There were significant discrepancies in the intensity estimations of Super Typhoon Lekima (2019) among the China Meteorological Administration (CMA), the United States Joint Typhoon Warning Center (JTWC), and the Japan Meteorological Agency (JMA) data sets, with a maximum difference of over 12 m/s and 16 m/s between the JTWC data set and the CMA and JMA data sets, respectively. During the intensification phase, disagreement on the maximum sustained wind (MSW) between these agencies was due to the use of different conversion tables for the current intensity number (CI) estimated by Dvorak technique-MSW. In addition, CI discrepancies and different available observational data were also important contributors to the different intensities estimated during the Lekima’s decay phase before landfall. The ability of various methods to minimize these discrepancies was evaluated in this study. Both the linear factor multiplication method and the remapping method using the same CI-MSW conversion table have substantially abilities to reduce intensity discrepancies, with the latter method being more effective. However, these improvements only hold for the intensification phase in the ocean. The CMA data set had more complete and accurate intensity estimations when Lekima made landfall in China. After its landfall, the intensity estimate of the CMA was comparable to that of the JMA, which differed greatly from that of the JTWC.

The predictions for Super Typhoon Lekima (2019) have been evaluated from official forecasts, global models, regional models and ensemble prediction systems (EPSs) at lead times of 1–5 days. Track errors from most deterministic forecasts are smaller than their annual mean errors in 2019. Compared to the propagation speed, the propagation direction of Lekima (2019) was much easier to determine for the official agency and numerical weather prediction (NWP) models. The National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP-GEFS), Japan Meteorological Agency Global Ensemble Prediction System (JMA-GEPS) and Meteorological Service of Canada Ensemble System (MSC-CENS) are underdispersed, and the Shanghai Typhoon Institute Typhoon Ensemble Data Assimilation and Prediction System (STI-TEDAPS) is overdispersed, while the ensemble prediction system from European Centre for Medium-Range Weather Forecasts (ECMWF) shows adequate dispersion at all lead times. Most deterministic forecasting methods underestimated the intensity of Lekima (2019), especially for the rapid intensification period after Lekima (2019) entered the East China Sea. All of the deterministic forecasts performed well at predicting the first landfall point at Wenling, Zhejiang Province with a lead time of 24 and 48 h.

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 conti-guous 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.

In this paper, a revised method for typhoon precipitation probability forecast, based on the frequency-matching method, is developed by combining the screening and the neighborhood methods. The frequency of the high-resolution precipitation forecasts is used as the reference frequency, and the frequency of the low-resolution ensemble forecasts is used as the forecast frequency. Based on frequency–matching method, the frequency of rainfall above the rainstorm magnitude increases. The forecast members are then selected by using the typhoon tracks of the short-term predictions, and the precipitation probability is calculated for each member using a combination of the neighbor and the traditional probability statistical methods. Moreover, four landfalling typhoons (i.e., STY Lekima and STS Bailu in 2019, and TY Hagupit and Higos in 2020) were chose to test the rainfall probability forecast. The results show that the method performs well with respect to the forecast rainfall area and magnitude for the four typhoons. The Brier and Brier skill scores are almost entirely positive for the probability forecast of 0.1–250 mm rainfall during Bailu, Hagupit and Higos (except for 0.1mm of Hagupit), and for<100 mm rainfall (except for 25 mm) during Lekima.

As one of the most devastating tropical cyclones over the western North Pacific Ocean, Super Typhoon Lekima (2019) has caused a wide range of heavy rainfall in China. Based on the CMA Multi-source merged Precipitation Analysis System (CMPAS)-hourly data set, both the temporal and spatial distribution of extreme rainfall is analyzed. It is found that the heavy rainfall associated with Lekima includes three main episodes with peaks at 3, 14 and 24 h after landfall, respectively. The first two rainfall episodes are related to the symmetric outburst of the inner rainband and the persistence of outer rainband. The third rainfall episode is caused by the influence of cold, dry air from higher latitudes and the peripheral circulation of the warm moist tropical storm. The averaged rainrate of inner rainbands underwent an obvious outburst within 6 h after landfall. The asymmetric component of the inner rainbands experienced a transport from North (West) quadrant to East (South) quadrant after landfall which was related to the storm motion other than the Vertical Wind Shear (VWS). Meanwhile the outer rainband in the vicinity of three times of the Radius of Maximum Wind (RMW) was active over a 12-h period since the decay of the inner rainband. The asymmetric component of the outer rainband experienced two significant cyclonical migrations in the northern semicircle.

