It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the rel...It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.展开更多
As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally...As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors(WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method(MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element(EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally,a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.展开更多
Although tropical cyclone(TC)track forecast errors(TFEs)of operational warning centres have substantially decreased in recent decades,there are still many cases with large TFEs.The International Grand Global Ensemble(...Although tropical cyclone(TC)track forecast errors(TFEs)of operational warning centres have substantially decreased in recent decades,there are still many cases with large TFEs.The International Grand Global Ensemble(TIGGE)data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction(NWP)models.Forty-four TCs in the western North Pacific during the period 2007-2014 with TFEs(+24 to+120 h)larger than the 75 th percentile of the annual error distribution(with a total of 93 cases)are identified.Four categories of situations are found to be associated with large TFEs.These include the interaction of the outer structure of the TC with tropical weather systems,the intensity of the TC,the extension of the subtropical high(SH)and the interaction with the westerly trough.The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions,the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE.The performance of different models in different situations is discussed.展开更多
A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated fo...A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system, and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected forecast is apparently better than the original, suggesting that the approach can be applied for improving monthly-scale regional climate dynamical forecast.展开更多
This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physic...This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.展开更多
Accounting concepts dictate that separately disclosed components should contain separate useful information. This paper examines the relations between income statement components and analysts＇ earnings forecasts and ...Accounting concepts dictate that separately disclosed components should contain separate useful information. This paper examines the relations between income statement components and analysts＇ earnings forecasts and forecast errors. Regressions explaining earnings forecasts using earnings components provide a better fit than regression using just aggregate income to explain forecasts. We interpret this as consistent with the hypothesis that analysts use incremental information in components not available in aggregate income. However, additional tests based on predictability of forecast errors indicate that analysts do not incorporate all information available in components into earnings forecasts. In addition, this inefficiency appears to increase at longer forecast horizons.展开更多
[Objective] The research aimed to study the reason of local heavy rainstorm forecast error in the subtropical high control. [Method] Started from summarizing the reason of forecast error, by using the conventional gro...[Objective] The research aimed to study the reason of local heavy rainstorm forecast error in the subtropical high control. [Method] Started from summarizing the reason of forecast error, by using the conventional ground observation data, the upper air sounding data, T639, T213 and European Center (ECMWF) numerical prediction product data, GFS precipitation forecast product of U.S. National Center for Environmental Prediction, the weather situation, physical quantity field in a heavy rainstorm process which happened in the north of Shaoyang at night on August 5, 2010 were fully analyzed. Based on the numerical analysis forecast product data, the reason of heavy rainstorm forecast error in the subtropical high was comprehensively analyzed by using the comparison and analysis method of forecast and actual situation. [Result] The forecasters didn’t deeply and carefully analyze the weather situation. On the surface, 500 hPa was controlled by the subtropical high, but there was the weak shear line in 700 and 850 hPa. Moreover, they neglected the influences of weak cold air and easterlies wave. The subtropical high quickly weakened, and the system adjustment was too quick. The wind field variations in 850, 700 and 500 hPa which were forecasted by ECMWF had the big error with the actual situation. It was by east about 2 longitudes than the actual situation. In summer forecast, they only considered the intensity and position variations of 500 hPa subtropical high, and neglected the situation variations in the middle, low levels and on the ground. It was the most key element which caused the rainstorm forecast error in the subtropical high. The forecast error of numerical forecast products on the height field situation variation was big. The precipitation forecasts of Japan FSAS, U.S. National Center for Environmental Prediction GFS, T639 and T213 were all small. The humidity field forecast value of T639 was small. In the rainstorm forecast, the local rainstorm forecast index and method weren’t used in the forecast practice. In the precipitation forecast process, they only paid attention to the score prediction of station and didn’t value the non-site prediction. Some important physical quantity factors weren’t carefully studied. [Conclusion] The research provided the reference basis for the forecast and early warning of local heavy rainstorm.展开更多
In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absenc...In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.展开更多
