Meteorological applications of precipitable water vapor measurements retrieved by the national GNSS network of China

In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sites, including 260 belonging to the Crustal Movement Observation Network of China （CMONOC） and 692 belonging to the China Meteorological Administration GNSS network （CMAGN）. Additionally, GNSS observation collecting and data processing procedures are presented and PWV data quality control methods are investigated. PWV levels as determined by GNSS and radiosonde are compared. The results show that GNSS estimates are generally in good agreement with measurements of radio- sondes and water vapor radiometers （WVR）. The PWV retrieved by the national GNSS network is used in weather forecasting, assimilation of data into numerical weather prediction models, the validation of PWV estimates by radiosonde, and plum rain monitoring. The network is also used to monitor the total ionospheric electron content.

Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System

An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.

VERIFICATION OF TROPICAL CYCLONE RAINFALL PREDICTIONS FROM CMA AND JMA GLOBAL MODELS

The number of tropical cyclone (TC) genesis over the South China Sea and the Northwest Pacific Ocean in 2009 is significantly less than the average (27.4). However, the number of landfall TC over China's Mainland and its associated rainfall is more than the average. This paper focuses on the performance of numerical weather prediction (NWP) of landfall TC precipitation over China in 2009. The China Meteorological Administration (CMA) and Japan Meteorological Agency (JMA) models are compared. Although the schemes of physical processes, the data assimilation system and the dynamic frame are entirely different for the two models, the results of forecast verification are similar to each other for TC rainfall and track except for TC Goni. In this paper, a day with daily rainfall amount greater than 50 mm was selected as a storm rain day when there was a TC affecting the mainland. There are 32 storm rain days related to the landing of typhoons and tropical depressions. The rainfall forecast verification methods of National Meteorological Centre (NMC) of CMA are selected to verify the models' rainfall forecast. Observational precipitation analyses related to TCs in 2009 indicate a U-shape spatial distribution in China. It is found that the rain belt forecasted by the two models within 60 hours shows good agreement with observations, both in the location and the maximum rainfall center. Beyond 3 days, the forecasted rainfall belt shifts northward on average, and the rainfall amount of the model forecasts becomes under-predicted. The rainfall intensity of CMA model forecast is more reasonable than that of JMA model. For heavy rain, the JMA model made more missing forecasts. The TC rainfall is verified in Guangdong, Guangxi, Fujian and Hainan where rainfall amount related to TCs is relatively larger than in other regions. The results indicate that the model forecast for Guangdong and Guangxi is more skillful than that for Hainan. The rainfall forecast for Hainan remains difficult for the models because of insufficient observation data and special tropical ocean climate.

A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System

How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts.In this study,a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System(GRAPES)Convection-Allowing Ensemble Prediction System(CAEPS).The nonlinear forcing singular vector(NFSV)approach,that is,conditional nonlinear optimal perturbation-forcing(CNOP-F),is applied in this study,to construct a nonlinear model perturbation method for GRAPES-CAEPS.Three experiments are performed:One of them is the CTL experiment,without adding any model perturbation;the other two are NFSV-perturbed experiments,which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint.Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment,which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts.Additionally,the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables.But for precipitation verification,the NFSV-S experiment performs better in forecasts for light precipitation,and the NFSV-L experiment performs better in forecasts for heavier precipitation,indicating that for different precipitation events,the perturbation magnitude constraint must be carefully selected.All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.

A new climate scenario for assessing the climate change impacts on soil moisture over the Huang-Huai-Hai Plain region of China