This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind Lidar (DWL). The data were collected as part of a field experiment in Zhoushan, Zhejiang Province (China), which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima (2019). The DWL measurements were compared with balloon-borne GPS radiosonde (GPS sonde) data, which were acquired using balloons launched from the DWL location. Results showed that wind speed measured by GPS sonde at heights of<100 m is unreliable owing to the drift effect. Optimal agreement (at heights of>100 m) was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m (correlation coefficient: 0.82; root mean square (RMS): 2.19 m·s⁻¹). Analysis revealed that precipitation intensity (PI) exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data; however, PI has minimal effect on the wind speed bias of DWL measurements. Specifically, the rate of missing DWL data increased with increasing measurement height and PI. For PI classed as heavy rain or less (PI<12 mm·h⁻¹), the DWL data below 300 m were considered valid, whereas for PI classed as a severe rainstorm (PI>90 mm·h⁻¹), only data below 100 m were valid. Up to the height of 300 m, the RMS of the DWL measurements was nearly half that of wind profile radar (WPR) estimates (4.32 m·s⁻¹), indicating that DWL wind data are more accurate than WPR data under typhoon conditions.

Gaofen-3 (GF-3) is the first Chinese spaceborne multi-polarization synthetic aperture radar (SAR) instrument at C-band (5.43 GHz). In this paper, we use data collected from GF-3 to observe Super Typhoon Lekima (2019) in the East China Sea. Using a VH-polarized wide ScanSAR (WSC) image, ocean surface wind speeds at 100m horizontal resolution are obtained at 21:56:59 UTC on 8 August 2019, with the maximum wind speed, 38.9 m·s⁻¹. Validating the SAR-retrieved winds with buoy-measured wind speeds, we find that the root mean square error (RMSE) is 1.86 m·s⁻¹, and correlation coefficient, 0.92. This suggests that wind speeds retrieved from GF-3 SAR are reliable. Both the European Centre for Medium-Range Weather Forecasts (ECMWF) fine grid operational forecast products with spatial resolution, and China Global/Regional Assimilation and Prediction Enhance System (GRAPES) have good performances on surface wind prediction under weak wind speed condition (<24 m·s⁻¹), but underestimate the maximum wind speed when the storm is intensified as a severe tropical storm (>24 m·s⁻¹). With respect to SAR-retrieved wind speeds, the RMSEs are 5.24 m·s⁻¹ for ECMWF and 5.17 m·s⁻¹ for GRAPES, with biases of 4.16 m·s⁻¹ for ECMWF and 3.84 m·s⁻¹ for GRAPES during Super Typhoon Lekima (2019).

Calibration error is one of the primary sources of bias in echo intensity measurements by ground-based radar systems. Calibration errors cause data discontinuity between adjacent radars and reduce the effectiveness of the radar system. The Global Precipitation Measurement Ku-band Precipitation Radar (GPM KuPR) has been shown to provide stable long-term observations. In this study, GPM KuPR observations were converted to S-band approximations, which were then matched spatially and temporally with ground-based radar observations. The measurements of stratiform precipitation below the melting layer collected by the KuPR during Typhoon Ampil were compared with those of multiple radar systems in the Yangtze River Delta to determine the deviations in the echo intensity between the KuPR and the ground-based radar systems. The echo intensity data collected by the ground-based radar systems was corrected using the KuPR observations as reference, and the correction results were verified by comparing them with rain gauge observations. It was found that after the correction, the consistency of the echo intensity measurements of the multiple radar systems improved significantly, and the precipitation estimates based on the revised ground-based radar observations were closer to the rain gauge measurements.

Typhoon Lekima (2019) struck Zhejiang Province on 10 August 2019 as a supertyphoon, which severely impacted Zhejiang Province. The typhoon killed 45 people and left three others missing, and the total economic loss reached 40.71 billion yuan. This paper reports a postdisaster survey that focuses on the storm precipitation, flooding, landslides, and weather services associated with Typhoon Lekima (2019) along the south-eastern coastline of Zhejiang Province. The survey was conducted by a joint survey team from the Shanghai Typhoon Institute and local meteorological bureaus from 26 to 28 August, 2019, approximately two weeks after the disaster. Based on this survey and subsequent analyses of the results, we hope to develop countermeasures to prevent future tragedies.

