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  • Author or Editor: Michael J. Foster x
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Andrew K. Heidinger
,
Michael J. Foster
,
Andi Walther
, and
Xuepeng (Tom) Zhao

The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoids spatial or temporal averaging of data, thus maintaining the flexibility to conduct multidimensional analysis. Comparison of this format against the unsampled record demonstrates the ability to reproduce the pixel distribution to a high level of accuracy. AVHRR PATMOS-x is composed of data from 17 different sensors. An examination of cloud amount and total-sky albedo time series demonstrates that intersatellite biases are less than 2%. The comparison of the cloud amount time series to the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) demonstrates a high degree of correlation, indicating that sensor-to-sensor differences are also not contributing significantly to the observed climate variability in PATMOS-x. AVHRR PATMOS-x data are hosted by the National Climatic Data Center (NCDC) (available at www.ncdc.noaa.gov/cdr/operationalcdrs.html).

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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.

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