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Daniel T. Lindsey and Louie Grasso

Abstract

Satellite retrieval of cirrus cloud microphysical properties is an important but difficult problem because of uncertainties in ice-scattering characteristics. Most methods have been developed for instruments aboard polar-orbiting satellites, which have better spatial and spectral resolution than geostationary sensors. The Geostationary Operational Environmental Satellite (GOES) series has the advantage of excellent temporal resolution, so that the evolution of thunderstorm-cloud-top properties can be monitored. In this paper, the authors discuss the development of a simple ice cloud effective radius retrieval for thick ice clouds using three bands from the GOES imager: one each in the visible, shortwave infrared, and window infrared portion of the spectrum. It is shown that this retrieval compares favorably to the MODIS effective radius algorithm. In addition, a comparison of the retrieval for clouds viewed simultaneously from GOES-East and GOES-West reveals that the assumed ice-scattering properties perform very well. The algorithm is then used to produce maps of mean ice cloud effective radius over the continental United States. A real-time version of this retrieval is currently running and may be used to study the evolution of thunderstorm-top ice crystal size in rapidly evolving convection.

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Daniel T. Lindsey, Dan Bikos, and Lewis Grasso

Abstract

Geostationary Operational Environmental Satellite-16 (GOES-16) was launched into geostationary orbit in late 2016 and began providing unprecedented spatial and temporal resolution imagery early in 2017. Its Advanced Baseline Imager has additional spectral bands including two in the “clear” window and “dirty window” portion of the infrared spectrum, and the difference of these two bands, sometimes called the split window difference, provides unique information about low-level water vapor. Under certain conditions, low-level convergence along a boundary can cause local water vapor pooling, and the signal of this pooling can sometimes be detected by GOES-16 prior to any cloud formation. This case study from 15 June 2017 illustrates how the technique might be used in an operational forecast setting. A boundary in western Kansas was detected using the split window difference more than 2 h before the first cloud formed.

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Daniel T. Lindsey and Matthew J. Bunkers

Abstract

A case study of a left-moving supercell with a rapid motion is presented to (i) elucidate differences in anvil orientations between left- and right-moving supercells and (ii) highlight the interaction of the left mover with a tornadic right mover. It is shown how anvil orientations, as viewed from satellite, may be used to assist in the identification of thunderstorms with differing motions and how this applies to splitting supercells. Additionally, the movement of the left mover into the forward flank of the right mover may have temporarily affected its tornadic circulation, as tornadoes occurred both before and after the merger, despite the structure of the right mover being interrupted during the merging process. Given the dearth of literature on thunderstorm mergers in general, and how mergers affect tornadic supercells in particular, this is an area that demands further research.

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Stephen Hodanish, Ronald L. Holle, and Daniel T. Lindsey

Abstract

Just prior to 1900 UTC 25 July 2000, an 18-year-old male was fatally wounded by a lightning flash on the summit of Pikes Peak, Colorado. This case is believed to be unique in that radar and satellite data indicated that the cell that produced the flash was quite shallow and exhibited marginal reflectivity characteristics typically associated with electrified storms. Additionally, the National Lightning Detection Network indicated that this was the first and only cloud-to-ground (CG) flash associated with this convective cell. It is believed the height and isolated nature of the Pikes Peak massif played a role in the initiation of this flash.

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Daniel T. Lindsey, Steven D. Miller, and Louie Grasso
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Daniel T. Lindsey, Louie Grasso, John F. Dostalek, and Jochen Kerkmann

Abstract

The depth of boundary layer water vapor plays a critical role in convective cloud formation in the warm season, but numerical models often struggle with accurate predictions of above-surface moisture. Satellite retrievals of water vapor have been developed, but they are limited by the use of a model’s first guess, instrument spectral resolution, horizontal footprint size, and vertical resolution. In 2016, Geostationary Operational Environmental Satellite-R (GOES-R), the first in a series of new-generation geostationary satellites, will be launched. Its Advanced Baseline Imager will provide unprecedented spectral, spatial, and temporal resolution. Among the bands are two centered at 10.35 and 12.3 μm. The brightness temperature difference between these bands is referred to as the split-window difference, and has been shown to provide information about atmospheric column water vapor. In this paper, the split-window difference is reexamined from the perspective of GOES-R and radiative transfer model simulations are used to better understand the factors controlling its value. It is shown that the simple split-window difference can provide useful information for forecasters about deepening low-level water vapor in a cloud-free environment.

