Forecasting Hurricanes using Large-Ensemble Output

2020 ◽  
Author(s):  
Jonathan Lin ◽  
Kerry Emanuel ◽  
Jonathan Vigh
Keyword(s):  
Wind Field ◽  
Field Model ◽  
Weather Prediction ◽  
Surface Wind ◽  
Large Ensemble ◽  
Model Framework ◽  
Wind Field Model ◽  
Probabilistic Forecasts ◽  
Nwp Model ◽  
Surface Wind Field

<p>This paper describes the development of a model framework for Forecasts of Hurricanes using Large-ensemble Outputs (FHLO). Computationally inexpensive, FHLO quantifies the forecast uncertainty of a particular tropical cyclone (TC) through O(1000) ensemble members. The model framework consists of three components: (1) a track model that generates synthetic tracks from the TC tracks of an ensemble numerical weather prediction (NWP) model, (2) an intensity model that predicts the intensity along each synthetic track, and (3) a TC wind field model that estimates the time-varying twodimensional surface wind field. In this framework, we consider the evolution of a TC’s intensity and wind field as though it were embedded in a timeevolving environmental field. The environmental fields are derived from the forecast fields of ensemble NWP models, leading to probabilistic forecasts of track, intensity, and wind speed that incorporate the flow-dependent uncertainty. Each component of the model is evaluated using four years (2015- 2018) of TC forecasts in the Atlantic and Eastern Pacific basins. We show that the synthetic track algorithm can generate tracks that are statistically similar to those of the underlying global ensemble models. We show that FHLO produces competitive intensity forecasts, especially when considering probabilistic verification statistics. We also demonstrate the reliability and accuracy of the probabilistic wind forecasts. Limitations of the model framework are also discussed.</p>

scholarly journals Forecasts of Hurricanes Using Large-Ensemble Outputs

2020 ◽  
Vol 35 (5) ◽  
pp. 1713-1731
Author(s):  
Jonathan Lin ◽  
Kerry Emanuel ◽  
Jonathan L. Vigh
Keyword(s):  
Wind Field ◽  
Large Scale ◽  
Weather Prediction ◽  
Surface Wind ◽  
Large Ensemble ◽  
Model Framework ◽  
Wind Field Model ◽  
Probabilistic Forecasts ◽  
Large Ensembles ◽  
Nwp Model

AbstractThis paper describes the development of a model framework for Forecasts of Hurricanes Using Large-Ensemble Outputs (FHLO). FHLO quantifies the forecast uncertainty of a tropical cyclone (TC) by generating probabilistic forecasts of track, intensity, and wind speed that incorporate the state-dependent uncertainty in the large-scale field. The main goal is to provide useful probabilistic forecasts of wind at fixed points in space, but these require large ensembles [O(1000)] to flesh out the tails of the distributions. FHLO accomplishes this by using a computationally inexpensive framework, which consists of three components: 1) a track model that generates synthetic tracks from the TC tracks of an ensemble numerical weather prediction (NWP) model, 2) an intensity model that predicts the intensity along each synthetic track, and 3) a TC wind field model that estimates the time-varying two-dimensional surface wind field. The intensity and wind field of a TC evolve as though the TC were embedded in a time-evolving environmental field, which is derived from the forecast fields of ensemble NWP models. Each component of the framework is evaluated using 1000-member ensembles and four years (2015–18) of TC forecasts in the Atlantic and eastern Pacific basins. We show that the synthetic track algorithm generates tracks that are statistically similar to those of the underlying global ensemble models. We show that FHLO produces competitive intensity forecasts, especially when considering probabilistic verification statistics. We also demonstrate the reliability and accuracy of the probabilistic wind forecasts. Limitations of the model framework are also discussed.

scholarly journals Evaluation of Wave Forecasts Consistent with Tropical Cyclone Warning Center Wind Forecasts

2013 ◽  
Vol 28 (1) ◽  
pp. 287-294 ◽  
Author(s):  
Charles R. Sampson ◽  
Paul A. Wittmann ◽  
Efren A. Serra ◽  
Hendrik L. Tolman ◽  
Jessica Schauer ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Wind Field ◽  
Weather Prediction ◽  
Surface Wind ◽  
Track Forecast ◽  
Surface Winds ◽  
Wave Fields ◽  
Wavewatch Iii ◽  
Surface Wind Field

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.

Influence of the diagnostic wind field model on the results of calculation of the microscale atmospheric dispersion in moderately complex terrain

2013 ◽  
Vol 79 ◽  
pp. 29-35 ◽  
Author(s):  
Ivan V. Kovalets ◽  
Vladimir Y. Korolevych ◽  
Alexander V. Khalchenkov ◽  
Ievgen A. Ievdin ◽  
Mark J. Zheleznyak ◽  
...  
Keyword(s):  
Wind Field ◽  
Complex Terrain ◽  
Field Model ◽  
Atmospheric Dispersion ◽  
Wind Field Model

scholarly journals Parameter Estimation in a Three-dimensional Wind Field Model Using Genetic Algorithms

2002 ◽  
pp. 950-959 ◽  
Author(s):  
Eduardo Rodríguez ◽  
Gustavo Montero ◽  
Rafael Montenegro ◽  
José María Escobar ◽  
José María González-Yuste
Keyword(s):  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Wind Field ◽  
Field Model ◽  
Three Dimensional ◽  
Wind Field Model

scholarly journals High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

2015 ◽  
Vol 8 (8) ◽  
pp. 2645-2653 ◽  
Author(s):  
C. G. Nunalee ◽  
Á. Horváth ◽  
S. Basu
Keyword(s):  
High Resolution ◽  
Vortex Shedding ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wrf Model ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Nwp Model ◽  
Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

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