Browse

You are looking at 101 - 110 of 201 items for :

  • Bulletin of the American Meteorological Society x
  • Refine by Access: All Content x
Clear All
Jill S. M. Coleman
,
Kaylee D. Newby
,
Karen D. Multon
, and
Cynthia L. Taylor
Full access
Olga Zolina
,
Clemens Simmer
,
Alice Kapala
,
Pavel Shabanov
,
Paul Becker
,
Hermann Mächel
,
Sergey Gulev
, and
Pavel Groisman

The STAMMEX (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe) project has developed a high-resolution gridded long-term precipitation dataset based on the daily-observing precipitation network of the German Weather Service DWD, which runs one of the world's densest rain gauge networks, comprising more than 7,500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931–onward (with 0.5° resolution), 1951–onward (0.5° and 0.25°), and 1971–2000 (0.5°, 0.25°, and 0.1°). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates, the STAMMEX datasets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/ dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS)—which include considerably less observations compared to those used in STAMMEX—demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability patterns and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models.

Full access
Anne Steinemann
Full access
Full access
Thomas Lafon
,
Jennifer Fowler
,
John Fredy Jiménez
, and
Gabriel Jaime Tamayo Cordoba

Radiosonde-collected data are of vital importance to a wide variety of studies that aim at understanding the interaction between land surface and the atmosphere, among others. However, atmospheric measurements in developing countries, some of which encompass areas critical to the regulation of global climate, are sparse due to the lack of funding allocated toward collecting such data, and therefore fail to meet the standards set by the World Meteorological Organization. We review current radiosonde technologies and an alternative that aims at lowering sounding costs by recovering the sondes: the glidersonde. Two major issues currently hamper future development and commercialization of this technology: 1) how to have reusable radiosondes while keeping the market viable for the sonde manufacturers, and 2) the need for consistent and effective governmental aviation regulations for developing and flying glidersondes. We conclude this review with an alternative consideration as an incentive for cooperation in the development and implementation of cost-effective sounding equipment.

Full access
Chris T. Jones
,
Todd D. Sikora
,
Paris W. Vachon
, and
Joseph R. Buckley

The Royal Canadian Navy produces a semiweekly map of major water mass boundaries in the Western North Atlantic using temperature measurements from several data sources, including satellite sea surface temperature (SST) images from the Advanced Very High Resolution Radiometer (AVHRR). Temporal–spatial detail that can be provided by AVHRR of the location of important SST boundaries such as the Gulf Stream North Wall is limited due to pervasive cloud cover. The ability of satellite-borne synthetic aperture radar (SAR) to image SST front signatures unrestrained by cloud cover makes it a potentially significant additional data source. The Spaceborne Ocean Intelligence Network project has developed an automated procedure to detect candidate SST front signatures in RADARSAT-2 SAR images of the ocean surface and classify them with greater than 80% accuracy.

Full access
Cristina L. Archer
,
Brian A. Colle
,
Luca Delle Monache
,
Michael J. Dvorak
,
Julie Lundquist
,
Bruce H. Bailey
,
Philippe Beaucage
,
Matthew J. Churchfield
,
Anna C. Fitch
,
Branko Kosovic
,
Sang Lee
,
Patrick J. Moriarty
,
Hugo Simao
,
Richard J. A. M. Stevens
,
Dana Veron
, and
John Zack
Full access
Edward N. Rappaport
Full access
John J. Cassano
Full access
Andreas Schiller
,
Mike Herzfeld
,
Richard Brinkman
, and
Greg Stuart
Full access