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Mitchell K. Kelleher and Kevin M. Grise


Clouds and their associated radiative effects are a large source of uncertainty in global climate models. One region with particularly large model biases in shortwave cloud radiative effects (CRE) is the Southern Ocean. Previous research has shown that many dynamical “cloud controlling factors” influence shortwave CRE on monthly time scales and that two important cloud controlling factors over the Southern Ocean are midtropospheric vertical velocity and estimated inversion strength (EIS). Model errors may thus arise from biases in representing cloud controlling factors (atmospheric dynamics) or in representing how clouds respond to those cloud controlling factors (cloud parameterizations), or some combination thereof. This study extends previous work by examining cloud controlling factors over the Southern Ocean on daily time scales in both observations and global climate models. This allows the cloud controlling factors to be examined in the context of transient weather systems. Composites of EIS and midtropospheric vertical velocity are constructed around extratropical cyclones and anticyclones to examine how the different dynamical cloud controlling factors influence shortwave CRE around midlatitude weather systems and to assess how models compare to observations. On average, models tend to produce a realistic cyclone and anticyclone, when compared to observations, in terms of the dynamical cloud controlling factors. The difference between observations and models instead lies in how the models’ shortwave CRE respond to the dynamics. In particular, the models’ shortwave CRE are too sensitive to perturbations in midtropospheric vertical velocity and, thus, they tend to produce clouds that excessively brighten in the frontal region of the cyclone and excessively dim in the center of the anticyclone.

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Jian Zhang, Kenneth Howard, Carrie Langston, Steve Vasiloff, Brian Kaney, Ami Arthur, Suzanne Van Cooten, Kevin Kelleher, David Kitzmiller, Feng Ding, Dong-Jun Seo, Ernie Wells, and Chuck Dempsey

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verification, and aviation product development.

Through about ten years of R&D, a real-time NMQ system has been implemented ( Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time.

The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.

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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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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
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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.

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Suzanne Van Cooten, Kevin E. Kelleher, Kenneth Howard, Jian Zhang, Jonathan J. Gourley, John S. Kain, Kodi Nemunaitis-Monroe, Zac Flamig, Heather Moser, Ami Arthur, Carrie Langston, Randall Kolar, Yang Hong, Kendra Dresback, Evan Tromble, Humberto Vergara, Richard A Luettich Jr., Brian Blanton, Howard Lander, Ken Galluppi, Jessica Proud Losego, Cheryl Ann Blain, Jack Thigpen, Katie Mosher, Darin Figurskey, Michael Moneypenny, Jonathan Blaes, Jeff Orrock, Rich Bandy, Carin Goodall, John G. W. Kelley, Jason Greenlaw, Micah Wengren, Dave Eslinger, Jeff Payne, Geno Olmi, John Feldt, John Schmidt, Todd Hamill, Robert Bacon, Robert Stickney, and Lundie Spence

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface.

Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and technologies that improve actionable hydrologic/hydrodynamic information to reduce the impacts of coastal flooding. Results are presented for Hurricane Isabel (2003), Hurricane Earl (2010), and Tropical Storm Nicole (2010) for the Tar–Pamlico and Neuse River basins of North Carolina. This area was chosen, in part, because of the tremendous damage inflicted by Hurricanes Dennis and Floyd (1999). The vision is to transition CI-FLOW research findings and technologies to other U.S. coastal watersheds.

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