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Timothy Marchok, Robert Rogers, and Robert Tuleya

Abstract

A scheme for validating quantitative precipitation forecasts (QPFs) for landfalling tropical cyclones is developed and presented here. This scheme takes advantage of the unique characteristics of tropical cyclone rainfall by evaluating the skill of rainfall forecasts in three attributes: the ability to match observed rainfall patterns, the ability to match the mean value and volume of observed rainfall, and the ability to produce the extreme amounts often observed in tropical cyclones. For some of these characteristics, track-relative analyses are employed that help to reduce the impact of model track forecast error on QPF skill. These characteristics are evaluated for storm-total rainfall forecasts of all U.S. landfalling tropical cyclones from 1998 to 2004 by the NCEP operational models, that is, the Global Forecast System (GFS), the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, and the North American Mesoscale (NAM) model, as well as the benchmark Rainfall Climatology and Persistence (R-CLIPER) model. Compared to R-CLIPER, all of the numerical models showed comparable or greater skill for all of the attributes. The GFS performed the best of all of the models for each of the categories. The GFDL had a bias of predicting too much heavy rain, especially in the core of the tropical cyclones, while the NAM predicted too little of the heavy rain. The R-CLIPER performed well near the track of the core, but it predicted much too little rain at large distances from the track. Whereas a primary determinant of tropical cyclone QPF errors is track forecast error, possible physical causes of track-relative differences lie with the physical parameterizations and initialization schemes for each of the models. This validation scheme can be used to identify model limitations and biases and guide future efforts toward model development and improvement.

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Zoltan Toth, Yuejian Zhu, and Timothy Marchok

Abstract

In the past decade ensemble forecasting has developed into an integral part of numerical weather prediction. Flow-dependent forecast probability distributions can be readily generated from an ensemble, allowing for the identification of forecast cases with high and low uncertainty. The ability of the NCEP ensemble to distinguish between high and low uncertainty forecast cases is studied here quantitatively. Ensemble mode forecasts, along with traditional higher-resolution control forecasts, are verified in terms of predicting the probability of the true state being in 1 of 10 climatologically equally likely 500-hPa height intervals. A stratification of the forecast cases by the degree of overall agreement among the ensemble members reveals great differences in forecast performance between the cases identified by the ensemble as the least and most uncertain. A new ensemble-based forecast product, the “relative measure of predictability,” is introduced to identify forecasts with below and above average uncertainty. This measure is standardized according to geographical location, the phase of the annual cycle, lead time, and also the position of the forecast value in terms of the climatological frequency distribution. The potential benefits of using this and other ensemble-based measures of predictability is demonstrated through synoptic examples.

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Manuel Lonfat, Robert Rogers, Timothy Marchok, and Frank D. Marks Jr.

Abstract

This study documents a new parametric hurricane rainfall prediction scheme, based on the rainfall climatology and persistence model (R-CLIPER) used operationally in the Atlantic Ocean basin to forecast rainfall accumulations. Although R-CLIPER has shown skill at estimating the mean amplitude of rainfall across the storm track, one underlying limitation is that it assumes that hurricanes produce rain fields that are azimuthally symmetric. The new implementations described here take into account the effect of shear and topography on the rainfall distribution through the use of parametric representations of these processes. Shear affects the hurricane rainfall by introducing spatial asymmetries, which can be reasonably well modeled to first order using a Fourier decomposition. The effect of topography is modeled by evaluating changes in elevation of flow parcels within the storm circulation between time steps and correcting the rainfall field in proportion to those changes. Effects modeled in R-CLIPER and those from shear and topography are combined in a new model called the Parametric Hurricane Rainfall Model (PHRaM). Comparisons of rainfall accumulations predicted from the operational R-CLIPER model, PHRaM, and radar-derived observations show some improvement in the spatial distribution and amplitude of rainfall when shear is accounted for and significant improvements when both shear and topography are modeled.

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Christian Buckingham, Timothy Marchok, Isaac Ginis, Lewis Rothstein, and Dail Rowe

