Barbero, Tyler W., Michael M Bell, Jan-Huey Chen, and Philip J Klotzbach, March 2024: A potential vorticity diagnosis of tropical cyclone track forecast errors. Journal of Advances in Modeling Earth Systems, 16(3), DOI:10.1029/2023MS004008. Abstract
Tropical cyclone (TC) track forecasting provides essential guidance for coastal communities. However, track forecast errors still occur, highlighting the need for continued research into error sources. Piecewise potential vorticity (PV) inversion is used systematically to quantitatively diagnose errors in track forecasts in four models during the 2017 Atlantic hurricane season. The deep layer mean steering flow (DLMSF) provides a sufficient proxy for hurricane movement, and DLMSF errors are correlated with TC track errors. Analysis of track forecasts for Hurricanes Harvey, Irma, and Maria reveals that their track errors are attributed to steering errors caused by misrepresentations of specific pressure systems. Harvey's westward track error in the GFS resulted from zonal wind errors from the Continental High, while Irma's northward track error in the SHiELD gfsIC resulted from meridional wind errors in the Bermuda High and Continental High. Maria's southward track error in the IFS resulted from meridional wind errors in the Bermuda High and a misrepresentation of Jose to Maria's northwest. The mean absolute error of the DLMSF shows that the Bermuda High contributed the most to steering flow errors in the cases examined. Our results show that piecewise PV inversion can identify the sources of biases in TC track forecasts. The correction of these biases may lead to improved track forecasts. Quantitative diagnostics presented here provide useful information for future model development.
Chen, Jan-Huey, Adam J Clark, Guoqing Ge, Lucas Harris, Kimberly Hoogewind, Anders Jensen, Hosmay Lopez, Joseph Mouallem, Breanna L Zavadoff, Xuejin Zhang, and Linjiong Zhou, January 2024: 2022-2023 Global-Nest Initiative Activity Summary: Recent Results and Future Plan, Princeton, NJ: NOAA Technical Memorandum OAR GFDL, 2023-001, DOI:10.25923/yx20-3k04 14pp. Abstract
The Global-Nest Initiative takes new technologies developed at Geophysical Fluid Dynamics Laboratory (GFDL) and partners to create convective-scale digital twins of the earth system to better simulate and predict extreme weather events, their impacts, and their role within the broader earth system, and to create actionable information at all time scales. This annual report describes the activities and results of the NOAA Global-Nest Initiative during Fiscal Year 2022-2023.
Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.
McTaggart-Cowan, Ron, David S Nolan, Rabah Aider, Martin Charron, and Jan-Huey Chen, et al., March 2024: Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system. Monthly Weather Review, 152(3), DOI:10.1175/MWR-D-23-0193.1837-863. 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.
Yoon, Donghyuck, Jan-Huey Chen, and Eunkyo Seo, in press: Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting. Weather and Climate Extremes. DOI:10.1016/j.wace.2024.100722. September 2024. Abstract
Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.
The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model’s warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.
Chen, Jan-Huey, Linjiong Zhou, Linus Magnusson, Ron McTaggart-Cowan, and M Köhler, July 2023: Tropical cyclone forecasts in the DIMOSIC project—medium-range forecast models with common initial conditions. Earth and Space Science, 10(7), DOI:10.1029/2023EA002821. Abstract
The tropical cyclone (TC) forecast skill of the eight global medium-range forecast models which are participating in the DIfferent Models, Same Initial Conditions project is investigated in this study. Each model was used to generate 10-day forecasts from the same initial conditions provided by the European Centre for Medium-Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 at 3-day intervals. The TC track and intensity forecasts are evaluated against the best track data set. TC-related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow-moving and right-of-track biases in their TC track forecasts. Also, a common dry bias in TC-related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments.
High-resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high-resolution (approximately 3 km grid-spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic-scale pattern and thereby to the steering flow. Our results suggest that optimizing small-scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (∼10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high-resolution models, and its often overlooked role in affecting larger-scale circulations and hurricane track prediction.