Weather manifests in spatiotemporally coherent structures.Weather forecasts hence are affected by both positional and structural or amplitude errors.This has been long recognized by practicing forecasters(cf.,e.g.,Tro...Weather manifests in spatiotemporally coherent structures.Weather forecasts hence are affected by both positional and structural or amplitude errors.This has been long recognized by practicing forecasters(cf.,e.g.,Tropical Cyclone track and intensity errors).Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors,most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED)method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field.The total error is then partitioned into three orthogonal components:(a)large scale positional,(b)large scale structural,and(c)small scale error variance.The use of FED is demonstrated over a month-long MSLP data set.As expected,positional errors are often characterized by dipole patterns related to the displacement of features,while structural errors appear with single extrema,indicative of magnitude problems.The most important result of this study is that over the test period,more than 50%of the total mean sea level pressure forecast error variance is associated with large scale positional error.The importance of positional error in forecasts of other variables and over different time periods remain to be explored.展开更多
We examine the relation between managerial ability and management forecast accuracy. We base our analysis on S＆P 500 Composite Index constituents for the period of 2006-2012. Data were collected from Thomson Reuteurs...We examine the relation between managerial ability and management forecast accuracy. We base our analysis on S＆P 500 Composite Index constituents for the period of 2006-2012. Data were collected from Thomson Reuteurs, Compustat and Demerjian, Lev, and McVay （2012）. We find that forecast accuracy is positively associated with managerial ability in the case of sales forecasts. Specifically, more able managers are associated with lower magnitude＇s forecast errors in the case of sales forecasts. Additional analysis finds that managerial ability is immaterial to EPS figures＇ forecast accuracy, i.e., EPS forecasts appear not to be affected by manager＇s superiority. Regarding sales forecasts, the results are consistent with the assertion that managers impact the quality of the delivered management forecasts. Regarding EPS forecasts, the results are in alignment with Demerjian, Lev, Lewis, and McVay （2013） who highlighted that managerial ability is an ability score related to the entire management team.展开更多
This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect ...This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.展开更多
Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri'...Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts,global models and regional models with lead times varying from 1 to 5 days.The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration(CMA).The typhoon also caused significant wind and rainfall impacts,with precipitation at several stations reaching historical extremes,ranking eighth in terms of total rainfall impact during the event.The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method(SSTC)and Fengwu Model are the most effective for short-term track forecasts.Meanwhile,the forecasts from the European Centre for Medium-Range Weather Forecasts(ECMWF)and United Kingdom Meteorological Office(UKMO)are optimal for long-term predictions.It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity.The objective forecast is terribly poor when there is a sudden change in intensity.CMA-National Digital Forecast System(CMA-NDFS)provides a better reference value for typhoon accumulated rainfall forecasts,and regional models perform well in forecasting extreme rainfall.The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model.This improves the effective application of model products.展开更多
The Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quali...The Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality forecasting.Meteorological data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the CCMM.In this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula.The nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA.When the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain.This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological predictions.Compared to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA.The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the case.Therefore,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.展开更多
Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented consi...Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares(KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.展开更多
Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System(GFS) are still subject to substantial forecast biases and rarely consid...Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System(GFS) are still subject to substantial forecast biases and rarely consider the impact of atmospheric aerosol, despite the consensus that aerosol is one of the most important sources of uncertainty in the climate system. Here we demonstrate that atmospheric aerosol is one of the important drivers biasing daily temperature prediction. By comparing observations and the GFS prediction, we find that the monthly-averaged bias in the 24-h temperature forecast varies between ± 1.5 ℃ in regions influenced by atmospheric aerosol. The biases depend on the properties of aerosol, the underlying land surface, and aerosol–cloud interactions over oceans. It is also revealed that forecast errors are rapidly magnified over time in regions featuring high aerosol loadings. Our study provides direct ‘‘observational" evidence of aerosol’s impacts on daily weather forecast, and bridges the gaps between the weather forecast and climate science regarding the understanding of the impact of atmospheric aerosol.展开更多
Flood control forecast operation mode is one of the main ways for determining the upper bound of dynamic control of flood limited water level during flood season. The floodwater utilization rate can be effectively inc...Flood control forecast operation mode is one of the main ways for determining the upper bound of dynamic control of flood limited water level during flood season. The floodwater utilization rate can be effectively increased by using flood forecast information and flood control forecast operation mode. In this paper, Dahuofang Reservoir is selected as a case study. At first, the distribution pattern and the bound of forecast error which is a key source of risk are analyzed. Then, based on the definition of flood risk, the risk of dynamic control of reservoir flood limited water level within different flood forecast error bounds is studied. The results show that, the dynamic control of reservoir flood limited water level with flood forecast information can increase the floodwater utilization rate without increasing flood control risk effectively and it is feasible in practice.展开更多
Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasti...Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasting is helpful to reduce above adverse impacts.There are two kinds of wind power forecasting.One is to forecast wind power only based on its time series data.The other is to forecast wind power based on wind speeds from weather forecast.For a big wind farm,due to its spatial scale and dynamics of wind,wind speeds at different wind turbines are obviously different,that is called wind speed spatial dispersion.Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper.An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion.A case study of several wind farms has demonstrated that even ifthe forecasting average wind speed is accurate,the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.展开更多
This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou,Henan...This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou,Henan Province China on 19−20 July 2021 with a record-breaking hourly rainfall exceeding 200 mm and a 24-h rainfall exceeding 600 mm.Results show that the strengths of the mid-level low-pressure system,the upper-level divergence,and the low-level jet determine both the amount of the extreme 24-h accumulated and hourly rainfall at 0800 UTC.The forecast uncertainties of the accumulated rainfall are insensitive to the magnitude and the spatial structure of the tiny,unobservable errors in the initial conditions of the ensemble forecasts generated with Global Ensemble Forecast System(GEFS)or sub-grid-scale perturbations,suggesting that the predictability of this event is intrinsically limited.The dominance of upscale rather than upamplitude error growth is demonstrated under the regime of k^(−5/3) power spectra by revealing the inability of large-scale errors to grow until the amplitude of small-scale errors has increased to an adequate amplitude,and an apparent transfer of the fastest growing scale from smaller to larger scales with a slower growth rate at larger scales.Moist convective activities play a critical role in enhancing the overall error growth rate with a larger error growth rate at smaller scales.In addition,initial perturbations with different structures have different error growth features at larger scales in different variables in a regime transitioning from the k^(−5/3) to k^(−3) power law.Error growth with conditional nonlinear optimal perturbation(CNOP)tends to be more upamplitude relative to the GEFS or sub-grid-scale perturbations possibly owing to the inherited error growth feature of CNOP,the inability of convective parameterization scheme to rebuild the k^(−5/3) power spectra at the mesoscales,and different error growth characteristics in the k^(−5/3) and k^(−3) regimes.展开更多
With the technical development of wind power forecasting,making wind power generation schedule in power systems become an inevitable tendency.This paper proposes a new dispatch method for wind farm(WF)cluster by consi...With the technical development of wind power forecasting,making wind power generation schedule in power systems become an inevitable tendency.This paper proposes a new dispatch method for wind farm(WF)cluster by considering wind power forecasting errors.A probability distribution model of wind power forecasting errors and a mathematic expectation of the power shortage caused by forecasting errors are established.Then,the total mathematic expectation of power shortage from all WFs is minimized.Case study with respect to power dispatch in a WF cluster is conducted using forecasting and actual wind power data within 30 days from sites located at Gansu Province.Compared with the variable proportion method,the power shortage of the WF cluster caused by wind power forecasting errors is reduced.Along with the increment of wind power integrated into power systems,the method positively influences future wind power operation.展开更多
基金funding from the National Natural Science Foundation of China (Grant Nos. 41375110 and 41522502)
文摘It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.
基金supported by the Joint Research Fund in Smart Grid (No.U1966601) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and State Grid Corporation of China。
文摘As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors(WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method(MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element(EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally,a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.
基金supported by the Research Grants Council(RGC)of Hong Kong,General Research Fund(City U11332816)supported by Japan Society for the Promotion of Science KAKENHI Grant 26282111 and 18H01283
文摘Although tropical cyclone(TC)track forecast errors(TFEs)of operational warning centres have substantially decreased in recent decades,there are still many cases with large TFEs.The International Grand Global Ensemble(TIGGE)data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction(NWP)models.Forty-four TCs in the western North Pacific during the period 2007-2014 with TFEs(+24 to+120 h)larger than the 75 th percentile of the annual error distribution(with a total of 93 cases)are identified.Four categories of situations are found to be associated with large TFEs.These include the interaction of the outer structure of the TC with tropical weather systems,the intensity of the TC,the extension of the subtropical high(SH)and the interaction with the westerly trough.The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions,the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE.The performance of different models in different situations is discussed.
