Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

A STUDY OF THE INFLUENCE OF MICROPHYSICAL PROCESSES ON TYPHOON NIDA(2016) USING A NEW DOUBLE-MOMENT MICROPHYSICS SCHEME IN THE WEATHER RESEARCH AND FORECASTING MODEL

The basic structure and cloud features of Typhoon Nida(2016) are simulated using a new microphysics scheme(Liuma) within the Weather Research and Forecasting(WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly-used microphysics scheme(WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme,it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.

A Methodological Study on Using Weather Research and Forecasting(WRF) Model Outputs to Drive a One-Dimensional Cloud Model

A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

Solar Energy Resource Characteristics of Photovoltaic Power Station in Shandong Province

[Objective] The aim was to analyze characters of solar energy in photo- voltaic power stations in Shandong Province. [Method] The models of total solar radiation and scattered radiation were determined, and solar energy resources in pho-tovoltaic power stations were evaluated based on illumination in horizontal plane and cloud data in 123 counties or cities and observed information in Jinan, Fushan and Juxian in 1988-2008. [Result] Solar energy in northern regions in Shandong proved most abundant, which is suitable for photovoltaic power generation; the optimal angle of tilt of photovoltaic array was at 35°, decreasing by 2°-3° compared with local latitude. Total solar radiation received by the slope with optimal angle of tilt exceeded 1 600 kw.h/（m2.a）, increasing by 16% compared with horizontal planes. The maximal irradiance concluded by WRF in different regions tended to be volatile in 1 020-1 060 W/m2. [Conclusion] The research provides references for construction of photovoltaic power stations in Shandong Province.

Simulation of Quasi-Linear Mesoscale Convective Systems in Northern China：Lightning Activities and Storm Structure

Two intense quasi-linear mesoscale convective systems（QLMCSs） in northern China were simulated using the WRF（Weather Research and Forecasting） model and the 3D-Var（three-dimensional variational） analysis system of the ARPS（Advanced Regional Prediction System） model.A new method in which the lightning density is calculated using both the precipitation and non-precipitation ice mass was developed to reveal the relationship between the lightning activities and QLMCS structures.Results indicate that,compared with calculating the results using two previous methods,the lightning density calculated using the new method presented in this study is in better accordance with observations.Based on the calculated lightning densities using the new method,it was found that most lightning activity was initiated on the right side and at the front of the QLMCSs,where the surface wind field converged intensely.The CAPE was much stronger ahead of the southeastward progressing QLMCS than to the back it,and their lightning events mainly occurred in regions with a large gradient of CAPE.Comparisons between lightning and non-lightning regions indicated that lightning regions featured more intense ascending motion than non-lightning regions;the vertical ranges of maximum reflectivity between lightning and non-lightning regions were very different;and the ice mixing ratio featured no significant differences between the lightning and non-lightning regions.

Simulations of Microphysics and Precipitation in a Stratiform Cloud Case over Northern China:Comparison of Two Microphysics Schemes

Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall event on 20–21 April 2010.The simulation output was compared with precipitation and aircraft observations.The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage.Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles.The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other.However,differences existed in the temporal and spatial evolutions of the precipitation distribution.An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison,while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed.The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles.P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.

Simulation of a torrential rainstorm in Xinjiang and gravity wave analysis

We used a weather research and forecasting model to simulate a torrential rainstorm that occurred in Xinjiang,China during June 16–17,2016.The model successfully simulated the rainfall area,precipitation intensity,and changes in precipitation.We identified a clear wave signal using the two-dimensional fast Fourier transform method;the waves propagated westwards,with wavelengths of 45–20 km,periods of 50–120 min,and phase velocities mainly concentrated in the-25 m/s to-10 m/s range.The results of wavelet cross-spectral analysis further confirmed that the waves were gravity waves,peaking at 11:00 UTC,June 17,2016.The gravity wave signal was identified along 79.17–79.93°E,81.35–81.45°E and 81.5–81.83°E.The gravity waves detected along 81.5–81.83°E corresponded well with precipitation that accumulated in 1 h,indicating that gravity waves could be considered a rainstorm precursor in future precipitation forecasts.

