scholarly journals Probabilistic Prediction of Tropical Cyclones. Part I: Position

2005 ◽  
Vol 133 (7) ◽  
pp. 1840-1852 ◽  
Author(s):  
Harry C. Weber
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Probabilistic Prediction ◽  
Automatic Production ◽  
Position Errors ◽  
Prediction Time ◽  
Forecasting System ◽  
The Mean ◽  
Geographical Maps

Abstract A new objective aid for operational prediction of the positions of tropical cyclones is presented. Its method is based on a simple analysis of the performance of all operationally available numerical models during a training period. In a subsequent forecast period, the results of this analysis are used to produce both high-quality deterministic (as by-product) and probabilistic storm position forecasts in the form of geographical maps of strike probability for all prediction times out to 120 h from a given base date and time. The model was developed using operationally available position predictions of all global tropical-cyclone events of the years 2000, 2001, and 2002 as provided in the U.S. Navy’s Automated Tropical Cyclone Forecasting System (ATCF). Forecasts have been carried out for the years 2001 and 2002, with corresponding training periods 2000 and 2001. The global annual mean deterministic position errors for the years 2001 (2002) were 148 (136), 266 (235), 393 (329), 541 (435), and 733 (554) km at 24-, 48-, 72-, 96-, and 120-h prediction times, respectively. The deterministic mean errors and their corresponding standard deviations were found to be lower than those of most statistical and dynamical operational models and approximately equal to those of all consensus approaches currently in operational use. The main feature of the new method is the automatic production of geographical strike probability maps. For all tropical-cyclone events during the year 2001 (2002), the mean annual diameters of, for example, the 66% strike probability regions (i.e., the regions inside which future storm positions can be expected with a probability of 66%) at 24-, 48-, 72-, 96-, and 120-h prediction time were found to be 290 (280), 490 (480), 710 (660), 1020 (780), and 1310 (1020) km, respectively. At all prediction times, the predicted sizes of areas of given strike probability represent conservative estimates in that the observed percentages of storm positions inside these areas are larger than the corresponding expected percentages.

scholarly journals Probabilistic Prediction of Tropical Cyclones. Part II: Intensity

2005 ◽  
Vol 133 (7) ◽  
pp. 1853-1864 ◽  
Author(s):  
Harry C. Weber
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Probabilistic Prediction ◽  
Wind Speed Prediction ◽  
Probabilistic Forecasts ◽  
Prediction Time ◽  
Speed Prediction ◽  
Forecasting System

Abstract A new objective aid for operational probabilistic intensity (defined as maximum wind speed) prediction of tropical cyclones is presented. Based on statistical analyses of the performance of all operationally available numerical models (using datasets of the U.S. Navy’s Automated Tropical Cyclone Forecasting System) during training periods defined by the years 2000 and 2001, probabilistic and, as a by-product, deterministic intensity predictions were carried out for all global tropical-cyclone events during subsequent forecast periods defined by the years 2001 and 2002, respectively. The annual mean deterministic intensity errors of the years 2001 (2002) at 24-, 48-, 72-, 96-, and 120-h prediction time were found to be 6.2 (6.5), 9.6 (10.6), 11.7 (12.4), 15.4 (15.3), and 17.2 (17.1) m s−1, respectively. On average, the deterministic forecasts were of approximately the same quality as those of all current consensus approaches and of superior quality than those of the majority of all operational dynamical models. The quality of the probabilistic forecasts, provided in the form of intensity probability intervals at given prediction times, was assessed by the annual mean sizes of given probability intervals. For example, in the years 2001 (2002) the annual mean sizes of the 66% confidence intervals at 24-, 48-, 72-, 96-, and 120-h prediction times were found to be 12.6 (13.3), 19.7 (21.5), 24.4 (24.8), 39.6 (27.8), and 40.4 (29.0) m s−1, respectively. Postanalyses showed that the sizes of all intensity probability intervals represented conservative and reliable estimates of future storm intensities in that the observed percentages of storm intensities inside given intervals were larger than the corresponding expected percentages.