基金National Natural Science Foundation of China (40875067, 40675040)Knowledge Innovation Program of the Chinese Academy of Sciences (IAP09306)National Basic Research Program of China. (2006CB400505)
文摘A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system, and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected forecast is apparently better than the original, suggesting that the approach can be applied for improving monthly-scale regional climate dynamical forecast.
基金supported by the Fulbright-Nehru Doctoral Research program(Award No.2447/DR/2019-2020).
文摘This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.
文摘Accounting concepts dictate that separately disclosed components should contain separate useful information. This paper examines the relations between income statement components and analysts＇ earnings forecasts and forecast errors. Regressions explaining earnings forecasts using earnings components provide a better fit than regression using just aggregate income to explain forecasts. We interpret this as consistent with the hypothesis that analysts use incremental information in components not available in aggregate income. However, additional tests based on predictability of forecast errors indicate that analysts do not incorporate all information available in components into earnings forecasts. In addition, this inefficiency appears to increase at longer forecast horizons.
文摘[Objective] The research aimed to study the reason of local heavy rainstorm forecast error in the subtropical high control. [Method] Started from summarizing the reason of forecast error, by using the conventional ground observation data, the upper air sounding data, T639, T213 and European Center (ECMWF) numerical prediction product data, GFS precipitation forecast product of U.S. National Center for Environmental Prediction, the weather situation, physical quantity field in a heavy rainstorm process which happened in the north of Shaoyang at night on August 5, 2010 were fully analyzed. Based on the numerical analysis forecast product data, the reason of heavy rainstorm forecast error in the subtropical high was comprehensively analyzed by using the comparison and analysis method of forecast and actual situation. [Result] The forecasters didn’t deeply and carefully analyze the weather situation. On the surface, 500 hPa was controlled by the subtropical high, but there was the weak shear line in 700 and 850 hPa. Moreover, they neglected the influences of weak cold air and easterlies wave. The subtropical high quickly weakened, and the system adjustment was too quick. The wind field variations in 850, 700 and 500 hPa which were forecasted by ECMWF had the big error with the actual situation. It was by east about 2 longitudes than the actual situation. In summer forecast, they only considered the intensity and position variations of 500 hPa subtropical high, and neglected the situation variations in the middle, low levels and on the ground. It was the most key element which caused the rainstorm forecast error in the subtropical high. The forecast error of numerical forecast products on the height field situation variation was big. The precipitation forecasts of Japan FSAS, U.S. National Center for Environmental Prediction GFS, T639 and T213 were all small. The humidity field forecast value of T639 was small. In the rainstorm forecast, the local rainstorm forecast index and method weren’t used in the forecast practice. In the precipitation forecast process, they only paid attention to the score prediction of station and didn’t value the non-site prediction. Some important physical quantity factors weren’t carefully studied. [Conclusion] The research provided the reference basis for the forecast and early warning of local heavy rainstorm.
文摘In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.
文摘Weather manifests in spatiotemporally coherent structures.Weather forecasts hence are affected by both positional and structural or amplitude errors.This has been long recognized by practicing forecasters(cf.,e.g.,Tropical Cyclone track and intensity errors).Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors,most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED)method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field.The total error is then partitioned into three orthogonal components:(a)large scale positional,(b)large scale structural,and(c)small scale error variance.The use of FED is demonstrated over a month-long MSLP data set.As expected,positional errors are often characterized by dipole patterns related to the displacement of features,while structural errors appear with single extrema,indicative of magnitude problems.The most important result of this study is that over the test period,more than 50%of the total mean sea level pressure forecast error variance is associated with large scale positional error.The importance of positional error in forecasts of other variables and over different time periods remain to be explored.
