A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in Northwest Pacific

Tropical cyclones are amongst the most powerful and destructive meteorological systems on earth. In this paper, we propose a novel deep learning model for tropical cyclone track prediction method. Specifically, the track task is regarded as a time series predicting challenge, and then a deep learning framework by Bi-directional Gate Recurrent Unit network (BiGRU) with attention mechanism is developed for track prediction. This proposed model can excavate the effective information of the historical track in a deeper and more accurate way. Data exepriments are conducted on tropical cyclone best track data provided by the Joint Typhoon Warning Center (JTWC) from 1988 to 2017 in the Northwest Pacific. As results, our model performs well in tracks of 6h, 12h, 24h, 48h and 72h in the future. The prediction results show that our proposed combined model are superior to state-of-the-art deep learning models, include Recurrent Neural Network (RNN), Long Short-Term Memory neural network (LSTM), Gate Recurrent Unit network (GRU) and BiGRU without the use of attention mechanism. Compared with China Meteorological Administration (CMA), Japan Meteorological Agency (JMA) and Joint Typhoon Warning Center (JTWC), our method has obvious advantage in the mid- to long-term track forecasting, especially in the next 72 hours.

Comparison of ARCHER MPERC to NHC Analysis

Eyewall replacement cycles (ERCs) are events that occur in intense tropical cyclones (TCs) and are difficult to predict. An ERC event involves a secondary outer eyewall that surrounds the inner eyewall. The outer eyewall slowly moves towards the eye and weakens the inner eyewall, eventually replacing the inner eyewall. During this process, wind speeds lower and the structure of a TC becomes disorganized, further weakening the storm. TCs often restrengthen after an ERC. Little is known about the process and as such, poses an obstacle to forecasters. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) Microwave-based Probability of Eyewall Replacement Cycle (MPERC) is an algorithm that uses 89-95 GHz passive microwave imagery and intensity estimates from the National Hurricane Center (NHC), Central Pacific Hurricane Center (CPHC), or the Joint Typhoon Warning Center (JTWC) to predict the possibility of an ERC. The effectiveness and ability of ARCHER MPERC was analyzed and compared to the NHC’s official reports on all Atlantic Basin tropical cyclones from 2017 to 2019. MPERC ultimately predicted seventeen ERCs in nine tropical cyclones. Of those, seven were valid ERCs. The algorithm works well, predicting approximately 41% of the total number of predictions correctly. However, MPERC did not predict five ERCs that were cited by the NHC. It was further found that it was true that MPERC produces incorrect results in sheared and dry environments.

Opportunity for Early Warnings of Typhoon Lekima from Two Global Ensemble Model Forecasts of Formation with 7-Day Intensities along Medium-Range Tracks

Typhoon Lekima (2019) with its heavy rains and floods is an excellent example of the need to provide the earliest possible warnings of the formation, intensification, and subsequent track before a typhoon makes landfall along a densely populated coast. To demonstrate an opportunity to provide early (10 days in advance) warnings of the threat of Typhoon Lekima, the ensemble models from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Predictions have been used to provide time-to-formation timing and positions along the weighted-mean vector motion track forecasts. In addition, the seven-day intensity forecasts after the formation using a weighted analog intensity prediction technique are provided. A detailed description of one European Center ensemble forecast is provided to describe the methodology for estimating the formation time and generating the intensity forecasts. Validation summary tables of the formation timing and position errors, and the intensity errors versus the Joint Typhoon Warning Center intensities, are presented. The availability of these ensemble forecasts would have been an opportunity to issue alerts/watches/warnings of Lekima even seven days in advance of when Lekima became a Tropical Storm. These ensemble forecasts also represent an opportunity to extend support on the 5–15 day timescale for the decision-making processes of water resource management and hydrological operations.

Did COVID-19 Lockdown Brew “Amphan” into a Super Cyclone?