scholarly journals Machine Learning in Tropical Cyclone Forecast Modeling: A Review

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 676
Author(s):  
Rui Chen ◽  
Weimin Zhang ◽  
Xiang Wang
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Weather Forecasts ◽  
Dynamic Mechanisms ◽  
Forecast Models ◽  
Strong Winds ◽  
The Many ◽  
Axisymmetric Structures

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

scholarly journals A direct aircraft observation of helical rolls in the tropical cyclone boundary layer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Tang ◽  
Jun A. Zhang ◽  
Pakwai Chan ◽  
Kaikwong Hon ◽  
Xiaotu Lei ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Correlation Method ◽  
Outer Core ◽  
Eddy Correlation ◽  
Turbulence Structure ◽  
Planetary Boundary Layer Parameterization ◽  
Tropical Cyclone Boundary Layer

AbstractHelical rolls are known to play a significant role in modulating both the mean and turbulence structure of the atmospheric boundary layer in tropical cyclones. However, in-situ measurements of these rolls have been limited due to safety restrictions. This study presents analyses of data collected by an aircraft operated by the Hong Kong Observatory in Typhoon Kalmaegi (1415) and Typhoon Nida (1604). Examination of the flight-level data at ~ 600 m altitude confirmed the existence of sub-kilometer-scale rolls. These rolls were mostly observed in the outer-core region. Turbulent momentum fluxes were computed using the eddy correlation method. The averaged momentum flux of flight legs with rolls was found to be ~ 2.5 times that of legs without rolls at a similar wind speed range. This result suggests that rolls could significantly modulate turbulent transfer in the tropical cyclone boundary layer. This roll effect on turbulent fluxes should be considered in the planetary boundary layer parameterization schemes of numerical models simulating and forecasting tropical cyclones.

scholarly journals ECMWF Activities for Improved Hurricane Forecasts

2019 ◽  
Vol 100 (3) ◽  
pp. 445-458 ◽  
Author(s):  
L. Magnusson ◽  
J.-R. Bidlot ◽  
M. Bonavita ◽  
A. R. Brown ◽  
P. A. Browne ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Weather Forecasts ◽  
Research Activities ◽  
Ocean Coupling ◽  
Hurricane Season ◽  
Medium Range ◽  
Future Challenges ◽  
Forecasting System ◽  
The Impact

AbstractTropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.

scholarly journals DSAEF_LTP Model Experiment to Forecast the Accumulated Precipitation of Landfalling Northward-Moving Typhoons in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Mei Yao ◽  
Yunqi Ma ◽  
Li Jia ◽  
Fumin Ren ◽  
Guoping Li ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Distribution Patterns ◽  
Vortex Identification ◽  
Initial Value ◽  
Forecast Performance ◽  
The North ◽  
Training Samples ◽  
Area North

We designed two groups of experiments to test the forecast performance of the Dynamical-Statistical-Analog Ensemble Forecast (DSAEF_LTP) model for precipitation caused by landfalling northward-moving typhoons. The first group DSAEF_LTP-1 had the generalized initial value containing three factors (tropical cyclone track, landfall season and tropical cyclone intensity) while the second group DSAEF_LTP-2 added multiple choices of similarity regions. We selected 33 typhoons that brought about maximum daily precipitation ≥100 mm to the area north of the Yangtze River from 2004–2019. We used 22 tropical cyclones from 2004–2015 as training samples to identify the best scheme, which was then used to conduct independent sample forecasting experiments for 11 tropical cyclones from 2016–2019. The results were compared with those of four numerical models (ECMWF, GFS, GRAPES and SMS-WARMS). The simulation ability of the DSAEF_LTP model was significantly improved after adding the similarity regions. The TSsum (TS250 + TS100) for accumulated precipitation ≥250 and ≥100 mm increased from 0.1239 (0 + 0.1239) to 0.1883 (0.0526 + 0.1357). The forecast performance of the DSAEF_LTP for TS100 was 0.1355 for DSAEF_LTP-1 and 0.099 for DSAEF_LTP-2 . Both exceeded the scores for two of the operational Numerical Models, GRAPES (0.0798) and SMS-WARMS (0.0943). The DSAEF_LTP model can capture the distribution patterns of the observed precipitation in most cases. The forecasting performance was good over the southern coast of China but was limited in the north. The development of vortex identification technology for residual vortices and the introduction of new environmental factors into the generalized initial value are required to improve the DSAEF_LTP model.

