Relationships between tropical cyclone (TC) precipitation, wind, and storm damage are analyzed for China based on TCs over the period from 1984 to 2013. The analysis shows that the maximum daily areal precipitation from stations with daily precipitation of ≥50 mm and the sum of wind gusts of ≥13.9 m/s can be used to estimate the main damage caused by TCs, and an index combining the precipitation and wind gust of a TC (IPWT) is defined to assess the severity of the combined impact of precipitation and wind. The correlation coefficient between IPWT and the damage index for affecting TCs is 0.80, which is higher than that for only precipitation or wind. All TCs with precipitation and wind affecting China are divided into five categories, Category 0 to Category 4, based on IPWT, where higher categories refer to higher combined impacts of precipitation and wind. The combined impact category is closely related to damage category and it can be used to estimate the potential damage category in operational work. There are 87.7%, 72.9%, 69.8%, and 73.4% of cases that have the same or one category difference between damage category and combined impact category for Categories 1, 2, 3, and 4, respectively. IPWT and its classification can be used to assess the severity of the TC impact and of combined precipitation and wind conveniently and accurately, and the potential damage caused by TCs. The result will be a good supplementary data for TC intensity, precipitation, wind, and damage. In addition, IPWT can be used as an index to judge the reliability of damage data. Further analysis of the annual frequency of combined precipitation-wind impact categories reveals no significant increasing or decreasing trend in impact over China over the past 30 years.
Rice production in China’s coastal areas is frequently affected by typhoons, since the associated severe storms, with heavy rain and the strong winds, lead directly to the rice plants becoming flooded or lodged. Long-term flooding and lodging can cause a substantial reduction in rice yield or even destroy the harvest completely. It is therefore urgent to obtain accurate information about paddy rice flooding and lodging as soon as possible after the passing of the storm. This paper proposes a workflow in Google Earth Engine (GEE) for mapping the flooding and lodging area of paddy rice in Wenzhou City, Zhejiang, following super typhoon Maria (Typhoon No.8 in 2018). First, paddy rice in the study area was detected by multi-temporal Sentinel-1 backscatter data combined with Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) using the Random Forests (RFs) algorithm. High classification accuracies were achieved, whereby rice detection accuracy was calculated at 95% (VH+ NDVI-based) and 87% (VV+ NDVI-based). Secondly, Change Detection (CD) based Rice Normalized Difference Flooded Index (RNDFI) and Rice Normalized Difference Lodged Index (RNDLI) were proposed to detect flooding and lodged paddy rice. Both RNDFI and RNDLI were tested based on four different remote sensing data sets, including the Sentinel-1-derived VV and VH backscattering coefficient, Sentinel-2-derived NDVI and Enhanced Vegetation Index (EVI). Overall agreement regarding detected area between the each two different data sets was obtained, with values of 79% to 93% in flood detection and 64% to 88% in lodging detection. The resulting flooded and lodged paddy rice maps have potential to reinforce disaster emergency assessment systems and provide an important resource for disaster reduction and emergency departments.
Physical models used to forecast the temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. Owing to the existing measuring technology and our knowledge of the physical laws controlling landslide initiation, model uncertainties are due to an inability to accurately quantify the model input parameters and rainfall forcing data. An uncertainty analysis of slope instability prediction provides a rationale for refining the geotechnical models. The Transient Rainfall Infiltration and Grid-based Regional Slope Stability-Probabilistic (TRIGRS-P) model adopts a probabilistic approach to compute the changes in the Factor of Safety (FS) due to rainfall infiltration. Slope Infiltration Distributed Equilibrium (SLIDE) is a simplified physical model for landslide prediction. The new code (SLIDE-P) is also modified by adopting the same probabilistic approach to allow values of the SLIDE model input parameters to be sampled randomly. This study examines the relative importance of rainfall variability and the uncertainty in the other variables that determine slope stability. The precipitation data from weather stations, China Meteorological Administration Land Assimilation System 2.0 (CLDAS2.0), China Meteorological Forcing Data set precipitation (CMFD), and China geological hazard bulletin are used to drive TRIGRS, SLIDE, TRIGRS-P and SLIDE-P models. The TRIGRS-P and SLIDE-P models are used to generate the input samples and to calculate the values of FS. The outputs of several model runs with varied input parameters and rainfall forcings are analyzed statistically. A comparison suggests that there are significant differences in the simulations of the TRIGRS-P and SLIDE-P models. Although different precipitation data sets are used, the simulation results of TRIGRS-P are more concentrated. This study can inform the potential use of numerical models to forecast the spatial and temporal occurrence of regional rainfall-induced shallow landslides.
