Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

Spatio-Temporal Characteristics of Heavy Precipitation Forecasts from ECMWF in Eastern China

This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China from the European Centre for Medium-Range Weather Forecasts(ECMWF) using the time-domain version of the Method for Object-based Diagnostic Evaluation(MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 are selected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assesses the overall simulation capability of models for the life history of precipitation systems. The results of multiple tests with different parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitation tracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for different classified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and longlifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and shortlifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for the duration and dissipation of precipitation objects with large-area or long-lifespan(such as typhoon precipitation) while having large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simulation results regarding the generation of precipitation objects show that it performs relatively well in simulating the generation of large-area and fast-moving precipitation objects. However, there are significant differences in the forecasted generation of small-area and slow-moving precipitation objects after 9 hours.

A Potential Risk Index Dataset for Landfalling Tropical Cyclones over the Chinese Mainland(PRITC dataset V1.0)

A dataset entitled“A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland”(PRITC dataset V1.0)is described in this paper,as are some basic statistical analyses.Estimating the severity of the impacts of tropical cyclones(TCs)that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study,including an index combining TC-induced precipitation and wind(IPWT)and further information,such as the corresponding category level(CAT_IPWT),an index of TC-induced wind(IWT),and an index of TC-induced precipitation(IPT).The current version of the dataset includes TCs that made landfall from 1949-2018;the dataset will be extended each year.Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased,as embodied by the annual mean IPWT values,and increases in TC-induced precipitation are the main contributor to this increase.TC Winnie(1997)and TC Bilis(2006)were the two TCs with the highest IPWT and IPT values,respectively.The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

Evaluation of Tropical Cyclone Intensity Forecasts from Five Global Ensemble Prediction Systems During 2015-2019

This study presented an evaluation of tropical cyclone(TC) intensity forecasts from five global ensemble prediction systems(EPSs) during 2015-2019 in the western North Pacific region. Notable error features include the underestimation of the TC intensity by ensemble mean forecast and the under-dispersion of the probability forecasts.The root mean square errors(brier scores) of the ensemble mean(probability forecasts) generally decrease consecutively at long lead times during the five years, but fluctuate between certain values at short lead times.Positive forecast skill appeared in the most recent two years(2018-2019) at 120 h or later as compared with the climatology forecasts. However, there is no obvious improvement for the intensity change forecasts during the 5-year period, with abrupt intensity change remaining a big challenge. The probability forecasts show no skill for strong TCs at all the lead times. Among the five EPSs, ECMWF-EPS ranks the best for the intensity forecast, while NCEPGEFS ranks the best for the intensity change forecast, according to the evaluation of ensemble mean and dispersion.As for the other probability forecast evaluation, ECMWF-EPS ranks the best at lead times shorter than 72 h, while NCEP-GEFS ranks the best later on.

ASSIMILATION OF REAL OBSERVATIONAL DATA WITH THE GSI-HYBRID DATA ASSIMILATION SYSTEM TO IMPROVE TYPHOON FORECAST

A hybrid GSI(Grid-point Statistical Interpolation)-ETKF(Ensemble Transform Kalman Filter) data assimilation system has been recently developed for the WRF(Weather Research and Forecasting) model and tested with simulated observations for tropical cyclone(TC) forecast. This system is based on the existing GSI but with ensemble background information incorporated. As a follow-up, this work extends the new system to assimilate real observations to further understand the hybrid scheme. As a first effort to explore the system with real observations, relatively coarse grid resolution(27 km) is used. A case study of typhoon Muifa(2011) is performed to assimilate real observations including conventional in-situ and satellite data. The hybrid system with flow-dependent ensemble covariance shows significant improvements with respect to track forecast compared to the standard GSI system which in theory is three dimensional variational analysis(3DVAR). By comparing the analyses, analysis increments and forecasts, the hybrid system is found to be potentially able to recognize the existence of TC vortex, adjust its position systematically, better describe the asymmetric structure of typhoon Muifa and maintain the dynamic and thermodynamic balance in typhoon initial field. In addition, a cold-start hybrid approach by using the global ensembles to provide flow-dependent error is tested and similar results are revealed with those from cycled GSI-ETKF approach.

