Storm Center Research Articles

Simulation of Atlantic Hurricane Tracks and Features: A Coupled Machine Learning Approach

Abstract The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain, from input data (storm features) available in or derived from the HURDAT2 database, models capable of simulating important hurricane properties (e.g., landfall location and wind speed) consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude and intensity models providing the central pressure and maximum 1-min wind speed at 10-m elevation were created. The trajectory and intensity models are coupled and must be advanced together, 6 h at a time, as the features that serve as inputs to the models at any given step depend on predictions at the previous time steps. Once a synthetic storm database is generated, properties of interest, such as the frequencies of large wind speeds, may be extracted from any part of the simulation domain. The coupling of the trajectory and intensity models obviates the need for an intensity decay model inland of the coastline. Prediction results are compared with historical data, and the efficacy of the storm simulation models is evaluated at four sites: New Orleans, Louisiana; Miami, Florida; Cape Hatteras, North Carolina; and Boston, Massachusetts.

On-site wave-wind observation and spectral investigation of dynamic behaviors for sea-crossing bridge during tropical cyclone

The dynamic behaviors of a sea-crossing bridge during a tropical cyclone event which passed through the bridge site in September 2015 are simulated using in-situ observation wave and wind data. The hourly observation data during the cyclone event at the bridge site including averaged wind speed, turbulence intensity, direction, significant wave height, mean period, spectral variations are recorded. The wave and wind spectra are analyzed from raw data and are fed to a frequency-domain framework for response modeling. The stochastic wave actions considering foundation geometry, diffraction effect, wave coherence and second-order quadratic wave force transfer functions are considered using boundary element methods and the wind actions including buffeting loads, aerodynamic stiffness and damping are modeled based on stationary buffeting theory. Results reveal that the maximal structural response occurs near the cyclone eye wall while the storm center is still 80–100 km away but experiences sudden drop when the cyclone eye reaches the bridge site. Deck responses are much more sensitive to wind, but wave dominates the tower and base force responses. Second-order wave load is found to be critical to responses because the sum-frequency wave force could undesirably coincide with the bridge eigenfrequencies.

Sensitivity of WRF multiple parameterization schemes to extreme precipitation event over the Poyang Lake Basin of China

Global warming leads to more frequent and more violent extreme weather events, which cause severe natural disasters. The sensitivity of physical schemes in numerical weather prediction models for extreme precipitation is a significant challenge. To simulate a heavy precipitation process from 1 July 2020 to 10 July 2020 over the Poyang Lake Basin, where floods occur frequently, the Weather Research and Forecast model (WRF) was employed. The observation (OBS) from 92 meteorological stations was applied to evaluate WRF performance. To assess the optimal parameter, 27 combinations of multiphysics schemes based on three cumulus schemes (CUs), three microphysical schemes (MPs) and three land surface schemes (LSMs) were employed in WRF simulation. The Euclid distance (ED) was derived to evaluate the performance of the modelled total cumulative precipitation (TCP). The results showed that the simulation generally reproduced the spatial distribution of precipitation and captured the storm centre. In general, WRF underestimated the observation for most areas but overestimated the observation in the northeastern part of the basin. For total cumulative precipitation (TCP), the spatial correlation coefficients ranged from 0.6 to 0.8. M11 had the highest value of 0.796. The scores (TS, POD, FAR) of M15 were 0.66, 0.79, and 0.21, respectively. With the maximum similarity of 0.518, M7 (BMJ-WDM6-Noah) showed the best performance in ED based on six evaluation metrics. The mean values of OBS and M19 were 259.34 and 218.33 mm, respectively. M19 (NT-Thompson-Noah) was closest to the OBS for the range of the TCP. In terms of daily precipitation, the CC of M3 was maximum (0.96), and the RMSE of M24 was minimum (11.9 mm. day-1). The minimum error between the simulation and OBS was found for M3 (NT-Thompson-Noah). Therefore, with a comprehensive evaluation, five optimal combinations of physical schemes (M22, M19, M9, M3, M21) were found for the PLB, which is of great significance for extreme precipitation simulation and flood forecasting.