The world witnessed one of the largest lockdowns in the history of mankind ever, spread over months in an attempt to contain the contact spreading of the novel coronavirus induced COVID-19. As billions around the world stood witness to the staggered lockdown measures, a storm brewed up in the urns of the rather hot Bay of Bengal (BoB) in the Indian Ocean realm. When Thailand proposed the name “Amphan” (pronounced as “Um-pun” meaning ‘the sky’), way back in 2004, little did they realize that it was the christening of the 1st super cyclone (Category-5 hurricane) of the century in this region and the strongest on the globe this year. At the peak, Amphan clocked wind speeds of 168 mph (Joint Typhoon Warning Center) with the pressure drop to 925 h.Pa. What started as a depression in the southeast BoB at 00 UTC on 16th May 2020 developed into a Super Cyclone in less than 48 hours and finally made landfall in the evening hours of 20th May 2020 through the Sundarbans between West Bengal and Bangladesh. Did the impact of the COVID-19 induced lockdown drive an otherwise typical pre-monsoon tropical depression into a super cyclone?

A Tropical Cyclone Rapid Intensification Prediction Aid for the Joint Typhoon Warning Center’s Areas of Responsibility

In late 2017, the Rapid Intensification Prediction Aid (RIPA) was transitioned to operations at the Joint Typhoon Warning Center (JTWC). RIPA probabilistically predicts seven rapid intensification (RI) thresholds over three separate time periods: 25-, 30-, 35-, and 40-kt (1 kt ≈ 0.51 m s−1) increases in 24 h (RI25, RI30, RI35, RI40); 45- and 55-kt increases in 36 h (RI45 and RI55); and 70-kt increases in 48 h (RI70). RIPA’s probabilistic forecasts are also used to produce deterministic forecasts when probabilities exceed 40%, and the latter are included as members of the operational intensity consensus forecast aid. RIPA, initially designed for the western North Pacific, performed remarkably well in all JTWC areas of responsibility (AOR) and is now incorporated into JTWC’s ever improving suite of intensity forecast guidance. Even so, making real-time operational RIPA forecasts exposed some methodological weaknesses such as overprediction of RI for weak/disorganized systems (i.e., systems with maximum winds less than 35 kt), prediction of RI during landfall, input data reliability, and statistical inconsistencies. Modifications to the deterministic forecasts that address these issues are presented, and newly derived and more statistically consistent models are developed using data from all of JTWC’s AORs. The updated RIPA is tested as it would be run in operations and verified using a 2-yr (2018–19) independent sample. The performance from this test indicates the new RIPA—when compared to its predecessor—has improved probabilistic verification statistics, and similar deterministic skill while using fewer predictors to make forecasts.

Characteristics of Typhoon Eyewalls According to World Wide Lightning Location Network Data

Methods for the estimation of typhoon eyewall characteristics (the center location, the radius and the width, and radii of inner and outer boundaries) based on World Wide Lightning Location Network (WWLLN) data are presented and discussed in this work. The center locations, the eyewall radii, and inner boundary radii estimated from WWLLN data for the typhoons of the northwestern Pacific from 2011 to 2015 were compared with the typhoon centers, radii of maximum winds, and the radii of the eyes obtained from Advanced Scatterometer (ASCAT) wind data, the Japan Meteorological Agency (JMA) archives, and the Joint Typhoon Warning Center (JTWC) archives. It is shown that the eyewall characteristics estimates based on the lightning discharge data are most closely related to characteristics of the ASCAT wind speed fields, and the radii of the eyewalls and their inner boundaries are linearly related to the radii of maximum winds and the radii of the eyes, with correlation coefficients reaching approximately 0.9 and 0.8, respectively. It was shown that the distances between locations of the eyewalls and typhoon centers estimated according to the WWLLN and those of the ASCAT, JMA, and JTWC data on average were 19, 16, and 17 km, respectively. The eyewall widths varied from 15 to 69 km, with an average of ~30 km.

