A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal ...A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal prediction skill of this system via a 21-year hindcast experiment for the period 2000–20 with eight ensemble members.Results showed moderate-to-high skill for the primary atmospheric variables. The most accurate predictions emerged in the cold season but were largely confined within tropical bands as the forecast lead time was increased. Compared with the NCEP S2S FS, the CAMS-CSM S2S FS showed comparable subseasonal skill for 500-h Pa geopotential height, but slightly higher(lower) skill for precipitation(2-m temperature). The skillful lead time in the CAMS-CSM S2S FS for the Madden–Julian Oscillation and North Atlantic Oscillation reached 20 and 10 days, respectively, consistent with the NCEP S2S FS. Consequently, these findings guide future research on subseasonal predictability based on the CAMS-CSM S2S FS, and where efforts should be focused to improve the prediction system.展开更多
The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predicta...The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predictability remains elusive. This study assessed the real-time one-month-lead prediction skill of the summer 2022 YRV heatwaves using 12operational seasonal forecast systems. Results indicate that most individual forecast systems and their multi-model ensemble(MME) mean exhibited limited skill in predicting the 2022 YRV heatwaves. Notably, after the removal of the linear trend, the predicted 2-m air temperature anomalies were generally negative in the YRV, except for the Met Office Glo Sea6 system, which captured a moderate warm anomaly. While the models successfully simulated the influence of La Ni?a on the East Asian–western North Pacific atmospheric circulation and associated YRV temperature anomalies, only Glo Sea6 reasonably captured the observed relationship between the YRV heatwaves and an atmospheric teleconnection extending from the North Atlantic to the Eurasian mid-to-high latitudes. Such an atmospheric teleconnection plays a crucial role in intensifying the YRV heatwaves. In contrast, other seasonal forecast systems and the MME predicted a distinctly different atmospheric circulation pattern, particularly over the Eurasian mid-to-high latitudes, and failed to reproduce the observed relationship between the YRV heatwaves and Eurasian mid-to-high latitude atmospheric circulation anomalies.These findings underscore the importance of accurately representing the Eurasian mid-to-high latitude atmospheric teleconnection for successful YRV heatwave prediction.展开更多
Impacts of land models and initial land conditions （ICs） on the Asian summer monsoon, especially its onset, were investigated using the NCEP Climate Forecast System （CFS）. Two land models, the Oregon State Univers...Impacts of land models and initial land conditions （ICs） on the Asian summer monsoon, especially its onset, were investigated using the NCEP Climate Forecast System （CFS）. Two land models, the Oregon State University （OSU） land model and the NCEP, OSU, Air Force, and Hydrologic Research Laboratory （Noah） land model, were used to get parallel experiments NCEP/Department of Energy （DOE） Global Reanalysis 2 System （GLDAS）. The experiments also used land ICs from the （GR2） and the Global Land Data Assimilation Previous studies have demonstrated that, a systematic weak bias appears in the modeled monsoon, and this bias may be related to a cold bias over the Asian land mass. Results of the current study show that replacement of the OSU land model by the Noah land model improved the model＇s cold bias and produced improved monsoon precipitation and circulation patterns. The CFS predicted monsoon with greater proficiency in E1 Nifio years, compared to La Nifia years model in monsoon predictions for individual years. and the Noah model performed better than the OSU These improvements occurred not only in relation to monsoon onset in late spring but also to monsoon intensity in summer. Our analysis of the monsoon features over the India peninsula, the Indo-China peninsula, and the South Chinese Sea indicates different degrees of improvement. Furthermore, a change in the land models led to more remarkable improvement in monsoon prediction than did a change from the GR2 land ICs to the GLDAS land ICs.展开更多
The Florida Current （FC） largely fills the Straits of Florida and is variable on a broad spectrum of time and space scales. Some portions of the variability are due to variable forcing by tides, winds, heating/cooli...The Florida Current （FC） largely fills the Straits of Florida and is variable on a broad spectrum of time and space scales. Some portions of the variability are due to variable forcing by tides, winds, heating/cooling, and throughflow; other portions are due to intrinsic instabilities of the FC. To predict, as well as to better understand this complex regime, a nowcast/forecast system （East Florida Shelf Information System （EFSIS）） has been implemented and assessed （http：//efsis. rsmas. miami. edu）. EFSIS is based on an implementation of the Princeton Ocean Model （POM） with mesoscale-admitting resolution on a curvilinear grid. It is forced by a mesoscale numerical weather prediction system （called Eta） run operationally by the National Centers for Environmental Prediction （NCEP）, eight tidal constituents from a global tidal model, and lateral boundary conditions from an operational global ocean prediction model, i.e., the Navy Coastal Ocean Model （NCOM）. Real-time observations of coastal sea level, coastal sea surface temperature, coastal HF radar-derived surface current maps, and FC volume transport are used to verify and validate EFSIS. EFSIS is part of an evolving strategy for real-time predictive coastal ocean modeling methodology, and for fostering the understanding of the variability of the regime on several time and space scales. Here, some of the verification and validation results are provided, as well as diagnostic analyses of dynamical processes. The central point is that an example is provided of a ＇scientific revolution＇ in progress that combines real-time observations and numerical circulation models to yield a credible sequence of synoptic views of coastal ocean circulation for the first time.展开更多