Complicated terrain was considered and simplified as two-dimensional (2D) terrain in a dynamical downscaling model and a parametric wind field model for typhoons developed by the Shanghai Typhoon Institute. The 2D terrain was further modeled as uphill and downhill segments with various slope angles relative to the incoming flow. The wind speed ratios and pressure characteristics around the 2D terrain were numerically and experimentally investigated in this study. Aerodynamic characteristics of the 2D terrain with a limited-length upper surface were first investigated in the wind tunnel with sheared incoming flow. The corresponding numerical investigation was also conducted by using the commercial computational fluid dynamics code FLUENT with the realizable k-ε turbulence model. Special efforts were made to maintain the inflow boundary conditions throughout the computational domain. Aerodynamic characteristics were then investigated for the ideal 2D terrain with an unlimited-length upper surface by using a numerical method with uniform incoming flow. Comparisons of the different terrain models and incoming flows from the above studies show that the wind pressure coefficients and the wind speed ratios are both affected by the slope angle. A negative peak value of the wind pressure coefficients exists at the escarpment point, where flow separation occurs, for the uphill and downhill terrain models with slope angles of 40° and 30°, respectively. Correspondingly, the streamwise wind speed ratios at the points above the escarpment point for the uphill terrain model increase with increasing slope angle, reach their peak values at the slope angle of α = 40° and decrease when the slope angle increases further. For the downhill terrain model, similar trends exist at the points above the escarpment point with the exception that the critical slope angle is α = 30°.
Inclusion of cloud processes is essential for precipitation prediction with a numerical weather prediction model. However, convective parameterization contains numerous parameters whose values are in large uncertainties. In particular, it is still not clear how the parameters of a sub-grid-scale convection scheme can be modified to improve high-resolution precipitation prediction. To address these issues, a micro-genetic (micro-GA) algorithm is coupled to the Kain-Fritsch (KF) convective parameterization scheme (CPS) in the WRF to improve the quantitative precipitation forecast (QPF). The optimization focuses on two parameters in the KF scheme: the convective time scale and the conversion rate. The optimizing process is controlled by the micro-GA using a QPF skill score as the fitness function. Two heavy rainfall events related to typhoons that made landfall over the south-east coastal region of China are selected, and for each case the parameter values are adjusted to achieve the best QPF skill. Significant improvements in QPF are evident with an increase in the average equitable threat score (ETS) by 5.8% for the first case, and by 18.4% for the second case. The results demonstrate that the micro-GA-KF coupling system is effective in optimizing the parameter values, which affect the applicability of CPS in a high-resolution model, and therefore improves the rainfall prediction in both ETS and spatial distribution.
The initial condition accuracy is a major concern for tropical cyclone (TC) numerical forecast. The ensemble-based data assimilation techniques have shown great promise to initialize TC forecast. In addition to initial condition uncertainty, representing model errors (e.g. physics deficiencies) is another important issue in an ensemble forecasting system. To improve TC prediction from both deterministic and probabilistic standpoints, a Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting (WRF) model, has been developed in Shanghai Typhoon Institute and running real-time since 2015. Performance of TEDAPS in the prediction of track, intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015–2018, and compared against the NCEP GEFS.
TEDAPS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS. The errors of TEDAPS track forecasts are comparative with (slightly worse than) those of GEFS at longer (shorter) forecast leads. TEDAPS ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times, whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical underestimation. Four case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs, as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.
A WRF (Weather Research and Forecasting Model)/CALMET (California Meteorological Model) coupled system is used to investigate the impact of physical representations in CALMET on simulations of the near-surface wind field of Super Typhoon Meranti (2016). The coupled system is configured with a horizontal grid spacing of 3 km in WRF and 500 m in CALMET, respectively. The model performance of the coupled WRF/CALMET system is evaluated by comparing the results of simulations with observational data from 981 automatic surface stations in Fujian Province. The root mean square error (RMSE) of the wind speed at 10 m in all CALMET simulations is significantly less than the WRF simulation by 20%–30%, suggesting that the coupled WRF/CALMET system is capable of representing more realistic simulated wind speed than the mesoscale model only. The impacts of three physical representations including blocking effects, kinematic effects of terrain and slope flows in CALMET are examined in a specified local region called Shishe Mountain. The results show that before the typhoon landfall in Xiamen, a net downslope flow that is tangent to the terrain is generated in the west of Shishe Mountain due to blocking effects with magnitude exceeding 10 m/s. However, the blocking effects seem to take no effect in the strong wind area after typhoon landfall. Whether being affected by the typhoon strong wind or not, the slope flows move downslope at night and upslope in the daytime due to the diurnal variability of the local heat flux with magnitude smaller than 3 m/s. The kinematic effects of terrain, which are speculated to play a significant role in the typhoon strong wind area, can only be applied to atmospheric flows in stable conditions when the wind field is quasi-nondivergent.
