Global/Regional Assimilation And Prediction System Research Articles

Ability of the GRAPES Ensemble Forecast Product to Forecast Extreme Temperatures over the Tibetan Plateau

Due to climate change, extreme temperature events are receiving increased attention. Based on the climate state deviation and threat score (TS), the ability of the Global/Regional Assimilation and Prediction System (GRAPES) ensemble model to forecast extreme temperature events was examined. The “optimal” Extreme Forecast Index (EFI) was derived for plateau forecasting, and its predictability was examined based on the receiver operating characteristic (ROC) curve method. Meanwhile, the applicability of the Shift of Tails (SOT) index to extreme temperature forecasting was analyzed using extreme temperature cases. Results showed that the GRAPES model has a warm bias for both summer extreme high temperature and winter extreme low temperature, and the warm bias decreases slightly with an increase in the forecasting lead time. The ensemble mean and median forecasts are less effective, and the maximum value is more predictable. However, for the ensemble forecast model, the extreme information in its forecast is more unstable, and the limitation of the extreme temperature forecast in the plateau region is higher. With different forecast lead times, the TS tends to increase and then decrease with an increase in the EFI threshold, which means that there is an optimal EFI. The optimal EFI thresholds for summer extreme high-temperature forecasts are all less than −0.5, while for winter extreme low-temperature forecasts, they are almost all less than 0. From the ROC curves, the EFI has a certain level of predictability for summer extreme high temperatures but poorer forecasting effects. Furthermore, the EFI has some predictability for extreme summer high temperatures, but the prediction effect is poor. For the extremely low temperatures in winter, which are poorly predicted by the model itself, post-processing of the extreme information predicted by the model with the EFI can improve the forecasting effect of the model. Through analysis of individual cases, it was found that the extreme intensity reflected by the SOT_+ (0.9) index of the model was closer to reality for the prediction of extremely high temperatures, whereas for the prediction of extremely low temperatures, the extreme intensity indicated by the SOT_− (0.1) index of the model was weaker. Therefore, the SOT index can play an important auxiliary role in the prediction of the intensity of extreme events based on the EFI.

Impact of the Detection Channels Added by Fengyun Satellite MWHS-II at 183 GHz on Global Numerical Weather Prediction

Fine spectral detection can basically solve the problem of low vertical resolution at the 183 GHz water-vapor absorption line, and it is expected to become one of the main methods for next-generation geostationary and polar-orbiting satellites. Here, using data from Microwave Humidity Sounder II (MWHS-II) onboard the Chinese Fengyun 3D (FY-3D) satellite in the Global/Regional Assimilation and Prediction System (GRAPES) Four-Dimensional Variational (4D-Var) system of the China Meteorological Administration (CMA), we explore the assimilation application of the water-vapor absorption line at 183.31 ± 1 GHz, 183.31 ± 3 GHz and 183.31 ± 7 GHz, as well as 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz, two added channels, to assess the impact of adding the 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz sampling channels on data assimilation and numerical weather prediction. Our findings reveal a significant increase in the specific-humidity increment, which in the middle–upper troposphere is numerically much larger than in the lower troposphere. Specifically, the assimilation of 183.31 ± 1.8 GHz observations, positioned near the center of the water-vapor absorption line, results in a pronounced adjustment compared with the 183.31 ± 4.5 GHz observations. And under the strong constraint of the numerical model, the Root Mean Square Error (RMSE) of the wind field diminishes more significantly (by an average of 2–4%) after assimilating the water-vapor observations at greater heights.

Improving the Forecasts of Surface Latent Heat Fluxes and Surface Air Temperature in the GRAPES Global Forecast System

The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature.

Dynamic downscaling ensemble forecast of an extreme rainstorm event in South China by COSMO EPS

