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Abstract
The Visible/Infrared Imager Radiometer Suite (VIIRS), scheduled to fly on the satellites of the National Polar-orbiting Operational Environmental Satellite System, will combine the missions of the Advanced Very High Resolution Radiometer (AVHRR), which flies on current National Oceanic and Atmospheric Administration satellites, and the Operational Linescan System aboard the Defense Meteorological Satellite Program satellites. VIIRS will offer a number of improvements to weather forecasters. First, because of a sophisticated downlink and relay system, VIIRS latencies will be 30 min or less around the globe, improving the timeliness and therefore the operational usefulness of the images. Second, with 22 channels, VIIRS will offer many more products than its predecessors. As an example, a true-color simulation is shown using data from the Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS), an application current geostationary imagers cannot produce because of a missing “green” wavelength channel. Third, VIIRS images will have improved quality. Through a unique pixel aggregation strategy, VIIRS pixels will not expand rapidly toward the edge of a scan like those of MODIS or AVHRR. Data will retain nearly the same resolution at the edge of the swath as at nadir. Graphs and image simulations depict the improvement in output image quality. Last, the NexSat Web site, which provides near-real-time simulations of VIIRS products, is introduced.
Abstract
The Visible/Infrared Imager Radiometer Suite (VIIRS), scheduled to fly on the satellites of the National Polar-orbiting Operational Environmental Satellite System, will combine the missions of the Advanced Very High Resolution Radiometer (AVHRR), which flies on current National Oceanic and Atmospheric Administration satellites, and the Operational Linescan System aboard the Defense Meteorological Satellite Program satellites. VIIRS will offer a number of improvements to weather forecasters. First, because of a sophisticated downlink and relay system, VIIRS latencies will be 30 min or less around the globe, improving the timeliness and therefore the operational usefulness of the images. Second, with 22 channels, VIIRS will offer many more products than its predecessors. As an example, a true-color simulation is shown using data from the Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS), an application current geostationary imagers cannot produce because of a missing “green” wavelength channel. Third, VIIRS images will have improved quality. Through a unique pixel aggregation strategy, VIIRS pixels will not expand rapidly toward the edge of a scan like those of MODIS or AVHRR. Data will retain nearly the same resolution at the edge of the swath as at nadir. Graphs and image simulations depict the improvement in output image quality. Last, the NexSat Web site, which provides near-real-time simulations of VIIRS products, is introduced.
It has been found that the day/night band of the Visible Infrared Imaging Radiometer Suite is capable of observing rapid motions of the aurora. The images that led to this discovery are shown. Shifts in the apparent position of the aurora boundary between consecutive scans of the instrument, which occur ~1.79 s apart, allow the cross-track relative speed of the aurora to be calculated. The physical basis for these observations and the method for determining the speed of auroral motions are discussed. These new satellite observations compare favorably with ground-based measurements presented in previous studies.
It has been found that the day/night band of the Visible Infrared Imaging Radiometer Suite is capable of observing rapid motions of the aurora. The images that led to this discovery are shown. Shifts in the apparent position of the aurora boundary between consecutive scans of the instrument, which occur ~1.79 s apart, allow the cross-track relative speed of the aurora to be calculated. The physical basis for these observations and the method for determining the speed of auroral motions are discussed. These new satellite observations compare favorably with ground-based measurements presented in previous studies.
Abstract
Microwave propagation conditions in the lower marine troposphere are evaluated using gradients of radio refractivity profiles. An inexpensive, weather-resistant system for Continuous monitoring of radio refractivity conditions in the lower marine troposphere would be useful for deciding when more detailed measurements should be made. Radio refractivity is largely dependent on vertical profiles of water vapor pressure. A high-power, dual-frequency, monostatic acoustic sounder was constructed to investigate the possibility of measuring water vapor pressure profiles in the marine boundary layer by acoustic means. These water vapor pressure profiles may be combined with surface measurements of atmospheric temperature and pressure to obtain estimated radio refractivity profiles. A fundamental assumption for this technique is that the pair of frequencies used should observe the same atmospheric backscatter. That is, the scattering coefficients of the two frequencies should remain a constant ratio. Measurements made with this acoustic sounder at the Boulder Atmospheric Observatory demonstrate that the 6- and 10-kHz frequencies used do not always observe the same atmospheric phenomena. Therefore, a different pair of frequencies should be sought to derive water vapor pressure profiles.
Abstract
Microwave propagation conditions in the lower marine troposphere are evaluated using gradients of radio refractivity profiles. An inexpensive, weather-resistant system for Continuous monitoring of radio refractivity conditions in the lower marine troposphere would be useful for deciding when more detailed measurements should be made. Radio refractivity is largely dependent on vertical profiles of water vapor pressure. A high-power, dual-frequency, monostatic acoustic sounder was constructed to investigate the possibility of measuring water vapor pressure profiles in the marine boundary layer by acoustic means. These water vapor pressure profiles may be combined with surface measurements of atmospheric temperature and pressure to obtain estimated radio refractivity profiles. A fundamental assumption for this technique is that the pair of frequencies used should observe the same atmospheric backscatter. That is, the scattering coefficients of the two frequencies should remain a constant ratio. Measurements made with this acoustic sounder at the Boulder Atmospheric Observatory demonstrate that the 6- and 10-kHz frequencies used do not always observe the same atmospheric phenomena. Therefore, a different pair of frequencies should be sought to derive water vapor pressure profiles.
