Search Results

You are looking at 1 - 7 of 7 items for

  • Author or Editor: Curtis J. Seaman x
  • All content x
Clear All Modify Search
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.

Full access
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.

Full access
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.

Open access
Yoo-Jeong Noh, Curtis J. Seaman, Thomas H. Vonder Haar, and Guosheng Liu

Abstract

The vertical distribution of liquid and ice water content and their partitioning is studied using 34 cases of in situ measured microphysical properties in midlatitude mixed-phase clouds, with liquid water path ranging from near zero to ~248 g m−2, total water path ranging from near zero to ~562 g m−2, and cloud-top temperature ranging from −2° to −38°C. The 34 profiles were further divided into three cloud types depending on their vertical extents and altitudes. It is found that both the vertical distribution of liquid water within a cloud and the liquid water fraction (of total condensed water) as a function of temperature or relative position in a cloud layer are cloud-type dependent. In particular, it is found that the partitioning between liquid and ice water for midlevel shallow clouds is relatively independent on the vertical position within the cloud while it clearly depends on cloud mean temperature. For synoptic snow clouds, however, liquid water fraction increases with the decrease of altitude within the cloud. While the liquid water fraction in synoptic clouds also decreases with lowering temperature, its magnitude is only about 50% near 0°C.

Full access
Steven D. Miller, William C. Straka III, Jia Yue, Curtis J. Seaman, Shuang Xu, Christopher D. Elvidge, Lars Hoffmann, and Irfan Azeem

Abstract

Hurricane Matthew (28 September–9 October 2016) was perhaps the most infamous storm of the 2016 Atlantic hurricane season, claiming over 600 lives and causing over $15 billion (U.S. dollars) in damages across the central Caribbean and southeastern U.S. seaboard. Research surrounding Matthew and its many noteworthy meteorological characteristics (e.g., rapid intensification into the southernmost category 5 hurricane in the Atlantic basin on record, strong lightning and sprite production, and unusual cloud morphology) is ongoing. Satellite remote sensing typically plays an important role in the forecasting and study of hurricanes, providing a top-down perspective on storms developing over the remote and inherently data-sparse tropical oceans. In this regard, a relative newcomer among the suite of satellite observations useful for tropical cyclone monitoring and research is the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB), a sensor flying on board the NOAA–NASA Suomi National Polar-Orbiting Partnership (SNPP) satellite. Unlike conventional instruments, the DNB’s sensitivity to extremely low levels of visible and near-infrared light offers new insight into storm properties and impacts. Here, we chronicle Matthew’s path of destruction and peer through the DNB’s looking glass of low-light visible observations, including lightning connected to sprite formation, modulation of the atmospheric nightglow by storm-generated gravity waves, and widespread power outages. Collected without moonlight, these examples showcase the wealth of unique information present in DNB nocturnal low-light observations without moonlight, and their potential to complement traditional satellite measurements of tropical storms worldwide.

Open access
Yoo-Jeong Noh, John M. Forsythe, Steven D. Miller, Curtis J. Seaman, Yue Li, Andrew K. Heidinger, Daniel T. Lindsey, Matthew A. Rogers, and Philip T. Partain

Abstract

Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

Full access
Steven D. Miller, Timothy L. Schmit, Curtis J. Seaman, Daniel T. Lindsey, Mathew M. Gunshor, Richard A. Kohrs, Yasuhiko Sumida, and Donald Hillger

Abstract

In 1967, at the dawn of the satellite era, the Applications Technology Satellite 3 (ATS-3) provided the first full-disk “true color” images of Earth. With its depiction of blue oceans, golden deserts, and green forestlands beneath white clouds, the imagery captured the iconic Blue Marble in a way that resonates strongly with human perception. After ATS-3, the standard fare of geostationary satellites entailed a single visible band with additional infrared spectral channels. While single-band visible satisfied the basic user requirements of daytime imagery, the loss of true-color capability and its inherent capability to distinguish myriad atmospheric and surface features via coloration left a notable void. Nearly half a century later, with the launch of Japan’s Himawari-8 in October 2014, there is once again a geostationary sensor—the Advanced Himawari Imager (AHI)—containing the multispectral visible bands required notionally for true color. However, it soon became apparent that AHI’s “green” band, centered at 0.51 μm, was not aligned with the chlorophyll reflectance signature near 0.55 μm. As a result, vegetation appears browner and deserts appear redder than legacy true-color imagery. Here, we describe a technique that attempts to mitigate these issues by blending information from a ref lective-infrared band at 0.86 μm to form a “hybrid” green band. When combining this method with Rayleigh corrections, AHI’s true-color performance is found to be consistent with that of the optimal 0.55-μm band, offering a stopgap solution adaptable to future satellites of similar design.

Full access