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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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Linjiong Zhou
,
Shian-Jiann Lin
,
Jan-Huey Chen
,
Lucas M. Harris
,
Xi Chen
, and
Shannon L. Rees

Abstract

The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new variable-resolution global model with the ability to represent convective-scale features that serves as a prototype of the Next Generation Global Prediction System (NGGPS). The goal of this prediction system is to maintain the skill in large-scale features while simultaneously improving the prediction skill of convectively driven mesoscale phenomena. This paper demonstrates the new capability of this model in convective-scale prediction relative to the current operational Global Forecast System (GFS). This model uses the stretched-grid functionality of the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to refine the global 13-km uniform-resolution model down to 4-km convection-permitting resolution over the contiguous United States (CONUS), and implements the GFDL single-moment 6-category cloud microphysics to improve the representation of moist processes. Statistics gathered from two years of simulations by the GFS and select configurations of the FV3-based model are carefully examined. The variable-resolution FV3-based model is shown to possess global forecast skill comparable with that of the operational GFS while quantitatively improving skill and better representing the diurnal cycle within the high-resolution area compared to the uniform mesh simulations. Forecasts of the occurrence of extreme precipitation rates over the southern Great Plains are also shown to improve with the variable-resolution model. Case studies are provided of a squall line and a hurricane to demonstrate the effectiveness of the variable-resolution model to simulate convective-scale phenomena.

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Jan-Huey Chen
,
Shian-Jiann Lin
,
Linjiong Zhou
,
Xi Chen
,
Shannon Rees
,
Morris Bender
, and
Matthew Morin

Abstract

A new global model using the GFDL nonhydrostatic Finite-Volume Cubed-Sphere Dynamical Core (FV3) coupled to physical parameterizations from the National Centers for Environmental Prediction’s Global Forecast System (NCEP/GFS) was built at GFDL, named fvGFS. The modern dynamical core, FV3, has been selected for the National Oceanic and Atmospheric Administration’s Next Generation Global Prediction System (NGGPS) due to its accuracy, adaptability, and computational efficiency, which brings a great opportunity for the unification of weather and climate prediction systems. The performance of tropical cyclone (TC) forecasts in the 13-km fvGFS is evaluated globally based on 363 daily cases of 10-day forecasts in 2015. Track and intensity errors of TCs in fvGFS are compared to those in the operational GFS. The fvGFS outperforms the GFS in TC intensity prediction for all basins. For TC track prediction, the fvGFS forecasts are substantially better over the northern Atlantic basin and the northern Pacific Ocean than the GFS forecasts. An updated version of the fvGFS with the GFDL 6-category cloud microphysics scheme is also investigated based on the same 363 cases. With this upgraded microphysics scheme, fvGFS shows much improvement in TC intensity prediction over the operational GFS. Besides track and intensity forecasts, the performance of TC genesis forecast is also compared between the fvGFS and operational GFS. In addition to evaluating the hit/false alarm ratios, a novel method is developed to investigate the lengths of TC genesis lead times in the forecasts. Both versions of fvGFS show higher hit ratios, lower false alarm ratios, and longer genesis lead times than those of the GFS model in most of the TC basins.

Open access
Xianan Jiang
,
Baoqiang Xiang
,
Ming Zhao
,
Tim Li
,
Shian-Jiann Lin
,
Zhuo Wang
, and
Jan-Huey Chen

Abstract

Motivated by increasing demand in the community for intraseasonal predictions of weather extremes, predictive skill of tropical cyclogenesis is investigated in this study based on a global coupled model system. Limited intraseasonal cyclogenesis prediction skill with a high false alarm rate is found when averaged over about 600 tropical cyclones (TCs) over global oceans from 2003 to 2013, particularly over the North Atlantic (NA). Relatively skillful genesis predictions with more than 1-week lead time are only evident for about 10% of the total TCs. Further analyses suggest that TCs with relatively higher genesis skill are closely associated with the Madden–Julian oscillation (MJO) and tropical synoptic waves, with their geneses strongly phase-locked to the convectively active region of the MJO and low-level cyclonic vorticity associated with synoptic-scale waves. Moreover, higher cyclogenesis prediction skill is found for TCs that formed during the enhanced periods of strong MJO episodes than those during weak or suppressed MJO periods. All these results confirm the critical role of the MJO and tropical synoptic waves for intraseasonal prediction of TC activity. Tropical cyclogenesis prediction skill in this coupled model is found to be closely associated with model predictability of several large-scale dynamical and thermodynamical fields. Particularly over the NA, higher predictability of low-level relative vorticity, midlevel humidity, and vertical zonal wind shear is evident along a tropical belt from the West Africa coast to the Caribbean Sea, in accord with more predictable cyclogenesis over this region. Over the extratropical NA, large-scale variables exhibit less predictability due to influences of extratropical systems, leading to poor cyclogenesis predictive skill.