文摘We examine the relation between managerial ability and management forecast accuracy. We base our analysis on S＆P 500 Composite Index constituents for the period of 2006-2012. Data were collected from Thomson Reuteurs, Compustat and Demerjian, Lev, and McVay （2012）. We find that forecast accuracy is positively associated with managerial ability in the case of sales forecasts. Specifically, more able managers are associated with lower magnitude＇s forecast errors in the case of sales forecasts. Additional analysis finds that managerial ability is immaterial to EPS figures＇ forecast accuracy, i.e., EPS forecasts appear not to be affected by manager＇s superiority. Regarding sales forecasts, the results are consistent with the assertion that managers impact the quality of the delivered management forecasts. Regarding EPS forecasts, the results are in alignment with Demerjian, Lev, Lewis, and McVay （2013） who highlighted that managerial ability is an ability score related to the entire management team.
文摘This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.
基金supported jointly by Innovation and Development Special Program of China Meteorological Administration (Grant Nos.CXFZ2024J006)National Natural Science Foundation of China (Grant Nos.42075056)+4 种基金Research Program from Science and Technology Committee of Shanghai (Grant Nos.23DZ204700,22ZR1476400)Shanghai Science and Technology Commission Project (Grant Nos.23DZ1204701)Ningbo Key R&D Program (Grant Nos.2023Z139)East China Regional Meteorological Science and Technology Collaborative Innovation Fund (Grant Nos.QYHZ202318)Special Fund Project of Basic Scientific Research Business Expenses of Shanghai Typhoon Institute, (Grant Nos.2024JB03).
文摘Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts,global models and regional models with lead times varying from 1 to 5 days.The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration(CMA).The typhoon also caused significant wind and rainfall impacts,with precipitation at several stations reaching historical extremes,ranking eighth in terms of total rainfall impact during the event.The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method(SSTC)and Fengwu Model are the most effective for short-term track forecasts.Meanwhile,the forecasts from the European Centre for Medium-Range Weather Forecasts(ECMWF)and United Kingdom Meteorological Office(UKMO)are optimal for long-term predictions.It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity.The objective forecast is terribly poor when there is a sudden change in intensity.CMA-National Digital Forecast System(CMA-NDFS)provides a better reference value for typhoon accumulated rainfall forecasts,and regional models perform well in forecasting extreme rainfall.The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model.This improves the effective application of model products.
基金Supported by the National Research Foundation of Korea(2021R1A2C1012572)funded by the South Korean government(Ministry of Science and ICT)Yonsei Signature Research Cluster Program of 2023(2023-22-0009)National Institute of Environmental Research(NIER-2022-01-02-076)funded by the Ministry of Environment(MOE)of the Republic of Korea。
文摘The Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality forecasting.Meteorological data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the CCMM.In this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula.The nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA.When the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain.This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological predictions.Compared to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA.The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the case.Therefore,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.
基金partly supported by National Natural Science Foundation of China(No.51190101)science and technology project of State Grid,Research on the combined planning method for renewable power base based on multi-dimensional characteristics of wind and solar energy
文摘Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares(KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.
基金supported by the National Natural Science Foundation of China (41725020 and 41922038)。
文摘Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System(GFS) are still subject to substantial forecast biases and rarely consider the impact of atmospheric aerosol, despite the consensus that aerosol is one of the most important sources of uncertainty in the climate system. Here we demonstrate that atmospheric aerosol is one of the important drivers biasing daily temperature prediction. By comparing observations and the GFS prediction, we find that the monthly-averaged bias in the 24-h temperature forecast varies between ± 1.5 ℃ in regions influenced by atmospheric aerosol. The biases depend on the properties of aerosol, the underlying land surface, and aerosol–cloud interactions over oceans. It is also revealed that forecast errors are rapidly magnified over time in regions featuring high aerosol loadings. Our study provides direct ‘‘observational" evidence of aerosol’s impacts on daily weather forecast, and bridges the gaps between the weather forecast and climate science regarding the understanding of the impact of atmospheric aerosol.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079015, 50979011)
文摘Flood control forecast operation mode is one of the main ways for determining the upper bound of dynamic control of flood limited water level during flood season. The floodwater utilization rate can be effectively increased by using flood forecast information and flood control forecast operation mode. In this paper, Dahuofang Reservoir is selected as a case study. At first, the distribution pattern and the bound of forecast error which is a key source of risk are analyzed. Then, based on the definition of flood risk, the risk of dynamic control of reservoir flood limited water level within different flood forecast error bounds is studied. The results show that, the dynamic control of reservoir flood limited water level with flood forecast information can increase the floodwater utilization rate without increasing flood control risk effectively and it is feasible in practice.
