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.     

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.     

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

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

Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events

The linkage between the Arctic and midlatitudes has received much attention recently due to the rapidly changing climate.Many investigations have been conducted to reveal the relationship between the Arctic and Eurasian extreme events from the perspective of climatological statistics.As a prediction source for extreme events in Eurasia,Arctic conditions are crucial for extreme event predictions.Therefore,it is urgent to explore the Arctic influence on the predictability of Eurasian extreme events due to the large uncertainties in Arctic conditions.Considering the sensitivity and nonlinearity of the atmospheric circulations in midlatitude to Arctic conditions,it is necessary to investigate the Arctic influences on Eurasian extreme weather events in case studies at weather time scales.Previous studies indicate that only perturbations in specific patterns have fast growth.Thus,the conditional nonlinear optimal perturbation approach is recommended for exploring the uncertainties in Arctic initial and boundary conditions and their synergistic effect on Eurasian extreme events.Moreover,the mechanism for extreme event formation may differ in different cases.Therefore,more extreme cases should be investigated to reach robust conclusions.     

The Impact of Assimilating Radar-estimated Rain Rates on Simulation of Precipitation in the 17--18 July 1996 Chicago Floods

Rainfall prediction remains one of the most challenging problems in weather forecasting. In order to improve high-resolution quantitative precipitation forecasts (QPF), a new procedure for assimilating rainfall rate derived from radar composite reflectivity has been proposed and tested in a numerical simulation of the Chicago floods of 17–18 July 1996. The methodology is based on the one-dimensional variation scheme (1DVAR) assimilation approach introduced by Fillion and Errico but applied here using the Ka...     

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.     

Heavy Rainfall Ensemble Prediction： Initial Condition Perturbation vs Multi-Physics Perturbation

Mesoscale ensemble is an encouraging technology for improving the accuracy of heavy rainfall predictions. Occurrences of heavy rainfall are closely related to convective instability and topography. In mid-latitudes, perturbed initial fields for medium-range weather forecasts are often configured to focus on the baroclinic instability rather than the convective instability. Thus, alternative approaches to generate initial perturba- tions need to be developed to accommodate the uncertainty of the convective instability. In this paper, an initial condition perturbation approach to mesoscale heavy rainfall ensemble prediction, named as Different Physics Mode Method （DPMM）, is presented in detail. Based on the PSU/NCAR mesoscale model MM5, an ensemble prediction experiment on a typical heavy rainfall event in South China is carried out by using the DPMM, and the structure of the initial condition perturbation is analyzed. Further, the DPMM ensem- ble prediction is compared with a multi-physics ensemble prediction, and the results show that the initial perturbation fields from the DPMM have a reasonable mesoscale circulation structure and could reflect the prediction uncertainty in the sensitive regions of convective instability. An evaluation of the DPMM ini- tial condition perturbation indicates that the DPMM method produces better ensemble members than the multi-physics perturbation method, and can significantly improve the precipitation forecast than the control non-ensemble run.     

Recent Advances in Predictability Studies in China （1999-2002）

Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealedby NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process.     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

The Impact of Moist Physics on the Sensitive Area Identification for Heavy Rainfall Associated Weather Systems

The impact of moist physics on the sensitive areas identified by conditional nonlinear optimal perturbation(CNOP)is examined based on four typical heavy rainfall cases in northern China through performing numerical experiments with and without moist physics.Results show that the CNOP with moist physics identifies sensitive areas corresponding to both the lower-(850?700 hPa)and upper-level(300?100 hPa)weather systems,while the CNOP without moist physics fails to capture the sensitive areas at lower levels.The reasons for the CNOP peaking at different levels can be explained in both algorithm and physics aspects.Firstly,the gradient of the cost function with respect to initial perturbations peaks at the upper level without moist physics which results in the upper-level peak of the CNOP,while it peaks at both the upper and lower levels with moist physics which results in both the upper-and lower-level peaks of the CNOP.Secondly,the upper-level sensitive area is associated with high baroclinicity,and these dynamic features can be captured by both CNOPs with and without moist physics.The lower-level sensitive area is associated with moist processes,and this thermodynamic feature can be captured only by the CNOP with moist physics.This result demonstrates the important contribution of the initial error of lower-level systems that are related to water vapor transportation to the forecast error of heavy rainfall associated weather systems,which could be an important reference for heavy rainfall observation targeting.     

An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction

An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors(MEs) in past intervals. Given the analyses, the ME in each interval(6 h) between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year(2001–2005) GRAPESGFS(Global Forecast System of the Global and Regional Assimilation and Prediction System) error patterns and evolution,a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP(National Centers for Environmental Prediction) FNL(final) data.The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.     

Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction

After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.     

