Bureau Research Report

Greenslade, Diana; Taylor, Andy; Freeman, Justin; Sims, Holly; Schulz, Eric J.; Colberg, Frank; Divakaran, Prasanth; Velic, Mirko; Kepert, Jeffrey D.

doi:10.22499/4.0031

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…On one hand, it can be observed a differentiation of predominantly active DWTs in consecutive IPO phases, most persistent during the off‐season months (May–October) as well as in transitional months (November–December and March–April). Salinger et al (2001) also showed that the IPO represents a major source of decadal climate variability in the SWP, which is consistent with the findings of Greenslade and Gregory (2023) who showed that before and after 2000 the relationship between TC activity and several climate indices in the SWP changed significantly. On the other hand, at the interannual time scale, ENSO has been proven to modulate climate variability through the equatorial SST gradient and thus influencing TC activity.…”

Section: Time‐scales Variabilitysupporting

confidence: 73%

Seasonal forecast of tropical cyclones in the Southwest Pacific Ocean

van Vloten,

Pozo,

Cagigal

et al. 2023

Intl Journal of Climatology

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Predictions of tropical cyclone (TC) activity have been a topic of recurrent interest and research in the past. Here we utilize reanalysis datasets of sea surface temperature (SST) and mixed layer depth (MLD) to build a statistical seasonal forecasting model that produces outlooks of expected TC counts in the region of the Southwest Pacific (SWP). Nevertheless, the model applicability can be extended to other regions and basins. A novel TC predictor index is developed at the daily scale and used to obtain an objective classification of synoptic weather patterns. This classification has been performed by clustering the daily index predictor fields, previously transformed into principal components, using a K‐mean algorithm. As a result, 49 daily weather types (DWTs) are presented which inform about the mean representative features and spatial patterns of both predictor and predictand variables. Thus, statistical relationships between TC activity and nonlinear combinations of predictor variables are found to assign daily rates of expected TCs. The cluster‐based model is calibrated from 1982 to 2019 and validated by recent TC season observations, demonstrating the operational application using ensembles of long‐term predictions in the Southwest Pacific. Results have shown which synoptic types of SST and MLD are favourable to cyclogenesis and activity, with additional information related to concurrent sea level pressure and precipitation synoptic patterns, as well as seasonal and interannual climate variability.

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Section: Time‐scales Variabilitysupporting

confidence: 73%

Seasonal forecast of tropical cyclones in the Southwest Pacific Ocean

van Vloten,

Pozo,

Cagigal

et al. 2023

Intl Journal of Climatology

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show abstract

“…Out of this ensemble, 200 tracks are selected, and these drive a 200‐member storm surge model ensemble. This results in forecasts for a worst‐case scenario (98th percentile surge height) and a most likely surge forecast based on the official forecast track (Greenslade et al., 2017). Similarly, the US National Weather Service uses a probabilistic TC storm surge (P‐Surge) model to assess storm surge risk.…”

Section: Introductionmentioning

confidence: 99%

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

Rezuanul Islam,

Duc,

Sawada

2023

JGR Atmospheres

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Ensemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

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“…Dullaart et al [40] examined the performance of global surge simulation forced by the climate reanalysis datasets, and showed that the accuracy of storm surge simulation depends on whether the reanalysis data can accurately describe the intensities of tropical cyclones (10 m winds and mean pressure). In order to overcome the problem of underestimating tropical cyclone intensities via reanalysis data, the meteorological forcing derived from parametric tropical cyclone models is often applied to drive storm surge models [41,42], such as a symmetric wind model- Holland (1980) [43]-some asymmetric wind field models (such as the generalized asymmetric Holland model, GAHM [44], the Willoughby model [45] and the Ueno model [46]). In addition, the Hurricane wind (H*wind) [47] and Weather Research and Forecasting for Hurricanes (HWRF) models [48] are also effective when it comes to improving the quality of typhoon wind field and further improving the accuracy of storm surge simulation.…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

Yang

Ren

et al. 2022

Remote Sensing

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Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively makes up for the uneven temporal and spatial distribution of satellite remote sensing and in situ data. However, the reanalysis data cannot accurately describe characteristic parameters of tropical cyclones, especially in high wind conditions. In this paper, the performance of the tropical cyclone representation in ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation) is investigated and analyzed with respect to IBTrACS (International Best Track Archive for Climate Stewardship) during the period 2018–2020. Comparisons demonstrate that ERA5 winds significantly underestimate the maximum wind speed during the tropical cyclones (>30 m/s) compared to those provided by IBTrACS. An effective wind reconstruction method is examined to enhance tropical cyclone intensity representation in reanalysis data in 94 cases of 31 tropical cyclones 2018–2020. The reconstructed wind speeds are in good agreement with the SFMR (Stepped Frequency Microwave Radiometer) measured data and SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The proposed wind reconstruction method can effectively improve the accuracy of the tropical cyclone representation in ERA5, and will benefit from the establishment of remote sensing satellite retrieval model and the forcing fields of the ocean model.

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Bureau Research Report

Cited by 3 publications

References 20 publications

Seasonal forecast of tropical cyclones in the Southwest Pacific Ocean

Seasonal forecast of tropical cyclones in the Southwest Pacific Ocean

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer

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