Search Results

You are looking at 11 - 13 of 13 items for :

  • Author or Editor: Michael J. Foster x
  • Refine by Access: All Content x
Clear All Modify Search
Michael J. Foster
,
Coda Phillips
,
Andrew K. Heidinger
,
Eva E. Borbas
,
Yue Li
,
W. Paul Menzel
,
Andi Walther
, and
Elisabeth Weisz

Abstract

A new version of the PATMOS-x multidecadal cloud record, version 6.0, has been produced and is available from the NOAA National Centers for Environmental Information. A description of the processes and methods used for generating the dataset are presented, with a focus on the differences between version 6.0 and the previous version of PATMOS-x, version 5.3. The new version appears both to be more stable, with less intersatellite variability, and to have more consistent polar cloud detection, phase distribution, and cloud-top height distribution when compared against the MODIS EOS record. Improvements in consistency and performance are attributed to the addition of multidimensional variables for cloud detection, constraining cloud retrievals to radiometric bands available throughout the record, and the addition of data from the HIRS instrument.

Significance Statement

The PATMOS-x project produces multidecadal cloudiness records from polar-orbiting satellites. Version 6.0 combines imager and sounder data from 15 satellites and shows significant improvements in accuracy and stability.

Free access
Kevin A. Scharfenberg
,
Daniel J. Miller
,
Terry J. Schuur
,
Paul T. Schlatter
,
Scott E. Giangrande
,
Valery M. Melnikov
,
Donald W. Burgess
,
David L. Andra Jr.
,
Michael P. Foster
, and
John M. Krause

Abstract

To test the utility and added value of polarimetric radar products in an operational environment, data from the Norman, Oklahoma (KOUN), polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) were delivered to the National Weather Service Weather Forecast Office (WFO) in Norman as part of the Joint Polarization Experiment (JPOLE). KOUN polarimetric base data and algorithms were used at the WFO during the decision-making and forecasting processes for severe convection, flash floods, and winter storms. The delivery included conventional WSR-88D radar products, base polarimetric radar variables, a polarimetric hydrometeor classification algorithm, and experimental polarimetric quantitative precipitation estimation algorithms. The JPOLE data collection, delivery, and operational demonstration are described, with examples of several forecast and warning decision-making successes. Polarimetric data aided WFO forecasters during several periods of heavy rain, numerous large-hail-producing thunderstorms, tornadic and nontornadic supercell thunderstorms, and a major winter storm. Upcoming opportunities and challenges associated with the emergence of polarimetric radar data in the operational community are also described.

Full access
David J. Stensrud
,
Ming Xue
,
Louis J. Wicker
,
Kevin E. Kelleher
,
Michael P. Foster
,
Joseph T. Schaefer
,
Russell S. Schneider
,
Stanley G. Benjamin
,
Stephen S. Weygandt
,
John T. Ferree
, and
Jason P. Tuell

The National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods because these phenomena are a threat to life and property. These warnings are presently based upon either visual confirmation of the phenomena or the observational detection of proxy signatures that are largely based upon radar observations. Convective-scale weather warnings are unique in the NWS, having little reliance on direct numerical forecast guidance. Because increasing severe thunderstorm, tornado, and flash-flood warning lead times are a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of these high-impact weather phenomena, a new warning paradigm is needed in which numerical model forecasts play a larger role in convective-scale warnings. This new paradigm shifts the warning process from warn on detection to warn on forecast, and it has the potential to dramatically increase warning lead times.

A warn-on-forecast system is envisioned as a probabilistic convective-scale ensemble analysis and forecast system that assimilates in-storm observations into a high-resolution convection-resolving model ensemble. The building blocks needed for such a system are presently available, and initial research results clearly illustrate the value of radar observations to the production of accurate analyses of convective weather systems and improved forecasts. Although a number of scientific and cultural challenges still need to be overcome, the potential benefits are significant. A probabilistic convective-scale warn-on-forecast system is a vision worth pursuing.

Full access