The probability of quantitative precipitation forecast (PQPF) of three Bayesian Model Averaging (BMA) models based on three raw super ensemble prediction schemes (i. e., A, B, and C) are established, which through calibration of their parameters using 1–3 day precipitation ensemble prediction systems (EPSs) from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) and observation during land-falling of three typhoons in south-east China in 2013. The comparison of PQPF shows that the performance is better in the BMA than that in raw ensemble forecasts. On average, the mean absolute error (MAE) of 1 day lead time forecast is reduced by 12.4%, and its continuous ranked probability score (CRPS) of 1–3 day lead time forecast is reduced by 26.2%, respectively. Although the amount of precipitation prediction by the BMA tends to be underestimated, but in view of the perspective of probability prediction, the probability of covering the observed precipitation by the effective forecast ranges of the BMA are increased, which is of great significance for the early warning of torrential rain and secondary disasters induced by it.
The impacts of multi-time-scale flows on northward and north-eastward moving tropical cyclones (TCs) near the east coast of China in August and September are investigated using reanalysis data from 1982 to 2012. TCs of interest are under the influence of the subtropical high-pressure system in the western North Pacific (WNP). In August when the subtropical high-pressure system is strong and close to the coast line, most TCs in the region move northward, while more TCs move north-eastward in September when the subtropical high-pressure system retreats to the east.
To investigate the influence from different time-scales, the environmental flow is divided into four components, the synoptic flow, the intraseasonal flow, the interannual flow and the climatological background field. Analysis of steering flows between 25°N and 30°N indicates that the meridional steering vectors from all time-scales point to the north, dominated by the intraseasonal component. The deciding factor on whether a TC moves to the north or north-east between 25°N and 30°N is the zonal steering vector. For the northward moving group, the sum of the zonal steering from all time-scales is very small. On the other hand, the north-east moving group has a net eastward zonal component mainly contributed by the climatological mean flow.
Several individual cases that stood out from the majority of the group are analyzed. For those cases, the intraseasonal flow plays an important role in affecting the movement of the TCs mainly through the wave train, in which a cyclonic circulation is located to the north-west (north) and an anticyclonic circulation to the south-east (east) of TCs. The analysis of the steering vectors indicates the importance of all components with different time-scales to the movement of TCs.
Through a cloud-resolving simulation of the rapid intensification (RI) of Typhoon Meranti (2016), the convections, warm core, and heating budget are investigated during the process of RI. By investigating the spatial distributions and temporal evolutions of both convective-stratiform precipitation and shallow-deep convections, we find that the inner-core convections take mode turns, from stratiform-precipitation (SP) dominance to convective-precipitation (CP) prevalence during the transition stages between pre-RI and RI. For the CP, it experiences fewer convections before RI, and the conversion from moderate/moderate-deep convections to moderate-deep/deep convections during RI. There is a clear upper-level warm-core structure during the process of RI. However, the mid-low-level warming begins first, before the RI of Meranti. By calculating the local potential temperature (q) budget of various convections, the link between convections and the warm core (and further to RI via the pressure drop due to the warming core) is established. Also, the transport pathways of heating toward the center of Meranti driven by pressure are illuminated. The total hydrostatic pressure decline is determined by the mid-low-level warm anomaly before RI, mostly caused by SP. The azimuthal-mean diabatic heating is the largest heating source, the mean vertical heat advection controls the vertical downwards transport by adiabatic warming of compensating downdrafts above eye region, and then the radial q advection term radially transports heat toward the center of Meranti in a slantwise direction. Accompanying the onset of RI, the heating efficiency of the upper-level warming core rises swiftly and overruns that of the mid-low-level warm anomaly, dominating the total pressure decrease and being mainly led by moderate-deep and deep convections. Aside from the characteristics in common with SP, for CP, the eddy component of radial advection also plays a positive role in warming the core, which enhances the centripetal transport effect and accelerates the RI of Meranti.
Data from the China Meteorological Administration and ERA-Interim are used to examine the environmental characteristics of landfalling tropical cyclones (TCs) with abrupt intensity change. The results show that, of all 657 landfalling TCs during 1979–2017, 71%, 70% and 65% of all landfalling TDs, TSs and TYs, respectively, intensify. Of all the 16595 samples, 4.0% and 0.2% of typhoons and tropical storms, respectively, experience over-water rapid intensification (RI) process during their life cycle. Meanwhile, 4.5% and 0.6% of typhoons and tropial storms, respectively, undergo over-water rapid decay (RD). These two kinds of cases, i.e., RI and RD, are used to analyze their associated large-scale conditions. Comparisons show that the RI cases are generally on the south side of the strong western Pacific subtropical high (WPSH); warm sea surface temperatures (SSTs) and sufficient water vapor fluxes existing in RI samples is a dominant feature that is conducive to the development of TCs. Also, the moderate low-level relative vorticity is favorable for TC intensification. On the contrary, the RD TCs are located on the west side of the WPSH; significant decreasing SSTs and low-level water vapor transport may synergistically contribute to RD. Simultaneously, low-level relative vorticity seems to be unfavorable for the development of TCs.