In this study, the high-resolution ensemble prediction system COSMO (Consortium for Small Scale) EPS is used to predict the extreme rainstorm that occurred from 27 to 31 August 2018 in Guangdong Province, China, which leads to intensities exceeding historical extreme values. COSMO EPS is run with a 2.8-km grid spacing, allowing for an explicit treatment of deep convection, and 24 members of the EPS are initialized and laterally driven by the ICON (ICOsahedral Nonhydrostatic) global model. We compare the predictions of COSMO EPS against observations derived from the global precipitation measurement (GPM) and with ensemble forecasts of both mesoscale EPS and global EPS provided by GRAPES (Global and Regional Assimilation and PrEdiction System), and with the deterministic forecasts of global models ICON and ECMWF (European Centre for Medium-Range Weather Forecasts). Model performances are evaluated both by gridpoint-based scores, such as the equitable threat score (ETS), and by the Method for Object-based Diagnostic Evaluation (MODE) for spatial verification. According to our results, COSMO EPS could perform better forecasts for the rainstorms taken place in eastern Guangdong than other models. However, the location and coverage area of its predicted rainstorm is eastward and smaller in contrast with the observations. Therefore, COSMO EPS exhibits relative high performance by object-based spatial evaluations, while it could not display evident superiority in terms of the gridpoint-based scores. The cause analysis of this extreme rainstorm shows that Guangdong Province of China is mainly affected by monsoon depression. Southwesterly and southerly winds continuously transport water vapor from the South China Sea to Guangdong Province. The southwest monsoon low-level jet advances northward over time, which promotes the occurrence and development of continuous heavy precipitation in the coastal areas of Guangdong. In an additional experiment, we investigate the benefit of assimilation of radar data, by applying the latent heat nudging (LHN) approach based on surface-based radar observations to the COSMO EPS. Subsequently, the prediction by assimilation of radar data more reasonably reproduces the spatial distribution of precipitation observations, while the coverage and intensity of the rainstorm in eastern Guangdong are still not reflected satisfactorily.

Impacts of Transition Approach of Water Vapor-Related Microphysical Processes on Quantitative Precipitation Forecasting

The water vapor-related microphysical processes (WVRMPs) in cloud microphysics schemes are crucial to the formation and dissipation of clouds, which have a significant impact on the quantitative precipitation forecasting of numerical weather prediction models. In this study, a well physics-based parallel-split transition approach (PSTA) to compute the WVRMPs from the same temperature and humidity state is developed and compared with the original sequential-update transition approach (SUTA) in a double-moment cloud microphysics scheme. Case study and batch experiments were carried out to investigate their different impacts on the clouds and precipitation simulated by the Global/Regional Assimilation and Prediction System (GRAPES) regional 3 km high-resolution model of the China Meteorological Administration (CMA), named CMA-MESO. The results show that the PSTA experiment tends to simulate a narrower and more concentrated precipitation area with a higher-intensity center compared to those of the SUTA experiment, which is more consistent with the observations. In the cold region, the net transition rates of WVRMPs from the PSTA experiment with more ice-phase hydrometeors are higher than those from the SUTA experiment. While in the warm region, the condensation and evaporation rates with violent fluctuation simulated by the SUTA are significantly larger than those from the PSTA experiment, resulting in less precipitation. The batch experiments indicate that the equitable threat scores (ETSs) of 24-h precipitation simulated by the PSTA are just slightly better than those of the SUTA, yet its ETSs of 48-h precipitation have been systematically improved for all magnitude levels against the SUTA. It is demonstrated that more attention should be paid to the reasonable treatments of the WVRMPs in developing cloud microphysics schemes.

Coupling a New Version of the Common Land Model (CoLM) to the Global/Regional Assimilation and Prediction System (GRAPES): Implementation, Experiment, and Preliminary Evaluation

Land surface processes can significantly influence weather and climate. The Common Land Model version 2005 (CoLM2005) has been coupled to the Global Forecast System of the Global/Regional Assimilation and Prediction System (GRAPES_GFS), which is independently developed by the China Meteorological Administration. Since a new version of CoLM has been developed (CoLM2014) with updated soil basic data and parts of hydrological processes, we coupled CoLM2014 with GRAPES_GFS to investigate whether the land surface model can help to improve the prediction skill of the weather forecast model. The forecast results were evaluated against global validation datasets at different forecasting lengths and over various regions. The results demonstrate that GRAPES_GFS coupled with CoLM2005 and CoLM2014 can both well reproduce the spatial patterns and magnitude of atmospheric variables, and the effective predictable lengths of time are up to 3 days on the global scale and even up to 6 days on regional scales. Moreover, the GRAPES_GFS coupled with CoLM2014 outperforms the original one in predicting atmospheric variables. In addition, GRAPES_GFS coupled with both versions of CoLM reproduce acceptably accurate spatial distribution and magnitude of land variables. GRAPES_GFS coupled with CoLM2014 significantly improves the forecast of land surface state variables compared to the one coupled with CoLM2005, and the improvement signal is more notable than that in atmospheric variables. Overall, this study shows that CoLM is suitable for coupling with GRAPES_GFS, and the improvement of the land surface model in a weather forecast model can significantly improve the prediction skill of both atmospheric and land variables.

