Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

A Numerical Study on Forecasting the Henan Extraordinarily Heavy Rainfall Event in August 1975

This study is essentially an experiment on the control experiment in the August 1975 catastrophe which was the heaviest rainfall in China's Mainland with a maximum 24-h rainfall of 1060.3 mm, and it significantly demonstrates that the limited area model can still skillfully give reasonable results even only the conventional data are available. For such a heavy rainfall event, a grid length of 90 km is too large while 45 km seems acceptable. Under these two grid sizes, the cumulus parameterization scheme is evidently superior to the explicit scheme since it restricts instabilities such as CISK to limited extent. The high resolution scheme for the boundary treatment does not improve forecasts significantly.The experiments also revealed some interesting phenomena such as the forecast rainfall being too small while affecting synoptic system so deep as compared with observations. Another example is the severe deformation of synoptic systems both in initial conditions and forecast fields in the presence of complicated topography. Besides, the fixed boundary condition utilized in the experiments along with current domain coverage set some limitations to the model performances.

Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of South China

This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.

Analysis of a Heavy Rainfall Event over Beijing During 21-22 July2012 Based on High Resolution Model Analyses and Forecasts

The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall season,and TC intensity.In the present study,we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added.Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018.The first experiment was designed as the DSAEF_LTP model with TC track,TC landfall season,and intensity(DSAEF_LTP-1).The other three experiments were based on the first experiment,but with new ensemble forecast schemes added(DSAEF_LTP-2),new similarity regions added(DSAEF_LTP-3),and both added(DSAEF_LTP-4),respectively.Results showed that,after new similarity regions added into the model(DSAEF_LTP-3),the forecasting performance of the DSAEF_LTP model for heavy rainfall(accumulated precipitation≥250 mm and≥100 mm)improved,and the sum of the threat score(TS250+TS100)increased by 4.44%.Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1,the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously(DSAEF_LTP-4),with the TS increasing by 25.36%.Moreover,the forecasting performance of the four experiments was compared with four operational numerical weather prediction models,and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall.Finally,some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.