Stationary and Transient Asymmetric Features in Tropical Cyclone Eye with Wavenumber-1 Instability: Case Study for Typhoon Haishen (2020) with Atmospheric Motion Vectors from 30-Second Imaging

Abstract Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 s. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward of the storm translation, consistently with a numerical study of the tropical cyclone (TC) boundary layer. Over the analysis period of 10 h, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenumber-1 asymmetry in ring-shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes. Significance Statement Axially asymmetric transient features in the inner cores of tropical storms have been suggested to profoundly affect the structures and the time evolutions of tropical storms. However, the scarcity of observations has hindered studying such processes observationally. By using a specially conducted high-frequency satellite imaging of Typhoon Haishen (2020), we derived atmospheric motion vectors nearly homogeneously at an unprecedentedly high spatiotemporal resolution. Various kinds of asymmetric motions in low-level flows in the eye were found. Of particular interest is a special type of wavenumber-1 instability whose role has not drawn much attention; the instability was found to provide angular momentum transport consistent with the measured homogenization of the rotation.

Assessing the performances of parametric wind models in predicting storm surges in the Pearl River Estuary

Portraying wind fields plays a crucial role in storm surge simulation, which includes the information of the storm track, central maximum wind speed, maximum wind speed radius, and pressure drop. In this study, five widely used parametric wind models and their performances in predicting storm surges have been estimated in the Pearl River Estuary. The synthetic wind field reveals that different wind models produce wind fields with diverse structures, each with its own focus and advantages for the storm's center or its periphery. By implementing these wind models into surge simulation, the wind and surge estimates show a strong dependence on model selection. Selection of parametric wind models may lead to a surge height error greater than 1.45m. The distribution of maximum wind speed and peak water level under various typhoons dominated by storm intensity is also investigated. As the storm intensity increases, the prediction deviations between these selected models are also enlarged and over 10% in Typhoon Hato. Overall, the model proposed by Emanuel and Rotunno (2011) yields accurate estimates among the selected parametric wind models. The uncertainty of the storm track parameters and drag coefficient are quantified based on Emanuel and Rotunno (2011) model. Small track changes can result in huge variations in surge heights with maximum values of 0.5–1m. The choice of drag coefficient also has a maximum difference of 5.4%, which contributes a significant uncertainty to the simulation. The multiple choice of parameters in wind models increases the risk of uncertainty propagation. This study is expected to provide a reference for the numerical modeling of wind field and storm surge.

Effects of Volcanic Aerosols on the Genesis of Tropical Cyclone Wukong (2018)

AbstractThe short‐range impacts of volcanic aerosols from the eruption of the Kilauea volcano on the genesis of tropical cyclone (TC) Wukong in 2018 are investigated by numerical simulations, as distinct from the studies on the aerosol climate effects. The aerosol−radiation and aerosol−cloud effects are isolated through sensitivity experiments. For the aerosol−radiation effect, the radiative cooling at the low levels and heating above by aerosols southeast of Wukong's periphery induce stable sinking and decrease latent heating in the environment far from the storm center. Therefore, the sea‐level pressure increases there, enhancing low‐level radial inflow to the storm, inner‐core convection and the release of latent heat, which is conducive to TC genesis. For the aerosol−cloud effect, volcanic aerosols in storm circulation can be converted into cloud condensation nuclei, invigorating inner‐core convection, and subsequently favoring TC genesis. However, the nonlinear effect resulting from the interaction between aerosol−radiation and aerosol−cloud effects is not conducive to TC genesis, because it weakens the aerosol−cloud effect through the deposition of aerosols and also limits the aerosol−radiation effect through the increasing of peripheral convection.

Stochastic tropical cyclone precipitation field generation

AbstractTropical cyclones are important drivers of coastal flooding which have severe negative public safety and economic consequences. Due to the rare occurrence of such events, high spatial and temporal resolution historical storm precipitation data are limited in availability. This article introduces a statistical tropical cyclone space‐time precipitation generator given limited information from storm track datasets. Given a handful of predictor variables that are common in either historical or simulated storm track ensembles such as pressure deficit at the storm's center, radius of maximal winds, storm center and direction, and distance to coast, the proposed stochastic model generates space‐time fields of quantitative precipitation over the study domain. Statistically novel aspects include that the model is developed in Lagrangian coordinates with respect to the dynamic storm center that uses ideas from low‐rank representations along with circular process models. The model is trained on a set of tropical cyclone data from an advanced weather forecasting model over the Gulf of Mexico and southern United States, and is validated by cross‐validation. Results show the model appropriately captures spatial asymmetry of cyclone precipitation patterns, total precipitation as well as the local distribution of precipitation at a set of case study locations along the coast. We additionally compare our model against a widely‐used statistical forecast, and illustrate that our approach better captures uncertainty, as well as storm characteristics such as asymmetry.

Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods

AbstractHurricane Laura was the strongest hurricane to make landfall in Louisiana since 1969 with maximum sustained winds of 130 knots. One University of Oklahoma Shared Atmospheric Mobile and Teaching Polarmetric Radar (SR1‐P), and four portable in situ precipitation stations (PIPSs) equipped with parsivel disdrometers were spatially and temporally collocated with two NASA Global Precipitation Measurement Mission Dual‐frequency Precipitation Radar overpasses. The combined retrieval methods were able to quantify and compare drop size distribution moments and radar‐inferred precipitation processes before, during, and after the storm center made landfall. It was found that the magnitude of collision‐coalescence dominant precipitation decreased from before to after landfall. Further, the presence of a bright‐band becomes more evident across all percentiles in the post‐landfall overpass, indicating an increase in stratiform precipitation compared to convective precipitation after Laura moved inland. The PIPS showed an increase in mean drop size from 1.0 mm before landfall to as high as 4.0 mm in the eyewall, while decreasing to below 1.0 mm as Laura continued to move inland with a decrease in maximum echo top height of 0.5–1.0 km. Last, the Dual‐frequency Precipitation Radar (DPR) algorithm overestimated the normalized intercept parameter by 0.5–1.0 m−3 mm−1 compared to the PIPS implying differences in measured drop number concentration, potentially due to differences in measurement footprint or assumptions in the DPR retrieval algorithm. These findings can potentially be used to improve the DPR particle size distribution algorithm in tropical cyclones.

Moisture transport contributing to precipitation at the centre of storm Bronagh

Analysis of moist airflows associated with a case study, storm Bronagh, is conducted to determine whether moisture contributing to observed precipitation near the cyclone centre is transported from the subtropics or local environment. Reanalysis data are used to identify the warm conveyor belt and dry intrusion, and an algorithm is developed to identify the presence of the feeder air stream (FA) during the storm's evolution. We conclude that during the intensification stage, moisture is transported by the FA from the local environment into which Bronagh is propagating and not from the reservoir of moist air in the subtropics.

Machine Learning–Based Hurricane Wind Reconstruction

Abstract Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.

Diurnal Variations in Contraction of the Radius of Maximum Tangential Wind in Tropical Cyclones

AbstractDiurnal variations in contraction of the radius of maximum tangential wind (RMW) of tropical cyclones (TCs) were assessed using best‐track data for 2001–2018. For RMW contractions before TC intensification, the largest RMW contraction rate in 6 hr at 0300–0900 local solar time (LST) was associated with the maximum coverage of very deep convective clouds with infrared brightness temperatures <208 K at 0300–0600 LST, which were found mostly outside the mean RMW. The maximum RMW contraction rate and maximum intensification rate occurred simultaneously in 6 hr at 0300–0900 LST for rapidly intensifying storms associated with the greatest coverage of very deep convective cloud at 0300–0600 LST at storm center. This was not the case for nonrapidly intensifying storms. The time‐dependence of RMW contraction indicates the importance of radiative forcing on TC structure.

Simulation of an Extreme Precipitation Event Using Ensemble-Based WRF Model in the Southeastern Coastal Region of China