Why does the 1909 typhoon, Lekima, become so destructive after making landfall in China? Using a newly developed mathematical apparatus, the multiscale window transform (MWT), and the MWT-based localized mutliscale energetics analysis and theory of canonical transfer, this study is intended to give a partial answer from a dynamical point of view. The ECMWF reanalysis fields are first reconstructed onto the background window, the TC-scale window, and the convection-scale window. A localized energetics analysis is then performed, which reveals to us distinctly different scenarios before and after August 8–9, 2019, when an eyewall replacement cycle takes place. Before that, the energy supply in the upper layer is mainly via a strong upper layer-limited baroclinic instability; the available potential energy thus-gained is then converted into the TC-scale kinetic energy, with a portion to fuel Lekima’s upper part, another portion carried downward via pressure work flux to maintain the cyclone’s lower part. After the eyewall replacement cycle, a drastic change in dynamics occurs. First, the pressure work is greatly increased in magnitude. A positive baroclinic transfer almost spreads throughout the troposphere, and so does barotropic transfer; in other words, the whole air column is now both barotropically and baroclinically unstable. These newly occurred instabilities help compensate the increasing consumption of the TC-scale kinetic energy, and hence help counteract the dissipation of Lekima after making landfalls.

This study explores the effect of the initial axisymmetric wind structure and moisture on the predictability of the peak intensity of Typhoon Lekima (2019) through a 20-member ensemble forecast using the WRF model. The ensemble members are separated into Strong and Weak groups according to the maximum 10-m wind speed at 48 h. In our study of Lekima (2019), the initial intensity defined by maximum 10-m wind speed is not a good predictor of the intensity forecast. The peak intensity uncertainty is sensitive to the initial primary circulation outside the radius of maximum wind (RMW) and the initial secondary circulation. With greater absolute angular momentum (AAM) beyond the RMW directly related to stronger primary circulation, and stronger radial inflow, Strong group is found to have larger AAM import in low-level, helping to spin up the TC. Initial moisture in inner-core is also critical to the intensity predictability through the development of inner-core convection. The aggregation and merger of convection, leading to the TC intensification, is influenced by both radial advection and gradient of system-scale vortex vorticity. Three sensitivity experiments are conducted to study the effect of model uncertainty in terms of model horizontal grid resolution on intensity forecast. The horizontal grid resolution greatly impacts the predictability of Lekima’s intensity, and the finer resolution is helpful to simulate the intensification and capture the observed peak value.

In 2019, the operational Global Regional Assimilation and Prediction System-Tropical Cyclone Model (GRAPES-TCM) was updated by adopting the characteristic parameters in the official real-time released TC data of CMA, introducing the horizontal sixth-order diffusion scheme and adjusting the operational flowchart. In the case of the Super Typhoon Lekima, the model exhibits a reliable prediction ability for the type of tropical cyclone (TC) with northwestern tracking. The track and intensity forecasts in 2019 are significantly better than those over the past five years on average. The updated model can provide a skillful forecast of landfall position and rapid weakening process. Moreover, the precipitation pattern is close to the observation. TC forecast in 2019 shows that the updated GRAPES-TCM has a smaller track error than that of the previous year, and the 24 h intensity forecasting ability is improved.

The impact of vertical resolution on the simulation of Typhoon Lekima (2019) is investigated using the Weather Research and Forecasting (WRF) model version 3.8.1. Results show that decreasing vertical grid spacing from approximately 1000 m to 100 m above 1 km height barely influences the simulated track. However, significant differences are found in the simulated tropical cyclone (TC) structure. The simulation with the coarsest vertical resolution shows a clear double warm-core structure. The upper warm core weakens and even disappears with the increase of vertical resolution. A broader eye and a more slantwise eyewall are observed with the increase of vertical resolution due to the vertically extended lower-level and upper-level outflow, which likely results in a weaker subsidence. Vertical grid convergence is evaluated with the simulated kinetic energy (KE) spectra. As the vertical grid spacing becomes finer than 200 m, convergent KE spectra are found in both the free atmosphere and the outer core of the TC. However, sensitivity tests reveal that the grid convergence is sensitive to the choice of the planetary boundary layer scheme.