Impacts of Roof/Ground Mitigation Strategies on Improving the Urban Thermal Environment and Human Comfort over the Yangtze River Delta, China

The combined effects of global warming and the urban heat islands exacerbate the risk of urban heat stress. It is crucial to implement effective cooling measures in urban areas to improve the comfort of the thermal environment. In this study, the Weather Research and Forecasting Model(WRF), coupled with a single-layer Urban Canopy Model(UCM), was used to study the impact of heat mitigation strategies. In addition, a 5-km resolution land-cover dataset for China(ChinaLC), which is based on satellite remote sensing data, was adjusted and used, and 18 groups of numerical experiments were designed, to increase the albedo and vegetation fraction of roof/ground parameters. The experiments were conducted for four heatwave events that occurred in the summer of 2013 in the Yangtze River Delta urban agglomeration of China. The simulated results demonstrated that, for the single roof/ground schemes, the mitigation effects were directly proportional to the albedo and greening. Among all the experimental schemes, the superposed schemes presented better cooling effects. For the ground greening scheme, with similar net radiation flux and latent heat flux, its storage heat was lower than that of the roof greening scheme, resulting in more energy flux into the atmosphere, and its daytime cooling effect was not as good as that of the roof greening scheme. In terms of human thermal comfort(HTC), the improvement achieved by the ground greening scheme was better than any other single roof/ground schemes, because the increase in the relative humidity was small. The comprehensive evaluation of the mitigation effects of different schemes on the thermal environment presented in this paper provides a theoretical basis for improving the urban environment through rational urban planning and construction.

Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques

Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.

Quantify Urbanization-Induced Precipitation and Runoff Anomalies over the Qinhuai River Basin of China: Sensitivity Experiments with WRF-Hydro

Urbanization-related precipitation and surface runoff changes have been widely investigated,but few studies have directly quantified these changes and their link to urbanization in the hydrological cycle.A two-way dynamically coupled atmospheric–hydrological modeling system,Weather Research and Forecasting(WRF)-Hydro,has been applied in this study to perform the quantification.The offline WRF-Hydro was first calibrated and validated for several flooding events against gauge observed streamflow data,with the Nash–Sutcliffe efficiency reaching 0.9.Compared to the WRF model,WRF-Hydro resolves more detailed rainfall pattern features and reproduces the gauge rainfall with a correlation coefficient of 0.8.Then,the impact of urbanization on hydrometeorological processes was investigated with coupled WRF-Hydro sensitivity simulations over the Qinhuai River basin of China during 2 June–31 July 2015.The results indicate that urbanization enhances regional precipitation,resulting in an indirect increase in surface runoff,overland flow,and streamflow by 16.7,93.5,and 111.2 mm,respectively;however,the impervious area results in higher surface runoff,overland flow,and streamflow.Moreover,changes in main hydrometeorological processes further impact the atmospheric–terrestrial water budget,resulting in a decrease in terrestrial water storage and an increase(a decrease)in precipitable water storage in the middle(lower)parts of the lower troposphere.These changes are likely associated with the warmer urban environment than rural areas.Increased water vapor and strengthened convective conditions in the middle part of the lower troposphere due to urban warming are advantageous to the formation of precipitation in urban areas,which in turn increases surface runoff,thereby facilitating the water cycle and altering the atmospheric–terrestrial water budget.

Validation of WRF model on simulating forcing data for Heihe River Basin

The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA （China Meteorological Administration） and hourly meteorological observation data from seven sites of WATER （Watershed Airborne Telemetry Experimental Research） are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE （Mean Bias Error） of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were -0.19 ℃, -4.49 hPa, 4.08% and 0.92 m/s, the average RMSE （Root Mean Square Error） of them were 2.11 ℃, 5.37 hPa, 9.55% and 1.73 m/s, and the average R （correlation coefficient） of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were-0.16 ℃,-6.62 hPa,-5.14%, 0.26 m/s, 33.0 W/m^2 and-6.44 W/m^2, the average RMSE of them were 2.62 ℃, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m^2 and 53.5 W/m^2, and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained： （1） regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; （2） the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; （3） both wind speed and rainfall simulated from WRF model did not agree well with observation data.

Increases in Anthropogenic Heat Release from Energy Consumption Lead to More Frequent Extreme Heat Events in Urban Cities

With economic development and rapid urbanization,increases in Gross Domestic Product and population in fastgrowing cities since the turn of the 21st Century have led to increases in energy consumption.Anthropogenic heat flux released to the near-surface atmosphere has led to changes in urban thermal environments and severe extreme temperature events.To investigate the effects of energy consumption on urban extreme temperature events,including extreme heat and cold events,a dynamic representation scheme of anthropogenic heat release(AHR)was implemented in the Advanced Research version of the Weather Research and Forecasting(WRF)model,and AHR data were developed based on energy consumption and population density in a case study of Beijing,China.Two simulations during 1999−2017 were then conducted using the developed WRF model with 3-km resolution with and without the AHR scheme.It was shown that the mean temperature increased with the increase in AHR,and more frequent extreme heat events were produced,with an annual increase of 0.02−0.19 days,as well as less frequent extreme cold events,with an annual decrease of 0.26−0.56 days,based on seven extreme temperature indices in the city center.AHR increased the sensible heat flux and led to surface energy budget changes,strengthening the dynamic processes in the atmospheric boundary layer that reduce AHR heating efficiency more in summer than in winter.In addition,it was concluded that suitable energy management might help to mitigate the impact of extreme temperature events in different seasons.

