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.

An Overview of Research and Forecasting on Rainfall Associated with Landfalling Tropical Cyclones

The ability to forecast heavy rainfall associated with landfalling tropical cyclones （LTCs） can be improved with a better understanding of the mechanism of rainfall rates and distributions of LTCs. Research in the area of LTCs has shown that associated heavy rainfall is related closely to mechanisms such as moisture transport, extratropical transition （ET）, interaction with monsoon surge, land surface processes or topographic effects, mesoscale convective system activities within the LTC, and boundary layer energy transfer etc.. LTCs interacting with environmental weather systems, especially the westerly trough and mei-yu front, could change the rainfall rate and distribution associated with these mid-latitude weather systems. Recently improved technologies have contributed to advancements within the areas of quantitative precipitation estimation （QPE） and quantitative precipitation forecasting （QPF）. More specifically, progress has been due primarily to remote sensing observations and mesoscale numerical models which incorporate advanced assimilation techniques. Such progress may provide the tools necessary to improve rainfall forecasting techniques associated with LTCs in the future.

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.

Research on the short-term fishery forecasting of Spanish mackerel (Scomberomerus niphonius)

-This paper presents the use of the hydrographic factors in short-term fishery forecasting of the spawning migration stock of the Spanish mackerel and salinity describes more concretely the correlativity of water temperature, salinity and air temperature with the fishing season in spring. The data have been collected from the hydrographic environmental investigation at the fixed position on the sea and the telegraph recordings of the drift net operation in the spring fishing season during the period of April and May from 1972 to 1980. The correlation coefficients of various factors with the data of the fishing season have been calculated by using the monadic regression method.The main reference targets of the forecasting are: (1) By using the upper-layer water temperature as the forecasting factor at the beginning of the fishing season, the accuracy is high; (2) the distribution and location of the isotherm of the upper-layer water at 10°C at the beginning of April are used as an important factor for determining the location and the range of the central fishing area of the Spanish mackerel; (3) whether a low temperature area at 8°C existing at the Estuary of the Changjiang River can be used as an important factor for forecasting the migration distribution of the Spanish mackerel moving to the north.

Improvement and Evaluation of the Latest Version of WRF-Lake at a Deep Riverine Reservoir

The WRF-lake vertically one-dimensional(1D)water temperature model,as a submodule of the Weather Research and Forecasting(WRF)system,is being widely used to investigate water-atmosphere interactions.But previous applications revealed that it cannot accurately simulate the water temperature in a deep riverine reservoir during a large flow rate period,and whether it can produce sufficiently accurate heat flux through the water surface of deep riverine reservoirs remains uncertain.In this study,the WRF-lake model was improved for applications in large,deep riverine reservoirs by parametric scheme optimization,and the accuracy of heat flux calculation was evaluated compared with the results of a better physically based model,the Delft3D-Flow,which was previously applied to different kinds of reservoirs successfully.The results show:(1)The latest version of WRF-lake can describe the surface water temperature to some extent but performs poorly in the large flow period.We revised WRF-lake by modifying the vertical thermal diffusivity,and then,the water temperature simulation in the large flow period was improved significantly.(2)The latest version of WRF-lake overestimates the reservoir-atmosphere heat exchange throughout the year,mainly because of underestimating the downward energy transfer in the reservoir,resulting in more heat remaining at the surface and returning to the atmosphere.The modification of vertical thermal diffusivity can improve the surface heat flux calculation significantly.(3)The longitudinal temperature variation and the temperature difference between inflow and outflow,which cannot be considered in the 1D WRF-lake,can also affect the water surface heat flux.

城市化对北京单次极端高温过程影响的数值模拟研究

城市化对高温热浪的频次和强度具有重要影响,但目前对于城市化影响高温热浪过程的机理了解还不充分。本文利用WRF模式,对2010年7月2~6日(北京时)北京一次高温过程进行了模拟,分析了城市化对此次高温过程的影响机理。采用优化后的WRF模式,能够模拟出北京连续5日高温的特征和城市热岛强度的变化。城市下垫面的不透水性决定了城区2 m高度处相对湿度低于乡村,削弱了城区通过潜热调节城市气温的能力。日落后,城市感热通量下降缓慢,城区降温速率小于乡村,夜间边界层稳定、高度低,风速小,抑制了城乡之间能量的传输,形成了夜间强的城市热岛强度,造成夜间城市气温明显高于乡村。日出后城乡地面感热通量、潜热通量迅速上升,边界层稳定性下降。午后,城市下垫面分别为地表感热通量和潜热通量的高、低值中心,通过潜热调节气温的能力被削弱;边界层稳定性降低,有利于能量的垂直扩散;此时,城市热岛强度小于夜间。因此,北京城市下垫面形成了明显的城市热岛效应,加重了城区极端高温事件的强度。此外,在这次高温热浪期间,中国东部大部分地区受到大陆暖高压控制,晴空少云,西北气流越山后形成焚风效应,是北京地区高温热浪形成的天气背景。

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.