Estimating horizontal winds in and around typhoons is important for improved monitoring and prediction of typhoons and mitigating their damages. Here, we present a new algorithm for estimating typhoon winds using multiple satellite observations and its application to Typhoon Soulik (2018). Four kinds of satellite remote sensing data, along with their relationship to typhoon intensity, derived statistically from hundreds of historical typhoon cases, were merged into the final product of typhoon wind (MT wind): 1) geostationary-satellite-based infrared images (IR wind), 2) passive microwave sounder (MW wind), 3) feature-tracked atmospheric motion vectors, and 4) scatterometer-based sea surface winds (SSWs). The algorithm was applied to two cases (A and B) of Typhoon Soulik and validated against SSWs independently retrieved from active microwave synthetic aperture radar (SAR) and microwave radiometer (AMSR2) images, and vertical profiles of wind speed derived from reanalyzed data and dropsonde observations. For Case A (open ocean), the algorithm estimated the realistic maximum wind, radius of maximum wind, and radius of 15 m/s, which could not be estimated using the reanalysis data, demonstrating reasonable and practical estimates. However, for Case B (when the typhoon rapidly weakened just before making landfall in the Korean Peninsula), the algorithm significantly overestimated the parameters, primarily due to the overestimation of typhoon intensity. Our study highlights that realistic typhoon winds can be monitored continuously in real-time using multiple satellite observations, particularly when typhoon intensity is reasonably well predicted, providing timely analysis results and products of operational importance.

Tropical hurricanes are among the most devastating hazards on Earth. Knowledge about its intense inner-core structure and dynamics will improve hurricane forecasts and advisories. The precise morphological parameters extracted from high-resolution spaceborne Synthetic Aperture Radar (SAR) images, can play an essential role in further exploring and monitoring hurricane dynamics, especially when hurricanes undergo amplification, shearing, eyewall replacements and so forth. Moreover, these parameters can help to build guidelines for wind calibration of the more abundant, but lower resolution scatterometer wind data, thus better linking scatterometer wind fields to hurricane categories. In this paper, we develop a new method for automatically extracting the hurricane eyes from C-band SAR data by constructing Gray Level-Gradient Co-occurrence Matrices (GLGCMs). The hurricane eyewall is determined with a two-dimensional vector, generated by maximizing the class entropy of the hurricane eye region in GLGCM. The results indicate that when the hurricane is weak, or the eyewall is not closed, the hurricane eye extracted with this automatic method still agrees with what is observed visually, and it preserves the texture characteristics of the original image. As compared to Du’s wavelet analysis method and other morphological analysis methods, the approach developed here has reduced artefacts due to factors like hurricane size and has lower programming complexity. In summary, the proposed method provides a new and elegant choice for hurricane eye morphology extraction.

The risk of wind waves in a bay is often overlooked, owing to the belief that peninsulas and islands will inhibit high waves. However, during the passage of a tropical cyclone, a semi-enclosed bay is exposed to two-directional waves: one generated inside the bay and the other propagated from the outer sea. Typhoon Faxai in 2019 resulted in the worst coastal disaster in Tokyo Bay in the last few decades. The authors conducted a post-disaster survey immediately after this typhoon. Numerical modeling was also performed to reveal the mechanisms of unusual high waves. No significant high-wave damage occurred on coasts facing the Pacific Ocean. By contrast, Fukuura-Yokohama, which faces Tokyo Bay, suffered overtopping waves that collapsed seawalls. To precisely reproduce multi-directional waves, the authors developed an extended parametric typhoon model, which was embedded in the JMA mesoscale meteorological model (JMA-MSM). The peak wave height was estimated to be 3.4 m off the coast of Fukuura, in which the contribution of the outer-sea waves was as low as 10%–20%. A fetch-limited wave developed over a short distance in the bay is considered the primary mechanism of the high wave. The maximum wave occurred on the left-hand side of the typhoon track in the bay, which appears to be contrary to the common understanding that it is safer within the semicircle of a storm than on the opposite side. Typhoon Faxai was a small typhoon; however, if the radius was tripled, it is estimated that the wave height would exceed 3 m over the entire bay and surpass 4 m off the coasts of Yokohama and Chiba.