Tropical Cyclone Center Research Articles

Identification of tropical cyclone centre based on satellite images via deep learning techniques

AbstractA tropical cyclone (TC) is a highly destructive natural disaster. Accurate identification of key parameters of TCs is prerequisite for most TC‐related research and practices. The centre position is one of TC's basic parameters. However, comparison of TC best track data released by different meteorological institutes usually indicates a noticeable discrepancy for this parameter among varied data sources. In this study, efforts are made towards identifying the centre location of TCs via deep learning techniques, based on TC satellite cloud images (SCIs). Six deep learning models are analysed and compared. YOLOv4 model achieved a confidence of 99.84%, which is better than other models. In addition, we further explore the factors affecting the positioning accuracy of the YOLOv4 model and its application to the location identification of multiple TCs and the tracking of individual TCs. Results demonstrate that the YOLOv4 model has a probability exceeding 99% for identifying multiple TC locations and also performs well for single TC tracking.

Introducing TC Translation Speed into the Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation Model and Simulating the Daily Precipitation of Supertyphoon Lekima (2019)

Abstract The Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation (DSAEF_LTP_D) model is introduced in this paper. To improve the DSAEF_LTP_D model’s forecasting ability, tropical cyclone (TC) translation speed was introduced. Taking Supertyphoon Lekima (2019), which produced widespread heavy rainfall from 9 to 11 August 2019 as the target TC, two simulation experiments associated with the prediction of daily precipitation were conducted: the first involving the DSAEF_LTP_D model containing only the TC track (the actual trajectory of the TC center), named DSAEF_LTP_D-1; and the second containing both TC track and translation speed, named DSAEF_LTP_D-2. The results show the following: 1) With TC translation speed added into the model, the forecasting performance for heavy rainfall (24-h accumulated precipitation exceeding 50 and 100 mm) on 9 and 10 August improves, being able to successfully capture the center of heavy rainfall, but the forecasting performance is the same as DSAEF_LTP_D-1 on 11 August. 2) Compared with four numerical weather prediction (NWP) models (i.e., ECMWF, GFS, GRAPES, and SMS-WARMS), the TS100 + TS50 (the sum of TS values for predicting 24-h accumulated precipitation of ≥100 and ≥50 mm) of DSAEF_LTP_D-2 is comparable to the best performer of the NWP models (ECMWF) on 9 and 10 August, while the performance of DSAEF_LTP_D model for predicting heavy rainfall on 11 August is poor. 3) The newly added similarity regions make up for the deficiency that the similarity regions are narrower when the TC track is northward, which leads to DSAEF_LTP_D-2 having a better forecasting performance for heavy rainfall on 11 August, with the TS100 + TS50 increasing from 0.3021 to 0.4286, an increase of 41.87%.

Structural Changes Preceding the Rapid Intensification of Typhoon Lekima (2019) Under Moderate Vertical Wind Shear

AbstractThe early development of Super Typhoon Lekima (2019) under moderate vertical wind shear (VWS) is examined on the basis of a convection‐permitting simulation with the Weather Research and Forecast model. The tropical cyclone (TC) evolves from a tilted vortex to an upright vortex with a meso‐γ‐scale high‐vorticity core and finally to a vortex with a well‐defined eyewall preceding rapid intensification. The shift of the upper‐middle‐tropospheric vortex to the upshear‐left quadrant is critical to the establishment of an upright TC vortex with high vorticity concentrated in the center. As the upper‐middle‐tropospheric vortex reaches the upshear‐left quadrant, it drifts toward the TC center under the horizontal advective effect of VWS, leading to a reduction of TC vortex tilt. The low stability and high humidity below the upper‐middle‐tropospheric vortex together with the fast increase of upper‐middle‐tropospheric vorticity promote the convection around the TC center, which is important to the maintenance of the nearly upright TC vortex and the development of the high‐vorticity core in TC vortex. The evolution of the TC from an upright vortex with a high‐vorticity core to a vortex with a well‐defined eyewall is bound with the convection near the radius of maximum azimuthal‐mean tangential velocity. The convection enhances the boundary‐layer inflow and then the supergradient force above the radially inward side of the strong boundary‐layer inflow. In this way, the convergence within and above the boundary layer is strengthened, favoring the formation of a ring of deep convection and high vorticity. Such processes are similar to those leading up to secondary eyewall formation.