To assess the impacts of temperature and precipitation changes on surface soil moisture CSSM） in the Huang-Huai-Hai Plain （3H） region of China, the approach of conditional nonlinear optimal perturbation related to parameters （CNOP-P） and the Common Land Model are employed. Based on the CNOP-P method and climate change projections derived from 22 global climate models from CMIP5 under a moderate emissions scenario （RCP4.5）, a new climate change scenario that leads to the maximal change magnitudes of SSM is acquired, referred to as the CNOP-P type temperature or precipitation change scenario. Different from the hypothesized climate change scenario, the CNOP-P-type scenario considers the variation of the temperature or precipitation variability. Under the CNOP-P-type temperature change, the SSM changes in the last year of the study period mainly fluctuate in the range from ,0.014 to ＋0.012 m^3 m^-3 （-5.0% to ＋10.0%）, and from ＋0.005 to ＋0.018 m^3 m^-3 （＋1.5% to ＋9.6%） under the CNOP-P-type precipitation change scenario. By analyzing the difference of the SSM changes between different types of climate change scenarios, it is found that this difference associated with SSM is obvious only when precipitation changes are considered. Besides, the greater difference mainly occurs in north of 35°N, where the semi-arid zone is mainly situated. It demonstrates that, in the semi-arid region, SSM is more sensitive to the precipitation variability. Compared with precipitation variability, temperature variability seems to play little role in the variations of SSM.

Improvement of the Semi-Lagrangian Advection Scheme in the GRAPES Model:Theoretical Analysis and Idealized Tests

ABSTRACT The Global/Regional Assimilation and PrEdiction System （GRAPES） is the newgeneration numerical weather predic- tion （NWP） system developed by the China Meteorological Administration. It is a fully compressible non-hydrostatical global/regional unified model that uses a traditional semi-Lagrangian advection scheme with cubic Lagrangian interpola tion （referred to as the SL_CL scheme）. The SL_CL scheme has been used in many operational NWP models, but there are still some deficiencies, such as the damping effects due to the interpolation and the relatively low accuracy. Based on Reich＇s semi-Lagrangian advection scheme （referred to as the R2007 scheme）, the Re_R2007 scheme that uses the low- and high-order B-spline function for interpolation at the departure point, is developed in this paper. One- and two-dimensional idealized tests in the rectangular coordinate system with uniform grid cells were conducted to compare the Re..R2007 scheme and the SL_CL scheme. The numerical results showed that： （1） the damping effects were remarkably reduced with the Re_R2007 scheme; and （2） the normalized errors of the Re_R2007 scheme were about 7.5 and 3 times smaller than those of the SL_CL scheme in one- and two-dimensional tests, respectively, indicating the higher accuracy of the Re..R2007 scheme. Furthermore, two solid-body rotation tests were conducted in the latitude-longitude spherical coordinate system with non uniform grid cells, which also verified the Re_R2007 scheme＇s advantages. Finally, in comparison with other global advection schemes, the Re_R2007 scheme was competitive in terms of accuracy and flow independence. An encouraging possibility for the application of the Re_R2007 scheme to the GRAPES model is provided.

Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes.Six experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are used.The results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed.SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesoscale systems,and all three stochastic schemes(SPP,SPPT,and SKEB)mainly affect the KE of mesoscale systems.This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.

A Step Forward toward Effectively Using Hyperspectral IR Sounding Information in NWP

Satellite-based atmospheric sounding measurements with high spectral resolution or from hyperspectral infrared （IR） sounders are important global observations for improving weather forecasts through assimilating them into operational numerical weather prediction （NWP） systems.

A New Approach for Parameter Optimization in Land Surface Model

In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

Validation and correction of sea surface salinity retrieval from SMAP

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.

