Browse

You are looking at 1 - 10 of 323 items for :

  • Refine by Access: All Content x
Clear All
David M. Schultz

Monthly Weather Review needs over a thousand peer reviews each year to maintain the high quality of articles that our readership have come to expect. We value our volunteer reviewers and recognize their investment that keeps our journal operating. As a result of their experience, professionalism, and generosity, the majority of these thousand reviews are thoughtful, thorough, and constructive. I hear about the negativity in peer review in journals from other disciplines, and I am thankful that we rarely see such serious issues in Monthly Weather Review.

We desire to maintain this character and quality of our reviews

Open access
David M. Schultz and Peter Lynch

While Monthly Weather Review is celebrating its 150th year, another milestone in meteorology this year is the 100th anniversary of the publication of Lewis Fry Richardson’s Weather Prediction by Numerical Process. As we describe in this month’s editorial, Richardson’s meticulous attempt at hand-calculating the weather failed spectacularly but showed future workers the way forward. Their efforts helped to make Monthly Weather Review one of the leading journals for numerical modeling of the atmosphere.

The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer

Open access
David M. Schultz

As part of the 150th volume of Monthly Weather Review, we are telling stories from its 150-yr history in a series of editorials. This month’s editorial describes how Monthly Weather Review helped to found the Journal of Climate, which is now one of the leading journals in its field and is celebrating its 35th year.

Climate modeling had its birth in the 1960s within the pages of Monthly Weather Review with the seminal work by Joseph Smagorinsky and Nobel Prize–winner Syukuro Manabe (e.g., Smagorinsky 1963; Smagorinsky et al. 1965

Open access
David M. Schultz

On 18–19 February 1979, a rapidly deepening cyclone moved up the East Coast of North America, eventually dumping up to 60 cm of snow in eastern Virginia, Maryland, and Delaware. The National Weather Service was caught unawares, with the operational forecast models at the time missing the cyclogenesis event entirely, and therefore, the public forecast omitting reference to the possibility of heavy snow. Because the storm happened on the Presidents’ Day holiday in the United States, the storm became known as the Presidents’ Day snowstorm.

Eighteen months later, in September 1980, Lance Bosart of the State University of New York

Open access
Amy McGovern and Anthony J. Broccoli

This editorial opens volume 1, issue 1, of Artificial Intelligence for the Earth Systems (AIES) , the American Meteorological Society’s (AMS) newest journal. The journal focuses on the development and application of methods in artificial intelligence (AI), machine learning (ML), data science, and statistics that are relevant to meteorology, atmospheric science, hydrology, climate science, and ocean sciences, that is, the “AMS sciences.”

AI and ML constitute a rapidly growing sector of the atmospheric, climate, and water-related sciences. In recent years, almost all of the AMS journals saw rapid growth in terms of publications on this topic, as have journals

Free access
David A. R. Kristovich

Since 1962, the American Meteorological Society (AMS) has had a journal dedicated to utilizing meteorological and climatological information to help solve some of society’s greatest challenges. This editorial is to celebrate a noteworthy anniversary of the Journal of Applied Meteorology and Climatology (JAMC).

Sixty years ago, the Journal of Applied Meteorology was launched by AMS, providing an avenue for publishing scientific research “with the application of the atmospheric sciences to operational and practical goals” Hilst (1969). Since its inception, this journal has had slightly varying names, signifying expansion of the scientific community

Open access
David M. Schultz, Altuğ Aksoy, Jeffrey Anderson, Tommaso Benacchio, Kristen L. Corbosiero, Matthew D. Eastin, Clark Evans, Jidong Gao, Almut Gassman, Joshua P. Hacker, Daniel Hodyss, Matthew R. Kumjian, Ron McTaggart-Cowan, Glen Romine, Paul Roundy, Angela Rowe, Elizabeth Satterfield, Russ S. Schumacher, Stan Trier, Christopher Weiss, Henry P. Huntington, and Gary M. Lackmann

Science requires evidence. Making data available lets other scientists confirm results, uncover errors, or find new insights. Moreover, gathering data can be expensive and time consuming. Because the same data can be used for a range of purposes, making data available can be an efficient use of limited research resources. Doing so can improve traceability and accountability when it comes to research findings.

These reasons and more lie behind recent efforts to promote data availability in research publications. The American Meteorological Society (AMS) recently updated its data policy guidelines (https://www.ametsoc.org/index.cfm/ams/publications/ethical-guidelines-and-ams-policies/data-policy-and-guidelines/) to require, among other things, that articles in

Open access
Gary M. Lackmann, Brian Ancell, Matthew Bunkers, Ben Kirtman, Karen Kosiba, Amy McGovern, Lynn McMurdie, Zhaoxia Pu, Elizabeth Ritchie, and Henry P. Huntington

Science requires evidence. Making data available lets other scientists confirm results, uncover errors, or find new insights. Moreover, gathering data can be expensive and time consuming. Since the same data can be used for a range of purposes, making data available can be an efficient use of limited research resources. Doing so can also improve traceability and, thus, accountability, when it comes to research findings.

These reasons and more lie behind recent efforts to promote data availability in research publications. The American Meteorological Society (AMS) recently updated its data policy guidelines (https://www.ametsoc.org/index.cfm/ams/publications/ethical-guidelines-and-ams-policies/data-policy-and-guidelines/) to require, among other things, that

Open access
Jerome A. Smith, Paola Cessi, Ilker Fer, Gregory Foltz, Baylor Fox-Kemper, Karen Heywood, Nicole Jones, Jody Klymak, and Joseph LaCasce

The American Meteorological Society (AMS) is moving toward strongly encouraging authors to make all data available, consistent with the “FAIR” principle: “findable, accessible, interoperable, and reusable.” While we at the Journal of Physical Oceanography (JPO) firmly support this move, we also recognize that sharing all of the data is in some cases not practical or even useful. Your feedback now could help to prevent some of the less desirable possible side effects of this policy. We hope that this editorial will help to prod the discussion of exactly what data should be shared, and in what

Open access
Luca Baldini and William Emery

Science requires evidence. Making data available lets other scientists confirm results, uncover errors, or find new insights. Moreover, gathering data can be expensive and time consuming. Since the same data can be used for a range of purposes, making data available can be an efficient use of limited research resources. Doing so can also improve traceability and, thus, accountability, when it comes to research findings.

These reasons and more lie behind recent efforts to promote data availability. The American Meteorological Society (AMS) recently updated its data policy guidelines (https://www.ametsoc.org/index.cfm/ams/publications/ethical-guidelines-and-ams-policies/data-policy-and-guidelines/) to require, among other things, that papers in its

Open access