A Physical Model of Tropical Cyclone Central Pressure Filling at Landfall

Abstract We derive a simple physically based analytic model that describes the pressure filling of a tropical cyclone (TC) over land. Starting from the axisymmetric mass continuity equation in cylindrical coordinates, we derive that the half-life of the decay of the pressure deficit between the environment and TC center is proportional to the initial radius of maximum surface wind speed. The initial pressure deficit and column-mean radial inflow speed into the core are the other key variables. The assumptions made in deriving the model are validated against idealized numerical simulations of TC decay over land. Decay half-lives predicted from a range of initial TC states are tested against the idealized simulations and are in good agreement. Dry idealized TC decay simulations show that without latent convective heating, the boundary layer decouples from the vortex above leading to a fast decay of surface winds while a midlevel vortex persists.

The characterization and impact of Aeolus wind profile observations in NOAA's regional tropical cyclone model (HWRF)

AbstractObservation system experiments (OSEs) are conducted to assess the potential impacts of horizontal line‐of‐sight wind profile observations from the Aeolus satellite on tropical cyclone (TC) forecasting. The OSEs utilize the operational Hurricane Weather and Research Forecasting (HWRF) model. The OSEs include 226 forecasts from seven TC cases in the Atlantic and Eastern Pacific basins. Comparisons between Aeolus and model background winds show that winds from Aeolus are consistently stronger than those from HWRF. Data assimilation statistics also demonstrate that the greatest potential impacts from the assimilation of Aeolus observations are likely to occur in the upper troposphere and lower stratosphere and within approximately 500 km from the TC centre. For TC forecasting applications, the assimilation of Aeolus observations improves TC intensity and size forecasts in the Eastern Pacific basin, while the results for track forecasts and results from the Atlantic basin are mixed. However, in both basins, the largest and most statistically significant, positive impacts from the assimilation of Aeolus observations occur when reconnaissance flight data are unavailable and during the early stages of TC development. The traditionally used forecast assessments of TC intensity, track and size are rooted in surface‐based metrics, and an additional investigation above the surface demonstrated larger improvements from assimilating Aeolus observations on TC wind structure above 400 hPa as compared to the lower troposphere. Several, different assessments throughout this study demonstrate higher uncertainty and the need for special consideration associated with assimilation techniques within 500 km from the TC centre.

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

Tropical cyclones (TCs) are destructive natural disasters. Accurate prediction and monitoring are important to mitigate the effects of natural disasters. Although remarkable efforts have been made to understand TCs, operational monitoring information still depends on the experience and knowledge of forecasters. In this study, a fully automated geostationary-satellite-based TC center estimation approach is proposed. The proposed approach consists of two improved methods: the setting of regions of interest (ROI) using a score matrix (SCM) and a TC center determination method using an enhanced logarithmic spiral band (LSB) and SCM. The former enables prescreening of the regions that may be misidentified as TC centers during the ROI setting step, and the latter contributes to the determination of an accurate TC center, considering the size and length of the TC rainband in relation to its intensity. Two schemes, schemes A and B, were examined depending on whether the forecasting data or real-time observations were used to determine the initial guess of the TC centers. For each scheme, two models were evaluated to discern whether SCM was combined with LSB for TC center determination. The results were investigated based on TC intensity and phase to determine the impact of TC structural characteristics on TC center determination. While both proposed models improved the detection performance over the existing approach, the best-performing model (i.e., LSB combined with SCM) achieved skill scores (SSs) of +17.4% and +20.8% for the two schemes. In particular, the model resulted in a significant improvement for strong TCs (categories 4 and 5), with SSs of +47.8% and +72.8% and +41.2% and +72.3% for schemes A and B, respectively. The research findings provide an improved understanding of the intensity- and phase-wise spatial characteristics of TCs, which contributes to objective TC center estimation.