Elevation Influence on Rainfall and a Parameterization Algorithm in the Beijing Area

Using a high-density automatic weather stations(AWS) dataset of hourly rainfall observations, the present study investigates the relationship between rainfall and elevation in the Beijing area, and further proposes a rainfall amount dependent parameterized algorithm considering the elevation effect on rainfall on hourly timescale. The parameterization equation is defined as a segmented nonlinear model, which calculates the mountain rainfall as a function of valley rainfall amount. Results show that there exists an evident enhancement of rainfall amount by elevation effect in the Beijing area. In particular, larger rainfall amount is generally found in higher mountains, especially for slight rain and moderate rain. Furthermore, six representative station pairs located in valleys and on mountains respectively are selected to estimate the values of optimal parameters in the parameterization equation. The parameterization algorithm of elevation dependence can produce a reduction in the root-mean-square error and obtain a much closer mountain rainfall total to the observations compared with those using no elevation dependence. Furthermore, the spatial distribution of rainfall is more realistic and accurate in mountainous terrain when elevation dependence is considered. This study helps to understand the variability of rainfall with complex terrain in the Beijing area, and gives a possible way to parameterize rainfall–elevation relationship on hourly timescale.     

An Impact Study of the Vertical Coordinate on a Non-hydrostatic Mesoscale High-Resolution Model

With the high-speed development of high-powered computer techniques, it is possible that a high-resolution and multi-scale unified numerical model is applied to the operational weather prediction. Some techniques about mesoscale non-hydrostatic numerical weather prediction are addressed, and the impact of the vertical coordinate system is one of them. Based on WRF (Weather Research and Forecasting) model, the influence of vertical coordinates on the non-hydrostatic mesoscale high-resolution model is compared. The results show that the error of various coordinates in lower levels is almost same when we use the geometry height (z) and the pressure (p) to set up a terrain-following coordinate; but the error of the height terrain-following coordinate in higher levels is smaller than that of the pressure terrain-following coordinate. The higher the resolution is, the bigger the error will be. The results of the high-resolution simulation exhibited that the trend of the difference in the two coordinates existed. In addition, the correlative coefficient and standard error are also analysed by the comparison between the forecast fields and the corresponding analysis fields.     

Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)

Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types:(1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following:(1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Nin o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.     

STUDY ON APPLICATIONS OF C-BAND DUAL LINEAR POLARIZATION RADAR IN METEOROLOGY

Theoretical study and development of a dual linear polarization weather radar in China are briefly presented.Also discussed are the potential uses of the new radar system in improving the accuracy of areal rainfall measure-ments and analysing the spacial structure of storms and distribution of hydrometecrs in clouds based on theradar observational data from the field experimcnts during the summers of 1987—1989. The results indicatethat a C-band dual polarization weather radar, after considering the microwave attenuation correction, may beemployed to quantitatively measure rainfall and to monitor heavy rain and flood events and becomes an impor-rant means to study storm structure.     

Reducing the Prediction Uncertainties of High-Impact Weather and Climate Events:An Overview of Studies at LASG

This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Ni?o–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection-based four-dimensional variational data assimilation(DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction,which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors(NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.     

Determination of the distance between two adjacent stations,the observational vertical increment and the observational time interval in optimum sense

Considering the observational error, the truncation error and the requirements of numerical weather prediction, three formulas for determining the distance between two adjacent stations d1, the observational vertical increment △p1 and the observational time interval △t1 in optimum sense, have been derived. Since they depend on the shortest wavelength concerned and the ratio of maximum observational error to wave amplitude, the results are quite different for different scale systems.For the filtered model the values of d1, △p1,, and △t1 in general come near those required in the MANUAL on the GOS published in 1980 by WMO. But for the primitive equation model the estimated value of △t1 is much less than those required in the filtered model case.Therefore, it is improper to study the fast moving and developing processes of the atmospheric motion only on the basis of the conventional observations. It seems to be necessary to establish an optimum composite observational system including the surface-based system and the space-based system.     

Effects of Different Initial Fields on GRAPES Numerical Prediction

In this paper,a heavy rainfall process occurring in the Huaihe River Basin during 9-10 July 2005 is studied by the new generation numerical weather prediction model system-GRAPES,from the view of different initial field effects on the prediction of the model.Several numerical experiments are conducted with the initial conditions and lateral boundary fields provided by T213 L31 and NCEP final analyses,respectively. The sensitivity of prediction products generated by GRAPES to different initial conditions,including effects of three-dimensional variational assimilation on the results,is discussed.After analyzing the differences between the two initial fields and the four simulated results,the memonic ability of the model to initial fields and their influences on precipitation forecast are investigated.Analyses show the obvious differences of sub-synoptic scale between T213 and NCEP initial fields,which result in the corresponding different simulation results,and the differences do not disappear with the integration running.It also shows that for the same initial field whether it has data assimilation or not,it only obviously influences the GRAPES model results in the initial 24 h.Then the differences reduce.In addition,both the Iocation and intensity of heavy rain forecasted by GRAPES model Further is very close to the fact,but the forecasting area of strong torrential rain has some differences from the fact.For the same initial field when it has assimilation, the 9-12-,12-24-,and 0-24-h precipitation forecasts of the model are better than those without assimilation. All these suggest that the ability of GRAPES numerical prediction depends on the different initial fields and lateral boundary conditions to some extent,and the differences of initial fields will determine the differences of GRAPES simulated results.