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  • Author or Editor: David E. Jahn x
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David E. Jahn
and
William A. Gallus Jr.

Abstract

The Great Plains low-level jet (LLJ) is influential in the initiation and evolution of nocturnal convection through the northward advection of heat and moisture, as well as convergence in the region of the LLJ nose. However, accurate numerical model forecasts of LLJs remain a challenge, related to the performance of the planetary boundary layer (PBL) scheme in the stable boundary layer. Evaluated here using a series of LLJ cases from the Plains Elevated Convection at Night (PECAN) program are modifications to a commonly used local PBL scheme, Mellor–Yamada–Nakanishi–Niino (MYNN), available in the Weather Research and Forecasting (WRF) Model. WRF forecast mean absolute error (MAE) and bias are calculated relative to PECAN rawinsonde observations. The first MYNN modification invokes a new set of constants for the scheme closure equations that, in the vicinity of the LLJ, decreases forecast MAEs of wind speed, potential temperature, and specific humidity more than 19%. For comparison, the Yonsei University (YSU) scheme results in wind speed MAEs 22% lower but specific humidity MAEs 17% greater than in the original MYNN scheme. The second MYNN modification, which incorporates the effects of potential kinetic energy and uses a nonzero mixing length in stable conditions as dependent on bulk shear, reduces wind speed MAEs 66% for levels below the LLJ, but increases MAEs at higher levels. Finally, Rapid Refresh analyses, which are often used for forecast verification, are evaluated here and found to exhibit a relatively large average wind speed bias of 3 m s−1 in the region below the LLJ, but with relatively small potential temperature and specific humidity biases.

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Alexandra Jahn
,
Kara Sterling
,
Marika M. Holland
,
Jennifer E. Kay
,
James A. Maslanik
,
Cecilia M. Bitz
,
David A. Bailey
,
Julienne Stroeve
,
Elizabeth C. Hunke
,
William H. Lipscomb
, and
Daniel A. Pollak

Abstract

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.

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Kevin E. Kelleher
,
Kelvin K. Droegemeier
,
Jason J. Levit
,
Carl Sinclair
,
David E. Jahn
,
Scott D. Hill
,
Lora Mueller
,
Grant Qualley
,
Tim D. Crum
,
Steven D. Smith
,
Stephen A. Del Greco
,
S. Lakshmivarahan
,
Linda Miller
,
Mohan Ramamurthy
,
Ben Domenico
, and
David W. Fulker

The NOAA NWS announced at the annual meeting of the American Meteorological Society in February 2003 its intent to create an Internet-based pseudo-operational system for delivering Weather Surveillance Radar-1988 Doppler (WSR-88D) Level II data. In April 2004, the NWS deployed the Next-Generation Weather Radar (NEXRAD) level II central collection functionality and set up a framework for distributing these data. The NWS action was the direct result of a successful joint government, university, and private sector development and test effort called the Collaborative Radar Acquisition Field Test (CRAFT) project. Project CRAFT was a multi-institutional effort among the Center for Analysis and Prediction of Storms, the University Corporation for Atmospheric Research, the University of Washington, and the three NOAA organizations, National Severe Storms Laboratory, WSR-88D Radar Operations Center (ROC), and National Climatic Data Center. The principal goal of CRAFT was to demonstrate the real-time compression and Internet-based transmission of level II data from all WSR-88D with the vision of an affordable nationwide operational implementation. The initial test bed of six radars located in and around Oklahoma grew to include 64 WSR-88D nationwide before being adopted by the NWS for national implementation. A description of the technical aspects of the award-winning Project CRAFT is given, including data transmission, reliability, latency, compression, archival, data mining, and newly developed visualization and retrieval tools. In addition, challenges encountered in transferring this research project into operations are discussed, along with examples of uses of the data.

Full access
Kevin E. Kelleher
,
Kelvin K. Droegemeier
,
Jason J. Levit
,
Carl Sinclair
,
David E. Jahn
,
Scott D. Hill
,
Lora Mueller
,
Grant Qualley
,
Tim D. Crum
,
Steven D. Smith
,
Stephen A. Del Greco
,
S. Lakshmivarahan
,
Linda Miller
,
Mohan Ramamurthy
,
Ben Domenico
, and
David W. Fulker
Full access