Statistical Regression Scheme for Intensity Prediction of Tropical Cyclones in the Northwestern Pacific

This study proposes a statistical regression scheme to forecast tropical cyclone (TC) intensity at 12, 24, 36, 48, 60, and 72 h in the northwestern Pacific region. This study utilizes best track data from the Shanghai Typhoon Institute (STI), China, and the Joint Typhoon Warning Center (JTWC), United States, from 2000 to 2015. In addition to conventional factors involving climatology and persistence, this study pays close attention to the land effect on TC intensity change by considering a new factor involving the ratio of seawater area to land area (SL ratio) in the statistical regression model. TC intensity changes are investigated over the entire life-span, over the open ocean, near the coast, and after landfall. Data from 2000 to 2011 are used for model calibration, and data from 2012 to 2015 are used for model validation. The results show that the intensity change during the previous 12 h (DVMAX), the potential future intensity change (POT), and the area-averaged (200–800 km) wind shear at 1000–300 hPa (SHRD) are the most significant predictors of the intensity change for TCs over the open ocean and near the coast. Intensity forecasting for TCs near the coast and over land is improved with the addition of the SL ratio compared with that of the models that do not consider the SL ratio. As this study has considered the TC intensity change over the entire TC life-span, the proposed models are valuable and practical for forecasting TC intensity change over the open ocean, near the coast, and after landfall.

Tropical Cyclone Gale Wind Radii Estimates, Forecasts, and Error Forecasts for the Western North Pacific

In 2016, the Joint Typhoon Warning Center extended forecasts of gale-force and other wind radii to 5 days. That effort and a thrust to perform postseason analysis of gale-force wind radii for the “best tracks” (the quality controlled and documented tropical cyclone track and intensity estimates released after the season) have prompted requirements for new guidance to address the challenges of both. At the same time, operational tools to estimate and predict wind radii continue to evolve, now forming a quality suite of gale-force wind radii analysis and forecasting tools. This work provides an update to real-time estimates of gale-force wind radii (a mean/consensus of gale-force individual wind radii estimates) that includes objective scatterometer-derived estimates. The work also addresses operational gale-force wind radii forecasting in that it provides an update to a gale-force wind radii forecast consensus, which now includes gale-force wind radii forecast error estimates to accompany the gale-force wind radii forecasts. The gale-force wind radii forecast error estimates are computed using predictors readily available in real time (e.g., consensus spread, initial size, and forecast intensity) so that operational reliability and timeliness can be ensured. These updates were all implemented in operations at the Joint Typhoon Warning Center by January 2018, and more updates should be expected in the coming years as new and improved guidance becomes available.

An Operational Rapid Intensification Prediction Aid for the Western North Pacific

This work describes tropical cyclone rapid intensification forecast aids designed for the western North Pacific tropical cyclone basin and for use at the Joint Typhoon Warning Center. Two statistical methods, linear discriminant analysis and logistic regression, are used to create probabilistic forecasts for seven intensification thresholds including 25-, 30-, 35-, and 40-kt changes in 24 h, 45- and 55-kt in 36 h, and 70-kt in 48 h (1 kt = 0.514 m s−1). These forecast probabilities are further used to create an equally weighted probability consensus that is then used to trigger deterministic forecasts equal to the intensification thresholds once the probability in the consensus reaches 40%. These deterministic forecasts are incorporated into an operational intensity consensus forecast as additional members, resulting in an improved intensity consensus for these important and difficult to predict cases. Development of these methods is based on the 2000–15 typhoon seasons, and independent performance is assessed using the 2016 and 2017 typhoon seasons. In many cases, the probabilities have skill relative to climatology and adding the rapid intensification deterministic aids to the operational intensity consensus significantly reduces the negative forecast biases.

Structure Analysis of Tropical Cyclone Hudhud Over Bay of Bengal Using WRF Model

Tropical cyclones (TCs) over Bay of Bengal (BoB) have significant socio-economic impacts on the countries bordering the BoB. In this study, we have examined the structure and thermodynamic features of the TC Hudhud (7th -14th October, 2014) using WRF model. Simulated outputs are in good agreement with the available observations of India Meteorological Department and Joint Typhoon warning Center. At maximum intensity stage, the system’s horizontal size is found around 690 km. Wind and vorticity distributions capture the circulation of the system very well. Most strong winds of 60 ms−1 are extended vertically from 850 hPa to about 700 hPa. Simulation has shown intensification of the system above 200 hPa with wind speed of about 30ms−1. Relative humidity of the order of 90 % is found up to 400 hPa. Dhaka Univ. J. Sci. 66(1): 79-86, 2018 (January)