Abstract

The NCEP Global Ensemble Forecasting System (GEFS) is examined in its ability to predict tropical cyclone and extratropical transition (ET) positions. Forecast and observed tracks are compared in Atlantic and western North Pacific basins for 2006–08, and the accuracy and consistency of the ensemble are examined out to 8 days. Accuracy is quantified by the average absolute and along- and cross-track errors of the ensemble mean. Consistency is evaluated through the use of dispersion diagrams, missing rate error, and probability within spread. Homogeneous comparisons are made with the NCEP Global Forecasting System (GFS). The average absolute track error of the GEFS mean increases linearly at a rate of 50 n mi day−1 [where 1 nautical mile (n mi) = 1.852 km] at early lead times in the Atlantic, increasing to 150 n mi day−1 at 144 h (100 n mi day−1 when excluding ET tracks). This trend is 60 n mi day−1 at early lead times in the western North Pacific, increasing to 150 n mi day−1 at longer lead times (130 n mi day−1 when excluding ET tracks). At long lead times, forecasts illustrate left- and right-of-track biases in Atlantic and western North Pacific basins, respectively; bias is reduced (increased) in the Atlantic (western North Pacific) when excluding ET tracks. All forecasts were found to lag behind observed cyclones, on average. The GEFS has good dispersion characteristics in the Atlantic and is underdispersive in the western North Pacific. Homogeneous comparisons suggest that the ensemble mean has value relative to the GFS beyond 96 h in the Atlantic and less value in the western North Pacific; a larger sample size is needed before conclusions can be made.

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Morris A. Bender, Timothy P. Marchok, Charles R. Sampson, John A. Knaff, and Matthew J. Morin

Abstract

The impact of storm size on the forecast of tropical cyclone storm track and intensity is investigated using the 2016 version of the operational GFDL hurricane model. Evaluation was made for 1529 forecasts in the Atlantic, eastern Pacific, and western North Pacific basins, during the 2014 and 2015 seasons. The track and intensity errors were computed from forecasts in which the 34-kt (where 1 kt = 0.514 m s−1) wind radii obtained from the operational TC vitals that are used to initialize TCs in the GFDL model were replaced with wind radii estimates derived using an equally weighted average of six objective estimates. It was found that modifying the radius of 34-kt winds had a significant positive impact on the intensity forecasts in the 1–2 day lead times. For example, at 48 h, the intensity error was reduced 10%, 5%, and 4% in the Atlantic, eastern Pacific, and western North Pacific, respectively. The largest improvements in intensity forecasts were for those tropical cyclones undergoing rapid intensification, with a maximum error reduction in the 1–2 day forecast lead time of 14% and 17% in the eastern and western North Pacific, respectively. The large negative intensity biases in the eastern and western North Pacific were also reduced 25% and 75% in the 12–72-h forecast lead times. Although the overall impact on the average track error was neutral, forecasts of recurving storms were improved and tracks of nonrecurving storms degraded. Results also suggest that objective specification of storm size may impact intensity forecasts in other high-resolution numerical models, particularly for tropical cyclones entering a rapid intensification phase.

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Ning Lin, James A. Smith, Gabriele Villarini, Timothy P. Marchok, and Mary Lynn Baeck

Abstract

Landfalling tropical cyclones present major hazards for the eastern United States. Hurricane Isabel (September 2003) produced more than $3.3 billion in damages from wind, inland riverine flooding, and storm surge flooding, and resulted in 17 fatalities. Case study analyses of Hurricane Isabel are carried out to investigate multiple hazards from landfalling tropical cyclones. The analyses focus on storm evolution following landfall and center on simulations using the Weather Research and Forecasting Model (WRF). WRF simulations are coupled with the 2D, depth-averaged hydrodynamic Advanced Circulation Model (ADCIRC), to examine storm surge in the Chesapeake Bay. Analyses of heavy rainfall and flooding include an examination of the structure and evolution of extreme rainfall over land. Intercomparisons of simulated rainfall from WRF with Hydro-NEXRAD rainfall fields and observations from rain gauge networks are presented. A particular focus of these analyses is the evolving distribution of rainfall, relative to the center of circulation, as the storm moves over land. Similar analyses are carried out for the wind field of Hurricane Isabel as it moves over the mid-Atlantic region. Outer rainbands, which are not well captured in WRF simulations, played a major role in urban flooding and wind damage, especially for the Baltimore metropolitan region. Wind maxima in outer rainbands may also have played a role in storm surge flooding in the upper Chesapeake Bay.

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Morris A. Bender, Timothy Marchok, Robert E. Tuleya, Isaac Ginis, Vijay Tallapragada, and Stephen J. Lord

Abstract

The hurricane project at the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) was established in 1970. By the mid-1970s pioneering research had led to the development of a new hurricane model. As the reputation of the model grew, GFDL was approached in 1986 by the director of the National Meteorological Center about establishing a collaboration between the two federal organizations to transition the model into an operational modeling system. After a multiyear effort by GFDL scientists to develop a system that could support rigorous requirements of operations, and multiyear testing had demonstrated its superior performance compared to existing guidance products, operational implementation was made in 1995. Through collaboration between GFDL and the U.S. Navy, the model was also made operational at Fleet Numerical Meteorology and Oceanography Center in 1996. GFDL scientists continued to support and improve the model during the next two decades by collaborating with other scientists at GFDL, the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), the National Hurricane Center, the U.S. Navy, the University of Rhode Island (URI), Old Dominion University, and the NOAA Hurricane Research Division. Scientists at GFDL, URI, and EMC collaborated to transfer key components of the GFDL model to the NWS new Hurricane Weather Research and Forecasting Model (HWRF) that became operational in 2007. The purpose of the article is to highlight the critical role of these collaborations. It is hoped that the experiences of the authors will serve as an example of how such collaboration can benefit the nation with improved weather guidance products.

