Convective-scale ensemble forecasts of the heavy precipitation of Typhoon Lekima (2019) in Zhejiang Province

This study explores the impact of assimilating radar radial velocity (RV) on the forecast of Super Typhoon Lekima (2019) using the Weather Research and Forecasting (WRF) model and three-dimensional variational (3DVAR) assimilation system with different background error length scales. The results of two single observation tests show that the smaller background error length scale is able to constrain the spread of radar observation information within a relatively reasonable range compared with the larger length scale. During the five data assimilation cycles, the position and structure of the near-land typhoon are found to be significantly affected by the setting of the background error length scale. With a reduced length scale, the WRF-3DVAR system could effectively assimilate the radar RV to produce more accurate analyses, resulting in an enhanced typhoon vortex with a dynamic and thermal balance. In the forecast fields, the experiment with a smaller length scale not only reduces the averaged track error for the 24-h forecasts to less than 20 km, but it also more accurately captures the evolutions of the typhoon vortex and rainband during typhoon landing. In addition, the spatial distribution and intensity of heavy precipitation are corrected. For the 24-h quantitative precipitation forecasts, the equitable threat scores of the experiment with a reduced length scale are greater than 0.4 for the threshold from 1 to 100 mm and not less than 0.2 until the threshold increases to 240 mm. The enhanced prediction performances are probably due to the improved TC analysis.

/pdf/the-impact-of-radar-radial-velocity-data-assimilation-using-3hbebz1b.pdf

The Impact of Radar Radial Velocity Data Assimilation Using WRF-3DVAR System with Different Background Error Length Scales on the Forecast of Super Typhoon Lekima (2019)


The Typhoon Landfall Forecast Demonstration Project (TLFDP) (2010–2022) was an international cooperative scientific project conducted under the framework of the WMO. The primary objectives of the TLFDP were to enhance the capability of tropical cyclone (TC) forecasters, and support related decision-makers in effective utilization of the most advanced forecasting techniques for the ultimate purpose of reducing and preventing disasters associated with TC landfall. Forty agencies/organizations/projects globally participated in the activities of the TLFDP following its inception in 2010, although the primary focus was on landfalling TCs in the western North Pacific. The TLFDP facilitated collaborations and workshops that realized notable achievements in four key areas: 1) the collection, production, and sharing of TC data; 2) the development and application of TC forecast verification metrics; 3) research on TC forecast skill; and 4) development of new techniques for TC forecasting. An obvious outcome was the shift from prediction of TC features, including track and intensity, toward prediction of TC impacts with more probabilistic conception. The final years of the project also promoted increasing application of artificial intelligence and machine learning techniques in various techniques for analysis and forecasting of TCs. Although the TLFDP ended in 2022, its core activities have continued to be extended through new WMO projects and regional cooperative initiatives.

WMO Typhoon Landfall Forecast Demonstration Project (2010–2022): A Decade of Transition from Track Forecasts to Impact Forecasts

Abstract An accurate center localization in near real‐time is critical for tropical cyclone (TC) monitoring and forecasting. This study presents a robust algorithm for localizing typhoon centers using the Chinese geostationary (GEO) meteorological satellite. The results using the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun‐4A (FY‐4A) satellite data, achieving a mean absolute error (MAE) of 29.4 km across various typhoon intensities in the Western North Pacific, superior to other baseline methods. By harnessing the multi‐spectral imagery from the FY‐4A and incorporating an attention mechanism, it significantly boosts the deep learning convolutional neural network's ability to identify typhoon cloud features and their centers, even during their initial and weakest stages, which is laudable because these are the most difficult for center fixing even for human analysts. Remarkably, it requires just a single moment satellite imagery to locate the center of typhoon, enabling automated updates of the typhoon centers in near real‐time applications. 

Enhanced Typhoon Center Localization Using Geostationary Satellite Imagery

Held every four years, the International Workshop on Tropical Cyclone (IWTC) organized by the World Meteorological Organization has been a global leading conference in the field of tropical cyclone. In preparation for the 10th IWTC (IWTC-10) in December 2022, a summary of research advances of landfalling tropical cyclone (LTC) rainfall during past four years of 2019-2022 has been prepared. Some of the latest research advances has been summarized in Lamers et al. (2023), which reviewed the latest forecast and disaster prevention methods related to TC precipitation. As a supplement, this article mainly focuses on the recent advances in LTC asymmetric rainfall evolution mechanisms and forecast verification results over China. Some new findings have been made in the LTC inner-core size relationship with the asymmetric rainfall distribution. Some major advances focused on asymmetric microphysical characteristics in the TC rainbands. Current simulation and forecast performances of LTC precipitation have been analyzed, and different forecast error sources for rainfall during different landfall stages of TC were compared. To estimate the risk of TC rainfall hazards in China, a parameterized Tropical Cyclone Precipitation Model was reviewed as well in this article. 

Recent Advances in Landfalling Tropical Cyclone Asymmetric Rainfall Mechanism and Forecast Verification over China

The rainfall forecast performance of the Tropical Cyclone (TC) version Model of Global and Regional Assimilation PrEdiction System (GRAPES-TCM) of the China Meteorological Administration for landfalling Super Typhoon Lekima (2019) is studied by using the object-oriented verification method of contiguous rain area (CRA). The major error sources and possible reasons for the rainfall forecast uncertainties in different landfall stages (including near landfall and moving further inland) are compared. Results show that different performance and errors of rainfall forecast exist in the different TC stages. In the near landfall stage the asymmetric rainfall distribution is hard to be simulated, which might be related to the too strong forecasted TC intensity and too weak vertical wind shear accompanied. As Lekima moves further inland, the rain pattern and volume errors gradually increase. The Equitable Threat Score of the 24 h forecasted rainfall over 100 mm declines quickly with the time-length over land. The diagnostic analysis shows that there exists an interaction between the TC and the mid-latitude westerlies, but too weak frontogenesis is simulated. The results of this research indicate that for the current numerical model, the forecast ability of persistent heavy rainfall is very limited, especially when the weakened landing TC moves further inland.

Rainfall forecast errors in different landfall stages of Super Typhoon Lekima (2019)

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Rainfall forecast errors in different landfall stages of Super Typhoon Lekima (2019)