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.

A numerical simulation of latent heating within Typhoon Molave

The weather research and forecasting（WRF） model is a new generation mesoscale numerical model with a fine grid resolution（2 km）, making it ideal to simulate the macro-and micro-physical processes and latent heating within Typhoon Molave（2009）. Simulations based on a single-moment, six-class microphysical scheme are shown to be reasonable, following verification of results for the typhoon track, wind intensity, precipitation pattern, as well as inner-core thermodynamic and dynamic structures. After calculating latent heating rate, it is concluded that the total latent heat is mainly derived from condensation below the zero degree isotherm, and from deposition above this isotherm. It is revealed that cloud microphysical processes related to graupel are the most important contributors to the total latent heat. Other important latent heat contributors in the simulated Typhoon Molave are condensation of cloud water, deposition of cloud ice, deposition of snow, initiation of cloud ice crystals, deposition of graupel, accretion of cloud water by graupel, evaporation of cloud water and rainwater,sublimation of snow, sublimation of graupel, melting of graupel, and sublimation of cloud ice. In essence, the simulated latent heat profile is similar to ones recorded by the Tropical Rainfall Measuring Mission, although specific values differ slightly.

Effects of Land Use Changes Across Different Urbanization Periods on Summer Rainfall in the Pearl River Delta Core Area

The Pearl River Delta(PRD)is one of the three urban agglomerations in China that have experienced rapid development.For this study,a core area of the PRD was identified,comprising the highly urbanized areas of Guangzhou,Foshan,Zhongshan,Zhuhai,Shenzhen,and Dongguan Cities.The expansion of these urban areas was tracked across three time periods—the year population urbanization rate exceeded 70%(2000),18 years before(1982),and 18 years after(2018).This study used the Weather Research and Forecasting(WRF)model to explore summer rainfall changes across different urbanization periods in the PRD core area.The results show that urban land expansion mainly occurred in the post urbanization period.Rainfall changes acros s different urbanization periods were roughly consistent with previously observed spatial and temporal changes accompanying urban expansion in the PRD core area.Extreme rainfall mainly increased in the post urbanization period,shifting rainstorm center towards the PRD core area.Further causal analysis revealed that land use changes affected rainfall by altering thermodynamics and water vapor transfer.The urban expansion changed the surface energy balance,resulting in increased surface heating and heat island effects.The heat island effects thickened the planetary boundary layer and increased vertical wind speeds,which initiated dry island effects,thereby causing more water vapor transportation to the atmosphere.Consequently,rainstorms and extreme rainfall events have become concentrated in urban areas.

The Effect of Moisture in Different Altitude Layers on the Eastward Propagation of MJO

In this study,driven by ERA5 reanalysis data,the Weather Research and Forecasting(WRF)version 4.0 was used to investigate the eastward propagation of the Madden-Julian oscillation(MJO)in the tropical atmosphere during December-February(DJF)of 2007/2008.The experiment with 11 cumulus parameterization schemes respectively shows that the Grell 3D scheme is one of several worse ones in describing MJO activities.In addition,still by use of the Grell 3D scheme,four nudging assimilation experiments for water vapor in all model vertical layers(Ndg_all),lower layers(Ndg_low),middle layers(Ndg_mid),and upper layers(Ndg_upp)were conducted.It is found that when the water vapor in the model approaches to the observed value,the model performance for MJO activities is improved greatly.Among the four nudging simulations,Ndg_all certainly performs best.Although Ndg_mid is important for the MJO-filtered profiles related to moisture,Ndg_low and Ndg_upp exhibit superiority to Ndg_mid in simulating MJO eastward propagation.Ndg_low has advantages when MJO features are represented by zonal wind at 850 hPa and precipitation because the lower-level MJO-filtered moisture is conducive to the existence of lower-level condensational heating to the east of the MJO convective center.Ndg_upp performs better when describing the MJO eastward propagation features by outgoing longwave radiation(OLR)since it can capture the moisture and cloud top temperature of deep convection associated with MJO,as well as front Walker cell.These results suggest that the lower-level moisture is more important in regulating the MJO eastward propagation,and the observed maximum MJO-filtered moisture in the middle troposphere might be a phenomenon accompanying the MJO deep convection,but not a factor controlling its eastward propagation.