An ERA5 global climatology of tropical cyclone size asymmetry

AbstractThe size of tropical cyclone (TC), defined as the radius of gale‐force (34‐kt) 10‐m winds (R34) from a TC centre, is often asymmetric but received limited attention. Understanding the asymmetry of TC size is particularly essential to enhance the applicability, predictability, and preparedness for the parties in the coastal regions. In this study, the ERA5 reanalysis data are first validated by the QuikSCAT satellite data. Results demonstrate that the radius of ERA5 32‐kt 10‐m winds estimates best R34 in the QuikSCAT satellite data with a promising linear relationship (r = .75). A 41‐year (1979–2019) homogeneous ERA5 global climatology of TC size asymmetry is hence established based on this linear regression model. Both the temporal and spatial characteristics of the TC size asymmetry are investigated and discussed. This study lays the important groundwork for future sophisticated research on TC size.

Observed Drag Coefficient Asymmetry in a Tropical Cyclone

AbstractThe behavior of drag coefficient () in two different motion‐relative quadrants of Typhoon Mujigae (2015) is investigated through the flux observations conducted on a fixed platform over the coastal region in the northern South China Sea. Observations reveal that the variation of is closely related to the location relative to the tropical cyclone (TC) center. The presents an enhancement when the typhoon is away from the observational site. The spatial distribution of on the periphery of a TC is asymmetric, and the in the right rear quadrant is much larger than that in the right front quadrant for the same wind speed range. This asymmetric distribution of can be explained by the differences in wave properties between the two quadrants. is smaller in cross‐swell conditions than that in the along‐wind wave conditions. Observations also confirm that tends to level off and even attenuate with the increase of wind speed, and the critical wind speed for saturation over the coastal region (∼20 m/s) is much lower than that over the open ocean (∼30 m/s). The observational spatial distribution of in TC quadrants not only improves our understanding on the air‐sea momentum flux but also provides a potential solution for the long‐standing scientific bottleneck on TC intensity forecasting.

Impacts of Assimilating Additional Reconnaissance Data on Operational GFS Tropical Cyclone Forecasts

Abstract This study reviews the recent addition of dropwindsonde wind data near the tropical cyclone (TC) center as well as the first-time addition of high-density, flight-level reconnaissance observations (HDOBs) into the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The main finding is that the additional data have profound positive impacts on subsequent TC track forecasts. For TCs in the North Atlantic (NATL) basin, statistically significant improvements in track extend through 4–5 days during reconnaissance periods. Further assessment suggests that greater improvements might also be expected at days 6–7. This study also explores the importance of comprehensively assessing data impact. For example, model or data assimilation changes can affect the so-called “early” and “late” versions of the forecast very differently. It is also important to explore different ways to describe the error statistics. In several instances the impacts of the additional data strongly differ depending on whether one examines the mean or median errors. The results demonstrate the tremendous potential for further improving TC forecasts. The data added here were already operationally transmitted and assimilated by other systems at NCEP, and many further improvements likely await with improved use of these and other reconnaissance observations. This demonstrates the need of not only investing in data assimilation improvements, but also enhancements to observational systems in order to reach next-generation hurricane forecasting goals. Significance Statement This study demonstrates that data gathered from reconnaissance missions into tropical cyclones substantially improves tropical cyclone track forecasts.

Parameterized Tropical Cyclone Precipitation Model for Catastrophe Risk Assessment in China

Abstract Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation. Significance Statement In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment.

Assessment of Tropical Cyclone Rainfall from GSMaP and GPM Products and Their Application to Analog Forecasting in the Philippines

Tropical cyclone (TC) rainfall is both a resource and a hazard in the Philippines. Observation of its spatiotemporal distribution is necessary for water and disaster mitigation management. This study evaluated the performance of two high-resolution satellite precipitation datasets—the GSMaP and GPM-IMERG—in estimating accumulated TC rainfall in the Philippines from 2000 to 2021. TC rain is defined as rainfall within 5° of a TC center. Several estimation algorithms were included in the assessment. The uncalibrated near-real-time GSMaP_NRT and early version GPM_ER, the reanalysis GSMaP_RNL, and the gauge-calibrated GSMaP_G and GPM_G. The assessment shows the worst scores for the uncalibrated GSMaP_NRT and GSMaP_RNL, followed by GPM_ER with station correlation coefficient (CC) values of 0.63, 0.67, and 0.73, respectively, compared to GSMaP_G CC of 0.79 and GPM_G CC of 0.77. GSMaP_G and GPM_G also gave the least bias and error, with a consistently high (>0.6) probability of detection (POD) and Pierce skill score (PSS) up to rainfall of 300 mm. In addition to the evaluation, the GSMaP_G and GPM_G were used in the analog forecasting of TC rain. Analog forecasting is based on the principle that past weather conditions can occur again. In TC rain analog forecasting, past TCs with similar intensities and tracks are assumed to bring similar rainfall amounts and distribution as a current TC. Composite mean TC rainfall from historical satellite precipitation estimations was produced to create TC rain forecasts. Results show the analog TC rain forecasts generally captured the spatial distribution of TC rain and performed better than the uncalibrated GSMaP_NRT, with a mean station correlation of 0.62–0.67, POD greater than 0.7, and positive PSS indicating good skills. However, forecasts have a false alarm ratio greater than 0.8 for 150 mm rain and have difficulty producing extreme rainfall (>250 mm).