ON USE OF LHN METHOD TO ASSIMILATE THE INTENSIFIED SURFACE PRECIPITATIONS FOR GRAPES_MESO MODEL INITIALIZATION

The quantitative precipitation forecast(QPF) in very-short range(0-12 hours) has been investigated in this paper by using a convective-scale(3km) GRAPES_Meso model. At first, a latent heat nudging(LHN) scheme to assimilate the hourly intensified surface precipitation data was set up to enhance the initialization of GRAPES_Meso integration. And then based on the LHN scheme, a convective-scale prediction system was built up in considering the initial "triggering"uncertainties by means of multi-scale initial analysis(MSIA), such as the three-dimensional variational data assimilation(3DVAR), the traditional LHN method(VAR0LHN3), the cycling LHN method(CYCLING), the spatial filtering(SS) and the temporal filtering(DFI) LHN methods. Furthermore, the probability matching(PM) method was used to generate the QPF in very-short range by combining the precipitation forecasts of the five runs. The experiments for one month were carried out to validate the MSIA and PM method for QPF in very-short range.The numerical simulation results showed that:(1) in comparison with the control run, the CYCLING run could generate the smaller-scale initial moist increments and was better for reducing the spin-up time and triggering the convection in a very-short time;(2) the DFI runs could generate the initial analysis fields with relatively larger-scale initial increments and trigger the weaker convections at the beginning time(0-3h) of integration, but enhance them at latter time(6-12h);(3) by combining the five runs with different convection triggering features, the PM method could significantly improve the QPF in very-short range in comparison to any single run. Therefore, the QPF with a small size of combining members proposed here is quite prospective in operation for its lower computation cost and better performance.

The Impact of Assimilating FY-3C GNOS GPS Radio Occultation Observations on GRAPES Forecasts

In the present study, a gross quality control (QC) procedure is proposed for the Global Navigation Satellite System Occultation Sounder (GNOS) Global Positioning System radio occultation (GPS RO) refractivity data to remove abnormal data before they are assimilated. It consists of a climate extreme check removing data outside the range of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) climate maxima and minima over approximately five years, and a vertical gradient check that rejects profiles containing super-refractions. These two QC steps were applied sequentially to identify outliers in GNOS GPS RO refractivity data during boreal winter 2013/2014.All of the abnormal refractivity profiles and the outliers at each level of the GNOS GPS RO observations were effectively removed by the proposed QC procedure. The post-QC GNOS GPS RO refractivity observations were then assimilated in the Global/Regional Analysis and PrEdiction System (GRAPES) using the three-dimensional variational(3D-Var) system. The impacts of the GNOS refractivity observation on GRAPES analysis and forecasting were evaluated and analyzed using an observation system experiment run over one whole winter season of 2013/2014. The experiment results demonstrated a positive impact of GNOS GPS RO data on analysis and forecast quality. The root mean squared error of GRAPES analysis temperature was reduced by 1%in the Southern Hemisphere (SH) extratropics and in the tropics, and the anomaly correlation scores of the forecasted 500-hPa geopotential height over the SH increased significantly during days 1 to 5. Overall, the benefits of using GNOS GPS RO data are significant in the SH and tropics.

Application of a Neural Network to Store and Compute the Optical Properties of Non-Spherical Particles

Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles.This paper proposes a deep learning(DL)scheme in conjunction with an optical property database to achieve this goal.Deep neural network(DNN)architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters,size parameters,and refractive indices.The dataset was computed through the invariant imbedding T-matrix method.Four separate DNN architectures were created to compute the extinction efficiency factor,single-scattering albedo,asymmetry factor,and phase matrix.The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters.The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database,which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy.In addition,the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database.Importantly,the ratio of the database size(~127 GB)to that of the DNN parameters(~20 MB)is approximately 6810,implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.

HEIGHT OF ATMOSPHERIC BOUNDARY LAYER AS DETECTED BY COSMIC GPS RADIO OCCULTATION DATA

The characteristics of the atmospheric boundary layer height over the global ocean were studied based on the Constellation Observation System of Meteorology,Ionosphere and Climate(COSMIC) refractivity data from 2007 to2012.Results show that the height is much characteristic of seasonal,inter-annual and regional variation.Globally,the spatial distribution of the annual mean top height shows a symmetrical zonal structure,which is more zonal in the Southern Hemisphere than in the Northern Hemisphere.The boundary layer top is highest in the tropics and gradually decreases towards higher latitudes.The height is in a range of 3 to 3.5 km in the tropics,2 to 2.5 km in the subtropical regions,and 1 to 1.5 km or even lower in middle and high latitudes.The diurnal variation of the top height is not obvious,with the height varying from tens to hundreds of meters.Furthermore,it is different from region to region,some regions have the maximum height during 9:00 to 12:00,others at 15:00 to18:00.

