The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified inWuhan,China in December 2019.While early cases of the disease were linked to a wet market,human-to-human transmission...The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified inWuhan,China in December 2019.While early cases of the disease were linked to a wet market,human-to-human transmission has driven the rapid spread of the virus throughout China.The Chinese government has implemented containment strategies of city-wide lockdowns,screening at airports and train stations,and isolation of suspected patients;however,the cumulative case count keeps growing every day.The ongoing outbreak presents a challenge for modelers,as limited data are available on the early growth trajectory,and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated.We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province,the epicenter of the epidemic,and for the overall trajectory in China,excluding the province of Hubei.We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China.Here,we provide 5,10,and 15 day forecasts for five consecutive days,February 5th through February 9th,with quantified uncertainty based on a generalized logistic growth model,the Richards growth model,and a sub-epidemic wave model.Our most recent forecasts reported here,based on data up until February 9,2020,largely agree across the three models presented and suggest an average range of 7409e7496 additional confirmed cases in Hubei and 1128e1929 additional cases in other provinces within the next five days.Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588e13,499 in other provinces by February 24,2020.Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates(February 7th e 9th).We also observe that each of the models predicts that the epidemic h展开更多
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
Distinct from the literature on the effects that management earnings forecasts(MEFs) properties, such as point, range and qualitative estimations, have on analyst forecasts, this study explores the effects of selectiv...Distinct from the literature on the effects that management earnings forecasts(MEFs) properties, such as point, range and qualitative estimations, have on analyst forecasts, this study explores the effects of selective disclosure of MEFs.Under China's mandatory disclosure system, this study proposes that managers issue frequent forecasts to take advantage of opportune changes in predicted earnings. The argument herein is that this selective disclosure of MEFs increases information asymmetry and uncertainty, negatively influencing analyst earnings forecasts. Empirical evidence shows that firms that issue more frequent forecasts and make significant changes in MEFs are less likely to attract an analyst following, which can lead to less accurate analyst forecasts. The results imply that the selective disclosure of MEFs damages information transmission and market efficiency, which can enlighten regulators seeking to further enhance disclosure policies.展开更多
Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,g...Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.展开更多
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
Mapping function errors are usually not taken into consideration, when space geodetic data observed by VLBI, GNSS and some other techniques are utilized to estimate troposphere delay, which could, however, probably br...Mapping function errors are usually not taken into consideration, when space geodetic data observed by VLBI, GNSS and some other techniques are utilized to estimate troposphere delay, which could, however, probably bring non-ignorable errors to solutions. After analyzing the variation of mapping function errors with elevation angles based on several-year meteorological data, this paper constructed a model of this error and then proposed a two-step estimation method of troposphere delay with consideration of mapping function errors. The experimental results indicate that the method put forward by this paper could reduce the slant path delay residuals efficiently and improve the estimation accuracy of wet tropospheric delay to some extent.展开更多
The authors apply the technique of conditional nonlinear optimal perturbations （CNOPs） as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic （QG） model in a...The authors apply the technique of conditional nonlinear optimal perturbations （CNOPs） as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic （QG） model in a perfect-model scenario. Ensemble forecasts for the medium range （14 days） are made from the initial states perturbed by CNOPs and singular vectors （SVs）. 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.展开更多
The attempt of this article is to provide a literature review on recent development and progress in seasonal forecasts for tropical cyclone(TC) activity over the western North Pacific(WNP). Since the predictability of...The attempt of this article is to provide a literature review on recent development and progress in seasonal forecasts for tropical cyclone(TC) activity over the western North Pacific(WNP). Since the predictability of seasonal TC activity mainly comes from the slowly-evolving sea surface temperature(SST) conditions and the large-scale atmospheric circulation teleconnection patterns, our current understanding on the relationships between the interannual TC variability and tropical SST forcing and variations of various climate modes is first reviewed. It serves as the scientific basis and gives us ideas how predictable the seasonal TC activity is. The main body of the article focuses on an overview of the forecast approaches and methodologies, including statistical and dynamical models and their combination, currently used in seasonal forecasts for TCs over the WNP, and an initial assessment of their prediction skills in the past decade or so. Some outstanding issues, including the intrinsic limitation of predictability due to various uncertainties and the areas for future developments, are also briefly discussed. It is expected that the quality of the scientifically based seasonal TC forecasts would be steadily improved with the advancement in the forecast techniques and the driving of society needs.展开更多