Climatology of Different Classifications of Tropical Cyclones Landfalling in Guangdong Province of China during 1951–2020

The climatology of different classifications (based on the intensity at the landfall time) of tropical cyclones (TCs) making landfall in Guangdong Province of China during 1951–2020 (70 years) is investigated using the best track data from the China Meteorological Administration and ERA5 reanalysis data. There were 234 TCs making landfall in Guangdong Province, with more severe tropical storms (STSs, 30.8%) and typhoons (TYs, 27.3%), and less tropical depressions (TDs, 19.7%) and tropical storms (TSs, 22.2%) during the past 70 years. The frequency of the landfall TCs had a significant interannual oscillation of 2–5 years. Landfall TCs generated over the western North Pacific (WNP) were usually more and stronger than those generated over the South China Sea (SCS). The TCs generated over the WNP had longer lifetime duration and shorter on-land duration than those generated over the SCS. TCs making landfall in western Guangdong were the most, followed by central Guangdong and eastern Guangdong. The composite analysis using TC-relative coordinates indicated that the precipitation of different classifications of TCs making landfall in Guangdong Province was asymmetric, which was stronger in the south of the TC center. The position of the maximum precipitation showed a cyclonic rotation around the TC center with increasing TC intensity. Generally, the vertical velocity, moisture flux, warm core, and vertical wind shear enhanced with the increasing landfall TC intensity. The vertical velocity and moisture flux of different classifications of TCs also showed an asymmetric structure related to the distribution of TC precipitation. TSs, STSs, and TYs had a double warm-core configuration. The precipitation of the TDs and TSs usually occurred over the down-shear of average vertical wind shear, those of the STSs and TYs over the left-of-shear.

Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960–2020

Extreme rainfall associated with landfalling typhoon (ERLTC) can cause severe disasters and economic impacts throughout China. Improving the accuracy of ERLTC forecasts is therefore crucial in disaster prevention and mitigation. The top 26 ERLTC events in China during 1960–2020 are investigated based on multi-source datasets. These ERLTC events are categorized into five main types according to the geographical location of the extreme precipitation and its position relative to the tropical cyclone (TC) center, namely: the typhoon inner-core rainfall in Taiwan (TWIC), typhoon inverted trough rainfall in Taiwan (TWIT), weak typhoon rainfall in Hainan (HNWK), strong typhoon rainfall in Zhejiang (ZJST) and inland typhoon remnant rainfall (ILRM). All the ERLTC events occurred in the weakening stage of TC after reaching its lifetime maximum intensity in convective cloud (TBB ≤ −32 °C) regions over complex local terrain. The translational speeds of 20 TCs (76.9% of the total) were smaller than the climatological average (20.6 km h−1) during the extreme precipitation events. The differences are as follows: the TWIC and TWIT types are featured with different season, track and water vapor channel although both occurred in Taiwan. The other three types are distinguished by spinning track and strong convective cloud for HNWK type, strong TC intensity and binary TC interactions for ZJST type; and stagnation and strong westerly trough activity for ILRM type, respectively. These results are expected to provide useful clues for an in-depth understanding of ERLTC events over China.

