), including lightning-illuminated thunderstorm tops. This research presents an automated technique for objectively identifying and enhancing the bright steaks associated with lightning flashes, even in the presence of lunar illumination, derived from OLS imagery. A line-directional filter is applied to the data in order to identify lightning strike features and an associated false color imagery product enhances this information while minimizing false alarms. Comparisons of this satellite product to U.S. National Lightning Detection Network (NLDN) data in one case as well as to a lightning mapping array (LMA) in another case demonstrate general consistency to within the expected limits of detection. This algorithm is potentially useful in either finding or confirming electrically active storms anywhere on the globe, particularly those occurring in remote areas where surface-based observations are not available. Additionally, the OLS nighttime visible sensor provides heritage data for examining the potential usefulness of the Visible-Infrared Imager-Radiometer Suite (VIIRS) Day/Night Band (DNB) on future satellites including the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). The VIIRS DNB will offer several improvements to the legacy OLS nighttime visible channel, including full calibration and collocation with 21 narrowband spectral channels.
Abstract
Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.
Abstract
Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.
Abstract
Intense wildfires occasionally generate fire-triggered storms, known as pyrocumulonimbus (pyroCb), that can inject smoke particles and trace gases into the upper troposphere and lower stratosphere (UTLS). This study develops the first pyroCb detection algorithm using three infrared (IR) channels from the imager on board GOES-West (GOES-15). The algorithm first identifies deep convection near active fires via the longwave IR brightness temperature, distinguishing between midtropospheric and UTLS injections. During daytime, unique pyroCb microphysical properties are characterized by a medium-wave brightness temperature that is significantly larger than that in the longwave, allowing for separation of pyroCb from other deep convection. A cloud-opacity test reduces potential false detections. Application of this algorithm to 88 intense wildfires observed during the 2013 fire season in western North America resulted in successful detection of individual intense events, pyroCb embedded within traditional convection, and multiple, short-lived pulses of pyroconvective activity. Comparisons with a community inventory indicate that this algorithm captures the majority of pyroCb. The primary limitation is that pyroCb anvils can be small relative to GOES-West pixel size, especially in regions with large viewing angles. The algorithm is also sensitive to some false positives from traditional convection that either ingests smoke or exhibits extreme updraft velocities. A total of 26 pyroCb events are inventoried, including 31 individual pulses, all of which can inject smoke into the UTLS. Six of the inventoried intense pyroCb were not previously documented. Near-real-time application of this algorithm can be extended to other regions and to next-generation geostationary sensors, which offer significant advantages for pyroCb and fire detection.
Abstract
Intense wildfires occasionally generate fire-triggered storms, known as pyrocumulonimbus (pyroCb), that can inject smoke particles and trace gases into the upper troposphere and lower stratosphere (UTLS). This study develops the first pyroCb detection algorithm using three infrared (IR) channels from the imager on board GOES-West (GOES-15). The algorithm first identifies deep convection near active fires via the longwave IR brightness temperature, distinguishing between midtropospheric and UTLS injections. During daytime, unique pyroCb microphysical properties are characterized by a medium-wave brightness temperature that is significantly larger than that in the longwave, allowing for separation of pyroCb from other deep convection. A cloud-opacity test reduces potential false detections. Application of this algorithm to 88 intense wildfires observed during the 2013 fire season in western North America resulted in successful detection of individual intense events, pyroCb embedded within traditional convection, and multiple, short-lived pulses of pyroconvective activity. Comparisons with a community inventory indicate that this algorithm captures the majority of pyroCb. The primary limitation is that pyroCb anvils can be small relative to GOES-West pixel size, especially in regions with large viewing angles. The algorithm is also sensitive to some false positives from traditional convection that either ingests smoke or exhibits extreme updraft velocities. A total of 26 pyroCb events are inventoried, including 31 individual pulses, all of which can inject smoke into the UTLS. Six of the inventoried intense pyroCb were not previously documented. Near-real-time application of this algorithm can be extended to other regions and to next-generation geostationary sensors, which offer significant advantages for pyroCb and fire detection.
The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and lightning flashes.
NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.
The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and lightning flashes.
NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.
Abstract
New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.
Abstract
New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.