Magnusson, Linus, Duncan Ackerley, Yves Bouteloup, Jan-Huey Chen, James D Doyle, Paul Earnshaw, Y C Kwon, M Köhler, Simon T K Lang, Y-J Lim, Mio Matsueda, Takumi Matsunobu, Ron McTaggart-Cowan, Alex Rienecke, Munehiko Yamaguchi, and Linjiong Zhou, September 2022: Skill of medium-range forecast models using the same initial conditions. Bulletin of the American Meteorological Society, 103(9), DOI:10.1175/BAMS-D-21-0234.1E2050-E2068. Abstract
In the Different Models, Same Initial Conditions (DIMOSIC) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are 1) to evaluate the forecast skill and how it depends on model formulation, 2) to assess systematic differences and errors at short lead times, 3) to compare multimodel ensemble spread to model uncertainty schemes, and 4) to identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time, we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multimodel ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations still are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
We describe the third version of the Geophysical Fluid Dynamics Laboratory cloud microphysics scheme (GFDL MP v3) implemented in the System for High-resolution prediction on Earth-to-Local Domains (SHiELD). Compared to the GFDL MP v2, the GFDL MP v3 is entirely reorganized, optimized, and modularized into functions. The particle size distribution (PSD) of all hydrometeor categories is redefined to better mimic observations, and the cloud droplet number concentration (CDNC) is calculated from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) aerosol data. In addition, the GFDL MP has been redesigned so all processes use the redefined PSD to ensure overall consistency and easily permit the introduction of new PSDs and microphysical processes. A year's worth of global 13-km, 10-day weather forecasts were performed with the new GFDL MP. Compared to the GFDL MP v2, the GFDL MP v3 significantly improves SHiELD's predictions of geopotential height, air temperature, and specific humidity in the Troposphere, as well as the high, middle and total cloud fractions and the liquid water path. The predictions are improved even further by the use of reanalysis aerosol data to calculate CDNC, and also by using the more realistic PSD available in GFDL MP v3. However, the upgrade of the GFDL MP shows little impact on the precipitation prediction. Degradations caused by the new scheme are discussed and provide a guide for future GFDL MP development.
Clouds play critical roles in our daily weather and in the global energy and water budgets that regulate the climate of the Earth (Lamb and Verlinde, 2011; Houze, 2014). The formation and evolution of clouds significantly impact precipitation forecasts in numerical weather prediction (Seifert and Beheng, 2005; Morrison and Grabowski, 2008; Baldauf et al., 2011; Bauer et al., 2015). Clouds and their impacts on solar and thermal radiation are among the most challenging aspects of climate prediction (Trenberth et al., 2009; Stephens et al., 2012; Wild et al., 2019). Therefore, the representation of clouds in atmospheric models has to be paid particular attention to. Among all physical processes in a model, cloud microphysics is less well represented but is of critical importance. Because the processes are not readily resolved in time and space, cloud microphysics parameterization is essential from large-eddy to global simulations (Morrison and Grabowski, 2008; Kogan, 2013; Nogherotto et al., 2016).
Harris, Lucas, Xi Chen, William M Putman, Linjiong Zhou, and Jan-Huey Chen, June 2021: A Scientific Description of the GFDL Finite-Volume Cubed-Sphere Dynamical Core, Princeton, NJ: NOAA Technical Memorandum OAR GFDL, 2021-001, DOI:10.25923/6nhs-5897 109pp.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
This technical note explains updates to the GFDL Finite-Volume Cubed-Sphere Dynamical Core, abbreviated FV3 or FV[superscript 3], and the Split GFDL Microphysics. It does not repeat the contents of earlier documentation, especially publications. A list of publications and prior technical notes describing FV3 is available on the GFDL website.
We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.