Prediction skill for the seasonal tropical cyclone(TC)activity in the Northern Hemisphere is investigated using the coupled climate forecast system(version 1.0)of Nanjing University of Information Science and Technolo...Prediction skill for the seasonal tropical cyclone(TC)activity in the Northern Hemisphere is investigated using the coupled climate forecast system(version 1.0)of Nanjing University of Information Science and Technology(NUISTCFS1.0).This assessment is based on the seven-month(May to November)hindcasts consisting of nine ensemble members during 1982–2019.The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere.The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations,although the seasonal mean TC frequency and accumulated cyclone energy(ACE)are underestimated in all basins due to the low resolution(T106)of the atmospheric component in the model.NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic(NA)and eastern North Pacific(ENP),which have a good relationship with indexes based on the sea surface temperature.In the western North Pacific(WNP),NUIST-CFS1.0 can closely capture ACE,which is significantly correlated with the El Ni?o–Southern Oscillation(ENSO),while it has difficulty forecasting the interannual variation of TC frequency in this area.When the WNP is further divided into eastern and western subregions,the model displays improved TC activity forecasting ability.Additionally,it is found that biases in predicted TC genesis locations lead to inaccurately represented TC–environment relationships,which may affect the capability of the model in reproducing the interannual variations of TC activity.展开更多
In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer ...In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts （QPF） and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method （proposed with the single case study in Part I） was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.展开更多
A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple...A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System （NCEP GEFS） forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts （PQPF）. However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.展开更多
To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic ...To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.展开更多
In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RM...In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.展开更多
Variations of surface air temperature （SAT） are key in affecting the hydrological cycle, ecosystems and agriculture in western China in summer. This study assesses the seasonal forecast skill and reliability of SAT ...Variations of surface air temperature （SAT） are key in affecting the hydrological cycle, ecosystems and agriculture in western China in summer. This study assesses the seasonal forecast skill and reliability of SAT in western China, using the GloSea5 operational forecast system from the UK Met Office. Useful predictions are demonstrated, with considerable skill over most regions of western China. The temporal correlation coefficients of SAT between model predictions and observations axe larger than 0.6, in both northwestern China and the Tibetan Plateau. There are two important sources of skill for these predictions in western China： interannual variation of SST in the western Pacific and the SST trend in the tropical Pacific. The tropical SST change in the recent two decades, with a warming in the western Pacific and cooling in the eastern Pacific, which is reproduced well by the forecast system, provides a large contribution to the skill of SAT in northwestern China. Additionally, the interannual variation of SST in the western Pacific gives rise to the reliable prediction of SAT around the Tibetan Plateau. It modulates convection around the Maritime Continent and further modulates the variation of SAT on the Tibetan Plateau via the surrounding circulation. This process is evident irrespective of detrending both in observations and the model predictions, and acts as a source of skill in predictions for the Tibetan Plateau. The predictability and reliability demonstrated in this study is potentially useful for climate services providing early warning of extreme climate events and could imply useful economic benefits.展开更多
The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known a...The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.展开更多
The general structure of ship-borne helicopter landing forecast system is presented, and a novel ship motion prediction model based on minor component analysis (MCA) is built up to improve the forecast effectiveness. ...The general structure of ship-borne helicopter landing forecast system is presented, and a novel ship motion prediction model based on minor component analysis (MCA) is built up to improve the forecast effectiveness. To validate the feasibility of this landing forecast system, time series for the roll, pitch and heave are generated by simulation and then forecasted based on MCA. Simulation results show that ship-borne helicopters can land safely in higher sea condition while carrying on rescue or replenishment tasks at sea in terms of the landing forecast system.展开更多
