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Thomas F. Lee, Steven D. Miller, Carl Schueler, and Shawn Miller

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.

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Curtis J. Seaman and Steven D. Miller

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.

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Steven M. Babin, Robert E. Miller, and John R. Rowland

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.

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Daniel T. Lindsey, Steven D. Miller, and Louie Grasso
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Steven D. Miller, Thomas F. Lee, and Robert L. Fennimore

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.

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

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.

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Steven J. Fletcher, Glen E. Liston, Christopher A. Hiemstra, and Steven D. Miller

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.

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Richard L. Bankert, Jeremy E. Solbrig, Thomas F. Lee, and Steven D. Miller

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.

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Steven D. Miller, Fang Wang, Ann B. Burgess, S. McKenzie Skiles, Matthew Rogers, and Thomas H. Painter

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.

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Cristian Mitrescu, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk

Abstract

Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat’s millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle.

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