An MPI+OpenACC-based PRM scalar advection scheme in the GRAPES model over a cluster with multiple CPUs and GPUs

A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor. The Piecewise Rational Method (PRM) scalar advection scheme in the Global/Regional Assimilation and Prediction System (GRAPES) solves the moisture flux advection equation based on PRM. Computation of the scalar advection involves boundary exchange, and computation of higher bandwidth requirements is complicated and time-consuming in GRAPES. Recently, Graphics Processing Units (GPUs) have been widely used to solve scientific and engineering computing problems owing to advancements in GPU hardware and related programming models such as CUDA/OpenCL and Open Accelerator (OpenACC). Herein, we present an accelerated PRM scalar advection scheme with Message Passing Interface (MPI) and OpenACC to fully exploit GPUs' power over a cluster with multiple Central Processing Units (CPUs) and GPUs, together with optimization of various parameters such as minimizing data transfer, memory coalescing, exposing more parallelism, and overlapping computation with data transfers. Results show that about 3.5 times speedup is obtained for the entire model running at medium resolution with double precision when comparing the scheme's elapsed time on a node with two GPUs (NVIDIA P100) and two 16-core CPUs (Intel Gold 6142). Further, results obtained from experiments of a higher resolution model with multiple GPUs show excellent scalability.

Evaluation of Forecast Performance for Four Meteorological Models in Summer Over Northwestern China

At present, numerical models, which have been used for forecasting services in northwestern China, have not been extensively evaluated. We used national automatic ground station data from summer 2016 to test and assess the forecast performance of the high-resolution global European Centre for Medium-Range Weather Forecast (ECMWF) model, the mesoscale Northwestern Mesoscale Numerical Prediction System (NW-MNPS), the global China Meteorological Administration T639 model, and the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model over northwestern China. The root mean square error (RMSE) of the 2-m temperature forecast by ECMWF was the lowest, while that by T639 was the highest. The distribution of RMSE for each model forecast was similar to that of the difference between the modeled and observed terrain. The RMSE of the 10-m wind speed forecast was lower for the global ECMWF and T639 models and higher for the regional NW-MNPS and GRAPES models. The 24-h precipitation forecast was generally higher than observed for each model, with NW-MNPS having the highest score for light rain and heavy storm rain, ECMWF for medium and heavy rain, and T639 for storm rain. None of the models could forecast small-scale and high-intensity precipitation, but they could forecast large-scale precipitation. Overall, ECMWF had the best stability and smallest prediction errors, followed by NW-MNPS, T639, and GRAPES.

Sensitivity of Tropical Tropopause Layer Cirrus Prediction in GRAPES Global Forecast System

Abstract A warm bias with a maximum value of over 4 K in the tropical tropopause layer (TTL) is detected in day-5 operational forecasts of the Global/Regional Assimilation and Prediction System (GRAPES) for global medium-range numerical weather prediction (GRAPES_GFS). In this study, the predicted temperature changes caused by different processes are examined, and the predicted cloud fractions are compared with the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data. It is found that the overprediction of the TTL cirrus fraction contributes to the warm bias due to cloud-radiative heating. The interactions among the ice nucleation, deposition/sublimation, and the large-scale condensation together determine the results of the TTL ice crystal content prediction. Moreover, a range of sensitivity experiments show that the TTL ice crystal content prediction is sensitive to the threshold relative humidity over ice (RHi) in the ice nucleation process. Then the uncertainties of the formulas for saturation vapor pressure over ice at very low temperatures are discussed. The RHi calculated based on the Magnus–Tetens formula is up to 10% higher than that based on the Goff–Gratch formula. As the Goff–Gratch formula is applicable over a broader range of 184–273 K, it is more suitable for the cold TTL. When the Goff–Gratch formula rather than the Magnus–Tetens formula is used in the microphysics scheme, the TTL cirrus forecasts are improved greatly, and the warm bias disappears completely. After investigating the interplay of the dynamical, microphysical, and radiative processes, we find a positive feedback mechanism that exacerbates the TTL cirrus prediction error.

