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Nannan Qin, Da-Lin Zhang, and Ying Li

Abstract

It is well known that hurricane intensification is often accompanied by continuous contraction of the radius of maximum wind (RMW) and eyewall size. However, a few recent studies have shown rapid and then slow contraction of the RMW/eyewall size prior to the onset and during the early stages of rapid intensification (RI) of hurricanes, respectively, but a steady state in the RMW (S-RMW) and eyewall size during the later stages of RI. In this study, a statistical analysis of S-RMWs associated with rapidly intensifying hurricanes is performed using the extended best-track dataset during 1990–2014 in order to examine how frequently, and at what intensity and size, the S-RMW structure tends to occur. Results show that about 53% of the 139 RI events of 24-h duration associated with 55 rapidly intensifying hurricanes exhibit S-RMWs, and that the percentage of the S-RMW events increases to 69% when RI events are evaluated at 12-h intervals, based on a new RI rate definition of 10 m s−1 (12 h)−1; both results satisfy the Student’s t tests with confidence levels of over 95%. In general, S-RMWs tend to appear more frequently in more intense storms and when their RMWs are contracted to less than 50 km. This work suggests a new fruitful research area in studying the RI of hurricanes with S-RMWs.

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Wan-Shu Wu, David F. Parrish, Eric Rogers, and Ying Lin

Abstract

At the National Centers for Environmental Prediction, the global ensemble forecasts from the ensemble Kalman filter scheme in the Global Forecast System are applied in a regional three-dimensional (3D) and a four dimensional (4D) ensemble–variational (EnVar) data assimilation system. The application is a one-way variational method using hybrid static and ensemble error covariances. To enhance impact, three new features have been added to the existing EnVar system in the Gridpoint Statistical Interpolation (GSI). First, the constant coefficients that assign relative weight between the ensemble and static background error are now allowed to vary in the vertical. Second, a new formulation is introduced for the ensemble contribution to the analysis surface pressure. Finally, in order to make use of the information in the ensemble mean that is disregarded in the existing EnVar in GSI, the trajectory correction, a novel approach, is introduced. Relative to the application of a 3D variational data assimilation algorithm, a clear positive impact on 1–3-day forecasts is realized when applying 3DEnVar analyses in the North American Mesoscale Forecast System (NAM). The 3DEnVar DA system was operationally implemented in the NAM Data Assimilation System in August 2014. Application of a 4DEnVar algorithm is shown to further improve forecast accuracy relative to the 3DEnVar. The approach described in this paper effectively combines contributions from both the regional and the global forecast systems to produce the initial conditions for the regional NAM system.

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Eric Rogers, Thomas L. Black, Dennis G. Deaven, Geoffrey J. DiMego, Qingyun Zhao, Michael Baldwin, Norman W. Junker, and Ying Lin

Abstract

This note describes changes that have been made to the National Centers for Environmental Prediction (NCEP) operational “early” eta model. The changes are 1) an decrease in horizontal grid spacing from 80 to 48 km, 2) incorporation of a cloud prediction scheme, 3) replacement of the original static analysis system with a 12-h intermittent data assimilation system using the eta model, and 4) the use of satellite-sensed total column water data in the eta optimum interpolation analysis. When tested separately, each of the four changes improved model performance. A quantitative and subjective evaluation of the full upgrade package during March and April 1995 indicated that the 48-km eta model was more skillful than the operational 80-km model in predicting the intensity and movement of large-scale weather systems. In addition, the 48-km eta model was more skillful in predicting severe mesoscale precipitation events than either the 80-km eta model, the nested grid model, or the NCEP global spectral model during the March-April 1995 period. The implementation of this new version of the operational early eta system was performed in October 1995.

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Andrew J. Monaghan, David H. Bromwich, He-Lin Wei, Arthur M. Cayette, Jordan G. Powers, Ying-Hwa Kuo, and Matthew A. Lazzara

Abstract

In late April 2001, an unprecedented late-season flight to Amundsen–Scott South Pole Station was made in the evacuation of Dr. Ronald Shemenski, a medical doctor seriously ill with pancreatitis. This case study analyzes the performance of four of the numerical weather prediction models that aided meteorologists in forecasting weather throughout the operation: 1) the Antarctic Mesoscale Prediction System (AMPS) Polar MM5 (fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model), 2) the National Centers for Environmental Prediction Aviation Model (AVN), 3) the European Centre for Medium-Range Weather Forecasts (ECMWF) global forecast model, and 4) the NCAR Global MM5. To identify specific strengths and weaknesses, key variables for each model are statistically analyzed for all forecasts initialized between 21 and 25 April for several points over West Antarctica at the surface and at 500- and 700-hPa levels. The ECMWF model performs with the highest overall skill, generally having the lowest bias and rms errors and highest correlations for the examined fields. The AMPS Polar MM5 exhibits the next best skill, followed by AVN and Global MM5. For the surface variables, all of the models show high skill in predicting surface pressure but demonstrate modest skill in predicting temperature, wind speed, and wind direction. In the free atmosphere, the models show high skill in forecasting geopotential height, considerable skill in predicting temperature and wind direction, and good skill in predicting wind speed. In general, the models produce very useful forecasts in the free atmosphere, but substantial efforts are still needed to improve the surface prediction. The spatial resolution of each model exerts an important influence on forecast accuracy, especially in the complex topography of the Antarctic coastal regions. The initial and boundary conditions for the AMPS Polar MM5 exert a significant influence on forecasts.

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John R. Gyakum, Marco Carrera, Da-Lin Zhang, Steve Miller, James Caveen, Robert Benoit, Thomas Black, Andrea Buzzi, Cliément Chouinard, M. Fantini, C. Folloni, Jack J. Katzfey, Ying-Hwa Kuo, François Lalaurette, Simon Low-Nam, Jocelyn Mailhot, P. Malguzzi, John L. McGregor, Masaomi Nakamura, Greg Tripoli, and Clive Wilson

Abstract

The authors evaluate the performance of current regional models in an intercomparison project for a case of explosive secondary marine cyclogenesis occurring during the Canadian Atlantic Storms Project and the Genesis of Atlantic Lows Experiment of 1986. Several systematic errors are found that have been identified in the refereed literature in prior years. There is a high (low) sea level pressure bias and a cold (warm) tropospheric temperature error in the oceanic (continental) regions. Though individual model participants produce central pressures of the secondary cyclone close to the observed during the final stages of its life cycle, systematically weak systems are simulated during the critical early stages of the cyclogenesis. Additionally, the simulations produce an excessively weak (strong) continental anticyclone (cyclone); implications of these errors are discussed in terms of the secondary cyclogenesis. Little relationship between strong performance in predicting the mass field and skill in predicting a measurable amount of precipitation is found. The bias scores in the precipitation study indicate a tendency for all models to overforecast precipitation. Results for the measurable threshold (0.2 mm) indicate the largest gain in precipitation scores results from increasing the horizontal resolution from 100 to 50 km, with a negligible benefit occurring as a consequence of increasing the resolution from 50 to 25 km. The importance of a horizontal resolution increase from 100 to 50 km is also generally shown for the errors in the mass field. However, little improvement in the prediction of the cyclogenesis is found by increasing the horizontal resolution from 50 to 25 km.

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Manuel S. F. V. De Pondeca, Geoffrey S. Manikin, Geoff DiMego, Stanley G. Benjamin, David F. Parrish, R. James Purser, Wan-Shu Wu, John D. Horel, David T. Myrick, Ying Lin, Robert M. Aune, Dennis Keyser, Brad Colman, Greg Mann, and Jamie Vavra

Abstract

In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.

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