Extreme weather events have increased significantly in the past decades due to global warming. As a robust forecast and monitoring tool of extreme weather events, regional climate models have been widely applied on local scales. This study presented a simulation of an extreme precipitation event in the Southeastern Coastal Region of China (SEC), where floods, typhoons, and mountain torrents occur frequently using the Weather Research and Forecast model (WRF) driven by GEFS (The Global Ensemble Forecast System) ensemble members (one control run and 20 ensemble members) from 01 UTC 14 June to 18 UTC 16 June 2010. The observations of hourly precipitation records from 68 meteorological stations in the SEC were applied to validate the WRF ensemble simulations with respect to 3-hourly cumulative precipitation (3hP), 6-hourly cumulative precipitation (6hP) and total cumulative precipitation (TCP). The results showed that all WRF 20 ensemble outputs could capture the extreme precipitation events fairly well with the Pearson correlation coefficient ranging from 0.01 to 0.82 and 0.16 to 0.89 for 3 and 6hP, respectively. The normalized root mean square error was comparable between the control run and 20 ensembles for 3hP (0.67 vs. 0.63) and 6hP (0.51 vs. 0.53). In general, WRF underestimated the observations for TCP. The control run (En00) modeled 28.1% less precipitation, while the 20 ensembles modeled 3.9% to 55.5% less precipitation than observations. The ensemble member 12 (En12) showed the best TCP simulation with the smallest bias. The average of 20 ensembles simulated 31.7% less precipitation than observations. The total precipitation was not captured by WRF with a significant bias that ranged from −203.1 to 112.3 mm. The storm centers were generally not captured by WRF in this case study. WRF ensembles underestimated the observation in the central Fujian Province while overestimated in the northern and southern Fujian Province. Although the average of ensembles can reduce the uncertainty to a certain extent, the individual ensemble (e.g., En12) may be more reliable on local scales.

Dropsonde observations and numerical simulations of intensifying/weakening tropical cyclones over the northern South China Sea

Vertical profiles of tangential and radial wind speeds in Cyclone Mangkhut at 0900–1200 utc 15 September 2018 are shown. Red triangles represent dropsonde locations. Orange solid dots and dashed lines respectively represent observed and simulated tangential wind speed U tan (in ms−1), anti-clockwise positive. Blue solid dots and dashed lines respectively represent observed and simulated radial wind speed U rad (in ms−1), outflow positive. z represents height above mean sea level (in km). d represents distance from storm centre (in km).

Potential links of gobi, dust, and desertification: A comprehensive understanding from aeolian landform evolution in a middle-latitude desert

Knowledge of desert landscape evolution and related aeolian deposits will increase our understanding of the earth system. Desertification and dust cycle in the middle latitudes are an important part of global change and an important window to understand the earth's climate system. The Hexi Corridor is one of the dust storm centers in northern China and Asia, but the reason of desertification process and its relationship with local aeolian landforms and deposits are still unclear. Based on the extensive survey of aeolian landforms and the analysis of elemental, depositional, and structural properties of diverse aeolian sediments (dune sands, Gobi silts and dust loess), this study explored the genetic connection between dune sands in the study area, gobi erosible silts in the upwind area and dust storms in the downwind area and their relationship with desertification in the Hexi Corridor. The analytical results suggest that the dune sands in the Hexi Corridor are weakly related to the gobi sediments in the upwind area and the dust loess in the downwind area from the perspective of genetic connection, but there is a close relationship between gobi and dust sediments. On the decadal scale, the change trend of dust storm events in the study area is consistent with the variational process of the regional-scale desertification, indicating that the dust storm is an effective indicator of desertification process in the study area on a large scale. Based on the genetic connection and differences between the dust storm, gobi topsoil and dune sand and combined with other environmental parameters, we identified two different processes of desertification in the Hexi Corridor. One is an abnormal, reverse desertification process that occurred on a regional scale, which is inconsistent with the change of global climate and global dust storms. The other is an increased desertification process that occurred in the oasis area on a local scale, which has no relationship to global and regional climate change but is closely related to the decline of groundwater level in the oasis area. This provides good evidence for us to understand the causes of different landsurface processes that occurred in a unified environment in middle latitudes.

Observation of the Tropical Cyclone Diurnal Cycle Using Hyperspectral Infrared Satellite Sounding Retrievals