Performance of WRF Large Eddy Simulations in Modeling the Convective Boundary Layer over the Taklimakan Desert, China

The maximum height of the convective boundary layer(CBL)over the Taklimakan Desert can exceed 5000 m during summer and plays a crucial role in the regional circulation and weather.We combined the Weather Research and Forecasting Large Eddy Simulation(WRF-LES)with data from Global Positioning System(GPS)radiosondes and from eddy covariance stations to evaluate the performance of the WRF-LES in simulating the characteristics of the deep CBL over the central Taklimakan Desert.The model reproduced the evolution of the CBL processes reasonably well,but the simulations generated warmer and moister conditions than the observation as a result of the over-prediction of surface fluxes and large-scale advection.Further simulations were performed with multiple configurations and sensitivity tests.The sensitivity tests for the lateral boundary conditions(LBCs)showed that the model results are sensitive to changes in the time resolution and domain size of the specified LBCs.A larger domain size varies the distance of the area of interest from the LBCs and reduces the influence of large forecast errors near the LBCs.Comparing the model results using the original parameterization of sensible heat flux with the Noah land surface scheme and those of the sensitivity experiments showed that the desert CBL is sensitive to the sensible heat flux produced by the land surface scheme during daytime in summer.A reduction in the sensible heat flux can correct overestimates of the potential temperature profile.However,increasing the sensible heat flux significantly reduces the total time needed to increase the CBL to a relatively low altitude(<3 km)in the middle and initial stages of the development of the CBL rather than producing a higher CBL in the later stages.

Evaluation of the Forecast Accuracy of Near-Surface Temperature and Wind in Northwest China Based on the WRF Model

This study investigated the performance of the mesoscale Weather Research and Forecasting(WRF) model in predicting near-surface atmospheric temperature and wind for a complex underlying surface in Northwest China in June and December 2015. The spatial distribution of the monthly average bias errors in the forecasts of 2-m temperature and 10-m wind speed is analyzed first. It is found that the forecast errors for 2-m temperature and 10-m wind speed in June are strongly correlated with the terrain distribution. However, this type of correlation is not apparent in December, perhaps due to the inaccurate specification of the surface albedo and freezing-thawing process of frozen soil in winter in Northwest China in the WRF model. In addition, the WRF model is able to reproduce the diurnal variation in 2-m temperature and 10-m wind speed, although with weakened magnitude. Elevations and land-use types have strong influences on the forecast of near-surface variables with seasonal variations. The overall results imply that accurate specification of the complex underlying surface and seasonal changes in land cover is necessary for improving near-surface forecasts over Northwest China.

Statistics of the Z–R Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

The relationship between the radar reflectivity factor （Z） and the rainfall rate （R） is recalculated based on radar ob- servations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze-Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z-R relationship is combined with an empirical qr-R relationship to obtain a new Z-qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational （3DVar） data assimilation system of the Weather Research and Forecasting （WRF） model to im-prove the analysis and prediction of severe convective weather over the Yangtze--Huaihe River basin. The perform- ance of the corrected reflectivity operator used in the WRF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z-R relationship. Three experiments are conducted with the WRF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected refleetivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better per-forrnance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original re-flectivity operator. This suggests that the new statistical Z-R relationship is more suitable for predicting severe con- vective weather over the Yangtze-Huaihe River basin than the Z-R relationships currently in use.

Effect of Using Land Use Data with Building Characteristics on Urban Weather Simulations:A High Temperature Event in Shanghai

Land use data with building characteristics are important for modeling the impacts of urban morphology on local climate.In this study,an extreme heat event in Shanghai,China,was simulated by using a WRF/BEP+BEM(Weather Research and Forecasting/Building Effect Parameterization+Building Energy Model)model.We incorporated local climate zone(LCZ)land use data that resolved urban morphology using 10 classes of building parameters.The simulation was compared to a control case based on MODIS(Moderate-resolution Imaging Spectroradiometer)land use data.The findings are as follows:(1)the LCZ data performed better than the MODIS data for simulating 10-m wind speed.An increase in building height led to the wind speed to decrease by 0.6-1.4 m s^(-1)in the daytime and by 0.2-0.7 m s^(-1)at nighttime.(2)High-rise buildings warmed the air by trapping radiation in the urban canyon.This warming effect was partially offset by the cooling effect of building shadows in the day.As a result,the 2-m temperature increased by 0.8℃ at night but only by 0.4℃ during the day.(3)Heterogeneous urban surfaces increased the 50-m turbulent kinetic energy by 0.4 m^(2) s^(-2),decreased the 10-m wind speed by 1.8 m s^(-1)in the daytime,increased the surface net radiation by 45.1 W m^(2)-,and increased the 2-m temperature by 1.5℃ at nighttime.(4)The LCZ data modified the atmospheric circulation between land and ocean.The shadowing effect reduced the air temperature differences between land and ocean and weakened the sea breeze.Moreover,high-rise buildings obstructed sea breezes,restricting their impact to a smaller portion(10 km along the wind direction)of inland areas compared to that with MODIS.