CIMSS-NOAA Weekly Report [ Archive ]

CIMSS-NOAA WEEKLY HIGHLIGHTS FOR THE WEEK ENDING DECEMBER 13, 2024

DATA, INFORMATION, AND USE-INSPIRED SCIENCE:

FUTURE OUTLOOK:

AWARDS AND RECOGNITION:

TRAVEL AND MEETINGS:

WMO GCW Data Requirements Workshop: Jeff Key (Cooperative Institute for Meteorological Satellite Studies, CIMSS) participated remotely in a Cryosphere and Polar Research Data Requirements Meeting organized by the World Meteorological Organization (WMO) Global Cryosphere Watch (GCW). The meeting was held in Geneva, Switzerland, December 9-11, 2024. The purpose of the meeting was to address the WMO Executive Council request "to establish a dialogue with international research communities regarding the access of their cryosphere data through the WMO Information System (WIS2) and in the framework of the WMO Unified Data Policy partnerships, and agree on principles and technical aspects for integration for mutual benefits and adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles for scientific data management and stewardship..." Participants were from Switzerland, Germany, Norway, Denmark, the UK, China, Indonesia, Canada, and the U.S. Key led one of three groups in drafting a whitepaper, which will be submitted to a scientific/data journal in the coming months. It will discuss the roles of data providers, data repositories, and data users, science and policy drivers for data sharing, challenges, and recommendations. (J. Key, CIMSS, 608-890-4239)

TRAINING AND EDUCATION:

CIMSS provides an AWIPS tutorial to AOS 441 Students: Scott Lindstrom and Lee Cronce from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) gave a brief AWIPS tutorial to students in Tristan L'Ecuyer's Atmospheric & Oceanic Sciences (AOS) 441 class ("Satellite and Radar Meteorology").  This was followed by the 15 students (a mix of undergraduate and graduate students) trying out AWIPS for themselves while directed by Lindstrom and Cronce.  A basic theme of the training was to consider multiple satellite channels and level 2 products and RGB imagery to determine what feature is being viewed.  This 90-minute briefing was a follow-on to a similar briefing last week given by Cronce alone to the first 15 students in the class. (L. Cronce, S. Lindstrom, CIMSS, 608 263 4425)

VISIT Training for WFO ILM on GOES-R IFR Probability fields: Scott Lindstrom from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) gave a Virtual Institute for Satellite Integration Training (VISIT) lesson on GOES-R Instrument Flight Rules (IFR) Probability fields, the GOES-R Derived Product for Fog/Low stratus detection, to three forecasters at the National Weather Service office in Wilmington NC (WFO ILM). (S. Lindstrom, CIMSS, 608 263 4425)

MEDIA INTERACTIONS AND REQUESTS:

SOCIAL MEDIA AND BLOG Posts:

This week in the CIMSS Satellite Blog: The Cooperative Institute for Meteorological Satellite Studies (CIMSS) Satellite Blog included this week the following examples of how satellite data can give you beautiful, compelling and useful information about weather events: Eruption of Kanlaon Volcano in the Philippines (https://cimss.ssec.wisc.edu/satellite-blog/archives/62026); 19 GOES (https://cimss.ssec.wisc.edu/satellite-blog/archives/61506); Fog detection with multiple cloud layers (https://cimss.ssec.wisc.edu/satellite-blog/archives/62018); Franklin Fire near Malibu, California (https://cimss.ssec.wisc.edu/satellite-blog/archives/62038). (S. Bachmeier, T. Schmit, S. Lindstrom, CIMSS, 608 263 4425) 

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Figure: The pantheon of GOES satellites, from pre-GOES 1966 to present. Figure from Tim Schmit, NOAA.

PUBLICATIONS:

Arctic cloud feedback paper published: A manuscript titled "Cold Season Cloud Response to Sea Ice Loss in the Arctic" coauthored by Yinghui Liu and Jeff Key has been published in the Journal of Climate. The study concludes that Arctic cloud responses to sea ice loss shifted from positive cloud feedback (1982–1999) to a negative feedback mechanism (2000–2018), suggesting a possible stabilizing effect of clouds on Arctic sea ice , though the causes of this shift remain unclear. Overall, this work advances our understanding of Arctic climate processes, with critical implications for scientific research, climate model improvement, climate policy, and global climate resilience. Details of this study can be found from the citation, Liu, Y., and J. R. Key, 2024: Cold Season Cloud Response to Sea Ice Loss in the Arctic. J. Climate, 38, 347–367, https://doi.org/10.1175/JCLI-D-23-0394.1. (Y. Liu, E/RA2, 608-890-1893, yinghui.liu@noaa.gov)

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Figure: Correlations of the APP-x cloud amounts (cmask) in (a) ON (October-November), (b) NDJ (November-December-January), and (c) JFM (January-February-March) with sea ice index in SO for the period 2000–18 over the East Siberian–Chukchi–Beaufort Seas (EsCB). The lag regression coefficients of the cloud amounts (cmask) on the SO sea ice index over EsCB for 2000–18 are in (d) ON, (e) NDJ, and (f) JFM. The dotted-line and dashed-line contours denote that correlations/regressions with higher than 90% and 95% confidence levels, respectively. The solid-line contours denote the areas with p values smaller than the false discovery rate probability, meaning that they pass the field significance tests.

OTHER: