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Evaluation of the CMA-GD(R3):Hourly Precipitation Forecasts in a Tropical Island 认领 引用
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作者 CHEN Xiao-lu FENG Xiao +2 位作者 YANG Wei WU Yu LIU Yu 《Journal of Tropical Meteorology》 SCIE CAS CSCD 2026年第3期255-270,共16页
Given the intent of the model for use in fine precipitation forecasting with adequate lead time,it is of interest to examine the way in which forecasts of warm season precipitation over Hainan Island behave as a resul... Given the intent of the model for use in fine precipitation forecasting with adequate lead time,it is of interest to examine the way in which forecasts of warm season precipitation over Hainan Island behave as a result of the rapidly updated hourly model throughout 24 initializations.Standard metrics were performed on the quantitative precipitation forecasts(QPFs)from CMA-GD(R3),a globalegional assimilation and prediction system,and the cycle of the hourly assimilation and forecast system,in April–September of 2022–2024 over Hainan Island,to understand the way in which the seasonal,diurnal,and spatial variations change in relation to decreasing forecast lead times.The findings indicated that“rain or no rain”QPFs generally exhibited a superior performance at short lead times,with the 1-h forecast being optimal.As the rainfall threshold increased,the forecast performance deteriorated in the first five forecast hours,whereas the nighttime QPF skill often exceeded the daytime performance.Significant instabilities were identified in the mountainous region and the surrounding areas when 24 forecasts that were valid at the same forecast hour were compared.The 1-h model update enhanced the reliability of the 2-m temperature,2-m dew point,and 10-m wind field across most of the island.However,persistent biases remained,including underestimation of temperature and dew point at night and in central areas,and overestimation of wind speed with strengthened afternoon wind convergence.These biases contributed to uncertainty in the model's prediction of rainfall events over Hainan Island. 展开更多
关键词 quantitative precipitation forecasts CMA-GD(R3) rapid update cycle stability Hainan Island
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Enhanced Load-Settlement Curve Forecasts for Open-Ended Pipe Piles Incorporating Soil Plug Constraints Using Shallow and Deep Neural Networks 认领 引用
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作者 Luttfi A.AL-HADDAD Mohammed Y.FATTAH +2 位作者 Wissam H.S.AL-SOUDANI Sinan A.AL-HADDAD Alaa Abdulhady JABER 《China Ocean Engineering》 SCIE EI CSCD 2025年第3期562-572,共11页
This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Levera... This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network(SNN) and deep neural network(DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value,applied load, installation type, and soil configuration(loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering,addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving R2 values ranging from0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with R2 values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures. 展开更多
关键词 pipe piles soil plug artificial neural network bearing capacity forecasts
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Exploring small‑scale optimization coupling learning approaches for enterprises’financial health forecasts 认领 引用
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作者 Lin Zhu Zhihua Zhang M.James C.Crabbe 《Financial Innovation》 2025年第1期2200-2217,共18页
The financial health of leading enterprises has a significant impact on the sustainable development of the global economy.Most data-driven financial health forecasts are based on the direct use of small-scale machine ... The financial health of leading enterprises has a significant impact on the sustainable development of the global economy.Most data-driven financial health forecasts are based on the direct use of small-scale machine learning.In this study,we proposed the idea of optimization coupling learning to improve these machine learning models in financial health forecasting.It not only revealed lagging,immediate,continuous impacts of various indicators in different fiscal year,but also had the same low computational cost and complexity as known small-scale machine learning models.We used our optimization coupling learning to investigate 3424 leading enterprises in China and revealed inner triggering mechanisms and differences of enterprises’financial health status from individual behavior to macro level. 展开更多
关键词 Financial health forecasts Optimization coupling learning Triggering mechanisms Small-scale models
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Evaluation of WRF-based Convection-Permitting Ensemble Forecasts for an Extreme Rainfall Event in East China during the Mei-yu Season 认领 引用
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作者 Chengyi ZHANG Mengwen WU Yali LUO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第10期2102-2124,共23页
This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hour... This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs. 展开更多
关键词 extreme rainfall mei-yu season convection-permitting ensemble forecasts forecast evaluation
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Impact of Assimilating Pseudo-Observations Derived from the“Z-RH”Relation on Analyses and Forecasts of a Strong Convection Case 认领 引用
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作者 Feifei SHEN Lixin SONG +4 位作者 Jinzhong MIN Zhixin HE Aiqing SHU Dongmei XU Jiajun CHEN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第5期1010-1025,共16页