A Comparison of Two Bulk Microphysics Parameterizations for the Study of Aerosol Impacts on an Idealized Supercell

Idealized supercell storms are simulated with two aerosol-aware bulk microphysics schemes(BMSs),the Thompson and the Chen-Liu-Reisner(CLR),using the Weather Research and Forecast(WRF)model.The objective of this study is to investigate the parameterizations of aerosol effects on cloud and precipitation characteristics and assess the necessity of introducing aerosols into a weather prediction model at fine grid resolution.The results show that aerosols play a decisive role in the composition of clouds in terms of the mixing ratios and number concentrations of liquid and ice hydrometeors in an intense supercell storm.The storm consists of a large amount of cloud water and snow in the polluted environment,but a large amount of rainwater and graupel instead in the clean environment.The total precipitation and rain intensity are suppressed in the CLR scheme more than in the Thompson scheme in the first three hours of storm simulations.The critical processes explaining the differences are the auto-conversion rate in the warm-rain process at the beginning of storm intensification and the low-level cooling induced by large ice hydrometeors.The cloud condensation nuclei(CCN)activation and auto-conversion processes of the two schemes exhibit considerable differences,indicating the inherent uncertainty of the parameterized aerosol effects among different BMSs.Beyond the aerosol effects,the fall speed characteristics of graupel in the two schemes play an important role in the storm dynamics and precipitation via low-level cooling.The rapid intensification of storms simulated with the Thompson scheme is attributed to the production of hail-like graupel.

Western North Pacific Tropical Cyclone Database Created by the China Meteorological Administration

This paper describes the access to, and the content, characteristics, and potential applications of the tropical cyclone(TC) database that is maintained and actively developed by the China Meteorological Administration, with the aim of facilitating its use in scientific research and operational services. This database records data relating to all TCs that have passed through the western North Pacific(WNP) and South China Sea(SCS) since 1949. TC data collection has expanded over recent decades via continuous TC monitoring using remote sensing and specialized field detection techniques,allowing collation of a multi-source TC database for the WNP and SCS that covers a long period, with wide coverage and many observational elements. This database now comprises a wide variety of information related to TCs, such as historical or real-time locations(i.e., best track and landfall), intensity, dynamic and thermal structures, wind strengths, precipitation amounts, and frequency. This database will support ongoing research into the processes and patterns associated with TC climatic activity and TC forecasting.

ESTIMATION OF OBSERVATION IMPACT WITH AN ENSEMBLE SENSITIVITY METHOD

The ensemble based forecast sensitivity to observation method by Liu and Kalnay is applied to the SPEEDY-LETKF system to estimate the observation impact of three types of simulated observations.The estimation results show that all types of observations have positive impact on short-range forecast.The largest impact in Northern Hemisphere is produced by rawinsondes,followed by satellite retrieved profiles and cloud drift wind data,which in Southern Hemisphere is produced by satellite retrieved profiles,rawinsondes and cloud drift wind data.Satellite retrieved profiles influence more on the Southern Hemisphere than on the Northern Hemisphere due to few observations from rawinsondes in the Southern Hemisphere.At the level of 200 to 300 h Pa,the largest impact is attributed to wind observations from rawinsondes and cloud drift wind.

Performance of tropical cyclone forecasts in the western North Pacific in 2017

The forecasts of tropical cyclones(TC)in 2017 from five official guides,six global models,six regional models and six ensemble systems were assessed to study the current capability of track and intensity forecasts for the western North Pacific.The results show that the position errors for official agencies were under 100,165,265,335 and 425 km at the lead times of 24,48,72,96 and 120 h,respectively.As the forecast lead times increased,the forecasted TCs propagated,on average,too slow for most official guides.It is encouraging to note that all the models had positive skill scores,there is an overall upward trend in the skill scores of the models during from 2010 to 2017.Furthermore,both global and regional models’intensity forecast skill was increasing year by year from 2010 to 2017.For the ensemble prediction systems(EPSs),ECMWF-EPS was the best forecast system for the lead time less than 72 h,beyond the 72 h,the best EPS belong to NCEP-GEFS.

A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China

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 intofive 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.

Ensemble data assimilation and prediction of typhoon and associated hazards using TEDAPS:evaluation for 2015-2018 seasons

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 realtime since 2015.Performance of TED APS 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.TED APS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS.The errors of TED APS 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.

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

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

Application of the frequency-matching method in the probability forecast of landfalling typhoon rainfall

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

Evaluation of forecast performance for Super Typhoon Lekima in 2019

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

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Predictability and dynamics of the rapid intensification of Super Typhoon Lekima (2019)

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

Validation of Doppler Wind Lidar during Super Typhoon Lekima (2019)

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

Impact of physical representations in CALMET on the simulated wind field over land during Super Typhoon Meranti(2016)

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 quasinondivergent.

Improvements in the GRAPES-TCM and the forecast performance analysis in 2019

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

The impact of Typhoon Lekima (2019) on East China: a postevent survey in Wenzhou City and Taizhou City

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