In order to understand the current and potential use of ensemble forecasts in operational tropical cyclone(TC)forecasting,a questionnaire on the use of dynamic ensembles was conducted at operational TC forecast center...In order to understand the current and potential use of ensemble forecasts in operational tropical cyclone(TC)forecasting,a questionnaire on the use of dynamic ensembles was conducted at operational TC forecast centers across the world,in association with the World Meteorological Organisation(WMO)High-Impact Weather Project(HIWeather).The results of the survey are presented,and show that ensemble forecasts are used by nearly all respondents,particularly in TC track and genesis forecasting,with several examples of where ensemble forecasts have been pulled through successfully into the operational TC forecasting process.There is still however,a notable difference between the high proportion of operational TC forecasters who use and value ensemble forecast information,and the slower pull-through into operational forecast warnings and products of the probabilistic guidance and uncertainty information that ensembles can provide.Those areas of research and development that would help TC forecasters to make increased use of ensemble forecast information in the future include improved access to ensemble forecast data,verification and visualizations,the development of hazard and impact-based products,an improvement in the skill of the ensembles(particularly for intensity and structure),and improved guidance on how to use ensembles and optimally combine forecasts from all available models.A change in operational working practices towards using probabilistic information,and providing and communicating dynamic uncertainty information in operational forecasts and warnings,is also recommended.展开更多
The orthogonal conditional nonlinear optimal perturbations (CNOPs) method, orthogonal singular vectors (SVs)method and CNOP+SVs method, which is similar to the orthogonal SVs method but replaces the leading SV (LSV) w...The orthogonal conditional nonlinear optimal perturbations (CNOPs) method, orthogonal singular vectors (SVs)method and CNOP+SVs method, which is similar to the orthogonal SVs method but replaces the leading SV (LSV) with the first CNOP, are adopted in both the Lorenz-96 model and Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Fifth-Generation Mesoscale Model (MM5) for ensemble forecasts. Using the MM5, typhoon track ensemble forecasting experiments are conducted for strong Typhoon Matsa in 2005. The results of the Lorenz-96 model show that the CNOP+SVs method has a higher ensemble forecast skill than the orthogonal SVs method, but ensemble forecasts using the orthogonal CNOPs method have the highest forecast skill. The results from the MM5 show that orthogonal CNOPs have a wider horizontal distribution and better describe the forecast uncertainties compared with SVs. When generating the ensemble mean forecast, equally averaging the ensemble members in addition to the anomalously perturbed forecast members may contribute to a higher forecast skill than equally averaging all of the ensemble members. Furthermore, for given initial perturbation amplitudes, the CNOP+SVs method may not have an ensemble forecast skill greater than that of the orthogonal SVs method, but the orthogonal CNOPs method is likely to have the highest forecast skill. Compared with SVs, orthogonal CNOPs fully consider the influence of nonlinear physical processes on the forecast results; therefore, considering the influence of nonlinearity may be important when generating fast-growing initial ensemble perturbations. All of the results show that the orthogonal CNOP method may be a potential new approach for ensemble forecasting.展开更多
Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate s...Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate service was developed in 2016, producing a prototype seasonal forecast product for use by stakeholders in the region, based on rainfall forecasts directly from a dynamical model. Here, we describe an improved service based on a simple statistical downscaling approach. Through using dynamical forecast of an East Asian summer monsoon(EASM) index, seasonal mean rainfall for the upper and middle/lower reaches of YRB can be forecast separately by use of the statistical downscaling, with significant skills for lead times of up to at least three months. The skill in different sub-basin regions of YRB varies with the target season. The rainfall forecast skill in the middle/lower reaches of YRB is significant in May–June–July(MJJ), and the forecast skill for rainfall in the upper reaches of YRB is significant in June–July–August(JJA). The mean rainfall for the basin as a whole can be skillfully forecast in both MJJ and JJA. The forecasts issued in 2019 gave good guidance for the enhanced rainfall in the MJJ period and the near-average conditions in JJA. Initial feedback from users in the basin suggests that the improved forecasts better meet their needs and will enable more robust decision-making.展开更多
Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calen...Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calendar season.Consequently,a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times,thereby leading to arbitrary fluctuations in the predicted time series.To overcome this problem and account for ENSO seasonality,we developed an all-season convolutional neural network(A_CNN)model.The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring,which is the most challenging season to predict.Moreover,activation map values indicated a clear time evolution with increasing forecast lead time.The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time,thus indicating the potential of the A_CNN model as a diagnostic tool.展开更多
基金GC is supported by NSF grants 1610429 and 1633381.