Moisture conditions are crucial for the maintenance and development of severe convection.In the indirect assimilation of radar reflectivity,hydrometeors and water vapor retrieved from reflectivity are assimilated to a... Moisture conditions are crucial for the maintenance and development of severe convection.In the indirect assimilation of radar reflectivity,hydrometeors and water vapor retrieved from reflectivity are assimilated to avoid the nonlinearity issues associated with the observation operator.In a widely applied water vapor retrieval scheme,a cloud is assumed to be saturated when the radar reflectivity exceeds a certain threshold.This study replaces the traditional retrieval scheme with the“Z-RH”(radar reflectivity and relative humidity)linear statistical relationship for estimating the water vapor content,which is implemented to reduce the uncertainty caused by empirical relationships.The“Z-RH”relationship is statistically obtained from the humidity and the observations for rainfall rate at different temperature intervals with the use of the Z-R(radar reflectivity-rain rate)relationship.The impacts of these two retrieval approaches are investigated in the analyses and forecasts based on the radar reflectivity.The results suggest that both water vapor retrieval schemes yield similar reflectivity analyses,with“Z-RH”showing slightly stronger reflectivity intensities.Utilizing a“Z-RH”scheme contributes significantly to the improved analyses and forecasts of humidity and wind fields,resulting in more reasonable thermodynamic and dynamic structures.As the“Z-RH”relationship obtained by real-time statistics in a specific area provides a scientific basis for the retrieval of water vapor,a“Z-RH”scheme is beneficial to obtain more accurate reflectivity forecasts.The overall scores for the predicted precipitation of a“Z-RH”scheme are roughly 10%-20%higher compared to those of the traditional scheme. 展开更多
关键词 radar reflectivity data indirect assimilation water vapor retrieval heavy precipitation forecast
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Impacts of lateral boundary conditions from numerical models and data-driven networks on convective-scale ensemble forecasts 认领 引用 被引量：4
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作者 Junjie Deng Jin Zhang +3 位作者 Haoyan Liu Hongqi Li Feng Chen Jing Chen 《Atmospheric and Oceanic Science Letters》 CSCD 2025年第2期78-85,共8页
The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzho... The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study. 展开更多
关键词 Ensemble forecast Convective scale Lateral boundary conditions Data-driven network
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AI-based Correction of Wave Forecasts Using the Transformer-enhanced UNet Model 认领 引用
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作者 Yanzhao CAO Shouwen ZHANG +2 位作者 Guannan LV Mengchao YU Bo AI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第1期221-231,共11页
Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products,thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity.T... Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products,thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity.The traditional method that relies on forecasters'subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting.To address this problem,this paper proposes a Transformer-enhanced UNet(TransUNet)model for wave forecast AI correction,which fuses wind and wave information.The Transformer structure is integrated into the encoder of the UNet model,and instead of using the traditional upsampling method,the dual-sampling module is employed in the decoder to enhance the feature extraction capability.This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data,wave height forecast data,and significant wave height reanalysis data provided by ECMWF.The experimental results indicate that the TransUNet model yields smaller root-meansquare errors,mean errors,and standard deviations of the corrected results for the next 24-h forecasts than does the UNet model.Specifically,the root-mean-square error decreased by more than 21.55%compared to its precorrection value.According to the statistical analysis,87.81%of the corrected wave height errors for the next 24-h forecast were within±0.2m,with only 4.56%falling beyond±0.3 m.This model effectively limits the error range and enhances the ability to forecast wave heights. 展开更多
关键词 TransUNet Transformer wave forecasting bias correction
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A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area 认领 引用 被引量：27
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作者 Haochen LI Chen YU +3 位作者 Jiangjiang XIA Yingchun WANG Jiang ZHU Pingwen ZHANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2019年第10期1156-1170,共15页
In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation... In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer. 展开更多
关键词 temperature forecasts MOS machine learning multiple linear regression Random Forest weather consultation feature engineering data structures
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Effects of Dropsonde Data in Field Campaigns on Forecasts of Tropical Cyclones over the Western North Pacific in 2020 and the Role of CNOP Sensitivity 认领 引用 被引量：14
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作者 Xiaohao QIN Wansuo DUAN +2 位作者 Pak-Wai CHAN Boyu CHEN Kang-Ning HUANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2023年第5期791-803,共13页
Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weat... Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020.The conditional nonlinear optimal perturbation(CNOP)method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time.The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity,using the Weather Research and Forecasting model.It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent.However,assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast,either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data.Therefore,these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity. 展开更多
关键词 tropical cyclones targeting observation field campaign CNOP sensitivity dropsonde intensity forecasts
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Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method 认领 引用 被引量：8
