Effect of Initial Vortex Intensity Correction on Tropical Cyclone Intensity Prediction:A Study Based on GRAPES_TYM

Predicting the intensity of tropical cyclones(TCs)is challenging in operational weather prediction systems,partly due to the difficulty in defining the initial vortex.In an attempt to solve this problem,this study investigated the effect of initial vortex intensity correction on the prediction of the intensity of TCs by the operational numerical prediction system GRAPES_TYM(Global and Regional Assimilation and Prediction System_Typhoon Model)of the National Meteorological Center of the China Meteorological Administration.The statistical results based on experiments using data for major TCs in 2018 show that initial vortex intensity correction can reduce the errors in mean intensity for up to 120-h integration,with a noticeable decrease in the negative bias of intensity and a slight increase in the mean track error.The correction leads to an increase in the correlation coefficient of Vmax(maximum wind speed at 10-m height)for the severe typhoon and super typhoon stages.Analyses of the errors in intensity at different stages of intensity(including tropical storms,severe tropical storms,typhoons,severe typhoons,and super typhoons)show that vortex intensity correction has a remarkable positive influence on the prediction of super typhoons from 0 to 120h.Analyses of the errors in intensity for TCs with different initial intensities indicate that initial vortex correction can significantly improve the prediction of intensity from 24 to 96 h for weak TCs(including tropical storms and severe tropical storms at the initial time)and up to 24 h for strong TCs(including severe typhoons and super typhoons at the initial time).The effect of the initial vortex intensity correction is more important for developing TCs than for weakening TCs.     

A correction method suitable for Dynamical Seasonal Prediction

Based on the hindcast results of summer rainfall anomalies over China for the period 1981–2000 by the Dynamical Climate Prediction System (IAP-DCP) developed by the Institute of Atmospheric Physics, a correction method that can account for the dependence of model’s systematic biases on SST anomalies is proposed. It is shown that this correction method can improve the hindcast skill of the IAP-DCP for summer rainfall anomalies over China, especially in western China and southeast China, which may imply its potential application to real-time seasonal prediction.     

Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction

A new data insertion approach is applied to the Derber and Rosati ocean data assimilation(ODA) system,a system that uses a variational scheme to analyze ocean temperature and provide ocean model corrections continuously.Utilizing the same analysis component as the original system,the new approach conducts analyses to derive model corrections intermittently at once-daily intervals.A technique similar to the Incremental Analysis Update(IAU) method of Bloom et al.is applied to incorporate the corrections into the model gradually and continuously.This approach is computationally more economical than the original.A 13-year global ocean analysis from 1986 to 1998 is produced using this new approach and compared with an analysis based on the original one.An examination of both analyses in the tropical Pacific Ocean shows that they have qualitatively similar annual and interannual temperature variability.Howerver,the new approach produces smoother monthly analyses.Moreover,compared to the independent observations from current meters,the new equatorial currents are significantly better than the original analyses,not only in maintaining the mean state but also in capturing the annual and interannual variations.     

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events？ Results from a Theoretical Model

Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ``spring predictability barrier' (SPB) for El Nino events. First, sensitivity experiments were respectively performed to the air--sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation (CNOP) approach was used to study the effect of the optimal mode (CNOP-P) of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nino events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode (i.e. CNOP error) of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nino events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.     

Bias Correction in Wind Direction Forecasting Using the Circular-Circular Regression Method

Wind direction forecasting plays an important role in wind power prediction and air pollution management. Weather quantities such as temperature, precipitation, and wind speed are linear variables in which traditional model output statistics and bias correction methods are applied. However, wind direction is an angular variable; therefore, such traditional methods are ineffective for its evaluation. This paper proposes an effective bias correction technique for wind direction forecasting of turbine height from numerical weather prediction models, which is based on a circular-circular regression approach. The technique is applied to a 24-h forecast of 65-m wind directions observed at Yangmeishan wind farm, Yunnan Province, China, which consistently yields improvements in forecast performance parameters such as smaller absolute mean error and stronger similarity in wind rose diagram pattern.     