The evolution of Typhoon Mujigae (2015) during the landfall period is determined using potential vorticity (PV) based on a high-resolution numerical simulation. Diabatic heating from deep moist convections in the eyewall produces a hollow PV tower extending from the lower troposphere to the middle levels. Since the potential temperature and wind fields could be highly asymmetric during landfall, the fields are divided into symmetric and asymmetric components. Thus, PV is split into three parts: symmetric PV, first-order asymmetric PV, and quadratic-order asymmetric PV. By calculating the azimuth mean, the first-order term disappears. The symmetric PV is at least one order of magnitude larger than the azimuthal mean quadratic-order term, nearly accounting for the mean cyclone. Furthermore, the symmetric PV tendency equation is derived in cylindrical coordinates. The budget terms include the symmetric heating term, flux divergence of symmetric PV advection due to symmetric flow, flux divergence of partial first-order PV advection due to asymmetric flow, and the conversion term between the symmetric PV and quadratic-order asymmetric term. The diagnostic results indicate that the symmetric heating term is responsible for the hollow PV tower generation and maintenance. The symmetric flux divergence largely offsets the symmetric heating contribution, resulting in a horizontal narrow ring and vertical extension structure. The conversion term contribution is comparable to the mean term contributions, while the contribution of the partial first-order PV asymmetric flux divergence is apparently smaller. The conversion term implicitly contains the combined effects of processes that result in asymmetric structures. This term tends to counteract the contribution of symmetric terms before landfall and favor horizontal PV mixing after landfall.
As a basic property of cloud, accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons. Millimeter-wave cloud radar is an important means of identifying cloud type. Here, we develop a fuzzy logic algorithm that depends on radar range-height-indicator (RHI) data and takes into account the fundamental physical features of different cloud types. The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar. The input parameters of the algorithm include average reflectivity factor intensity, ellipse long axis orientation, cloud base height, cloud thickness, presence/absence of precipitation, ratio of horizontal extent to vertical extent, maximum echo intensity, and standard variance of intensities. The identified cloud types are stratus (St), stratocumulus (Sc), cumulus (Cu), cumulonimbus (Cb), nimbostratus (Ns), altostratus (As), altocumulus (Ac) and high cloud. The cloud types identified using the algorithm are in good agreement with those identified by a human observer. As a case study, the algorithm was applied to typhoon Khanun (1720), which made landfall in south-eastern China in October 2017. Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.
A spiral cloud belt matching (SCBeM) technique is proposed for automatically locating the tropical cyclone (TC) center position on the basis of multi-band geo-satellite images. The technique comprises four steps: fusion of multi-band geo-satellite images, extraction of TC cloud systems, construction of a spiral cloud belt template (CBT), and template matching to locate the TC center. In testing of the proposed SCBeM technique on 97 TCs over the western North Pacific during 2012–2015, the median error (ME) was 50 km. An independent test of another 29 TCs in 2016 resulted in a ME of 54 km. The SCBeM performs better for TCs with intensity above “typhoon” level than it does for weaker systems, and is not suitable for use on high-latitude or landfall TCs if their cloud band formations have been destroyed by westerlies or by terrain. The proposed SCBeM technique provides an additional solution for automatically and objectively locating the TC center and has the potential to be applied conveniently in an operational setting. Intercomparisons between the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) and SCBeM methods using events from 2014 to 2016 reveal that ARCHER has better location accuracy. However, when IR imagery alone is used, the ME of SCBeM is 54 km, and in the case of low latitudes and low vertical wind shear the ME is 45–47 km, which approaches that of ARCHER (49 km). Thus, the SCBeM method is simple, has good time resolution, performs well and is a better choice for those TC operational agencies in the case that the microwave images, ASCAT, or other observations are unavailable.
The wind environment around tall buildings in a central business district (CBD) was numerically investigated. The district covers an area of ~4.0 km2 and features a high density of tall buildings. In this study, only buildings taller than 20 m were considered, resulting in 173 tall buildings in the analysis. The numerical investigation was realized using the commercial computational fluid dynamics code FLUENT with the realizable