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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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 (HiRAM) 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 five-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-yr period. The correlations between the 21-yr 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 persistent 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. It is shown that intensity distribution of TCs can be captured well by the model if the central sea level pressure is used as the threshold variable instead of the commonly used 10-m wind speed. This demonstrates 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.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We use the fvGFS model developed at the Geophysical Fluid Dynamics Laboratory 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 five‐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.
Plain Language Summary
When using European Centre for Medium‐Range Weather Forecasts initial conditions, a new global weather model built at NOAA's Geophysical Fluid Dynamics Laboratory produces better hurricane forecast skill than the world‐leading European model.
Key Points
The GFDL fvGFS model shows superior hurricane track forecasts than the world‐leading ECMWF model when using ECMWF's initial condition
The poor tropical cyclone intensity forecasts shown in both the operational GFS and the ECWMF model are significantly improved in the fvGFS
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.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Plain Language Summary
High‐resolution models (approximately 3 km grid spacing or finer) covering a large domain are emerging powerful tools for hurricane prediction. However, the use of high resolution in the model can be a double‐edged sword—while it helps improve hurricane intensity prediction, it can also make the model more prone to develop errors in the prediction of steering flow and hurricane tracks due to the possible impact of prevalent small‐scale features resolved by the model. Our results suggest that regulating small‐scale convection activity in a high‐resolution model can significantly improve hurricane track predictions at days 4 and 5.
Key Points
Better regulation of explicit convection reduces North Atlantic hurricane track errors in a high‐resolution model by 10% at days 4 and 5
Improved track forecasts are related to a more realistic representation of the North Atlantic subtropical ridge
Explicit convection is modulated by implicit diffusivity in the model's advection scheme
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.
Key Points
The GFDL HiRAM is used to predict the seasonal TC activity at 25‐km resolution
The interannual Atlantic hurricane counts is highly predictable during 2000–2010
A successful prediction of a MJO event initialized 7‐day in advance of its onset
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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 grid configurations: 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. Particularly, the 8‐km nested model shows improved skills in predicting major hurricane frequency and accumulated cyclone energy. 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 sea surface temperature 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.
Plain Language Summary
This study provides the first attempt to predict hurricane and especially major hurricane activity over a month using a global atmospheric model. We highlight the performance of a two‐way‐nested grid configuration, in which the model resolution is locally enhanced in the tropical North Atlantic. Such configuration is a computationally affordable way to permit better simulation of intense hurricanes and demonstrates promising skills in predicting the occurrence of major hurricanes and their wind energy over a month.
Key Points
For the first time prediction skill on anomalous monthly hurricane and major hurricane activity is demonstrated in a dynamical model
The 8‐km nest better resolves intense hurricanes and hence better predicts monthly major hurricane occurrence and accumulated cyclone energy
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