To support navigational and environmental applications in coastal waters, marine opera- tional forecast models must be developed and implemented. A forecast model must guarantee that it is scientifically sound and pra...To support navigational and environmental applications in coastal waters, marine opera- tional forecast models must be developed and implemented. A forecast model must guarantee that it is scientifically sound and practically robust for performance and must meet or excel all target frequencies or durations before being released to the public. This paper discusses the standard policies and procedures for evaluation of operational marine forecast models. The primary variables to be evaluated are water lev- els, currents and water density (water temperature and salinity).展开更多
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using...It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.展开更多
In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hour...In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging manageme...Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation sin...An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.展开更多
In this article, a model of a weed control threshold forecast system has been established, with related model solving, data checking, database setting up, and system engineering illustration. Moreover, it is tested by...In this article, a model of a weed control threshold forecast system has been established, with related model solving, data checking, database setting up, and system engineering illustration. Moreover, it is tested by a software with data from a sugar cane planting experimental field in Yunnan, China. The methodology behind the detailed system analysis, design, and engineering has been discussed. The issue of how to create a dynamic data-dependent forecast model of a threshold forecast system, whose threshold changes according to the change of planting environment has been solved. Hence an effective solution has been initiated for further development on an agricultural expert system.展开更多
基金jointly funded by the National Key Research and Development Program (Grant No.2022YFC3004203)the National Natural Science Foundation of China (Grant No.42375033)the Basic Scientific Research and Operation Foundation of the Chinese Academy of Meteorological Sciences (Grant Nos.2023Z018, 2024KJ013, and 2023KJ036)。
文摘A subseasonal-to-seasonal(S2S) forecast system(FS) has recently been released based on the fully coupled Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM). This study evaluated the subseasonal prediction skill of this system via a 21-year hindcast experiment for the period 2000–20 with eight ensemble members.Results showed moderate-to-high skill for the primary atmospheric variables. The most accurate predictions emerged in the cold season but were largely confined within tropical bands as the forecast lead time was increased. Compared with the NCEP S2S FS, the CAMS-CSM S2S FS showed comparable subseasonal skill for 500-h Pa geopotential height, but slightly higher(lower) skill for precipitation(2-m temperature). The skillful lead time in the CAMS-CSM S2S FS for the Madden–Julian Oscillation and North Atlantic Oscillation reached 20 and 10 days, respectively, consistent with the NCEP S2S FS. Consequently, these findings guide future research on subseasonal predictability based on the CAMS-CSM S2S FS, and where efforts should be focused to improve the prediction system.
基金jointly supported by the National Key Research and Development Program of China (2023YFC3007503)the Joint Research Project for Meteorological Capacity Improvement (22NLTSZ002)+4 种基金the National Natural Science Foundations of China (Grant Nos.42375064, 41975102, 41730964, 42175047)the China Meteorological Administration Key Innovation Team for Climate Prediction (CMA2023ZD03)the Met Office Climate Science for Service Partnership (CSSP) China project under the International Science Partnerships Fund (ISPF)the Special Project of Innovation and Development of China Meteorological Administration (CXFZ2024J004)the China Yangtze Power Co.,Ltd.Research Project (Grant No.2423020054)。
文摘The Yangtze River Valley(YRV) of China experienced record-breaking heatwaves in July and August 2022. The characteristics, causes, and impacts of this extreme event have been widely explored, but its seasonal predictability remains elusive. This study assessed the real-time one-month-lead prediction skill of the summer 2022 YRV heatwaves using 12operational seasonal forecast systems. Results indicate that most individual forecast systems and their multi-model ensemble(MME) mean exhibited limited skill in predicting the 2022 YRV heatwaves. Notably, after the removal of the linear trend, the predicted 2-m air temperature anomalies were generally negative in the YRV, except for the Met Office Glo Sea6 system, which captured a moderate warm anomaly. While the models successfully simulated the influence of La Ni?a on the East Asian–western North Pacific atmospheric circulation and associated YRV temperature anomalies, only Glo Sea6 reasonably captured the observed relationship between the YRV heatwaves and an atmospheric teleconnection extending from the North Atlantic to the Eurasian mid-to-high latitudes. Such an atmospheric teleconnection plays a crucial role in intensifying the YRV heatwaves. In contrast, other seasonal forecast systems and the MME predicted a distinctly different atmospheric circulation pattern, particularly over the Eurasian mid-to-high latitudes, and failed to reproduce the observed relationship between the YRV heatwaves and Eurasian mid-to-high latitude atmospheric circulation anomalies.These findings underscore the importance of accurately representing the Eurasian mid-to-high latitude atmospheric teleconnection for successful YRV heatwave prediction.