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作者 JIE Weihua WU Tongwen +2 位作者 WANG Jun LI Weijing LIU Xiangwen 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2014年第2期293-304,共12页
A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions... A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau. 展开更多
关键词 time-lagged ensemble system lagged average forecast 6-15 day forecasts precipitation
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Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF 认领 引用 被引量：11
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作者 Xiaohao QIN Wansuo DUAN Hui XU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2020年第3期291-306,共16页
The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts... The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution. 展开更多
关键词 sensitivity tendency perturbation tropical cyclone intensity forecasts
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Bias correction of sea surface temperature retrospective forecasts in the South China Sea 认领 引用 被引量：3
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作者 Guijun Han Jianfeng Zhou +7 位作者 Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2022年第2期41-50,共10页
Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have bee... Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data. 展开更多
关键词 sea surface temperature retrospective forecasts bias correction backpropagation neural network empirical orthogonal function analysis South China Sea
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Sensitivity of Medium-Range Weather Forecasts to the Use of Reference Atmosphere 认领 引用 被引量：2
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作者 陈嘉滨 A.J.Simmons 《Advances in Atmospheric Sciences》 SCIE CAS 1990年第3期275-293,共19页
In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represen... In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represented variables, temperature, geopotential height and orography, are replaced by their deviations from the reference atmosphere. Two modified semi- implicit schemes have been proposed to alleviate the computational instability due to the introduction of reference atmosphere. Concerning the deviation of surface geopotential height from reference atmosphere, an exact computational formulation has been used instead of the approximate one in the earlier work. To re duce aliasing errors in the computations of the deviation of the surface geopotential height, a spectral fit has been used slightly to modify the original Gaussian grid-point values of orography.A series of experiments has been performed in order to assess the impact of the reference atmosphere on ECMWF medium- range forecasts at the resolution T21, T42 and T63. The results we have obtained reveal that the reference atmosphere introduced in ECMWF spectral model is generally beneficial to the mean statistical scores of 1000-200 hPa height 10-day forecasts over the globe. In the Southern Hemisphere, it is a clear improvement for T21, T42 and T63 throughout the 10-day forecast period. In the Northern Hemisphere, the impact of the reference atmos phere on anomaly correlation is positive for resolution T21, a very slightly damaging at T42 and almost neutral at T63 in the range of day 1 to day 4. Beyond the day 4 there is a clear improvement at all resolutions. 展开更多
关键词 Sensitivity of Medium-Range Weather Forecasts to the Use of Reference Atmosphere ECMWF
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A Spatiotemporal Interactive Processing Bias Correction Method for Operational Ocean Wave Forecasts 认领 引用
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作者 AI Bo YU Mengchao +5 位作者 GUO Jingtian ZHANG Wei JIANG Tao LIU Aichao WEN Lianjie LI Wenbo 《Journal of Ocean University of China》 SCIE CAS CSCD 2022年第2期277-290,共14页
Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictio... Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictions of numerical models and the actual sea state has been observed,predictions can only be released after correction by forecasters.This paper proposes a spati-otemporal interactive processing bias correction method to correct numerical prediction fields applied to the production and release of operational ocean wave forecasting products.The proposed method combines the advantages of numerical models and Forecast Discussion;specifically,it integrates subjective and objective information to achieve interactive spatiotemporal correc-tions for numerical prediction.The method corrects the single-time numerical prediction field in space by spatial interpolation and sub-zone numerical analyses using numerical model grid data in combination with real-time observations and the artificial judg-ment of forecasters to achieve numerical prediction accuracy.The difference between the original numerical prediction field and the spatial correction field is interpolated to an adjacent time series by successive correction analysis,thereby achieving highly efficient correction for multi-time forecasting fields.In this paper,the significant wave height forecasts from the European Centre for Medium-Range Weather Forecasts are used as background field for forecasting correction and analysis.Results indicate that the proposed method has good application potential for the bias correction of numerical predictions under different sea states.The method takes into account spatial correlations for the numerical prediction field and the time series development of the numerical model to correct numerical predictions efficiently. 展开更多
关键词 numerical models ocean wave forecasts spatial interpolation time series interpolation successive correction
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Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data 认领 引用
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作者 Seung-Woo LEE Dong-Kyou LEE 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2011年第4期758-774,共17页
Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper di... Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated. 展开更多
关键词 3DVAR background error covariances retrieved satellite data assimilation ensemble forecasts.
