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Kristopher M. Bedka and Konstantin Khlopenkov

associated with OTs given that most exhibit a clear BT minimum that extends across several 4-km pixels. Resampling also dampens small-scale BT variability in convective anvils that could confuse the pattern-recognition scheme and lead to false detection. MODIS 1-km-resolution VIS imagery is also input to the detection algorithm, which also matches the resolution of current and historical GEO VIS data. All resampling of imagery described in this paper is implemented by using a resampling function over a

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Martin Bergemann, Christian Jakob, and Todd P. Lane

priori assumptions are met (see section 3 for the heuristics used here) and the remaining part of the image can be considered as the detected pattern. We start the pattern detection by applying the rainfall intensity threshold and converting the rainfall data to a binary image. Areas less than the threshold are set to 0 and the ones above to 1. After converting the rainfall data to a binary image, small holes within the rainfall areas (1-domains) are closed. Closing of small holes within a

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DáithíA. Stone, Myles R. Allen, Frank Selten, Michael Kliphuis, and Peter A. Stott

EBM parameters. Now that we have estimated the responses of the GCM to individual forcings, we can proceed with the standard detection and attribution methodology ( Allen and Tett 1999 ). Under this, we express the observed temperature response pattern T obs as a linear sum of the simulated responses determined for each forcing ( T i ) plus a residual ( υ 0 ): Here β i is the scaling factor corresponding to the response to forcing i that is to be estimated in the regression. This relational

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Kate Marvel, Mark Zelinka, Stephen A. Klein, Céline Bonfils, Peter Caldwell, Charles Doutriaux, Benjamin D. Santer, and Karl E. Taylor

structure ( Santer et al. 2003 , 2013 ). In this study we use these properties to perform the first formal detection and attribution study on observed cloud trends. This requires that we first consider a number of related questions: Can we identify the fingerprints of external forcing on model cloud properties, and if so, are they distinct from patterns that arise from internal variability alone? How long an observational record is theoretically required to ensure detection of an externally forced

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Qiaohong Sun, Francis Zwiers, Xuebin Zhang, and Jun Yan

-Young and Zhang 2020 ), and in some regions, including notably dry and wet regions ( Donat et al. 2016 ), the high latitudes of the Northern Hemisphere ( Groisman et al. 2005 ; Westra et al. 2013 ), central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia ( Sun et al. 2021 ). Previous detection and attribution analyses ( Min et al. 2011 ; Zhang et al. 2013 ; Li et al. 2017 ; Dong et al. 2020 ; Kirchmeier

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Kate Marvel, Michela Biasutti, Céline Bonfils, Karl E. Taylor, Yochanan Kushnir, and Benjamin I. Cook

. D&A toolkit In this paper, we will use a standard set of “fingerprinting” and signal detection methods presented in, for example, Santer et al. (2005 , 2011 , 2013) . 1) Fingerprinting The fingerprint F ( ϕ ) of climate change is the spatial pattern that characterizes the climate system response to external forcing ( Allen and Stott 2003 ; Gillett et al. 2002 ; Hegerl et al. 1996 ; Stott et al. 2000 ; Tett et al. 2002 ). Following, for example, Hasselmann (1993) and Santer et al

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Laurent Terray, Lola Corre, Sophie Cravatte, Thierry Delcroix, Gilles Reverdin, and Aurélien Ribes

associated with quasi-uniform tropical SST warming ( Xie et al. 2010 ). Despite common features among projected spatial patterns of SST change, deviations from uniform tropical SST warming differ between models leading to large uncertainty with regard to the future distribution of tropical precipitation and evaporation changes. Furthermore, early detection and attribution of these changes is also hampered by the difficulty and lack of long-term freshwater flux observations over the oceans and their high

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Stanislaw Moszkowicz, Grzegorz J. Ciach, and Witold F. Krajewski

1026JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGYVOLUME 11NOTES AND CORRESPONDENCEStatistical Detection of Anomalous Propagation in Radar Refiecfivity Patterns $TANISLAW MOSZKOWICZ.4erology Division. Institute of Meteorology and Water Management, Legionowo, Poland GRZEGORZ J. CIACH AND WITOLD F. KRAJEWSKIIowa Institute of Hydraulic Research, University of lowa, Iowa City, lowa19 January 1993 and 16 November 1993 ABSTRACT The problem of anomalous propagation

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Rachel Dryden, M. Granger Morgan, and Stephen Broomell

climate attribution ( Parker et al. 2017 ; Sippel et al. 2015 ), lay perceptions of this issue have yet to be adequately explored. Here, we use signal detection theory (SDT) to explore whether and how laypeople attribute hurricanes to climate change and the circumstances under which they make this connection. SDT allows us to quantify the extent to which people identify changes in the occurrence of hurricanes as evidence of global warming and how those judgments are influenced by their beliefs

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Tran Vu La, Christophe Messager, Marc Honnorat, Rémi Sahl, Ali Khenchaf, Claire Channelliere, and Philippe Lattes

by the convective-scale ones. Synthetic Aperture Radar (SAR) devices were not recently mentioned with regard to the detection of surface winds associated with the CS. Alpers et al. (2016) discussed the observation of the convective rain cells on many SAR images, but this reference did not show the detailed results of surface wind patterns induced by the CS downdrafts. Based on the advantages of SAR, including high spatial resolution, wide swath, and availability in most weather conditions, the

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