基金partially supported by the International S & T Cooperation Project of the Ministry of Science and Technology of China (Grant No. 2009DFA21430)the National Natural Science Foundation of China (Grant No. 40921003)the Basic Scientific Research and Operation Foundation of the CAMS (Grant No. 2010Z003)
文摘Impacts of land models and initial land conditions （ICs） on the Asian summer monsoon, especially its onset, were investigated using the NCEP Climate Forecast System （CFS）. Two land models, the Oregon State University （OSU） land model and the NCEP, OSU, Air Force, and Hydrologic Research Laboratory （Noah） land model, were used to get parallel experiments NCEP/Department of Energy （DOE） Global Reanalysis 2 System （GLDAS）. The experiments also used land ICs from the （GR2） and the Global Land Data Assimilation Previous studies have demonstrated that, a systematic weak bias appears in the modeled monsoon, and this bias may be related to a cold bias over the Asian land mass. Results of the current study show that replacement of the OSU land model by the Noah land model improved the model＇s cold bias and produced improved monsoon precipitation and circulation patterns. The CFS predicted monsoon with greater proficiency in E1 Nifio years, compared to La Nifia years model in monsoon predictions for individual years. and the Noah model performed better than the OSU These improvements occurred not only in relation to monsoon onset in late spring but also to monsoon intensity in summer. Our analysis of the monsoon features over the India peninsula, the Indo-China peninsula, and the South Chinese Sea indicates different degrees of improvement. Furthermore, a change in the land models led to more remarkable improvement in monsoon prediction than did a change from the GR2 land ICs to the GLDAS land ICs.
文摘The Florida Current （FC） largely fills the Straits of Florida and is variable on a broad spectrum of time and space scales. Some portions of the variability are due to variable forcing by tides, winds, heating/cooling, and throughflow; other portions are due to intrinsic instabilities of the FC. To predict, as well as to better understand this complex regime, a nowcast/forecast system （East Florida Shelf Information System （EFSIS）） has been implemented and assessed （http：//efsis. rsmas. miami. edu）. EFSIS is based on an implementation of the Princeton Ocean Model （POM） with mesoscale-admitting resolution on a curvilinear grid. It is forced by a mesoscale numerical weather prediction system （called Eta） run operationally by the National Centers for Environmental Prediction （NCEP）, eight tidal constituents from a global tidal model, and lateral boundary conditions from an operational global ocean prediction model, i.e., the Navy Coastal Ocean Model （NCOM）. Real-time observations of coastal sea level, coastal sea surface temperature, coastal HF radar-derived surface current maps, and FC volume transport are used to verify and validate EFSIS. EFSIS is part of an evolving strategy for real-time predictive coastal ocean modeling methodology, and for fostering the understanding of the variability of the regime on several time and space scales. Here, some of the verification and validation results are provided, as well as diagnostic analyses of dynamical processes. The central point is that an example is provided of a ＇scientific revolution＇ in progress that combines real-time observations and numerical circulation models to yield a credible sequence of synoptic views of coastal ocean circulation for the first time.
基金supported in part by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Nature Science Foundation of China(Grant Nos.42005002,42030605,and 42105003)。
文摘Prediction skill for the seasonal tropical cyclone(TC)activity in the Northern Hemisphere is investigated using the coupled climate forecast system(version 1.0)of Nanjing University of Information Science and Technology(NUISTCFS1.0).This assessment is based on the seven-month(May to November)hindcasts consisting of nine ensemble members during 1982–2019.The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere.The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations,although the seasonal mean TC frequency and accumulated cyclone energy(ACE)are underestimated in all basins due to the low resolution(T106)of the atmospheric component in the model.NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic(NA)and eastern North Pacific(ENP),which have a good relationship with indexes based on the sea surface temperature.In the western North Pacific(WNP),NUIST-CFS1.0 can closely capture ACE,which is significantly correlated with the El Ni?o–Southern Oscillation(ENSO),while it has difficulty forecasting the interannual variation of TC frequency in this area.When the WNP is further divided into eastern and western subregions,the model displays improved TC activity forecasting ability.Additionally,it is found that biases in predicted TC genesis locations lead to inaccurately represented TC–environment relationships,which may affect the capability of the model in reproducing the interannual variations of TC activity.