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Application Research of Temperature Forecasts on Elman Neural Network 认领 引用
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作者 王芳 涂春丽 勾永尧 《Agricultural Science & Technology》 CAS 2011年第11期1680-1681,1686,共2页
[Objective] The aim was to establish Elman neural network model to predict the dynamic changes of temperature. [Method] Considering the inherent nature of temperature, and dy dint of the temperature in Chongqing durin... [Objective] The aim was to establish Elman neural network model to predict the dynamic changes of temperature. [Method] Considering the inherent nature of temperature, and dy dint of the temperature in Chongqing during 1951-2010, the Elman artificial neural network model was applied to predict the temperature. [Result] This simulation result suggested that the relative error was small and can have a good simulation to the future temperature changes. [Conclusion] The prediction result can guide agricultural production and further apply to the field of pricing the weather derivative products. 展开更多
关键词 Temperature forecasts Elman neural network Agricultural production
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A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts 认领 引用
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作者 ZHAO Bin ZHANG Bo LI Zi-liang 《Journal of Tropical Meteorology》 SCIE 2021年第1期24-33,共10页
Traditional precipitation skill scores are affected by the well-known"double penalty"problem caused by the slight spatial or temporal mismatches between forecasts and observations.The fuzzy(neighborhood)meth... Traditional precipitation skill scores are affected by the well-known"double penalty"problem caused by the slight spatial or temporal mismatches between forecasts and observations.The fuzzy(neighborhood)method has been proposed for deterministic simulations and shown some ability to solve this problem.The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts.We developed an ensemble precipitation verification skill score,i.e.,the Spatial Continuous Ranked Probability Score(SCRPS),and used it to extend spatial verification from deterministic into ensemble forecasts.The SCRPS is a spatial technique based on the Continuous Ranked Probability Score(CRPS)and the fuzzy method.A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency,which were then used in the reference score to calculate the skill score of the SCRPS.The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained.The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used. 展开更多
关键词 ECMWF ensemble forecasts Spatial Continuous Ranked Probability Score(SCRPS) traditional skill score consistent assessment OPERA quantitative precipitation estimation datasets
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Hurricane Forecasts in the FSU Models 认领 引用
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作者 T. N. Krishnamurti H. S. Bedi +1 位作者 K. S. Yap D. Oosterhof 《Advances in Atmospheric Sciences》 SCIE CAS 1993年第1期121-132,共12页
A brief account of our studies on the hurricane forecast problem is presented here. This covers recent prediction results from the Florida State University (FSU) regional and global numerical weather prediction models... A brief account of our studies on the hurricane forecast problem is presented here. This covers recent prediction results from the Florida State University (FSU) regional and global numerical weather prediction models. The regions covered are the Indian and the Pacific Oceans. The life cycle of the onset vortex (a hurricane) of the summer monsoon, typhoons over the western Pacific Ocean and tropical cyclones over the Bay of Bengal (Andhra Pradesh and the Bangladesh storms) are covered here. The essential elements in the storm formaton are the strong horizontal shear in the cyclogenetic areas, a lack of vertical shear and warn sea surface temperatures. The storm motion has a steering component largely described by the advection of vorticity by a vertically averaged layer mean wind, the recurvature of a storm appears to invoke physical processes via the advection of divergence by the divergent part of the wind especially in the outflow layers of the storm. Very high resolution global models seem to be able to handle the motion and structure during the entire life of typhoons quite reasonably. The scope for better diagnosis of the storms life cycle appears very promising in view of the realistic simulation of the life cycle. 展开更多
关键词 Hurricane Forecasts in the FSU Models
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Sensitivities of Numerical Model Forecasts of Extreme Cyclone Events 认领 引用 被引量：6
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作者 A. C. Yih J. E. Walsh 《Advances in Atmospheric Sciences》 SCIE CAS 1991年第1期51-66,共16页
A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of ... A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of January 1978, the New England blizzard of February 1978, and the Mid-Atlantic cyclone of February 1979. While medium-resolution simulations capture much of the intensification, the forecasts of the precise timing and intensity levels suffer from various degrees of error. The coastal cyclones show a 5-10 hPa dependence on the western North Atlantic sea surface temperature, which is varied within a range (± 2.5℃) compatible with interannual fluctuations. The associated vertical velocities and precipitation rates show proportionately stronger dependences on the ocean temperature perturbations. The Ohio Valley blizzard, which intensified along a track 700-800 km from the coast, shows little sensitivity to ocean temperature. The effect of a shift of - 10?latitude in the position of the snow boundary is negligible in each case. The forecasts depend strongly on the model resolution, and the coarse-resolution forecasts are consistently inferior to the medium-resolution forecasts. Studies of the corresponding sensitivities of extreme cyclonic events over eastern Asia are encouraged in order to identify characteristics that are common to numerical forecasts for the two regions. 展开更多
关键词 Sensitivities of Numerical Model Forecasts of Extreme Cyclone Events SST
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Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall 认领 引用 被引量：16
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作者 Philip E. BETT Adam A. SCAIFE +8 位作者 Chaofan LI Chris HEWITT Nicola GOLDING Peiqun ZHANG Nick DUNSTONE Doug M. SMITH Hazel E. THORNTON Riyu LU Hong-Li REN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2018年第8期22-30,共9页
The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage... The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from E1 Nifio to Yangtze River basin rainfall meant that the strong E1 Nifio in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements. 展开更多
关键词 seasonal forecasting flood forecasting Yangtze basin rainfall ENSO hydroelectricity
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