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known"double penalty"problem caused by the slight spatial or temporal mismatches between forecasts and observations.The fuzzy(neighborhood)method has been proposed for deterministic simulations and shown some ability to solve this problem.The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts.We developed an ensemble precipitation verification skill score,i.e.,the Spatial Continuous Ranked Probability Score(SCRPS),and used it to extend spatial verification from deterministic into ensemble forecasts.The SCRPS is a spatial technique based on the Continuous Ranked Probability Score(CRPS)and the fuzzy method.A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency,which were then used in the reference score to calculate the skill score of the SCRPS.The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained.The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.

Variations in soil moisture over the ‘Huang-Huai-Hai Plain＇ in China due to temperature change using the CNOP-P method and outputs from CMIP5

In this study, the variations in surface soil liquid water(SSLW) due to future climate change are explored in the‘Huang-Huai-Hai Plain'(‘3H') region in China with the Common Land Model(CoLM). To evaluate the possible maximum response of SSLW to climate change, the combination of the conditional nonlinear optimal perturbation related to the parameter(CNOP-P) approach and projections from 10 general circulation models(GCMs) of the Coupled Model Intercomparison Project5(CMIP5) are used. The CNOP-P-type temperature change scenario, a new type of temperature change scenario, is determined by using the CNOP-P method and constrained by the temperature change projections from the 10 GCMs under a high-emission scenario(the Representative Concentration Pathway 8.5 scenario). Numerical results have shown that the response of SSLW to the CNOP-P-type temperature scenario is stronger than those to the 11 temperature scenarios derived from the 10 GCMs and from their ensemble average in the entire ‘3H' region. In the northern region, SSLW under the CNOP-P-type scenario increases to0.1773 m^3 m^(-3); however, SSLW in the scenarios from the GCMs fluctuates from 0.1671 to 0.1748 m^3 m^(-3). In the southern region,SSLW decreases, and its variation(–0.0070 m^3 m^(-3)) due to the CNOP-P-type scenario is higher than each of the variations(–0.0051 to –0.0026 m^3 m^(-3)) due to the scenarios from the GCMs.

Development and Testing of the GRAPES Regional Ensemble-3DVAR Hybrid Data Assimilation System

Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.

Features of rainfall and latent heating structure simulated by two convective parameterization schemes

Using the latest version of SAMIL (Spectral Atmosphere Model of IAP LASG) developed by LASG/IAP,we evaluate the model performance by analyzing rainfall,latent heating structure and other basic fields with two different convective parameterization schemes:Manabe Scheme and Tiedtke Scheme.Results show that convective precipitation is excessively overestimated while stratiform precipitation is underestimated by Tiedtke scheme,thus causing less stratiform rainfall proportion compared with TRMM observation.In contrast,for Manabe scheme stratiform rainfall belt is well simulated,although precipitation center near Bay of Bengal (BOB) spreads eastward and northward associated with unrealistic strong rainfall downstream of the Tibet Plateau.The simulated latent heating structure indicates that Tiedtke scheme has an advantage over Manabe scheme,as the maximum convective latent heating near middle of troposphere is well reproduced.Moreover,the stratiform latent heating structure is also well simulated by Tiedtke scheme with warming above freezing level and cooling beneath freezing level.As for Manabe scheme,the simulated maximum convective latent heating lies near 700 hPa,lower than the observation.Additionally,the warming due to stratiform latent heating extends to the whole vertical levels,which is unreasonable compared with observation.Taylor diagram further indicates that Tiedtke scheme is superior to Manabe scheme as higher correlation between model output and observation data is achieved when Tiedtke scheme is employed,especially for the temperature near 200 hPa.Finally,a possible explanation is addressed for the unrealistic stratiform rainfall by Tiedtke scheme,which is due to the neglect of detrained cloud water and cloud ice during convective process.The speculation is verified through an established sensitivity experiment.