基金supported by a project of the National Natural Science Foundation of China (Grant No. 40875079)
文摘In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts （QPF） and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method （proposed with the single case study in Part I） was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.
基金supported by the project of the NSFC (Grants No. 40875079)
文摘A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System （NCEP GEFS） forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts （PQPF）. However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.
基金The National Key Research and Development Program of China under contract Nos 2018YFC1407205 and2018YFA0605901the Basic Scientific Fund for National Public Research Institute of China(ShuXingbei Young Talent Program)under contract No.2019S06+1 种基金the National Natural Science Foundation of China under contract Nos 41821004,42022042 and 41941012the China-Korea Cooperation Project on Northwestern Pacific Climate Change and its Prediction。
文摘To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.
基金Supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(grant no.2019QZKK0105)the National Key Research and Development Program of China(2018YFC1506603).
文摘In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.
基金supported by the National Key R&D Program of China(Grant No.2016YFA0600603)the National Natural Science Foundation of China(Grant Nos.U1502233,41320104007 and 41775083)supported by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP) China as part of the Newton Fund
文摘Variations of surface air temperature （SAT） are key in affecting the hydrological cycle, ecosystems and agriculture in western China in summer. This study assesses the seasonal forecast skill and reliability of SAT in western China, using the GloSea5 operational forecast system from the UK Met Office. Useful predictions are demonstrated, with considerable skill over most regions of western China. The temporal correlation coefficients of SAT between model predictions and observations axe larger than 0.6, in both northwestern China and the Tibetan Plateau. There are two important sources of skill for these predictions in western China： interannual variation of SST in the western Pacific and the SST trend in the tropical Pacific. The tropical SST change in the recent two decades, with a warming in the western Pacific and cooling in the eastern Pacific, which is reproduced well by the forecast system, provides a large contribution to the skill of SAT in northwestern China. Additionally, the interannual variation of SST in the western Pacific gives rise to the reliable prediction of SAT around the Tibetan Plateau. It modulates convection around the Maritime Continent and further modulates the variation of SAT on the Tibetan Plateau via the surrounding circulation. This process is evident irrespective of detrending both in observations and the model predictions, and acts as a source of skill in predictions for the Tibetan Plateau. The predictability and reliability demonstrated in this study is potentially useful for climate services providing early warning of extreme climate events and could imply useful economic benefits.
文摘The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.
文摘The general structure of ship-borne helicopter landing forecast system is presented, and a novel ship motion prediction model based on minor component analysis (MCA) is built up to improve the forecast effectiveness. To validate the feasibility of this landing forecast system, time series for the roll, pitch and heave are generated by simulation and then forecasted based on MCA. Simulation results show that ship-borne helicopters can land safely in higher sea condition while carrying on rescue or replenishment tasks at sea in terms of the landing forecast system.
基金Supported by the National Natural Science Foundation of China (No. 40376010).
文摘To support navigational and environmental applications in coastal waters, marine opera- tional forecast models must be developed and implemented. A forecast model must guarantee that it is scientifically sound and practically robust for performance and must meet or excel all target frequencies or durations before being released to the public. This paper discusses the standard policies and procedures for evaluation of operational marine forecast models. The primary variables to be evaluated are water lev- els, currents and water density (water temperature and salinity).
基金supported by the National Natural Science Foundation of China(Grant Nos.42375062 and 42275158)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)the Natural Science Foundation of Gansu Province(Grant No.22JR5RF1080)。
文摘It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.
基金funded in part by Grant No.DF-091-135-1441 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
文摘Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金China-Korea Cooperation Project on the development of oceanic monitoring and prediction system on nuclear safetythe Project of the National Programme on Global Change and Air-sea Interaction under contract No.GASI-03-IPOVAI-05
文摘An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.
文摘In this article, a model of a weed control threshold forecast system has been established, with related model solving, data checking, database setting up, and system engineering illustration. Moreover, it is tested by a software with data from a sugar cane planting experimental field in Yunnan, China. The methodology behind the detailed system analysis, design, and engineering has been discussed. The issue of how to create a dynamic data-dependent forecast model of a threshold forecast system, whose threshold changes according to the change of planting environment has been solved. Hence an effective solution has been initiated for further development on an agricultural expert system.