Numerical Simulation of the Heavy Rainfall in the Yangtze-Huai River Basin during Summer 2003 Using the WRF Model

In this study, a 47-day regional climate simulation of the heavy rainfall in the Yangtze-Huai River Basin during the summer of 2003 was conducted using the Weather Research and Forecast （WRY） model. The simulation reproduces reasonably well the evolution of the rainfall during the study period＇s three successive rainy phases, especially the frequent heavy rainfall events occurring in the Huai River Basin. The model captures the major rainfall peak observed by the monitoring stations in the morning. Another peak appears later than that shown by the observations. In addition, the simulation realistically captures not only the evolution of the low-level winds but also the characteristics of their diurnal variation. The strong southwesterly （low-level jet, LLJ） wind speed increases beginning in the early evening and reaches a peak in the morning; it then gradually decreases until the afternoon. The intense LLJ forms a strong convergent circulation pattern in the early morning along the Yangtze-Huai River Basin. This pattern partly explains the rainfall peak observed at this time. This study furnishes a basis for the further analysis of the mechanisms of evolution of the LLJ and for the further study of the interactions between the LLJ and rainfall.

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.

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.

Deriving Changjiang coastal zone wind from C-band SAR and its application to salinity simulation

Wind plays an important role in hydrodynamic processes such as the expansion of Changjiang （Yangtze） River Diluted Water （CDW）, and shelf circulation in the Changjiang estuary. Thus, it is essential to include wind in the numerical simulation of these phenomena. Synthetic aperture radar （SAR） with high resolution and wide spatial coverage is valuable for measuring spatially inhomogeneous ocean surface wind fields. We have collected 87 ERS-2 SAR images with wind-induced streaks that cover the Cbangjiang coastal area, to verify and improve the validity of wind direction retrieval using the 2D fast Fourier transform method. We then used these wind directions as inputs to derive SAR wind speeds using the C-band model. To demonstrate the applicability of the algorithms, we validated the SAR-retrieved wind fields using QuikSCAT measurements and the atmospheric Weather Research Forecasting model. In general, we found good agreement between the datasets, indicating the reliability and applicability of SAR- retrieved algorithms under different atmospheric conditions. We investigated the main error sources of this process, and conducted sensitivity analyses to estimate the wind speed errors caused by the effect of speckle, uncertainties in wind direction, and inaccuracies in the normalized radar cross section. Finally, we used the SAR-retrieved wind fields to simulate the salinity distribution off the Changjiang estuary. The findings of this study will be valuable for wind resource assessment and the development of future numerical ocean models based on SAR images.

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.

Evaluation of Precipitation Datasets from TRMM Satellite and Down-scaled Reanalysis Products with Bias-correction in Middle Qilian Mountain,China

Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies,but are more difficult in high mountainous areas because of the high elevation and complex terrain.This study compares and evaluates two kinds of precipitation datasets,the reanalysis product downscaled by the Weather Research and Forecasting(WRF)output,and the satellite product,the Tropical Rainfall Measuring Mission(TRMM)Multisatellite Precipitation Analysis(TMPA)product,as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China.Results show that the WRF output with finer resolution perfonns well in both estimating precipitation and hydrological simulation,while the TMPA product is unreliable in high mountainous areas.Moreover,bias-corrected WRF output also performs better than bias-corrected TMPA product.Combined with the previous studies,atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas.Climate is more important than altitude for the\falseAlarms'events of the TRMM product.Designed to focus on the tropical areas,the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas,thus causing significant"falseAlarms"events and leading to significant overestimations and unreliable performance.Simple linear bias correction method,only removing systematical errors,can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity.Evaluated by hydrological simulations,the bias-corrected WRF output is more reliable than the gauge dataset.Thus,data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.

Rainfall Sensitivity to Microphysics and Planetary Boundary Layer Parameterizations in Convection-Permitting Simulations over Northwestern South America

Convection-permitting modeling allows us to understand mechanisms that influence rainfall in specific regions.However,microphysics parameterization(MP) and planetary boundary layer(PBL) schemes remain an important source of uncertainty,affecting rainfall intensity,occurrence,duration,and propagation.Here,we study the sensitivity of rainfall to three MP [Weather Research and Forecasting(WRF) Single-Moment 6-class(WSM6),Thompson,and Morrison] and two PBL [the Yonsei University(YSU) and Mellor–Yamada Nakanishi Niino(MYNN)] schemes with a convection-permitting resolution(4 km) over northwestern South America(NWSA).Simulations were performed by using the WRF model and the results were evaluated against soundings,rain gauges,and satellite data,considering the spatio-temporal variability of rainfall over diverse regions prone to deep convection in NWSA.MP and PBL schemes largely influenced simulated rainfall,with better results for the less computationally expensive WSM6 MP and YSU PBL schemes.Regarding rain gauges and satellite estimates,simulations with Morrison MP overestimated rainfall,especially westward of the Andes,whereas the MYNN PBL underestimated precipitation in the Amazon–Savannas flatlands.We found that the uncertainty in the rainfall representation is highly dependent on the region,with a higher influence of MP in the Colombian Pacific and PBL in the Amazon–Savannas flatlands.When analyzing rainfall-related processes,the selection of both MP and PBL parameterizations exerted a large influence on the simulated lower tropospheric moisture flux and moisture convergence.PBL schemes significantly influenced the downward shortwave radiation,with MYNN simulating a greater amount of low clouds,which decreased the radiation income.Furthermore,latent heat fluxes were greater for YSU,favoring moist convection and rainfall.MP schemes had a marked impact on vertical velocity.Specifically,Morrison MP showed stronger convection and higher precipitation rates,which is associated with a greater latent heat release due to solid-phase hydrometeor formation.This study provides insights into assessing physical parameterizations in numerical models and suggests key processes for rainfall representation in NWSA.

Improving the Forecasts of Coastal Wind Speeds in Tianjin,China Based on the WRF Model with Machine Learning Algorithms

Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind forecasting.In this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for Tianjin.The results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing time.The back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE.In regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)winds.In addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed.This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.