This document describes the nonhydrostatic solver of the GFDL Finite-Volume Cubed-Sphere Dynamical Core, FV3. The nonhydrostatic solver works identically to the hydrostatic solver except for the need to solve for two new prognostic variables, the vertical velocity and geometric layer depth; and to use the full nonhydrostatic pressure in computing the pressure gradient force. In particular the Lagrangian dynamics described within L04 remains valid and all vertical processes (advection, wave propagation) remain implicit while all horizontal processes are explicit. This document assumes working knowledge of the hydrostatic discretization of FV3 described in LR96, LR97, L97, L04, PL07, and HL13. It is strongly recommended that anyone who wishes to understand the nonhydrostatic FV3 solver read and understand these documents first. Additional relevant material may be found in LPH17 and LH18. All of these documents may be found at www. gfdl.noaa.gov/fv3/fv3-documentation-and-references/.
Sun, Yongqiang, F Zhang, Linus Magnusson, R Buizza, Jan-Huey Chen, and Kerry A Emanuel, February 2020: Reply to “Comments on ‘What Is the Predictability Limit of Midlatitude Weather?’”. Journal of the Atmospheric Sciences, 77(2), DOI:10.1175/JAS-D-19-0308.1. Abstract
In their comment, Žagar and Szunyogh raised concerns about a recent study by Zhang et al. that examined the predictability limit of midlatitude weather using two up-to-date global models. Zhang et al. showed that deterministic weather forecast may, at best, be extended by 5 days, assuming we could achieve minimal initial-condition uncertainty (e.g., 10% of current operational value) with a nearly perfect model. Žagar and Szunyogh questioned the methodology and the experiments of Zhang et al. Specifically, Žagar and Szunyogh raised issues regarding the effects of model error on the growth of the forecast uncertainty. They also suggested that estimates of the predictability limit could be obtained using a simple parametric model. This reply clarifies the misunderstandings in Žagar and Szunyogh and demonstrates that experiments conducted by Zhang et al. are reasonable. In our view, the model error concern in Žagar and Szunyogh does not apply to the intrinsic predictability limit, which is the key focus of Zhang et al. and the simple parametric model described in Žagar and Szunyogh does not serve the purpose of Zhang et al.
Subseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time‐mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium‐Range Weather Forecasts (ECMWF) model 2‐4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode), and three modes in North America (North Pacific Oscillation, Pacific‐North America teleconnection mode and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence.
The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land‐atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary‐scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower‐boundary snow anomaly, and the snow‐atmosphere coupling helps sustain this mode and provides a source of predictability.
We use the fvGFS model developed at the Geophysical Fluid Dynamics Laboratory (GFDL) to demonstrate the potential of the upcoming United States Next Generation Global Prediction System for hurricane prediction. The fvGFS retrospective forecasts initialized with the European Centre for Medium‐Range Weather Forecasts (ECMWF) data showed much‐improved track forecasts for the 2017 Atlantic hurricane season compared to the best performing ECMWF operational model. The fvGFS greatly improved the ECMWF's poor track forecast for Hurricane Maria (2017). For Hurricane Irma (2017), a well‐predicted case by the ECMWF model, the fvGFS produced even lower 5‐day track forecast errors. The fvGFS also showed better intensity prediction than both the United States and the ECMWF operational models, indicating the robustness of its numerical algorithms.
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 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.
We demonstrate that two‐way nesting significantly improves the structure of simulated hurricane in an atmospheric general circulation model. Two sets of 30‐day hindcast experiments are conducted, one with the global‐uniform‐resolution (approximately 25‐km nominal horizontal resolution) and the other with a regionally refined two‐way nest (approximately 8 km over the tropical North Atlantic). The increase in the horizontal resolution on the nested grid improves the representation of storm intensity and intensification rate. When normalized by the radius of maximum wind (RMW), composite hurricane structures are generally similar in both simulations and compare well to observations. However, the hurricanes in the globally uniform configuration have much larger RMWs than observed, while those in the two‐way‐nested configuration have more realistic RMWs. We also find that the representation of the RMW has a critical impact on the simulation of inertial stability and boundary‐layer convergence in the inner‐core region. The more realistic inner‐core size (indicated by RMW) and structure are possible reasons for the improved intensification rates in the two‐way‐nested configuration.