Modelled impact of ocean warming on tropical cyclone size and destructiveness over the Bay of Bengal: A case study on FANI cyclone

Ocean warming influences tropical cyclone (TC) destructive parameters and hence their guidance certainly helps the disaster management agencies to reduce the damage. The present study modelled the sensitivity of TC size, intensity, rainfall, and destructive potential parameters under various ocean warming conditions. A recent extremely severe cyclonic storm FANI (2019) over the Bay of Bengal is chosen for this purpose. The 9-km grid-spacing Weather Research and Forecasting (WRF) model simulations are performed by altering the default sea surface temperature (SST) by −1 °C, +1 °C, +2 °C and, +3 °C respectively along with the control run. The model simulations revealed that ocean warming causes the FANI cyclone to turn northeastward direction. The observed changes of tangential wind speed due to large sea surface enthalpy fluxes associated with ocean warming result in TC size changes and then guide the cyclone to northeastward movement. The radius of 34-knot wind (R34) is more sensitive to the SST warming compared to the radius of maximum winds. The modelled R34 values ranged between ~180–600 km against observations (~100–400 km) during the TC life period. The increased tangential wind speeds (~50–60 m s−1) and convective updrafts (~1.3–1.5 m s−1) in the TC area are responsible for TC intensity and size changes. A linear and exponential growth is seen for the TC destructive potential indicators those estimated using TC intensity and R34 values. The SST increase could result in peak heavy rainfall (>65 mm day−1) in the TC inner-core region, especially the rear sector. The categorical rainfall distribution analysis also proved that heavy rainfall areas extend to greater distances (>300 km) around the TC center with SST warming. The study helps in assessing the hydro-meteorological destruction associated with the TCs in the future warming climate.

Determining Tropical Cyclone Center and Rainband Size in Geostationary Satellite Imagery

Brightness temperature (TB) observations at an infrared channel (10.3 μm) of the Advanced Baseline Imager (ABI) on board the U. S. 16th Geostationary Operational Environmental Satellite (GOES-16) are used for determining tropical cyclone (TC) center positions and rainband sizes. Firstly, an azimuthal spectral analysis method is employed to obtain an azimuthally symmetric center of a TC. Then, inner and outer rainbands radii, denoted as RIR and ROR, respectively, are estimated based on radial gradients of TB observations at different azimuthal angles. The radius RIR describes the size of the TC inner-core region, and the radius ROR reflects the maximum radial extent of TC rainbands. Compared with the best track centers, the root mean square differences of ABI-determined centers for tropical storms and hurricanes, which totals 108 samples, are 45.35 and 29.06 km, respectively. The larger the average wavenumber-0 amplitude, the smaller the difference between the ABI-determined center and the best track center. The TB-determined RIR is close but not identical to the radius of the outermost closed isobar and usually coincides with the radius where the strongest wavenumber 1 asymmetry is located. The annulus defined by the two circles with radii of ROR and RIR is the asymmetric area of rainbands described by azimuthal wavenumbers 1–3. In general, amplitudes of wavenumber 0 component centered on the ABI-determined center are greater than or equal to those from the best track. For a case of a 60 km distance between the ABI-determined and the best track TC center, the innermost azimuthal waves of wavenumbers 1–3 are nicely distributed along or within the radial distance RIR that is determined based on the ABI-determined TC center. If RIR is determined based on the best track, the azimuthal waves of wavenumbers 1–3 are found at several radial distances that are smaller than RIR. The TC center positions, and rainband size radii are important for many applications, including specification of a bogus vortex for hurricane initialization and verification of propagation mechanism of vortex Rossby waves.

Modulation of Pacific Sea Surface Temperature on Two Types of Tropical Cyclone Tracks Affecting Northeast China

In this study, the northward-moving tropical cyclones (TCs) that influence Northeast China (NEC) in July–September are identified based on the distance between the TC center and the boundaries of NEC. Then, based on a machine learning algorithm named hierarchical agglomerative clustering, the tracks of northward-moving TCs that influence NEC are classified into the eastern-track type and the turning type. In NEC, the precipitation induced by eastern-track type TCs gradually decreases from east to west, and the precipitation induced by turning-type TCs gradually decreases from south to north. For eastern-track type TCs, negative Niño3 sea surface temperature (SST) anomalies in preceding January–March can induce cyclonic circulation anomalies and positive vorticity anomalies over the Philippine Sea during subsequent July–September, which favors the genesis of TCs. Moreover, the westerly anomaly in the subtropical western north Pacific and the strengthening of cyclonic steering flow over the Philippines jointly steer the TCs to move northward along the northerly airflow on the west of the western Pacific subtropical high, which favors the genesis of eastern-track type TCs. For turning-type TCs, the positive SST anomalies in the West Wind Drift area during preceding May–July cause positive vorticity anomalies from the northern Philippines to Taiwan from July to September. The cyclonic steering flow over the Philippines and the anticyclonic steering flow over the Sea of Japan lead the TCs to move northwestwards and then turn to northeast, which is conducive to the genesis of turning type northward-moving TCs. Finally, the results of numerical experiments have confirmed these findings.