Abstract
The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.
Abstract
The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.
Abstract
This paper presents two multispectral enhancement techniques for distinguishing between regions of cloud and snow cover using optical spectrum passive radiometer satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Fundamental to the techniques are the 1.6- and 2.2-μm shortwave infrared bands that are useful in distinguishing between absorbing snow cover (having low reflectance) and less absorbing liquid-phase clouds (higher reflectance). The 1.38-μm band helps to overcome ambiguities that arise in the case of optically thin cirrus. Designed to provide straightforward, stand-alone environmental characterization for operational forecasters (e.g., military weather forecasters in the context of mission planning), these products portray the information that is contained within complex scenes as value-added, readily interpretable imagery at the highest available spatial resolution. Their utility in scene characterization and quality control of digital snow maps is demonstrated.
Abstract
This paper presents two multispectral enhancement techniques for distinguishing between regions of cloud and snow cover using optical spectrum passive radiometer satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Fundamental to the techniques are the 1.6- and 2.2-μm shortwave infrared bands that are useful in distinguishing between absorbing snow cover (having low reflectance) and less absorbing liquid-phase clouds (higher reflectance). The 1.38-μm band helps to overcome ambiguities that arise in the case of optically thin cirrus. Designed to provide straightforward, stand-alone environmental characterization for operational forecasters (e.g., military weather forecasters in the context of mission planning), these products portray the information that is contained within complex scenes as value-added, readily interpretable imagery at the highest available spatial resolution. Their utility in scene characterization and quality control of digital snow maps is demonstrated.
Abstract
In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.
Abstract
In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.
Abstract
Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter’s ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.
Abstract
Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter’s ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.
Abstract
The next-generation U.S. polar-orbiting environmental satellite program, the Joint Polar Satellite System (JPSS), promises unprecedented capabilities for nighttime remote sensing by way of the day/night band (DNB) low-light visible sensor. The DNB will use moonlight illumination to characterize properties of the atmosphere and surface that conventionally have been limited to daytime observations. Since the moon is a highly variable source of visible light, an important question is where and when various levels of lunar illumination will be available. Here, nighttime moonlight availability was examined based on simulations done in the context of Visible/Infrared Imager Radiometer Suite (VIIRS)/DNB coverage and sensitivity. Results indicate that roughly 45% of all JPSS-orbit [sun-synchronous, 1330 local equatorial crossing time on the ascending node (LTAN)] nighttime observations in the tropics and midlatitudes would provide levels of moonlight at crescent moon or greater. Two other orbits, 1730 and 2130 LTAN, were also considered. The inclusion of a 2130 LTAN satellite would provide similar availability to 1330 LTAN in terms of total moonlit nights, but with approximately a third of those nights being additional because of this orbit’s capture of a different portion of the lunar cycle. Nighttime availability is highly variable for near-terminator orbits. A 1-h shift from the 1730 LTAN near-terminator orbit to 1630 LTAN would nearly double the nighttime availability globally from this orbit, including expanded availability at midlatitudes. In contrast, a later shift to 1830 LTAN has a negligible effect. The results are intended to provide high-level guidance for mission planners, algorithm developers, and various users of low-light applications from these future satellite programs.
Abstract
The next-generation U.S. polar-orbiting environmental satellite program, the Joint Polar Satellite System (JPSS), promises unprecedented capabilities for nighttime remote sensing by way of the day/night band (DNB) low-light visible sensor. The DNB will use moonlight illumination to characterize properties of the atmosphere and surface that conventionally have been limited to daytime observations. Since the moon is a highly variable source of visible light, an important question is where and when various levels of lunar illumination will be available. Here, nighttime moonlight availability was examined based on simulations done in the context of Visible/Infrared Imager Radiometer Suite (VIIRS)/DNB coverage and sensitivity. Results indicate that roughly 45% of all JPSS-orbit [sun-synchronous, 1330 local equatorial crossing time on the ascending node (LTAN)] nighttime observations in the tropics and midlatitudes would provide levels of moonlight at crescent moon or greater. Two other orbits, 1730 and 2130 LTAN, were also considered. The inclusion of a 2130 LTAN satellite would provide similar availability to 1330 LTAN in terms of total moonlit nights, but with approximately a third of those nights being additional because of this orbit’s capture of a different portion of the lunar cycle. Nighttime availability is highly variable for near-terminator orbits. A 1-h shift from the 1730 LTAN near-terminator orbit to 1630 LTAN would nearly double the nighttime availability globally from this orbit, including expanded availability at midlatitudes. In contrast, a later shift to 1830 LTAN has a negligible effect. The results are intended to provide high-level guidance for mission planners, algorithm developers, and various users of low-light applications from these future satellite programs.
Abstract
Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.
Abstract
Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.