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Fuqing Zhang
,
Y. Qiang Sun
,
Linus Magnusson
,
Roberto Buizza
,
Shian-Jiann Lin
,
Jan-Huey Chen
, and
Kerry Emanuel

Abstract

Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

Open access
Baoqiang Xiang
,
Shian-Jiann Lin
,
Ming Zhao
,
Shaoqing Zhang
,
Gabriel Vecchi
,
Tim Li
,
Xianan Jiang
,
Lucas Harris
, and
Jan-Huey Chen

Abstract

While tropical cyclone (TC) prediction, in particular TC genesis, remains very challenging, accurate prediction of TCs is critical for timely preparedness and mitigation. Using a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the authors studied the predictability of two destructive landfall TCs: Hurricane Sandy in 2012 and Super Typhoon Haiyan in 2013. Results demonstrate that the geneses of these two TCs are highly predictable with the maximum prediction lead time reaching 11 days. The “beyond weather time scale” predictability of tropical cyclogenesis is primarily attributed to the model’s skillful prediction of the intraseasonal Madden–Julian oscillation (MJO) and the westward propagation of easterly waves. Meanwhile, the landfall location and time can be predicted one week ahead for Sandy’s U.S landfall, and two weeks ahead for Haiyan’s landing in the Philippines. The success in predicting Sandy and Haiyan, together with low false alarms, indicates the potential of using the GFDL coupled model for extended-range predictions of TCs.

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Chun-Chieh Wu
,
Jan-Huey Chen
,
Sharanya J. Majumdar
,
Melinda S. Peng
,
Carolyn A. Reynolds
,
Sim D. Aberson
,
Roberto Buizza
,
Munehiko Yamaguchi
,
Shin-Gan Chen
,
Tetsuo Nakazawa
, and
Kun-Hsuan Chou

Abstract

This study compares six different guidance products for targeted observations over the northwest Pacific Ocean for 84 cases of 2-day forecasts in 2006 and highlights the unique dynamical features affecting the tropical cyclone (TC) tracks in this basin. The six products include three types of guidance based on total-energy singular vectors (TESVs) from different global models, the ensemble transform Kalman filter (ETKF) based on a multimodel ensemble, the deep-layer mean (DLM) wind variance, and the adjoint-derived sensitivity steering vector (ADSSV). The similarities among the six products are evaluated using two objective statistical techniques to show the diversity of the sensitivity regions in large, synoptic-scale domains and in smaller domains local to the TC. It is shown that the three TESVs are relatively similar to one another in both the large and the small domains while the comparisons of the DLM wind variance with other methods show rather low similarities. The ETKF and the ADSSV usually show high similarity because their optimal sensitivity usually lies close to the TC. The ADSSV, relative to the ETKF, reveals more similar sensitivity patterns to those associated with TESVs. Three special cases are also selected to highlight the similarities and differences among the six guidance products and to interpret the dynamical systems affecting the TC motion in the northwestern Pacific. Among the three storms studied, Typhoon Chanchu was associated with the subtropical high, Typhoon Shanshan was associated with the midlatitude trough, and Typhoon Durian was associated with the subtropical jet. The adjoint methods are found to be more capable of capturing the signal of the dynamic system that may affect the TC movement or evolution than are the ensemble methods.

