Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts.Recently developed retrospective forecast experiments,based on a Community Earth System Model ensemble prediction system,offer a great opportunity to comprehensively explore OHC predictability.Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends.The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill,indicating that the persistence of OHC serves as the primary predictive signal for its predictability.The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean,particularly within tropical regions.Additionally,notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability.The potential predictability of OHC generally surpasses the actual prediction skill at all lead times,highlighting significant room for improvement in current OHC predictions,especially for the North Indian Ocean and the Atlantic Ocean.Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations,develop effective data assimilation methods,and reduce model bias.

Evaluation of the Climate Prediction System CMA-CPSv3 on Performances of Precipitation and Temperature Prediction over the Tibetan Plateau

In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration (CMA-CPSv3) over the Tibetan Plateau region, the precipitation and temperature predicted by CMA-CPSv3 at different lead time was evaluated for the period of 2001-2023, by comparing with observations—the monthly precipitation of the CMAP and 2 m temperature data of the NCEP/DOE reanalysis data. The main conclusions were as follows: 1) the forecast skill of the model is very sensitive to the initial conditions and value, and the model forecast capability decreases rapidly as the lead time is extended. 2) CPSv3 performed well in capturing the spatial distribution of precipitation over Tibet in January, August, October, November and December at 0-month lead, and the forecast skill is relatively poor in March and April. CPSv3 has better performance in the east-central part of the land in January, in the central and western part of the region in March, in the whole region in April, in the northern part of Nagchu, northern part of Chamdo and central part of Shigatse in July, in the eastern part of Chamdo, northern part of Nagchu and northern part of Ali in August, in the whole region in September, and in the cities of Shigatse and Shannan in November. 3) At each lead time, the forecast skill for 2-m temperatures was higher than that for precipitation. For 2-m temperature, CPSv3 performed best in January, May, July, August, September, October, and December, while performing relatively poor in March, April, and November. Specifically, the prediction skill is higher in January and December for most of the regions, for Shigatse and Ali in May, for southern Shannan in July, and for northern Nagchu and southern Shannan in August, and there is some prediction skill in March, April and October, while CPSv3 performed poorly in November.

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

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

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones.PartⅠ:Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.

The ‘Two oceans and one sea' extended range numerical prediction system with an ultra-high resolution atmosphere-ocean-land regional coupled model

The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national defense.With the increasing demand for disaster prevention and mitigation,the importance of 10–30-day extended range prediction,between the conventional short-term(around seven days)and the climate scale(longer than one month),is apparent.However,marine extended range prediction is still a‘blank point’in China,making the early warning of marine disasters almost impossible.Here,the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the‘Two Oceans and One Sea’area based on a regional ultra-high resolution multi-layer coupled model,including the scientific aims,technical scheme,innovation,and expected achievements.The completion of this prediction system is of considerable significance for the economic development and national security of China.

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

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

The Potential Predictability of the South China Sea Summer Monsoon in a Dynamical Seasonal Prediction System

The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.

Video-Based Crowd Density Estimation and Prediction System for Wide-Area Surveillance

Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.

Study of perturbing method in regional BGM ensemble prediction system

Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation （SSP, including Initial Random Perturbation and Perturbation Restriction） and Dynamic State Perturbation （DSP） are designed. The impacts of both perturbations on precipitation prediction are studied by analyzing a slrong precipitation process oc- curring during July 20-21, 2008. The results show that both SSP and DSP play a positive role in prediction of mesoscale precipita- tion, such as lowering the （missing） rate of precipitation prediction. SSP is mainly helpful for the 24-hour prediction, while DSP can improve both 24-hour and 48-hour prediction. DSP is better than the two SSPs in the hit rate of regional precipitation prediction. However, the former also has a little higher false alarm rate than the latter. DSP enlarges in some extent the dispersion of EPS, which is good for EPS.

A New Prediction System of Sepsis: A Retrospective, Clinical Study

Objective: Analyzing 6 biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), fibrinogen (Fib), lactate concentration (Lac), D-dimer (D-d), neutrophil ratio (NEUT%) to figure out several sensitive indicators and establish a new prediction system of sepsis, which could achieve a higher sensitivity and specificity to predict sepsis. Methods: We collected 113 SIRS patients in ICU. According to their prognosis, all the patients were divided into two groups named sepsis and non-sepsis group according to the diagnostic criteria of sepsis. We recorded the general information and detected the plasma levels of the 6 biomarkers. Results: The plasma levels of NEUT% and Fib between the two groups had no significant difference. PCT had the highest prediction accuracy of sepsis compared with other biomarkers. A predictive model was established, in which Lac, PCT, CRP were enrolled. The final prediction model was: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L). The area under the curve of the prediction model was 0.893, which was higher than every single biomarker involved in this study. Conclusion: The three serum biomarkers of Lac, PCT, CRP are used to establish a prediction model of sepsis: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L), which can better predict the occurrence of sepsis compared with other biomarkers.