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Charles R. Sampson, Paul A. Wittmann, Efren A. Serra, Hendrik L. Tolman, Jessica Schauer, and Timothy Marchok

Abstract

An algorithm to generate wave fields consistent with forecasts from the official U.S. tropical cyclone forecast centers has been made available in near–real time to forecasters since summer 2007. The algorithm removes the tropical cyclone from numerical weather prediction model surface wind field forecasts, replaces the removed winds with interpolated values from surrounding grid points, and then adds a surface wind field generated from the official forecast into the background. The modified wind fields are then used as input into the WAVEWATCH III model to provide seas consistent with the official tropical cyclone forecasts. Although this product is appealing to forecasters because of its consistency and its superior tropical cyclone track forecast, there has been only anecdotal evaluation of resulting wave fields to date. This study evaluates this new algorithm for two years’ worth of Atlantic tropical cyclones and compares results with those of WAVEWATCH III run with U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) surface winds alone. Results show that the new algorithm has generally improved forecasts of maximum significant wave heights and 12-ft seas’ radii in proximity to tropical cyclones when compared with forecasts produced using only the NOGAPS surface winds.

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Morris A. Bender, Isaac Ginis, Robert Tuleya, Biju Thomas, and Timothy Marchok

Abstract

The past decade has been marked by significant advancements in numerical weather prediction of hurricanes, which have greatly contributed to the steady decline in forecast track error. Since its operational implementation by the U.S. National Weather Service (NWS) in 1995, the best-track model performer has been NOAA’s regional hurricane model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). The purpose of this paper is to summarize the major upgrades to the GFDL hurricane forecast system since 1998. These include coupling the atmospheric component with the Princeton Ocean Model, which became operational in 2001, major physics upgrades implemented in 2003 and 2006, and increases in both the vertical resolution in 2003 and the horizontal resolution in 2002 and 2005. The paper will also report on the GFDL model performance for both track and intensity, focusing particularly on the 2003 through 2006 hurricane seasons. During this period, the GFDL track errors were the lowest of all the dynamical model guidance available to the NWS Tropical Prediction Center in both the Atlantic and eastern Pacific basins. It will also be shown that the GFDL model has exhibited a steady reduction in its intensity errors during the past 5 yr, and can now provide skillful intensity forecasts. Tests of 153 forecasts from the 2004 and 2005 Atlantic hurricane seasons and 75 forecasts from the 2005 eastern Pacific season have demonstrated a positive impact on both track and intensity prediction in the 2006 GFDL model upgrade, through introduction of a cloud microphysics package and an improved air–sea momentum flux parameterization. In addition, the large positive intensity bias in sheared environments observed in previous versions of the model is significantly reduced. This led to the significant improvement in the model’s reliability and skill for forecasting intensity that occurred in 2006.

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Jeffrey S. Gall, Isaac Ginis, Shian-Jiann Lin, Timothy P. Marchok, and Jan-Huey Chen

Abstract

This paper describes a forecasting configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Atmospheric Model (HiRAM). HiRAM represents an early attempt in unifying, within a global modeling framework, the capabilities of GFDL’s low-resolution climate models for Intergovernmental Panel on Climate Change (IPCC) type climate change assessments and high-resolution limited-area models for hurricane predictions. In this study, the potential of HiRAM as a forecasting tool is investigated by applying the model to the near-term and intraseasonal hindcasting of tropical cyclones (TCs) in the Atlantic basin from 2006 to 2009. Results demonstrate that HiRAM provides skillful near-term forecasts of TC track and intensity relative to their respective benchmarks from t = 48 h through t = 144 h. At the intraseasonal time scale, a simple HiRAM ensemble provides skillful forecasts of 21-day Atlantic basin TC activity at a 2-day lead time. It should be noted that the methodology used to produce these hindcasts is applicable in a real-time forecasting scenario. While the initial experimental results appear promising, the HiRAM forecasting system requires various improvements in order to be useful in an operational setting. These modifications are currently under development and include a data assimilation system for forecast initialization, increased horizontal resolution to better resolve the vortex structure, 3D ocean model coupling, and wave model coupling. An overview of these ongoing developments is provided, and the specifics of each will be described in subsequent papers.

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