Analysis of Wind Power Assessment Based on the WRF Model

Assessing wind energy is a key step in selecting a site for a wind farm. The accuracy of the assessment is essential for the future operation of the wind farm. There are two main methods for assessing wind power： one is based on observational data and the other relies on mesoscale numerical weather prediction（NWP）. In this study, the wind power of the Liaoning coastal wind farm was evaluated using observations from an anemometer tower and simulations by the Weather Research and Forecasting（WRF） model, to see whether the WRF model can produce a valid assessment of the wind power and whether the downscaling process can provide a better evaluation. The paper presents long-term wind data analysis in terms of annual, seasonal, and diurnal variations at the wind farm, which is located on the east coast of Liaoning Province. The results showed that, in spring and summer, the wind speed, wind direction, wind power density, and other main indicators were consistent between the two methods. However, the values of these parameters from the WRF model were significantly higher than the observations from the anemometer tower. Therefore, the causes of the differences between the two methods were further analyzed. There was much more deviation in the original material, National Centers for Environmental Prediction（NCEP） final（FNL） Operational Global Analysis data, in autumn and winter than in spring and summer. As the region is vulnerable to cold-air outbreaks and windy weather in autumn and winter, and the model usually forecasted stronger high or low systems with a longer duration, the predicted wind speed from the WRF model was too large.     

基于改进Noah LSM和数值天气预报耦合模式对表层土壤湿度在典型暴雨过程中的模拟

Surface soil moisture has great impact on both meso- and microscale atmospheric processes, especially on severe local convection processes and on the dynamics of short-lived torrential rains. To promote the performance of the land surface model (LSM) in surface soil moisture simulations, a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin’anjiang model and TOPography based hydrological MODEL (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM. Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM, a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted. The results suggested that the surface, 10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations. Improvements from the simulated results were found, especially over eastern Shandong. The simulated results, compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets, indicated a consistent spatial pattern over all of China. The temporal variation of surface soil moisture was validated with the data at an observation station, also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events, even though biases and systematic trends may still exist.     

An Initialization Scheme for Weak Tropical Cyclones in the South China Sea

Variations in the initial structure of tropical cyclones(TCs) inevitably affect prediction results; however, the bogus model cannot accurately describe the structure of a weak tropical cyclone with increased initial field resolution. This study aims to construct a model to improve the prediction of weak TC in southern China. Based on the ECMWF 0.1°analysis data, several vortices were filtered out from tropical depressions and tropical storms in 2018 and 2019 to represent a weak TC reservoir in the South China Sea. For different simulation objects, filtered vortices were combined with the TC environmental field to form ensemble members. The observed TC information was assimilated for simulating TCs Bebinca, Mun, and Ewiniar to verify the feasibility of the proposed model, based on the Global/Regional Assimilation and Prediction Enhanced System(GRAPES) 9-km model developed by the Guangzhou Institute of Tropical and Marine Meteorology. The results show that the initialization scheme of the weak tropical cyclone model improved the intensity prediction of the TC by 26.81%(Bebinca), 18.65%(Mun), and 47.00%(Ewiniar), compared with the control experiment. Because typhoon intensity forecasting has not notably improved for many years, this scheme has certain scientific and operational significance.     

Seasonal Prediction of Spring Dust Weather Frequency in Beijing

In this paper, seasonal prediction of spring dust weather frequency (DWF) in Beijing during 1982-2008 has been performed. First, correlation analyses are conducted to identify antecedent climate signals during last winter that are statistically significantly related to spring DWF in Beijing. Then, a seasonal prediction model of spring DWF in Beijing is established through multivariate linear regression analysis, in which the systematic error between the result of original prediction model and the observation, averaged over the last 10 years, is corrected. In addition, it is found that climate signals occurring synchronously with spring dust weather, particularly meridional wind at 850 hPa over western Mongolian Plateau, are also linked closely to spring DWF in Beijing. As such, statistical and dynamic prediction approaches should be combined to include these synchronous predictors into the prediction model in the real-time operational prediction, so as to further improve the prediction accuracy of spring DWF in Beijing, even over North China. However, realizing such a prediction idea in practice depends essentially on the ability of climate models in predicting key climate signals associated with spring DWF in Beijing.     

Preliminary evaluations of FGOALS-g2 for decadal predictions

The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature (SAT), particularly in that of Nin o3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate (the control) by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 (LICOM2). The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.     

Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.     