We investigate the monthly prediction of North Atlantic hurricane and especially major hurricane activity based on the Geophysical Fluid Dynamics Laboratory High‐Resolution Atmospheric Model (HiRAM). We compare the performance of two versions of HiRAM: a globally‐uniform 25‐km grid and the other with an 8‐km interactive nest over the tropical North Atlantic. Both grid configurations show skills in predicting anomalous monthly hurricane frequency and accumulated cyclone energy (ACE). Particularly the 8‐km nested model shows improved skills in predicting major hurricane frequency and ACE. The skill in anomalous monthly hurricane occurrence prediction arises from the accurate prediction of zonal wind shear anomalies in the Main Development Region, which in turn arises from the SST anomalies persisted from the initialization time. The enhanced resolution on the nested grid permits a better representation of hurricanes and especially intense hurricanes, thereby showing the ability and the potential for prediction of major hurricanes on subseasonal timescales.
Understanding the root causes of forecast errors and occasional very poor forecasts is essential but difficult. In this paper we investigate the relative importance of initial conditions and model formulation for medium‐range errors in 500‐hPa geopotential height. The question is addressed by comparing forecasts produced with ECMWF‐IFS and NCEP‐GFS forecasting systems, and with the GFDL‐fvGFS model initialised with ECMWF and NCEP initial conditions. This gives two pairs of configurations that use the same initial conditions but different models, and one pair with the same model but different initial conditions. The first conclusion is that the initial conditions play the major role for differences between the configurations in terms of the average root‐mean‐square error for both northern and southern hemispheres as well as Europe and the contiguous U.S (CONUS), while the model dominates the systematic errors. A similar conclusion is also found by verifying precipitation over low latitudes and the CONUS. The day‐to‐day variations of 500‐hPa geopotential height scores are exemplified by one case of a forecast bust over Europe, where the error is found to be dominated by initial errors. The results are generalised by calculating correlations between errors integrated over Europe, CONUS and a region in southeastern Pacific respectively from the different configurations. For Europe and southeast Pacific, the correlations in the medium‐range are highest between the pairs that use the same initial conditions, while over CONUS it is for the pair with the same model. This suggests different mechanisms behind the day‐to‐day variability of the score for these regions. Over CONUS the link is made to the propagation of troughs over the Rockies, and the result suggests that the large differences in parameterisations of orographic drag between the models plays a role.
Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.
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 Center for Medium Range Weather Forecasting (ECMWF, 9-km operational model) and identical-twin experiments of the US next-generation global prediction system (NGGPS, 3-km). Results suggest that predictability limit for mid-latitude 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.
The variable-resolution version of a Finite-Volume Cubed-Sphere Dynamical Core (FV3)-based global model improves the prediction of convective-scale features while maintaining skillful global forecasts.
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 six-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.
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 one-week lead time are only evident for about 10 percent 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 thermo-dynamical fields. Particularly over the NA, higher predictability of low-level relative vorticity, mid-level humidity, and vertical zonal wind shear are evident along a tropical belt from the West Africa coast to Caribbean Seas, 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.
In this two-part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part I, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode – with prescribed sea surface temperatures (SSTs) and sea ice distribution – is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top-of-atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part II, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.
In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.
The Tropical Cyclones (TC) that form over the warm waters in the Gulf of Mexico region pose a major threat to the surrounding coastal communities. Skillful sub-seasonal prediction of TC activity is important for early preparedness and reducing the TC damage in this region. In this study, we evaluate the performance of a 25-km resolution Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) in simulating the modulation of the TC activity in the Gulf of Mexico and western Caribbean Sea by the Intraseasonal Oscillation (ISO) based on multi-year retrospective seasonal predictions. We demonstrate that the HiRAM faithfully captures the observed influence of ISO on TC activity over the region of interest, including the formation of tropical storms and (major) hurricanes, as well as the landfalling storms. This is likely because of the realistic representation of the large-scale anomalies associated with boreal summer ISO over Northeast Pacific in HiRAM, especially the enhanced (reduced) moisture throughout the troposphere during the convectively enhanced (suppressed) phase of ISO. The reasonable performance of HiRAM suggests its potential for the subseasonal prediction of regional TC risk.