Adaptive prediction system of sintering through point based on self-organize artificial neural network

A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[

Improvement of the global prediction system of the COVID-19 pandemic based on the ensemble empirical mode decomposition(EEMD)and autoregressive moving average(ARMA)model in a hybrid approach

In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.

Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System

In this study, the predictability of the El Nino-South Oscillation(ENSO) in an operational prediction model from the perspective of initial errors is diagnosed using the seasonal hindcasts of the Beijing Climate Center System Model,BCC;SM1.1(m). Forecast skills during the different ENSO phases are analyzed and it is shown that the ENSO forecasts appear to be more challenging during the developing phase, compared to the decay phase. During ENSO development, the SST prediction errors are significantly negative and cover a large area in the central and eastern tropical Pacific, thus limiting the model skill in predicting the intensity of El Nino. The large-scale SST errors, at their early stage, are generated gradually in terms of negative anomalies in the subsurface ocean temperature over the central-western equatorial Pacific,featuring an error evolutionary process similar to that of El Nino decay and the transition to the La Nina growth phase.Meanwhile, for short lead-time ENSO predictions, the initial wind errors begin to play an increasing role, particularly in linking with the subsurface heat content errors in the central-western Pacific. By comparing the multiple samples of initial fields in the model, it is clearly found that poor SST predictions of the Nino-3.4 region are largely due to contributions of the initial errors in certain specific locations in the tropical Pacific. This demonstrates that those sensitive areas for initial fields in ENSO prediction are fairly consistent in both previous ideal experiments and our operational predictions,indicating the need for targeted observations to further improve operational forecasts of ENSO.

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system:comparison of full-field and anomaly initialization

Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches--anomaly and full-field initializations--in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics （lAP）. IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called＇ensemble optimal interpolation-incremental analysis update＇ （EnOI-IAU）. It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Nino Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Nino/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4-7 months in advance.The predictive skill of the anomaly hindcasts for El Nino Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Nino Modoki winter.

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty

This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.

Potential Off-Grid User Prediction System Based on Spark

With the increasingly fierce competition among communication operators,it is more and more important to make an accurate prediction of potential off grid users.To solve the above problem,it is inevitable to consider the effectiveness of learning algo rithms,the efficiency of data processing,and other factors.Therefore,in this paper,we,from the practical application point of view,propose a potential customer off grid predic tion system based on Spark,including data pre processing,feature selection,model build ing,and effective display.Furthermore,in the research of off grid system,we use the Spark parallel framework to improve the gcForest algorithm which is a novel decision tree ensemble approach.The new parallel gcForest algorithm can be used to solve practical problems,such as the off grid prediction problem.Experiments on two real world datasets demonstrate that the proposed prediction system can handle large scale data for the off grid user prediction problem and the proposed parallel gcForest can achieve satisfying per formance.

Validation and evaluation of clinical prediction systems for first and repeated transarterial chemoembolization in unresectable hepatocellular carcinoma: A Chinese multicenter retrospective study

BACKGROUND The treatment outcome of transarterial chemoembolization(TACE)in unresectable hepatocellular carcinoma(HCC)varies greatly due to the clinical heterogeneity of the patients.Therefore,several prognostic systems have been proposed for risk stratification and candidate identification for first TACE and repeated TACE(re-TACE).AIM To investigate the correlations between prognostic systems and radiological response,compare the predictive abilities,and integrate them in sequence for outcome prediction.METHODS This nationwide multicenter retrospective cohort consisted of 1107 unresectable HCC patients in 15 Chinese tertiary hospitals from January 2010 to May 2016.The Hepatoma Arterial-embolization Prognostic(HAP)score system and its modified versions(mHAP,mHAP2 and mHAP3),as well as the six-and-twelve criteria were compared in terms of their correlations with radiological response and overall survival(OS)prediction for first TACE.The same analyses were conducted in 912 patients receiving re-TACE to evaluate the ART(assessment for re-treatment with TACE)and ABCR(alpha-fetoprotein,Barcelona Clinic Liver Cancer,Child-Pugh and Response)systems for post re-TACE survival(PRTS).RESULTS All the prognostic systems were correlated with radiological response achieved by first TACE,and the six-and-twelve criteria exhibited the highest correlation(Spearman R=0.39,P=0.026)and consistency(Kappa=0.14,P=0.019),with optimal performance by area under the receiver operating characteristic curve of 0.71[95%confidence interval(CI):0.68-0.74].With regard to the prediction of OS,the mHAP3 system identified patients with a favorable outcome with the highest concordance(C)-index of 0.60(95%CI:0.57-0.62)and the best area under the receiver operating characteristic curve at any time point during follow-up;whereas,PRTS was well-predicted by the ABCR system with a C-index of 0.61(95%CI:0.59-0.63),rather than ART.Finally,combining the mHAP3 and ABCR systems identified candidates suitable for TACE with an improved median PRTS of 36.6 mo,compared with non-candidates with a median PRTS of 20.0 mo(logrank test P<0.001).CONCLUSION Radiological response to TACE is closely associated with tumor burden,but superior prognostic prediction could be achieved with the combination of mHAP3 and ABCR in patients with unresectable liver-confined HCC.