Seasonal forecasting of tropical cyclones over the Bay of Bengal using a hybrid statistical/dynamical model

Sabeerali, C. T.; Sreejith, O. P.; Acharya, Nachiketa; Surendran, Divya E.; Pai, D. S.

doi:10.1002/joc.7651

Cited by 5 publications

(7 citation statements)

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“…When two events occur at the same date, we consider only the closest to the BMD network using the spatial average across the 34 stations. The categories for the available 6,679 three‐hourly time steps between 1998 and 2019 are almost always (except two cases categorized as “low” and one case not categorized) at least “depression.” We merge the categories “depression” (maximum sustained wind 17–27 knots, i.e., 31–49 km·h −1 ) and “deep depression” (maximum sustained wind 28–33 knots, i.e., 50–61 km·h −1 ) into a single “depression” category and all other “cyclonic storms” (maximum sustained wind ≥34 knots, i.e., ≥ 62 km·h −1 ) categories as a single “cyclone” category (Sabeerali et al ., 2022).…”

Section: Methodsmentioning

confidence: 99%

Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events

Moron

Acharya

Hassan³

2022

Intl Journal of Climatology

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We explore the characteristics of 96190 wet events (WE) defined as consecutive 3-hourly rainfall >= 1 mm/3h from a network of 34 stations across Bangladesh. Nearly 60% (5%) of wet events last <= 3 (>= 15) hours. The WEs are dynamically clustered into four "canonical" storm types (ST), mostly discretized by their duration, but also their mean and maximal intensity. While durations, total amounts and wet contiguous areas of WEs are positively related, their mean intensity is nearly independent of them. ~ 60% of WEs are associated with ST#1, that is short and small WEs and very low rainfall amounts (usually < 10 mm), ~15% of WEs are associated with either (ST#2) short/small WEs but with intense rainfall, probably mostly related to scattered thunderstorms, or (ST#3) longer/larger WEs but with less intense rainfall. The last ST (ST#4) is rare (~ 6%), related to very long durations and large wet areas, and includes the wettest WEs. It is especially frequent over southeastern Bangladesh. ST#2 to #4 contribute almost equally to the local-scale total amount of rainfall (27-29% each in mean) while ST#1, despite its individual low rainfall amount, still includes ~ 15% of it. ST#2 (ST#4) is related to the highest probability of occurrence of 3-hourly (daily) extremes. ST#4 occurrence is the most impacted by synoptic Indian lows/depressions as well as the main modes of intraseasonal variation, while ST#1 and #2 are also significantly impacted by intraseasonal modes but in reverse manner than ST#4.

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Section: Methodsmentioning

confidence: 99%

Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events

Moron

Acharya

Hassan³

2022

Intl Journal of Climatology

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“…Seasonal forecasts of diverse hydroclimatic variables such as precipitation, evaporation, sea water level, sea level pressure or large-scale climate indices have also been used to drive ML models of precipitation (Madadgar et al, 2016), streamflow, and tropical cyclone activity (Sabeerali et al, 2022). For instance, atmosphere-ocean teleconnections obtained from the Initialization times are 0.5, 5.5 and 9.5 months ahead of the summer season.…”

Section: Sub-seasonal To Decadal Hybrid Forecastsmentioning

confidence: 99%

“…A study over the Netherlands using streamflow, precipitation, and evaporation found that the hybrid ML approach outperformed climatological reference forecasts by approximately 60% and 80% for streamflow and surface water level, respectively, using various machine learning models (Hauswirth et al, 2022). Another study employed predictions of large-scale indices by the CFSv2 model to predict the frequency of tropical cyclones in the Bay of Bengal using principal component regression (Sabeerali et al, 2022).…”

Section: Sub-seasonal To Decadal Hybrid Forecastsmentioning

confidence: 99%

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

Slater

Arnal²,

Boucher³

et al. 2022

Preprint

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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

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“…With the exception of few papers (Nath et al, 2015(Nath et al, , 2016Sabeerali et al, 2022), the majority of preceding studies have successfully documented the concurrent association between a number of large-scale meteorological factors and TC activity with limited work being put into developing the prediction models. For the mitigation and prevention of disasters, reliable seasonal forecasting is essential for predicting the number of seasonal TCs in NIO.…”

Section: Introductionmentioning

confidence: 99%

“…The forecast skill for MLP, GRNN and RBF‐based ANN models were found to be 0.82, 0.79 and 0.80, respectively. In a recent work, Sabeerali et al, 2022 developed a hybrid statistical/dynamical model for estimating seasonal TC frequency in BoB using CFSv2 model, with a 2‐month lead time.…”

Section: Introductionmentioning

confidence: 99%

Seasonal prediction of tropical cyclones activity in the North Indian Ocean during post‐monsoon months

Jaiswal,

Singh

2024

Intl Journal of Climatology

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The frequency and power dissipation index (PDI) of tropical cyclones (TCs) in the North Indian Ocean (NIO) has dramatically grown over the last 10 years, according to our analysis utilizing the European Centre for Medium Range Weather Forecasts reanalysis version‐5 (ERA5) dataset and the India Meteorological Department (IMD) best track data over the period 1982–2021. Our findings indicate that the recent increase in the post‐monsoon TC PDI over NIO is caused by a reduction in wind shear and an increase in convective available potential energy over the Bay of Bengal. The importance of improving our understanding and developing more accurate predictions of the TCs activity has increased as TCs become more frequent and intense owing to the effects of global warming. This study therefore develops TC prediction models for frequency and PDI over NIO for various lead times that vary from 2 to 8 months in addition to the trend analysis employing the ERA5 and IMD best track data. The potential predictors used in proposed model are surface temperature (2 m temperature over land and sea surface temperature over oceanic regions), vertical wind shear, winds (zonal, meridional), geopotential height and temperature at different pressure levels during January–August. The developed models are remarkably accurate (skill is ~94%) in forecasting TCs frequency and PDI. The proposed models outperform the best performing models that are already in use for long‐range TC activity prediction. With a lead‐time as long as up to 8 months, this effort is the first to investigate the possibility of forecasting the frequency and PDI of post‐monsoon TC activity over the NIO with good accuracy. Therefore, the developed models show an operational potential for seasonal TC activity forecast over the NIO.

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Seasonal forecasting of tropical cyclones over the Bay of Bengal using a hybrid statistical/dynamical model

Cited by 5 publications

References 50 publications

Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events

Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

Seasonal prediction of tropical cyclones activity in the North Indian Ocean during post‐monsoon months

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