Global satellite composites — 20 years of evolution

Kohrs, Richard A.; Lazzara, Matthew A.; Robaidek, J.; Santek, David; Knuth, Shelley L.

doi:10.1016/j.atmosres.2013.07.023

Cited by 15 publications

(18 citation statements)

References 21 publications

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“…Note that the mean 2004 SAM index (for data see http://www.nerc-bas.ac.uk/public/icd/gjma/newsam.1957.2007.txt) was 0.35 that is twice as small compared to its annual STD (0.69) for the period 1957 to 2017. The mosaics are spatial composites assembling observations from both geostationary and polar orbiting satellites [ Lazzara et al , ; Kohrs et al , ]. The composites cover the squared area encapsulated by the mosaics (Figure a) with 5 km spatial and 3‐hourly temporal resolution (increased to 4 km and hourly since June 2013).…”

Section: Methodsmentioning

confidence: 99%

“…The composites cover the squared area encapsulated by the mosaics (Figure a) with 5 km spatial and 3‐hourly temporal resolution (increased to 4 km and hourly since June 2013). Over ice‐covered areas, where it is difficult to accurately distinguish the brightness temperatures of cloud tops and of ice, we also employed water vapor channel (~6.7 μm) mosaics, compositing geostationary, and polar orbiting satellite data such as Aqua and Terra via the Moderate Resolution Spectroradiometer (MODIS) [ Kohrs et al ., ]. This water vapor channel has not been used so far for MCs identification but provides useful information over the ice‐covered areas [ International Workshop on Atmospheric Water Vapor et al , ].…”

Section: Methodsmentioning

confidence: 99%

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Southern Ocean mesocyclones and polar lows from manually tracked satellite mosaics

Verezemskaya

Tilinina

Gulev

et al. 2017

Geophysical Research Letters

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A new reference data set of mesocyclone activity over the Southern Ocean has been developed from the manual analysis of high‐resolution infrared satellite mosaics for winter 2004. Of the total 1735 mesocyclones which were identified and analyzed, about three quarters were classified as being “polar lows” (i.e., intense systems; see Rasmussen and Turner, 2003). The data set includes mesocyclone track, size, associated cloud vortex type, and background synoptic conditions. Maxima in track density were observed over the Bellingshausen Sea and around East Antarctica and are highly correlated with cyclogenesis regions. A comparison against QuikSCAT and reanalyses wind characteristics shows that the reanalyses, while capturing mesocyclone events, tend to considerably underestimate their wind speed (by up to 10 m s−1). This mesocyclone data set is available as a reference for further analysis of mesocyclones and for the evaluation and development of cyclone‐tracking algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Southern Ocean mesocyclones and polar lows from manually tracked satellite mosaics

Verezemskaya

Tilinina

Gulev

et al. 2017

Geophysical Research Letters

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“…Satellite image composites have been generated at the University of Wisconsin Space Science and Engineering Center (UW-SSEC) over the Antarctic for over 20 yr (Lazzara et al 2003(Lazzara et al , 2011Kohrs et al 2013), and over the Arctic for approximately 5 yr (Lazzara and Knuth 2009;Lazzara et al 2011;Kohrs et al 2013). These composites are a mosaic of satellite images from both polar and geostationary platforms.…”

Section: A Arctic and Antarctic Satellite Compositesmentioning

confidence: 99%

“…One requirement for this investigation was to increase the temporal resolution of the composites to 1 h from the traditional 3-h interval found in the Antarctic and Arctic composites generated before the start of this effort (Lazzara et al 2003;Kohrs et al 2013), thereby providing image information closer to the temporal frequency used for GEO AMVs-typically hourly (Lazzara et al 2010). In these second-generation composites used in this project, the spatial resolution of the composites was increased to 4-km nominal resolution.…”

Section: A Arctic and Antarctic Satellite Compositesmentioning

confidence: 99%

High-Latitude Atmospheric Motion Vectors from Composite Satellite Data

Lazzara

Dworak

Santek

et al. 2014

Journal of Applied Meteorology and Climatology

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Atmospheric motion vectors (AMVs) are derived from satellite-observed motions of clouds and water vapor features. They provide crucial information in regions void of conventional observations and contribute to forecaster diagnostics of meteorological conditions, as well as numerical weather prediction. AMVs derived from geostationary (GEO) satellite observations over the middle latitudes and tropics have been utilized operationally since the 1980s; AMVs over the polar regions derived from low-earth (polar)orbiting (LEO) satellites have been utilized since the early 2000s. There still exists a gap in AMV coverage between these two sources in the latitude band poleward of 608 and equatorward of 708 (both hemispheres). To address this AMV gap, the use of a novel approach to create image sequences that consist of composites derived from a combination of LEO and GEO observations that extend into the deep middle latitudes is explored. Experiments are performed to determine whether the satellite composite images can be employed to generate AMVs over the gap regions. The derived AMVs are validated over both the Southern Ocean/Antarctic and the Arctic gap regions over a multiyear period using rawinsonde wind observations. In addition, a two-season numerical model impact study using the Global Forecast System indicates that the assimilation of these AMVs can improve upon the control (operational) forecasts, particularly during lower-skill (dropout) events.

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“…This difference is often ignored [8], set to a fixed value [21], and it can also be estimated by use of radiative transfer modeling with NWP input [17]. A clear-sky brightness temperature reference could be created without NWP model input by slot-wise compositing a monthly maximum brightness temperature [22]. However, important synoptic variations in surface skin temperature would be lost, and areas with a persistent temperature inversion layer would be a challenge.…”

Section: Introductionmentioning

confidence: 99%

Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

et al. 2019

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Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991-2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations-the diurnal cycle of reflectance and brightness temperature-is fully exploited instead of just accounting for single "imagery". The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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Global satellite composites — 20 years of evolution

Cited by 15 publications

References 21 publications

Southern Ocean mesocyclones and polar lows from manually tracked satellite mosaics

Southern Ocean mesocyclones and polar lows from manually tracked satellite mosaics

High-Latitude Atmospheric Motion Vectors from Composite Satellite Data

Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

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