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Jeffrey Anderson, Huug van den Dool, Anthony Barnston, Wilbur Chen, William Stern, and Jeffrey Ploshay

A statistical model and extended ensemble integrations of two atmospheric general circulation models (GCMs) are used to simulate the extratropical atmospheric response to forcing by observed SSTs for the years 1980 through 1988. The simulations are compared to observations using the anomaly correlation and root-mean-square error of the 700-hPa height field over a region encompassing the extratropical North Pacific Ocean and most of North America. On average, the statistical model is found to produce considerably better simulations than either numerical model, even when simple statistical corrections are used to remove systematic errors from the numerical model simulations. In the mean, the simulation skill is low, but there are some individual seasons for which all three models produce simulations with good skill.

An approximate upper bound to the simulation skill that could be expected from a GCM ensemble, if the model's response to SST forcing is assumed to be perfect, is computed. This perfect model predictability allows one to make some rough extrapolations about the skill that could be expected if one could greatly improve the mean response of the GCMs without significantly impacting the variance of the ensemble. These perfect model predictability skills are better than the statistical model simulations during the summer, but for the winter, present-day statistical forecasts already have skill that is as high as the upper bound for the GCMs. Simultaneous improvements to the GCM mean response and reduction in the GCM ensemble variance would be required for these GCMs to do significantly better than the statistical model in winter. This does not preclude the possibility that, as is presently the case, a statistical blend of GCM and statistical predictions could produce a simulation better than either alone.

Because of the primitive state of coupled ocean–atmosphere GCMs, the vast majority of seasonal predictions currently produced by GCMs are performed using a two-tiered approach in which SSTs are first predicted and then used to force an atmospheric model; this motivates the examination of the simulation problem. However, it is straightforward to use the statistical model to produce true forecasts by changing its predictors from simultaneous to precursor SSTs. An examination of the decrease in skill of the statistical model when changed from simulation to prediction mode is extrapolated to draw conclusions about the skill to be expected from good coupled GCM predictions.

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Jeffrey Anderson, Tim Hoar, Kevin Raeder, Hui Liu, Nancy Collins, Ryan Torn, and Avelino Avellano

The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DART's ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a model's state and an observation's expected value. Forward operators for standard, in situ observations and novel types, like GPS radio occultation soundings, are available. DART algorithms scale well on a variety of parallel architectures, allowing large data assimilation problems to be studied. DART also includes many low-order models and an ensemble assimilation tutorial appropriate for undergraduate and graduate instruction.

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Jason A. Otkin, Mark Shafer, Mark Svoboda, Brian Wardlow, Martha C. Anderson, Christopher Hain, and Jeffrey Basara
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Jason A. Otkin, Mark Svoboda, Eric D. Hunt, Trent W. Ford, Martha C. Anderson, Christopher Hain, and Jeffrey B. Basara

Abstract

Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects.

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Robert E. Dickinson, Stephen E. Zebiak, Jeffrey L. Anderson, Maurice L. Blackmon, Cecelia De Luca, Timothy F. Hogan, Mark Iredell, Ming Ji, Ricky B. Rood, Max J. Suarez, and Karl E. Taylor

A common modeling infrastructure ad hoc working group evolved from an NSF/NCEP workshop in 1998, in recognition of the need for the climate and weather modeling communities to develop a more organized approach to building the software that underlies modeling and data analyses. With its significant investment of pro bono time, the working group made the first steps in this direction. It suggested standards for model data and model physics and explored the concept of a modeling software framework. An overall software infrastructure would facilitate separation of the scientific and computational aspects of comprehensive models. Consequently, it would allow otherwise isolated scientists to effectively contribute to core U.S. modeling activities, and would provide a larger market to computational scientists and computer vendors, hence encouraging their support.

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Edward J. Zipser, Cynthia H. Twohy, Si-Chee Tsay, K. Lee Thornhill, Simone Tanelli, Robert Ross, T. N. Krishnamurti, Q. Ji, Gregory Jenkins, Syed Ismail, N. Christina Hsu, Robbie Hood, Gerald M. Heymsfield, Andrew Heymsfield, Jeffrey Halverson, H. Michael Goodman, Richard Ferrare, Jason P. Dunion, Michael Douglas, Robert Cifelli, Gao Chen, Edward V. Browell, and Bruce Anderson

In 2006, NASA led a field campaign to investigate the factors that control the fate of African easterly waves (AEWs) moving westward into the tropical Atlantic Ocean. Aircraft and surface-based equipment were based on Cape Verde's islands, helping to fill some of the data void between Africa and the Caribbean. Taking advantage of the international African Monsoon Multidisciplinary Analysis (AMMA) program over the continent, the NASA-AMMA (NAMMA) program used enhanced upstream data, whereas NOAA aircraft farther west in the Atlantic studied several of the storms downstream. Seven AEWs were studied during AMMA, with at least two becoming tropical cyclones. Some of the waves that did not develop while being sampled near Cape Verde likely intensified in the central Atlantic instead. NAMMA observations were able to distinguish between the large-scale wave structure and the smaller-scale vorticity maxima that often form within the waves. A special complication of the east Atlantic environment is the Saharan air layer (SAL), which frequently accompanies the AEWs and may introduce dry air and heavy aerosol loading into the convective storm systems in the AEWs. One of the main achievements of NAMMA was the acquisition of a database of remote sensing and in situ observations of the properties of the SAL, enabling dynamic models and satellite retrieval algorithms to be evaluated against high-quality real data. Ongoing research with this database will help determine how the SAL influences cloud microphysics and perhaps also tropical cyclogenesis, as well as the more general question of recognizing the properties of small-scale vorticity maxima within tropical waves that are more likely to become tropical cyclones.

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