基金funded by National Basic Research Program of China(973 Program)(No.2013CB228201)National Natural Science Foundation of China(No.51307017)
文摘Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasting is helpful to reduce above adverse impacts.There are two kinds of wind power forecasting.One is to forecast wind power only based on its time series data.The other is to forecast wind power based on wind speeds from weather forecast.For a big wind farm,due to its spatial scale and dynamics of wind,wind speeds at different wind turbines are obviously different,that is called wind speed spatial dispersion.Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper.An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion.A case study of several wind farms has demonstrated that even ifthe forecasting average wind speed is accurate,the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030604,41875051)the National Science Foundation(Grant No.AGS-1712290)+3 种基金China Postdoctoral Science Foundation(Grant No.2021M702725)sponsored by the MEL Outstanding Postdoctoral Scholarship from Xiamen Universitysupported by the East China Regional Meteorological Science and Technology Collaborative Innovation Fund(Grant No.QYHZ201801)the project from the Qingdao Meteorological Bureau(Grant No.2021qdqxz01)。
文摘This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou,Henan Province China on 19−20 July 2021 with a record-breaking hourly rainfall exceeding 200 mm and a 24-h rainfall exceeding 600 mm.Results show that the strengths of the mid-level low-pressure system,the upper-level divergence,and the low-level jet determine both the amount of the extreme 24-h accumulated and hourly rainfall at 0800 UTC.The forecast uncertainties of the accumulated rainfall are insensitive to the magnitude and the spatial structure of the tiny,unobservable errors in the initial conditions of the ensemble forecasts generated with Global Ensemble Forecast System(GEFS)or sub-grid-scale perturbations,suggesting that the predictability of this event is intrinsically limited.The dominance of upscale rather than upamplitude error growth is demonstrated under the regime of k^(−5/3) power spectra by revealing the inability of large-scale errors to grow until the amplitude of small-scale errors has increased to an adequate amplitude,and an apparent transfer of the fastest growing scale from smaller to larger scales with a slower growth rate at larger scales.Moist convective activities play a critical role in enhancing the overall error growth rate with a larger error growth rate at smaller scales.In addition,initial perturbations with different structures have different error growth features at larger scales in different variables in a regime transitioning from the k^(−5/3) to k^(−3) power law.Error growth with conditional nonlinear optimal perturbation(CNOP)tends to be more upamplitude relative to the GEFS or sub-grid-scale perturbations possibly owing to the inherited error growth feature of CNOP,the inability of convective parameterization scheme to rebuild the k^(−5/3) power spectra at the mesoscales,and different error growth characteristics in the k^(−5/3) and k^(−3) regimes.
基金This work was supported by the Nation High Technology R&D Program of China(No.2011AA05A104)funded by Ministry of Science and Technology,and the Key Technological Projects“Research on Integrated Supervisory and Control Technolo-gies of Wind Farm Containing Wind Power Prediction System”“Application and Research on the Key Techniques for Large-scale Grid Friendly Wind Farm”funded by State Grid Corporation of China。
文摘With the technical development of wind power forecasting,making wind power generation schedule in power systems become an inevitable tendency.This paper proposes a new dispatch method for wind farm(WF)cluster by considering wind power forecasting errors.A probability distribution model of wind power forecasting errors and a mathematic expectation of the power shortage caused by forecasting errors are established.Then,the total mathematic expectation of power shortage from all WFs is minimized.Case study with respect to power dispatch in a WF cluster is conducted using forecasting and actual wind power data within 30 days from sites located at Gansu Province.Compared with the variable proportion method,the power shortage of the WF cluster caused by wind power forecasting errors is reduced.Along with the increment of wind power integrated into power systems,the method positively influences future wind power operation.