文摘The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified inWuhan,China in December 2019.While early cases of the disease were linked to a wet market,human-to-human transmission has driven the rapid spread of the virus throughout China.The Chinese government has implemented containment strategies of city-wide lockdowns,screening at airports and train stations,and isolation of suspected patients;however,the cumulative case count keeps growing every day.The ongoing outbreak presents a challenge for modelers,as limited data are available on the early growth trajectory,and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated.We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province,the epicenter of the epidemic,and for the overall trajectory in China,excluding the province of Hubei.We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China.Here,we provide 5,10,and 15 day forecasts for five consecutive days,February 5th through February 9th,with quantified uncertainty based on a generalized logistic growth model,the Richards growth model,and a sub-epidemic wave model.Our most recent forecasts reported here,based on data up until February 9,2020,largely agree across the three models presented and suggest an average range of 7409e7496 additional confirmed cases in Hubei and 1128e1929 additional cases in other provinces within the next five days.Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588e13,499 in other provinces by February 24,2020.Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates(February 7th e 9th).We also observe that each of the models predicts that the epidemic h
基金supported by the National Key Research and Development Program of China (Grant Nos. 2018YFF0300104 and 2017YFC0209804)the National Natural Science Foundation of China (Grant No. 11421101)Beijing Academy of Artifical Intelligence (BAAI)
文摘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.
基金supported by the National Natural Science Foundation of China (Project 71102124)grants from the Beijing Municipal Commission of Education "Pilot Reform of Accounting Discipline Clustering"grants from the Beijing Municipal Commission of Education "Joint Construction Project"
文摘Distinct from the literature on the effects that management earnings forecasts(MEFs) properties, such as point, range and qualitative estimations, have on analyst forecasts, this study explores the effects of selective disclosure of MEFs.Under China's mandatory disclosure system, this study proposes that managers issue frequent forecasts to take advantage of opportune changes in predicted earnings. The argument herein is that this selective disclosure of MEFs increases information asymmetry and uncertainty, negatively influencing analyst earnings forecasts. Empirical evidence shows that firms that issue more frequent forecasts and make significant changes in MEFs are less likely to attract an analyst following, which can lead to less accurate analyst forecasts. The results imply that the selective disclosure of MEFs damages information transmission and market efficiency, which can enlighten regulators seeking to further enhance disclosure policies.
基金Authors acknowledge financial support from the NSF grant 1610429 and the NSF grant 1414374 as part of the joint NSFNIH-USDA Ecology and Evolution of Infectious Diseases programUK BiotechnologyBiological Sciences Research Council grant BB/M008894/1 and the Division of International Epidemiology and Population Studies,National Institutes of Health.
文摘Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.
基金jointly sponsored by the National Key Research and Development Program of China (Grant No. 2018YFC1506402)the National Natural Science Foundation of China (Grant Nos. 41930971, 41575061 and 41775061)
文摘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.
基金National Natural Science Foundation of China(No.41674082)National Natural Science Foundation of China(No.41774018)。
文摘Mapping function errors are usually not taken into consideration, when space geodetic data observed by VLBI, GNSS and some other techniques are utilized to estimate troposphere delay, which could, however, probably bring non-ignorable errors to solutions. After analyzing the variation of mapping function errors with elevation angles based on several-year meteorological data, this paper constructed a model of this error and then proposed a two-step estimation method of troposphere delay with consideration of mapping function errors. The experimental results indicate that the method put forward by this paper could reduce the slant path delay residuals efficiently and improve the estimation accuracy of wet tropospheric delay to some extent.
基金supported by State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences Program for Basic Research of China (No. 2008LASWZI01)the Chinese Academy of Sciences (Grant No. KZCX3-SW-230)the National Natural Science Foundation of China (Grant No. 40675030)
文摘The authors apply the technique of conditional nonlinear optimal perturbations （CNOPs） as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic （QG） model in a perfect-model scenario. Ensemble forecasts for the medium range （14 days） are made from the initial states perturbed by CNOPs and singular vectors （SVs）. 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.
文摘The attempt of this article is to provide a literature review on recent development and progress in seasonal forecasts for tropical cyclone(TC) activity over the western North Pacific(WNP). Since the predictability of seasonal TC activity mainly comes from the slowly-evolving sea surface temperature(SST) conditions and the large-scale atmospheric circulation teleconnection patterns, our current understanding on the relationships between the interannual TC variability and tropical SST forcing and variations of various climate modes is first reviewed. It serves as the scientific basis and gives us ideas how predictable the seasonal TC activity is. The main body of the article focuses on an overview of the forecast approaches and methodologies, including statistical and dynamical models and their combination, currently used in seasonal forecasts for TCs over the WNP, and an initial assessment of their prediction skills in the past decade or so. Some outstanding issues, including the intrinsic limitation of predictability due to various uncertainties and the areas for future developments, are also briefly discussed. It is expected that the quality of the scientifically based seasonal TC forecasts would be steadily improved with the advancement in the forecast techniques and the driving of society needs.