Abstract Hyperspectral infrared satellite sounding retrievals are used to examine thermodynamic changes in the tropical cyclone (TC) environment associated with the diurnal cycle of radiation. Vertical profiles of temperature and moisture are retrieved from the Suomi National Polar-Orbiting Partnership ( SNPP ) satellite system, National Oceanic and Atmospheric Administration-20 ( NOAA-20 ), and the Meteorological Operational ( MetOp-A/B ) satellite system, leveraging both infrared and microwave sounding technologies. Vertical profiles are binned radially based on distance from the storm center and composited at 4-h intervals to reveal the evolution of the diurnal cycle. For the three cases examined—Hurricane Dorian (2019), Hurricane Florence (2018), and Hurricane Irma (2017)—a marked diurnal signal is evident that extends through a deep layer of the troposphere. Statistically significant differences at the 95% level are observed in temperature, moisture, and lapse rate profiles, indicating a moistening and destabilization of the mid- to upper troposphere that is more pronounced near the inner core of the TC at night. Observations support a favorable environment for the formation of deep convection caused by diurnal differences in radiative heating tendencies, which could partially explain why new diurnal pulses tend to form around sunset. These findings demonstrate that the diurnal cycle of radiation affects TC thermodynamics through a deep layer of the troposphere, and suggest that hyperspectral infrared satellite sounding retrievals are valuable assets in detecting thermodynamic variations in TCs.

Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: a case study

Abstract. With the rising share of renewable energy sources like wind energy in the energy mix, high-impact weather events like mid-latitude storms increasingly affect energy production, grid stability and safety and reliable forecasting becomes very relevant for e.g. transmission system operators to allow for actions to reduce imbalances. Traditionally, meteorological forecasts are provided by limited-area weather prediction models (LAMs), which can use high enough model resolution to represent the range of atmospheric scales of motions associated with such storm structures. While generally satisfactory, deterioration and insufficient deepening of large-scale storm structures are observed when they are introduced near the lateral boundaries of the LAM due to inadequate spatial and temporal interpolation. Global models with regional mesh refinement capabilities like the Model for Prediction Across Scales (MPAS) have the potential to provide an alternative, while avoiding sharp resolution jumps and lateral boundaries. In this study, MPAS' capabilities of simulating key evaluation metrics like storm intensity, storm location and storm duration are investigated based on a case study and assessed in comparison with buoy measurements, forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) LAM. Quasi-uniform and variable-resolution MPAS mesh configurations with different model physics settings are designed to analyze the impact of the mesh refinement and model physics on the model performance. MPAS shows good performance in predicting storm intensity based on the local minimum sea level pressure, while time of local minimum sea level pressure (storm duration) was generally estimated too late (too long) in comparison with the buoy measurements in part due to an early west-wards shift of the storm center in MPAS. The variable-resolution configurations showed a combination of an additional south-westwards shift and deviations in the sea level pressure field south-west of the storm center that introduced additional bias to the time of local minimum sea level pressure at some locations. The study highlights the need for a more detailed analysis of applied mesh refinements for particular applications and emphasizes the importance of methods like data assimilation techniques to prevent model drifts.

Effect of typhoon‐induced intertidal‐flat erosion on dominant macrobenthic species (Meretrix meretrix)

AbstractBenthic animal populations inhabiting intertidal flats provide important ecosystem functions and services that may be disrupted by physical disturbances such as tropical cyclones, which are predicted to increase in frequency and intensity under future climate change. However, the spatial reach at which tropical cyclones impact macrobenthos populations in intertidal flats remains poorly understood. We examined whether a typhoon with a center that remained more than 1400 km away from the study site could trigger severe erosion of intertidal flats and adversely affect macrobenthos populations. We undertook simultaneous measurements of hydrodynamics (waves, currents), morphodynamics (erosion, accretion) and density and biomass of the macrobenthic bivalve Meretrix meretrix during the passage of this typhoon, on an intertidal flat on the Chinese coast. Our results showed that, in spite of the considerable distance from the storm center, the bed shear stress greatly exceeded the critical value for erosion, resulting in rapid erosion of approximately 10 cm and a nearly 50% reduction in both the density and the biomass of the clam species. Our findings suggest that if storms become more frequent and more intense in a future warmer climate, they may increase physical disturbances to intertidal flats and their benthic animal populations, even at great distances from storm centers.