Discovering Precursors to Tropical Cyclone Rapid Intensification in the Atlantic Basin Using Spatiotemporal Data Mining

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification.

Mechanism of Asymmetric Precipitation by Tropical Cyclone Nada Over the Indian Peninsula

The Tropical Rainfall Measuring Mission (TRMM) and ERA5 reanalysis data are used to research the asymmetric precipitation induced by tropical cyclone (TC) Nada, which made landfall on the southeastern coast of the Indian Peninsula on December 2, 2016. The mechanism of asymmetric precipitation is analyzed during the passage of the TC. The results show that the spatial average precipitation rate increased to a maximum of 1.78 mm/hr on the southeastern coast of the Indian Peninsula, where the vorticity of the TC center increased to more than 5 × 10^–4 s^–1 at 850 hPa at 06:00 UTC on December 1. Near the center of Nada, the troposphere had strong upward convection at a vertical velocity of 0.7 pa/s. The positive (negative) vertical wind shear was located on the left (right) side of the TC path. The maximum value area of wind shear was consistent with the distribution area of extreme precipitation. TC-induced asymmetric rainfall was mainly caused by the superposition of Nada and low-level cold air intrusion, which resulted in unstable atmospheric stratification, uplifting warm and humid air and producing strong convection. This study offers new insights into precipitation induced by the interaction of cold air intrusion and a TC in the northern Indian Ocean.

Characteristics and Preliminary Causes of Extremely Persistent Heavy Rainfall Generated by Landfalling Tropical Cyclones Over China

AbstractExtremely persistent heavy rainfall (EPHR) generated by landfalling tropical cyclones (LTCs) can cause destructive natural disasters. In this study, hourly precipitation data and best‐track data set for 2009–2019 are used to investigate the basic characteristics and preliminary causes of LTC‐generated EPHR over China. Using the relative threshold method, heavy precipitation lasting more than 7 hr is defined as EPHR. The result shows that most of the EPHR events occur in the south and southeast China, especially in the northwest of Hainan Island and the coastal areas of Zhejiang. The maximum duration of EPHR can reach 13 hr with a maximum cumulative rainfall of 668.9 mm. EPHR in the intermediate region (150–300 km away from the tropical cyclone (TC) center) of the LTCs tends to have a higher frequency and greater intensity, and the development process of the EPHR events exhibits a significant asymmetrical feature. Although EPHR accounts for a relatively small proportion (29.4%) of duration in EPHR events, it accounts for a large proportion (61.0%) of cumulative precipitation. The combination effects of topographic forcing and tracks of the TCs directly determines the high incidence areas of EPHR. And the preliminary causes of EPHR are mainly due to the upliftment or blocking effects of the topography. In addition, the translation speed and intensity of the TC can also restrict the generation of EPHR. In general, a stronger and slower TC is more likely to cause EPHR events when it passes through north of Hainan Island or makes landfall at Zhejiang‐Fujian area.

A Tropical Cyclone Center Location Method Based on Satellite Image.

Accurately detecting and locating the center of the tropical cyclone is critical for the trajectory forecasting. This study proposed an automatic method for centers' location of the tropical cyclones based on the visible or the infrared satellite images. The morphological structure of the tropical cyclone is modeled using the circular pattern. The tropical cyclone center is located based on regional pixels instead of skeleton points. All pixels in a segmented cloud cluster vote for a 2-dimensional accumulator. The center of the cloud cluster is computed by the mean voting distances, which are calculated by fitting quadratic functions in every column of the two-dimensional (2D) accumulator. Then, a linear function is fitted according to the functional relationship between the mean voting distance and voting angle. The fitted coefficients of the linear function are the center coordinates of the tropical cyclone. The proposed method for centers location of the tropical cyclones is tested using visible and infrared satellite images. The results of center location are compared with the best track provided in JMA datasets.