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Wei Zhang
,
Gabriel A. Vecchi
,
Hiroyuki Murakami
,
Thomas Delworth
,
Andrew T. Wittenberg
,
Anthony Rosati
,
Seth Underwood
,
Whit Anderson
,
Lucas Harris
,
Richard Gudgel
,
Shian-Jiann Lin
,
Gabriele Villarini
, and
Jan-Huey Chen

Abstract

This study aims to assess whether, and the extent to which, an increase in atmospheric resolution of the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-Oriented Low Ocean Resolution version of CM2.5 (FLOR) with 50-km resolution and the High-Resolution FLOR (HiFLOR) with 25-km resolution improves the simulation of the El Niño–Southern Oscillation (ENSO)–tropical cyclone (TC) connections in the western North Pacific (WNP). HiFLOR simulates better ENSO–TC connections in the WNP including TC track density, genesis, and landfall than FLOR in both long-term control experiments and sea surface temperature (SST)- and sea surface salinity (SSS)-restoring historical runs (1971–2012). Restoring experiments are performed with SSS and SST restored to observational estimates of climatological SSS and interannually varying monthly SST. In the control experiments of HiFLOR, an improved simulation of the Walker circulation arising from more realistic SST and precipitation is largely responsible for its better performance in simulating ENSO–TC connections in the WNP. In the SST-restoring experiments of HiFLOR, more realistic Walker circulation and steering flow during El Niño and La Niña are responsible for the improved simulation of ENSO–TC connections in the WNP. The improved simulation of ENSO–TC connections with HiFLOR arises from a better representation of SST and better responses of environmental large-scale circulation to SST anomalies associated with El Niño or La Niña. A better representation of ENSO–TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track, and landfall; improve understanding of the interannual variation of TC activity; and provide better projection of TC activity under climate change.

Full access
Hiroyuki Murakami
,
Gabriel A. Vecchi
,
Seth Underwood
,
Thomas L. Delworth
,
Andrew T. Wittenberg
,
Whit G. Anderson
,
Jan-Huey Chen
,
Richard G. Gudgel
,
Lucas M. Harris
,
Shian-Jiann Lin
, and
Fanrong Zeng

Abstract

A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model [the High-Resolution Forecast-Oriented Low Ocean Resolution (FLOR) model (HiFLOR)] has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean component. HiFLOR was developed from FLOR by decreasing the horizontal grid spacing of the atmospheric component from 50 to 25 km, while leaving most of the subgrid-scale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, as well as a comparable simulation of storm-induced cold wakes and TC-genesis modulation induced by the Madden–Julian oscillation (MJO). Moreover, HiFLOR is able to simulate and predict extremely intense TCs (Saffir–Simpson hurricane categories 4 and 5) and their interannual variations, which represents the first time a global coupled model has been able to simulate such extremely intense TCs in a multicentury simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.

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Ron McTaggart-Cowan
,
David S. Nolan
,
Rabah Aider
,
Martin Charron
,
Jan-Huey Chen
,
Jean-François Cossette
,
Stéphane Gaudreault
,
Syed Husain
,
Linus Magnusson
,
Abdessamad Qaddouri
,
Leo Separovic
,
Christopher Subich
, and
Jing Yang

Abstract

The operational Canadian Global Deterministic Prediction System suffers from a weak-intensity bias for simulated tropical cyclones. The presence of this bias is confirmed in progressively simplified experiments using a hierarchical system development technique. Within a semi-idealized, simplified-physics framework, an unexpected insensitivity to the representation of relevant physical processes leads to investigation of the model’s semi-Lagrangian dynamical core. The root cause of the weak-intensity bias is identified as excessive numerical dissipation caused by substantial off-centering in the two time-level time integration scheme used to solve the governing equations. Any (semi)implicit semi-Lagrangian model that employs such off-centering to enhance numerical stability will be afflicted by a misalignment of the pressure gradient force in strong vortices. Although the associated drag is maximized in the tropical cyclone eyewall, the impact on storm intensity can be mitigated through an intercomparison-constrained adjustment of the model’s temporal discretization. The revised configuration is more sensitive to changes in physical parameterizations and simulated tropical cyclone intensities are improved at each step of increasing experimental complexity. Although some rebalancing of the operational system may be required to adapt to the increased effective resolution, significant reduction of the weak-intensity bias will improve the quality of Canadian guidance for global tropical cyclone forecasting.

Significance Statement

Global numerical weather prediction systems provide important guidance to forecasters about tropical cyclone development, motion, and intensity. Despite recent improvements in the Canadian operational model’s ability to predict tropical cyclone formation, the system systematically underpredicts the intensity of these storms. In this study, we use a set of increasingly simplified experiments to identify the source of this error, which lies in the numerical time-stepping scheme used to solve the model equations. By decreasing numerical drag on the tropical cyclone circulation, intensity predictions that resemble those of other global modeling systems are achieved. This will improve the quality of Canadian tropical cyclone guidance for forecasters around the world.

Open access