scholarly journals The use of the Conway-Maxwell-Poisson in the seasonal forecasting of tropical cyclones

2021 ◽  
Author(s):  
Timothy D. Mitchell ◽  
Joanne Camp
Keyword(s):  
Tropical Cyclone ◽  
Poisson Process ◽  
Tropical Cyclones ◽  
Dispersion Characteristic ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Mean And Variance ◽  
The Mean ◽  
Over Dispersion ◽  
Tropical Cyclone Landfall

AbstractThe Conway-Maxwell-Poisson distribution improves the precision with which seasonal counts of tropical cyclones may be modelled. Conventionally the Poisson is used, which assumes that the formation and transit of tropical cyclones is the result of a Poisson process, such that their frequency distribution has equal mean and variance (‘equi-dispersion’). However, earlier studies of observed records have sometimes found over-dispersion, where the variance exceeds the mean, indicating that tropical cyclones are clustered in particular years. The evidence presented here demonstrates that at least some of this over-dispersion arises from observational inhomogeneities. Once this is removed, and particularly near the coasts, there is evidence for equi-dispersion or under-dispersion. In order to more accurately model numbers of tropical cyclones, we investigate the use of the Conway-Maxwell-Poisson as an alternative to the Poisson that represents any dispersion characteristic. An example is given for east China where using it improves the skill of a prototype seasonal forecast of tropical cyclone landfall.

scholarly journals Seasonal Tropical Cyclone Rain Volumes over Australia

2013 ◽  
Vol 26 (16) ◽  
pp. 5958-5964 ◽  
Author(s):  
Richard A. Dare
Keyword(s):  
Tropical Cyclone ◽  
Interannual Variability ◽  
Tropical Cyclones ◽  
Correlation Coefficients ◽  
Rainfall Data ◽  
Valuable Resource ◽  
Land Area ◽  
The Mean ◽  
Two Parameters

Abstract For a continent as dry as Australia, where water is a valuable resource, it is important to understand the sources of rainfall. The volume of water contributed by tropical cyclones (TCs) during the November–April season is investigated using 42 seasons of TC and rainfall data. The seasonal total TC rain volume (SRV) ranges from a minimum of 34.2 km3 in 1987/88 to a maximum of 564.4 km3 in 2000/01, with a long-term mean of 198.6 ± 107.4 km3. In terms of mean percentage, TCs contribute 7.6% to the seasonal total rain volume over Australia. The number of landfalling TCs and the number of TCs that individually produce more than the mean individual TC rain volume (25.7 km3) during a season are significant influences on the SRV. The TCs passing near the coast without landfalling have little impact on the SRV. The two parameters that correlate best with SRV are the total time spent over land by TCs during a season and the total land area covered by TCs during a season (correlation coefficients of 0.79 and 0.84, respectively). Although the highest SRVs occur almost exclusively during La Niña and neutral seasons, there is a mixture of ENSO seasons corresponding to the lowest SRVs. In general, the large interannual variability, even within a particular ENSO class, indicates that caution should be used when attempting to relate SRV to the phase of ENSO.

Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

2003 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  
Keyword(s):  
Real Time ◽  
Tropical Cyclones ◽  
Forecasting System

scholarly journals Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamish Steptoe ◽  
Nicholas Henry Savage ◽  
Saeed Sadri ◽  
Kate Salmon ◽  
Zubair Maalick ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Tropical Cyclones ◽  
Weather Forecasting ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Medium Range ◽  
Gust Speed

AbstractHigh resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to −27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from 10.5281/zenodo.3600201.

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