文摘In order to understand the current and potential use of ensemble forecasts in operational tropical cyclone(TC)forecasting,a questionnaire on the use of dynamic ensembles was conducted at operational TC forecast centers across the world,in association with the World Meteorological Organisation(WMO)High-Impact Weather Project(HIWeather).The results of the survey are presented,and show that ensemble forecasts are used by nearly all respondents,particularly in TC track and genesis forecasting,with several examples of where ensemble forecasts have been pulled through successfully into the operational TC forecasting process.There is still however,a notable difference between the high proportion of operational TC forecasters who use and value ensemble forecast information,and the slower pull-through into operational forecast warnings and products of the probabilistic guidance and uncertainty information that ensembles can provide.Those areas of research and development that would help TC forecasters to make increased use of ensemble forecast information in the future include improved access to ensemble forecast data,verification and visualizations,the development of hazard and impact-based products,an improvement in the skill of the ensembles(particularly for intensity and structure),and improved guidance on how to use ensembles and optimally combine forecasts from all available models.A change in operational working practices towards using probabilistic information,and providing and communicating dynamic uncertainty information in operational forecasts and warnings,is also recommended.
基金sponsored by the National Natural Science Foundation of China (Grant Nos. 41525017 & 41475100)the National Programme on Global Change and Air-Sea Interaction (Grant No. GASI-IPOVAI-06)the GRAPES Development Program of China Meteorological Administration (Grant No. GRAPES-FZZX-2018)
文摘The orthogonal conditional nonlinear optimal perturbations (CNOPs) method, orthogonal singular vectors (SVs)method and CNOP+SVs method, which is similar to the orthogonal SVs method but replaces the leading SV (LSV) with the first CNOP, are adopted in both the Lorenz-96 model and Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Fifth-Generation Mesoscale Model (MM5) for ensemble forecasts. Using the MM5, typhoon track ensemble forecasting experiments are conducted for strong Typhoon Matsa in 2005. The results of the Lorenz-96 model show that the CNOP+SVs method has a higher ensemble forecast skill than the orthogonal SVs method, but ensemble forecasts using the orthogonal CNOPs method have the highest forecast skill. The results from the MM5 show that orthogonal CNOPs have a wider horizontal distribution and better describe the forecast uncertainties compared with SVs. When generating the ensemble mean forecast, equally averaging the ensemble members in addition to the anomalously perturbed forecast members may contribute to a higher forecast skill than equally averaging all of the ensemble members. Furthermore, for given initial perturbation amplitudes, the CNOP+SVs method may not have an ensemble forecast skill greater than that of the orthogonal SVs method, but the orthogonal CNOPs method is likely to have the highest forecast skill. Compared with SVs, orthogonal CNOPs fully consider the influence of nonlinear physical processes on the forecast results; therefore, considering the influence of nonlinearity may be important when generating fast-growing initial ensemble perturbations. All of the results show that the orthogonal CNOP method may be a potential new approach for ensemble forecasting.
基金Supported by the UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund。
文摘Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate service was developed in 2016, producing a prototype seasonal forecast product for use by stakeholders in the region, based on rainfall forecasts directly from a dynamical model. Here, we describe an improved service based on a simple statistical downscaling approach. Through using dynamical forecast of an East Asian summer monsoon(EASM) index, seasonal mean rainfall for the upper and middle/lower reaches of YRB can be forecast separately by use of the statistical downscaling, with significant skills for lead times of up to at least three months. The skill in different sub-basin regions of YRB varies with the target season. The rainfall forecast skill in the middle/lower reaches of YRB is significant in May–June–July(MJJ), and the forecast skill for rainfall in the upper reaches of YRB is significant in June–July–August(JJA). The mean rainfall for the basin as a whole can be skillfully forecast in both MJJ and JJA. The forecasts issued in 2019 gave good guidance for the enhanced rainfall in the MJJ period and the near-average conditions in JJA. Initial feedback from users in the basin suggests that the improved forecasts better meet their needs and will enable more robust decision-making.
基金This work was supported by the National Research Foundation of Korea(NRF)(NRF-2020R1A2C2101025).
文摘Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calendar season.Consequently,a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times,thereby leading to arbitrary fluctuations in the predicted time series.To overcome this problem and account for ENSO seasonality,we developed an all-season convolutional neural network(A_CNN)model.The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring,which is the most challenging season to predict.Moreover,activation map values indicated a clear time evolution with increasing forecast lead time.The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time,thus indicating the potential of the A_CNN model as a diagnostic tool.