This study aims to assess whether, and the extent to which, an increase in atmospheric resolution in versions of the Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) with 50 km and HiFLOR with 25 km improves the simulation of the El Niño Southern Oscillation-tropical cyclone (ENSO-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/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/La Niña. A better representation of ENSO-TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track and landfall, improve our understanding of the interannual variation of TC activity, and provide better projection of TC activity under climate change.
A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model (HiFLOR) has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises of high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean components. HiFLOR was developed from the Forecast Oriented Low Resolution Ocean model (FLOR) by decreasing the horizontal grid spacing of the atmospheric component from 50-km to 25-km, while leaving most of the sub-gridscale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, and 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 (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 multi-century simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.
The hindcasts of the Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Atmospheric Model (HiRAM), which skillfully predicted the interannual variability of Atlantic tropical cyclone (TC) frequency, were analyzed to investigate what key circulation systems a model must capture in order to skillfully predict TCs. The HiRAM reproduced the leading EOF mode (M1) of the interannual variability of the Atlantic Hadley circulation and its impacts on environmental conditions. M1 represents the variability of the ITCZ intensity and width, and the predictability of Atlantic TCs can be explained by the lag correlation between M1 and SST in preceding months. Although the ITCZ displacement was not well predicted by the HiRAM, it does not affect the prediction of the basin-wide hurricane count. The analyses suggest that the leading mode of the variability of the regional Hadley circulation can serve as a useful metric to evaluate the performance of global models in TC seasonal prediction.
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 using the GFDL coupled model for operational extended-range predictions of TCs.
Retrospective seasonal predictions of tropical cyclones (TCs) in the three major ocean basins of the Northern Hemisphere are performed from 1990 to 2010 using the Geophysical Fluid Dynamics Laboratory High-Resolution Atmospheric Model at 25-km resolution. Atmospheric states are initialized for each forecast, with the sea surface temperature anomaly (SSTA) persisted from that at the starting time during the 5-month forecast period (July-November). Using a 5-member ensemble, it is shown that the storm counts of both tropical storm (TS) and hurricane categories are highly predictable in the North Atlantic basin during the 21-year period. The correlations between the 21-year observed and model predicted storm counts are 0.88 and 0.89 for hurricanes and TSs, respectively. The prediction in the eastern North Pacific is skillful, but it is not as outstanding as that in the North Atlantic. The persisted SSTA assumption appears to be less robust for the western North Pacific, contributing to less skillful predictions in that region. The relative skill in the prediction of storm counts is shown to be consistent with the quality of the predicted large-scale environment in the three major basins.
Finally, we show that intensity distribution of TCs can be captured well by our model if the central sea-level pressure were used as the threshold variable instead of the commonly used 10-meter wind speed. This demonstrated the feasibility of using the 25-km resolution HiRAM, a general circulation model designed initially for long-term climate simulations, to study the impacts of climate change on the intensity distribution of TCs.
Chen, S-G, C-C Wu, Jan-Huey Chen, and K-H Chou, May 2011: Validation and interpretation of adjoint-derived sensitivity steering vector as targeted observation guidance. Monthly Weather Review, 139(5), DOI:10.1175/2011MWR3490.1. Abstract
The adjoint-derived sensitivity steering vector (ADSSV) has been proposed and applied as a guidance for targeted observation in the field programs for improving tropical cyclone predictability, such as THORPEX–Pacific Asian Regional Campaign (T-PARC). The ADSSV identifies sensitive areas at the observing time to the steering flow at the verifying time through adjoint calculation. In addition, the ability of the ADSSV to represent signals of influence from synoptic systems such as the midlatitude trough and the subtropical high prior to the recurvature of Typhoon Shanshan (2006) has also been demonstrated.