Effects of Terrain-Following Vertical Coordinates on High-Resolution NWP Simulations

With increasing resolution in numerical weather prediction （NWP） models, the model topography can be described with finer resolution and includes steeper slopes. Consequently, negative effects of the traditional terrain-following vertical coordinate on high-resolution numerical simulations become more distinct due to larger errors in the pressure gradient force （PGF） calculation and associated distortions of the gravity wave along the coordinate surface. A series of numerical experiments have been conducted in this study, including idealized test cases of gravity wave simulation over a complex mountain, error analysis of the PGF estimation over a real topography, and a suite of real-data test cases. The GRAPES-Meso model is utilized with four different coordinates, i.e., the traditional terrain-following vertical coordinate proposed by Gal-Chen and Somerville （hereinafter referred to as the Gal.C.S coordinate）, the one-scale smoothed level （SLEVE1）, the two-scale smoothed level （SLEVE2）, and the COSINE （COS） coordinates. The results of the gravity wave simulation indicate that the GRAPES-Meso model generally can reproduce the mountain-induced gravity waves, which are consistent with the analytic solution. However, the shapes, vertical structures, and intensities Of the waves are better simulated with the SLEVE2 coordinate than with the other three coordinates. The model with the COS coordinate also performs well, except at lower levels where it is not as effective as the SLEVE2 coordinate in suppressing the PGF errors. In contrast, the gravity waves simulated in both the Gal.C.S and SLEVE1 coordinates are relatively distorted. The estimated PGF errors in a rest atmosphere over the real complex topography are much smaller （even disappear at the middle and upper levels） in the GRAPES-Meso model using the SLEVE2 and COS coordinates than those using the Gal.C.S and SLEVE1 coordinates. The results of the real-data test cases conducted over a one-month period suggest that the three modified vertical coordinates （SLEVE1, SLEVE2, and COS coordinates） give better results than the traditional Gal.C.S coordinate in terms of forecasting bias and root mean square error, and forecasting anomaly correlation coefficients. In conclusion, the SLEVE2 coordinate is proved to be the best option for the GRAPES-Meso model.

Modification of Cumulus Convection and Planetary Boundary Layer Schemes in the GRAPES Global Model

Cumulus convection is a key linkage between hydrological cycle and large-scale atmospheric circulation. Cumulus parameterization scheme is an important component in numerical weather and climate modeling studies. In the Global/Regional Assimilation and Prediction Enhanced System （GRAPES）, turbulent mixing and diffusion approach is applied in its shallow convection scheme. This method overestimates the vertical transport of heat and moisture fluxes but underestimates cloud water mixing ratio over the region of stratocumulus clouds. As a result, the simulated low stratocumulus clouds are less than observations. To overcome this problem, a mass flux method is employed in the shallow convection scheme to replace the original one. Meanwhile, the deep convection scheme is adjusted correspondingly. This modification is similar to that in the US NCEP Global Forecast System （GFS）, which uses the simplified Arakawa Schubert Scheme （SAS）. The planetary boundary layer scheme （PBL） is also revised by considering the coupling between the PBL and stratocumulus clouds. With the modification of both the cumulus and PBL schemes, the GRAPES simulation of shallow convective heating rate becomes more reasonable; total amounts of stratocumulus clouds simulated over the eastern Pacific and their vertical structure are more consistent with observations; the underestimation of stratocumulus clouds simulated by original schemes is less severe with the revised schemes. Precipitation distribution in the tropics becomes more reasonable and spurious precipitation is effectively suppressed. The westward extension and northward movement of the western Pacific subtropical high simulated with the revised schemes are more consistent with Final Operational Global Analysis （FNL） than that simulated with the original schemes. The statistical scores for the global GRAPES forecast are generally improved with the revised schemes, especially for the simulation of geopotential height in the Northern Hemisphere and winds in the tropics. Root mean square errors （RMSEs） decrease in the lower and upper troposphere with the revised schemes. The above results indicate that with the revised cumulus and PBL schemes, model biases in the tropics decrease and the global GRAPES performance is greatly improved.