Sensitivity of snowfall forecast over North China to ice crystal deposition/sublimation parameterizations in the WSM6 cloud microphysics scheme

AbstractIce‐phase cloud microphysical processes are very complicated, and there are still many uncertainties in current microphysics parameterization schemes. In this study, two alternative ice crystal deposition/sublimation (ICDS) parameterizations, following the Harrington et al., Journal of the Atmospheric Sciences, 1995, 52, 4344–4366 and Koenig, Monthly Weather Review, 1972, 100, 417–423 methods, were implemented into the Weather Research and Forecast (WRF) Single‐Moment 6‐class Microphysics (WSM6) scheme in the Global/Regional Assimilation and Prediction System (GRAPES) regional operational model to investigate their impact on overestimation of snowfall over North China. The results show that the snowfall amount and cloud particle composition are very sensitive to the ICDS parameterization. Sensitivity tests with WSM6 using the Harrington and Koenig ICDSs, referred to as WSM6_H and WSM6_K, respectively, notably reduced overestimation of the snowfall amount and simulated more realistic supercooled cloud water content compared with ERA5 reanalysis data. The vertical distributions, intensities, and duration of radar echoes simulated by WSM6_H are the most consistent with millimeter‐wavelength cloud radar observations. There is competition for water vapor in WSM6 due to the sequential order in which processes are updated. This tends to weaken the deposition of snow and graupel and the condensation of cloud water when the deposition of ice crystals is too strong in a cold region. In both WSM6_H and WSM6_K, the deposition processes of ice crystals are reduced and the other microphysical transformation processes become more active compared with WSM6. Overall, the WSM6_H configuration improves the forecast performance as evaluated by Taylor diagrams for eight snowfall days over North China. The ICDS parameterizations must therefore be handled carefully due to their large uncertainties in the development of the cloud microphysics schemes.

Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

The existence of outliers can seriously influence the analysis of variational data assimilation. Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields. In particular, variational quality control (VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems. In this study, we derived the governing equations of two VarQC algorithms that utilize different contaminated Gaussian distributions (CGDs): Gaussian plus flat distribution and Huber norm distribution. As such, these VarQC algorithms can handle outliers that have non-Gaussian innovations. We then implemented these VarQC algorithms in the Global/Regional Assimilation and PrEdiction System (GRAPES) model-level three-dimensional variational data assimilation (m3DVAR) system. Tests using artificial observations indicated that the VarQC using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than that of the VarQC using the Gaussian plus flat distribution. Furthermore, real observation experiments show that the distribution of observation analysis weights conform well with theory, indicating that the VarQC is effective in the GRAPES m3DVAR system. Subsequent case study and long-period data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the mass field’s (geopotential height and temperature) analysis increments. Compared to the control experiment, VarQC experiments have noticeably better posterior mass fields. Finally, the VarQC using the Huber distribution is superior to the VarQC using the Gaussian plus flat distribution, especially at the middle and lower levels.

A Simulated Radar Reflectivity Calculation Method in Numerical Weather Prediction Models

AbstractThe simulated radar reflectivity used by current mesoscale numerical weather prediction models can reflect the grid precipitation but cannot reflect the subgrid precipitation generated by a cumulus parameterization scheme. To solve this problem, this study developed a new simulated radar reflectivity calculation method to obtain the new radar reflectivity corresponding to the subgrid-scale and grid-scale precipitation based on the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model of the China Meteorological Administration. Based on this new method, two 15-day forecast experiments were carried out for two different time periods (11–25 April 2019 and 1–15 August 2019), and the radar reflectivity products obtained by the new method and previous method were compared. The results show that the radar reflectivity obtained by the new simulated radar reflectivity calculation method gives a clear indication of the subgrid-scale precipitation in the model. Verification results show that the threat scores of the improved experiments are better than those of the control experiments in general and that the reliability of the simulated radar reflectivity for the indication of precipitation is improved. It is concluded that the new simulated radar reflectivity calculation method is effective and significantly improves the reflectivity products. This method has good prospects for providing more information about forecasting precipitation and convective activity in operational models.