In this study, the impact of initial perturbations associated with the high or low ADSSV sensitivity on model simulations is investigated by systematically perturbing initial vorticity fields in the case of Shanshan. Results show that experiments with the perturbed initial conditions located in the high ADSSV area (i.e., the midlatitude trough and the subtropical high) lead to more track deflection relative to the unperturbed control run than experiments with perturbations in the low sensitivity area. The evolutions of the deep-layer-mean steering flow and the direction of the ADSSV are compared to provide conceptual interpretation and validation on the physical meaning of the ADSSV. Concerning the results associated with the perturbed regions in high sensitivity regions, the variation of the steering flow within the verifying area due to the initial perturbations is generally consistent with that of the direction of the ADSSV. In addition, the bifurcation between the ADSSV and the steering change becomes larger with the increased integration time. However, the result for the perturbed region in low sensitivity region indicates that the steering change does not have good agreement with the ADSSV. The large initial perturbations to the low sensitivity region may interact with the trough to the north due to the nonlinearity which may not be accounted for in the ADSSV. Furthermore, the effect of perturbations specifically within the sensitive vertical layers is investigated to validate the vertical structure of the ADSSV. The structure of kinetic energy shows that the perturbation associated with the trough (subtropical high) specifically in the middle to upper (middle to lower) troposphere evolves similarly to that in the deep-layer troposphere, leading to comparable track changes. A sensitivity test in which perturbations are locally introduced in higher sensitivity area is conducted to examine the different impact as compared to that perturbed with the broader synoptic feature.
A newly developed global model, the Geophysical Fluid Dynamics Laboratory (GFDL)
High-Resolution Atmospheric Model (HiRAM) which is designed for both weather predictions
and climate-change simulations, is used to predict the tropical cyclone activity at 25-km
resolution. Assuming the persistence of the sea surface temperature anomaly during the forecast
period, we show that the inter-annual variability of seasonal prediction for hurricane counts in
the North Atlantic basin is highly predictable during the past decade (2000-2010). A remarkable
correlation of 0.96 between the observed and model predicted hurricane counts is achieved. The
root mean square error of the predicted hurricane number is less than 1 per year after correcting
the model’s negative bias. The predictive skill of the model in the tropics is further supported by
the successful prediction of a Madden-Julian Oscillation event initialized 7-day in advance of its
onset.
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 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 near-term and intraseasonal hindcasting of tropical cyclones (TCs) in the Atlantic basin from 2006 – 2009. Results demonstrate that HiRAM provides skillful near-term forecasts of TC track and intensity relative to their respective benchmarks from t = 48 hr through t = 144 hr. At the intraseasonal timescale, 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 currently under development include a data assimilation system for forecast initialization, increased horizontal resolution to better resolve the vortex structure, 3-D ocean model coupling, and wave model coupling. An overview of these ongoing developments are provided, and the specifics of each will be described in subsequent papers.
Wu, C-C, G-Y Lien, Jan-Huey Chen, and F Zhang, December 2010: Assimilation of tropical cyclone track and structure based on the Ensemble Kalman Filter (EnKF). Journal of the Atmospheric Sciences, 67(12), DOI:10.1175/2010JAS3444.1. Abstract
A new tropical cyclone vortex initialization method based on the ensemble Kalman filter (EnKF) is proposed in this study. Three observed parameters that are related to the tropical cyclone (TC) track and structure center position, velocity of storm motion, and surface axisymmetric wind structure are assimilated into the high-resolution Weather Research and Forecasting (WRF) model during a 24-h initialization period to develop a dynamically balanced TC vortex without employing any extra bogus schemes. The first two parameters are available from the TC track data of operational centers, which are mainly based on satellite analysis. The radial wind profile is constructed by fitting the combined information from both the best-track and the dropwindsonde data available from aircraft surveillance observations, such as the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR).
The initialized vortex structure is consistent with the observations of a typical vertical TC structure, even though only the surface wind profile is assimilated. In addition, the subsequent numerical integration shows minor adjustments during early periods, indicating that the analysis fields obtained from this method are dynamically balanced. Such a feature is important for TC numerical integrations. The results here suggest that this new method promises an improved TC initialization and could possibly contribute to some high-resolution numerical experiments to better understand the dynamics of TC structure and to improve operational TC model forecasts. Further applications of this method with sophisticated data from The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) will be shown in a follow-up paper.