Simulation of Rapid Intensification of Super Typhoon Lekima (2019). Part I: Evolution Characteristics of Asymmetric Convection Under Upper-Level Vertical Wind Shear

The role of the upper-level vertical wind shear (VWS) on the rapid intensification (RI) of super typhoon Lekima (2019) is investigated with a high-resolution numerical simulation. Our simulation shows that under moderate upper-level easterly VWS, the tilting-induced convective asymmetry is transported from the initially downshear quadrant to the upshear quadrant and wrapped around the storm center by the cyclonic flow of the storm while moving inward. This process enhances upward motions at the upshear flank and creates upper-level divergent flow. As such, the establishment of outflow acts against the environmental flow to reduce the VWS, allowing vertical alignment of the storm. The organized outflow plays an important role in sustaining the inner-core deep convection by modulating the environmental upper-level thermal structure. Accompanying deep convective bursts (CBs), cold anomalies are generated in the tropopause layer due to the adiabatic cooling by the upward motion and radiative process associated with the cloud anvil. Physically, cold anomalies at the tropopause locally destabilize the atmosphere and enhance the convections and the secondary circulation. The CBs continue to develop episodically through this process as they wrap around the storm center to form a symmetric eyewall. The results suggest that deep convections are capable of reducing the upper-level VWS, promoting the development of upper-level outflow. Lekima overcame the less favorable environment and eventually intensified to become a super typhoon.

Monitoring the super typhoon lekima by GPM-based near-real-time satellite precipitation estimates

As the fifth-largest super tropical cyclone landed on Mainland China in history, typhoon Lekima occurred in August 2019 caused at least 71 deaths, 14 million disaster victims, and cost RMB 65.37 billion in damages. Here we use six latest GPM-based near-real-time satellite precipitation estimates (IMERG-Early, IMERG-Late, GSMaP-NOW, GSMaP-NRT, GSMaP-MVK, and CMORPH-RT) to quantify the spatiotemporal pattern of typhoon rainfall and meanwhile the potentials of these satellite precipitation products in detecting heavy storms are systematically investigated. Retrieval results indicate that the expansive rain belt of typhoon Lekima brought approximately 93.2 billion m3 water in total onto the Chinese mainland during the typhoon period and the precipitation process maintained higher intensities on the earlier two days (August 10 and 11). Specifically, it is found that a “precipitation window” with higher precipitation intensities appeared between 5:00 a.m. and 10:00 a.m., implying that a greater probability of rainstorms may occur during this period. Moreover, at the storm center around the Dongying City of Shandong Province, the dual-frequency precipitation radar of GPM (GPM-DPR) successfully revealed the existence of precipitation columns with the peak value of 121 mm·h−1, which seem to play a key role in the regional torrential rainfall. Additionally, our evaluation shows IMERG-Late and CMORPH-RT perform better under higher rain rates relative to other satellite precipitation estimates. This can be primarily attributed to their sufficient inputs from PMW sensors and, respectively, the former benefits well from both DPR and GMI while the latter significantly reduces its IR inputs. This study illustrates an example of monitoring the extreme heavy precipitation storms by the latest GPM-based near-real-time satellite precipitation estimates and highlights future possible improvements for algorithm developers.

On the extreme value statistics of spatio-temporal maximum sea waves under cyclone winds

Principles of the spatio-temporal statistics are used to investigate the characteristics of short-term/range extreme sea waves and related sea-state parameters under cyclone winds (northern hemisphere). We base our analysis upon consistent stereo-imaging observations of the 3D (2D space + time) sea surface elevation field, and spectral wave model results in the Northwestern Pacific during tropical storm Kong-rey (2018). The focus is on the extreme value analysis of individual maximum sea surface elevations (crest heights) and maximum crest-to-trough wave heights. Results highlight the sea areas around the storm centre where the spatio-temporal highest waves are more likely, and, via scale analysis, the principal mechanisms responsible for the occurrence of extreme conditions in bimodal (composed of wind-sea and swell) and short-crested storm seas. We find that individual waves are the highest to the north-east of the translating cyclone, where sea states are more energetic. However, in the south/south-west of the centre, where opposing wind-sea/swell sea states dominate, directional spread and bound nonlinear interactions are enhanced. In this area, more extreme waves may occur, having the maximum crest and wave heights mean values in excess of 1.3 and 2.1 times the significant wave height, respectively. This set of results provides insights into the role of the dispersive and directional focusing enhanced by nonlinearities up to the second order as an effective mechanism for the formation of extreme waves under cyclone winds. To examine what physical mechanism is behind the generation of extreme waves in different regions around the cyclone, we also explore for comparison areas where nonlinear four-wave interactions are more likely to occur.