Properties of High-Order Finite Difference Schemes and Idealized Numerical Testing

Construction of high-order difference schemes based on Taylor series expansion has long been a hot topic in computational mathematics, while its application in comprehensive weather models is still very rare. Here, the properties of high-order finite difference schemes are studied based on idealized numerical testing, for the purpose of their application in the Global/Regional Assimilation and Prediction System (GRAPES) model. It is found that the pros and cons due to grid staggering choices diminish with higher-order schemes based on linearized analysis of the one-dimensional gravity wave equation. The improvement of higher-order difference schemes is still obvious for the mesh with smooth varied grid distance. The results of discontinuous square wave testing also exhibits the superiority of high-order schemes. For a model grid with severe non-uniformity and non-orthogonality, the advantage of high-order difference schemes is inapparent, as shown by the results of two-dimensional idealized advection tests under a terrain-following coordinate. In addition, the increase in computational expense caused by high-order schemes can be avoided by the precondition technique used in the GRAPES model. In general, a high-order finite difference scheme is a preferable choice for the tropical regional GRAPES model with a quasi-uniform and quasi-orthogonal grid mesh.

Spin-up characteristics with three types of initial fields and the restart effects on forecast accuracy in the GRAPES global forecast system

Abstract. The spin-up refers to the dynamic and thermal adjustments made at the initial stage of numerical integration in order to reach a statistical equilibrium state. The analyses on the characteristics and effects of spin-ups are of great significance for optimizing the initial field of the model and improving its forecast skills. In this paper, three different initial fields are used in the experiments: the analysis field of four-dimensional variational (4D-VAR) assimilation, the 3 h prediction field in the operational forecasting system, and the Final (FNL) Operational Global Analysis data provided by National Centers for Environmental Prediction (NCEP). Following this, the characteristics of spin-ups in the version 2.3.1 of GRAPES (Global/Regional Assimilation and Prediction System) global forecast system (GRAPES_GFS2.3.1) under different initial fields are compared and analyzed. In addition, the influence of the lost cloud-field information on the spin-up and forecast results of the GRAPES model in the current operation is discussed as well. The results are as follows. With any initial field, the spin-up of GRAPES_GFS2.3.1 has to go through two stages – the dramatic adjustment in the first half-hour of integration and the slow dynamic and thermal adjustments afterwards. The spin-up in GRAPES_GFS2.3.1 lasts for at least 6 h, and the adjustment is gradually completed from lower to upper layers in the model. Therefore, in the evaluation of the GRAPES_GFS2.3.1, the forecast results in the first 6 h should be avoided, and the GRAPES_GFS2.3.1 with its own analysis field performs better than the one using FNL reanalysis data for the cold start in the spin-up because the variations in amplitude of the temperature and humidity tendency are smaller and the spin-up time is slightly shorter. Based on the 4D-VAR assimilation analysis field, the forecast in the operational model is artificially interrupted and restarted after 3 h of integration. In this process, as the cloud-field information is not retained, the spin-up should repeat in the model. The characteristics of spin-up are mostly consistent with those using the 4D-VAR assimilation analysis field as the initial field. However, as the cloud-field information is not retained in the current operation, the hydrometeor content in the atmosphere at the early stage of the forecast is underestimated, affecting the calculation accuracy of the radiation and causing a systematic positive bias of temperature and geopotential height fields at 500 hPa. In addition, the precipitation is also underestimated at the early stage of the simulation, affecting the forecast of typhoon tracks.

Impact of the One-Stream Cloud Detection Method on the Assimilation of AMSU-A Data in GRAPES

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.

Helmholtz solving and performance optimization in global/regional assimilation and prediction system

Despite efficient parallelism in the solution of physical parameterization in the Global/Regional Assimilation and Prediction System (GRAPES), the Helmholtz equation in the dynamic core, with the increase of resolution, can hardly achieve sufficient parallelism in the solving process due to a large amount of communication and irregular access. In this paper, optimizing the Helmholtz equation solution for better performance and higher efficiency has been an urgent task. An optimization scheme for the parallel solution of the Helmholtz equation is proposed in this paper. Specifically, the geometrical multigrid optimization strategy is designed by taking advantage of the data anisotropy of grid points near the pole and the isotropy of those near memory equator in the Helmholtz equation, and the Incomplete LU (ILU) decomposition preconditioner is adopted to speed up the convergence of the improved Generalized Conjugate Residual (GCR), which effectively reduces the number of iterations and the computation time. The overall solving performance of the Helmholtz equation is improved by thread-level parallelism, vectorization, and reuse of data in the cache. The experimental results show that the proposed optimization scheme can effectively eliminate the bottleneck of the Helmholtz equation as regards the solving speed. Considering the test results on a 10-node two-way server, the solution of the Helmholtz equation, compared with the original serial version, is accelerated by 100×, with one-third of iterations reduced.