Extended Range(10–30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model

Extended range(10–30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper,a nonlinear cross prediction error(NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First,nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process,after which the local change characteristics of the attractors are analyzed. Second,the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5,and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently,without failure,based on the NCPE model; the prediction validity periods for 1–2 d,3–9 d and 10–30 d are 4,22 and 74 cases,respectively,without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10–30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability,and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.     

数值预报误差订正技术中相似-动力方法的发展

Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.     

A forecast error correction method in numerical weather prediction by using recent multiple-time evolution data

The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polynomial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria: (1) the length of past data sufficient for estimation of the model errors, (2) a proper method of estimating the term "model integration with the exact solution" when solving the inverse problem, and (3) the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection-diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that (1) a few data limited by the order of the corrector can be used, (2) trapezoidal approximation can be employed to estimate the "term" in this study; however, a more accurate method should be explored for an operational NWP model, and (3) the correction is sensitive to observational error.     

INFLUENCE OF DIFFERENT-SCALE ERRORS INTERACTIONS ON ANALYSIS AND FORECAST OF REGIONAL NWP MODEL

In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the covariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The variations of analysis increments with different-scale errors are consistent with those of variances and correlations of background errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Temperature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more accurate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- (smaller-) scale errors will be beneficial for the accuracy of forecasts of heavy (light) precipitation at large (small) scales because of the amplification (diminution) of the intensity and area in precipitation forecasts.     

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results. In recent years, post-processing models based on deep learning have become prominent. In this paper, a deep learning model named ED-ConvLSTM based on encoder-decoder structure and ConvLSTM is developed, which appears to be able to effectively correct numerical weather forecasts. Compared with traditional post-processing methods and convolutional neural networks, ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field. In this paper, the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics, convolutional neural network postprocessing methods, and the original prediction by the ECMWF. The results show that the correction effect of ED-ConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes, especially in the long forecast time.     

An Effective Approach for Improving the Real-Time Prediction of Summer Rainfall over China

This paper has two purposes. One is to evaluate the ability of an atmospheric general circulation model (IAP9L-AGCM) to predict summer rainfall over China one season in advance. The other is to propose a new approach to improve the predictions made by the model. First, a set of hindcast experiments for summer climate over China during 1982-2010 are performed from the perspective of real-time prediction with the IAP9L-AGCM model and the IAP ENSO prediction system. Then a new approach that effectively combines the hind-cast with its correction is proposed to further improve the model’s predictive ability. A systematic evaluation reveals that the model’s real-time predictions for 41 stations across China show significant improvement using this new approach, especially in the lower reaches between the Yellow River and Yangtze River valleys.     

EVALUATION OF THE PARAMETERIZATION SCHEMES AND NUDGING TECHNIQUES IN GRAPES FOR WARM SECTOR TORRENTIAL RAINS USING SURFACE OBSERVATIONS

Sensitivities of parameterization schemes were conducted based on the Global/Regional Assimilation and Prediction System (GRAPES) model. Surface observations were used to evaluate the simulations and to improve the model’s ability to simulate the extreme precipitation over southern China on 20 July 2016. The results showed that GRAPES captured the large-scale precipitation over southern China but failed to predict the extreme precipitation over Xinyi. The model showed a systematic cold biases by adopting different parameterization schemes. In particular, the ECMWF analyses data showed a strong cold bias over Guangdong province and Guangxi Region. Observational nudging results showed that the surface temperature could largely help to alleviate the cold bias. The alleviation in the warm sector accounted for main improvement by the nudging scheme, and the RMSE was reduced by 1.56 degree from 3.25 degree to 1.69 degree by 1-h simulation and with 1.3 degree alleviation by 2-h simulation. Sensitivities using different parameterizations and the nudging scheme showed that the model’s underestimation of the precipitation was still present despite improvements in the predictions of surface temperature.     

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

In this study, a method of analogue-based correction of errors（ACE） was introduced to improve El Ni？o-Southern Oscillation（ENSO） prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.     

A Hybrid Dynamical-Statistical Approach for Predicting Winter Precipitation over Eastern China

Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields.It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China(herein after called the hybrid approach).In this connection,seasonal real-time prediction models for winter precipitation were established for the six regions.The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis.To improve the prediction accuracy,the systematic error between the original regression model result and the corresponding observation was corrected.Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend,sign,and interannual variation of regionally averaged winter precipitation in the six regions of concern.Averaged over the six target regions,the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%,respectively.This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.