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Howard B. Bluestein, Daniel T. Lindsey, Daniel Bikos, Dylan W. Reif, and Zachary B. Wienhoff

Abstract

This is a study of a tornadic supercell in Kansas on 14 May 2018 in which data of relatively high spatiotemporal resolution from a mobile, polarimetric, X-band, Doppler radar were integrated with GOES-16 geosynchronous satellite imagery, and with fixed-site, surveillance, S-band polarimetric Doppler radar data. The data-collection period spanned the early life of the storm from when it was just a series of ordinary cells, with relatively low cloud tops, through its evolution into a supercell with much higher cloud tops, continuing through the formation and dissipation of a brief tornado, and ending after the supercell came to a stop and reversed direction, produced another tornado, and collided with a quasi-linear convective system. The main goal of this study was to examine the relationship between the overshooting tops and radar observed features prior to and during tornadogenesis. The highest radar echo top was displaced about 10 km, mainly to the north or northeast of the main updraft and cloud top, from the supercell phase through the first tornado phase of the supercell phase, after which the updraft and the cloud top became more closely located and then jumped ahead; this behavior is consistent with what would be expected during cyclic mesocyclogenesis. The change in direction of the supercell later on occurred while the nocturnal low-level jet was intensifying. No relationship was apparent between changes in the highest cloud-top height and tornadogenesis, but changes in cloud-top heights (rapid increases and rapid decreases) were related to two phases in multicell evolution and to supercell formation.

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Curtis J. Seaman, Yoo-Jeong Noh, Steven D. Miller, Andrew K. Heidinger, and Daniel T. Lindsey

Abstract

The operational VIIRS cloud-base height (CBH) product from the Suomi–National Polar-Orbiting Partnership (SNPP) satellite is compared against observations of CBH from the cloud profiling radar (CPR) on board CloudSat. Because of the orbits of SNPP and CloudSat, these instruments provide nearly simultaneous observations of the same locations on Earth for a ~4.5-h period every 2–3 days. The methodology by which VIIRS and CloudSat observations are spatially and temporally matched is outlined. Based on four 1-month evaluation periods representing each season from June 2014 to April 2015, statistics related to the VIIRS CBH retrieval performance have been collected. Results indicate that when compared against CloudSat, the VIIRS CBH retrieval does not meet the error specifications set by the Joint Polar Satellite System (JPSS) program, with a root-mean-square error (RMSE) of 3.7 km for all clouds globally. More than half of all matching VIIRS pixels and CloudSat profiles have CBH errors exceeding the 2-km error requirement. Underscoring the significance of these statistics, it is shown that a simple estimate based on a constant cloud geometric thickness of 2 km outperforms the current operational CBH algorithm. It was found that the performance of the CBH product is impacted by the accuracy of upstream retrievals [primarily cloud-top height (CTH)] and the a priori information used by the CBH retrieval algorithm. However, even when CTH errors were small, CBH errors still exceed the JPSS program error specifications with an RMSE of 2.3 km.

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William E. Line, Timothy J. Schmit, Daniel T. Lindsey, and Steven J. Goodman

Abstract

The Geostationary Operational Environmental Satellite-14 (GOES-14) Imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of 2012, 2013, 2014, and 2015. This scan mode, known as the Super Rapid Scan Operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling that will be provided by the Advanced Baseline Imager on the next-generation GOES-R series. NOAA/National Weather Service/Storm Prediction Center (SPC) forecasters utilized the 1-min imagery extensively in operations when available over convectively active regions. They found it provided them with unique insight into relevant features and processes before, during, and after convective initiation. This paper introduces how the SRSOR datasets from GOES-14 were used by SPC forecasters and how these data are likely to be applied when available operationally from GOES-R. Several animations, included as supplemental material, showcase the rapid change of severe weather–related phenomena observed during the 2014 and 2015 SRSOR campaigns from the GOES-14 Imager.

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Steven D. Miller, Daniel T. Lindsey, Curtis J. Seaman, and Jeremy E. Solbrig

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.

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