Vertical Inhomogeneity Effect of Frozen Hydrometeor Habits in All‐Sky Passive Microwave Simulations

AbstractIn this study, we attempted to address the frozen hydrometeor habit vertical inhomogeneity (FHHVI) effects on microwave radiative transfer simulations. For simplification, we focused on a “two habits and two layers” FHHVI scheme in which two different ice habits can be assumed to be above and below a fixed habit boundary line of 325 hPa as suggested by studies of tropical cyclone vertical structure. The aforementioned FHHVI scheme was implemented into a radiative transfer for the Television Infrared Observation Satellite (TIROS) operational vertical sounder (RTTOV), which allows for close examination of the FHHVI effect on the radiative transfer process. Four scenarios (thin plate, dendrite, thin plate over dendrite, and dendrite over thin plate) were considered in the simulations at frequencies ranging from 10 to 183 GHz. We found that FHHVI has an obvious impact on the brightness temperature (BT) simulations. For real case studies, we considered the 2018 tropical cyclone Jebi in the Western Pacific basin. The hydrometeor profiles forecasted by the Global/Regional Assimilation and Prediction System (GRAPES) regional numerical weather prediction (NWP) model were used, and the simulated results were compared with observations from the instruments aboard on the Fengyun‐3D satellite, which recorded 61 overpasses during the life span of typhoon Jebi. We found that the overall performance of the BT simulations over multichannels could be improved by considering FHHVI effects. Specifically, the total penalty values that quantify the spectral consistency were 63.05 (dendrite), 43.88 (thin plate), 46.23 (dendrite over thin plate), and 39.03 (thin plate over dendrite), respectively.

Assessing forecasting performance of daily reference evapotranspiration using public weather forecast and numerical weather prediction

Accurate estimation of reference evapotranspiration (ET0) is important for water resource management and irrigation scheduling design. Limited by data availability of the numerical weather prediction (NWP), the public weather forecasting (PWF) has been widely used to forecast daily ET0 in China. Recently, two Chinese independently developed NWP products have been open to the public, namely, the Global/Regional Assimilation and Prediction System (GRAPES) and the T639 Global Medium-term Numerical Forecast Model (T639L60). In this paper, the forecast of ET0 is evaluated based on the recently released NWP outputs and the PWF using the FAO-56 PM equation. The daily ET0 forecast of the two datasets was compared against the ET0 that was calculated using weather variables observed from 31 automatic weather stations across a wide range of climate zones in China, including the temperate continental zone (TC), temperate monsoon zone (TM), mountain plateau zone (MP) and subtropical monsoon zone (SM). The results showed that the forecast performance of NWP for maximum and minimum air temperature (Tmax, Tmin) was worse due to the large errors in the altitude estimation process. The forecast performance of NWP for net radiation (Rnet) forecasted was better than that of PWF in the MP, TC and TM, while slightly worse in the SM. The VPD from the NWP was more accurate in TC and TM, while showed little difference with that from PWF in MP and SM. For the wind speed (u2), the NWP showed better forecast accuracy than PWF in all climate zones. Furthermore, the better forecast performance of ET0 was obtained by the NWP in the MP and TC. In the TM and SM, the PWF can provide a more accurate ET0 forecast compared to the NWP. Overall, the NWP can serve as a promising data source to generate acceptable ET0 forecasts across China. The high spatial resolution of the NWP provides the ability to capture the high resolution of the spatial variability in ET0, especially in the MP and TC where weather stations are sparse.

Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan

In this study, Fengyun-3D (FY-3D) MicroWave Radiation Imager (MWRI) radiance data were directly assimilated into the Global/Regional Assimilation and PrEdiction System (GRAPES) four-dimensional variational (4DVar) system. Quality control procedures were developed for MWRI applications by using algorithms from similar microwave instruments. Compared with the FY-3C MWRI, the bias of FY-3D MWRI observations did not show a clear node-dependent difference from the numerical weather prediction background simulation. A conventional bias correction approach can therefore be used to remove systematic biases before the assimilation of data. After assimilating the MWRI radiance data into GRAPES, the geopotential height and humidity analysis fields were improved relative to the control experiment. There was a positive impact on the location of the subtropical high, which led to improvements in forecasts of the track of Typhoon Shanshan.

Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction (NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community. Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since 1949 is summarized. The current status and recent progress of the domestically developed NWP system—GRAPES (Global/Regional Assimilation and PrEdiction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3–10 km for regional and 25–50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.