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Decadal forecasts of Large Earthquakes along the Northern San Andreas Fault System, California: Increased Activity on Regional Creeping Faults Prior to Major and Great Events

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International Journal of Geosciences 2024年第2期15卷 204-230页

作者： Lynn R. Sykes Lamont-Doherty Earth Observatory of Columbia University Palisades New York USA

The three largest earthquakes in northern California since 1849 were preceded by increased decadal activity for moderate-size shocks along surrounding nearby faults. Increased seismicity, double-difference precise locations of earthquakes since 1968, geodetic data and fault offsets for the 1906 great shock are used to re-examine the timing and locations of possible future large earthquakes. The physical mechanisms of regional faults like the Calaveras, Hayward and Sargent, which exhibit creep, differ from those of the northern San Andreas, which is currently locked and is not creeping. Much decadal forerunning activity occurred on creeping faults. Moderate-size earthquakes along those faults became more frequent as stresses in the region increased in the latter part of the cycle of stress restoration for major and great earthquakes along the San Andreas. They may be useful for decadal forecasts. Yearly to decadal forecasts, however, are based on only a few major to great events. Activity along closer faults like that in the two years prior to the 1989 Loma Prieta shock needs to be examined for possible yearly forerunning changes to large plate boundary earthquakes. Geodetic observations are needed to focus on identifying creeping faults close to the San Andreas. The distribution of moderate-size earthquakes increased significantly since 1990 along the Hayward fault but not adjacent to the San Andreas fault to the south of San Francisco compared to what took place in the decades prior to the three major historic earthquakes in the region. It is now clear from a re-examination of the 1989 mainshock that the increased level of moderate-size shocks in the one to two preceding decades occurred on nearby East Bay faults. Double-difference locations of small earthquakes provide structural information about faults in the region, especially their depths. The northern San Andreas fault is divided into several strongly coupled segments based on differences in seismicity.

关键词： San Andreas and Hayward Faults California Fault Creep forecasts Double-Difference Relocations

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Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and forecasts to 2150

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Journal of Environmental & Earth Sciences 2023年第1期5卷 17-32页

作者： Mazurkin Peter Matveevich Volga State University of Technology Yoshkar-Olathe Republic of Mari El424000Russia

The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming.

关键词： India 4 weather stations Average monthly temperature Waves of behavior Sum of wavelets Verification forecasts

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The application of the orthogonal conditional nonlinear optimal perturbations method to typhoon track ensemble forecasts

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Science China Earth Sciences 2019年第2期62卷 376-388页

作者： Zhenhua HUO Wansuo DUAN National Meteorological Center China Meteorological Administration State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences University of Chinese Academy of Sciences

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.

关键词： Ensemble forecasts Initial perturbation Conditional nonlinear optimal perturbation Singular vector Typhoon track

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Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity forecasts Generated by WRF

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Advances in Atmospheric Sciences 2020年第3期37卷 291-306页

作者： Xiaohao QIN Wansuo DUAN Hui XU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences

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.

关键词： sensitivity tendency perturbation tropical cyclone intensity forecasts

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A Model Output Machine Learning Method for Grid Temperature forecasts in the Beijing Area

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Advances in Atmospheric Sciences 2019年第10期36卷 1156-1170页

作者： Haochen LI Chen YU Jiangjiang XIA Yingchun WANG Jiang ZHU Pingwen ZHANG School of Mathematical Sciences Peking University School of Science Beijing University of Posts and Telecommunications Institute of Atmospheric Physics Chinese Academy of Sciences Beijing Meteorological Service

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.

关键词： temperature forecasts MOS machine learning multiple linear regression Random Forest weather consultation feature engineering data structures

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Evaluation of TIGGE Daily Accumulated Precipitation forecasts over the Qu River Basin, China

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Journal of Meteorological Research 2019年第4期33卷 747-764页

作者： Li LIU Chao GAO Qian ZHU Yue-Ping XU Institute of Hydrology and Water Resources College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058 School of Civil Engineering Southeast UniversityNanjing 210096

Quantitative precipitation forecasts(QPFs)provided by three operational global ensemble prediction systems(EPSs)from the THORPEX(The Observing System Research and Predictability Experiment)Interactive Grand Global Ensemble(TIGGE)archive were evaluated over the Qu River basin,China during the plum rain and typhoon seasons of 2009–13.Two post-processing methods,the ensemble model output statistics based on censored shifted gamma distribution(CSGD-EMOS)and quantile mapping(QM),were used to reduce bias and to improve the QPFs.The results were evaluated by using three incremental precipitation thresholds and multiple verification metrics.It is demonstrated that QPFs from NCEP and ECMWF presented similarly skillful forecasts,although the ECMWF QPFs performed more satisfactorily in the typhoon season and the NCEP QPFs were better in the plum rain season.Most of the verification metrics showed evident seasonal discriminations,with more satisfactory behavior in the plum rain season.Lighter precipitation tended to be overestimated,but heavier precipitation was always underestimated.The post-processed QPFs showed a significant improvement from the raw forecasts and the effects of post-processing varied with the lead time,precipitation threshold,and EPS.Precipitation was better corrected at longer lead times and higher thresholds.CSGD-EMOS was more effective for probabilistic metrics and the root-mean-square error.QM had a greater effect on removing bias according to bias and categorical metrics,but was unable to warrant reliabilities.In general,raw forecasts can provide acceptable QPFs eight days in advance.After post-processing,the useful forecasts can be significantly extended beyond 10 days,showing promising prospects for flood forecasting.

关键词： TIGGE quantitative precipitation forecasts quantile mapping censored shifted gamma distribution

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Fitting dynamic models to epidemic outbreaks with quantified uncertainty:A primer for parameter uncertainty,identifiability,and forecasts

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Infectious Disease Modelling 2017年第3期2卷 379-398页

作者： Gerardo Chowell Division of Epidemiology&Biostatistics School of Public HealthGeorgia State UniversityAtlantaGAUSA Division of International Epidemiology and Population Studies Fogarty International CenterNational Institutes of HealthBethesdaMDUSA

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.

关键词： Parameter estimation Uncertainty quantification Bootstrap Parameter identifiability Model performance forecasts Uncertainty propagation

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forecasts

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Beijing Review 2011年第9期54卷 28-29页

Ba Shusong, Deputy Director of the Research Institute of Finance at the Development Research Center of the State Council: Since 2010, China has raised the reserve requirement ratio eight times

关键词： CPI forecasts

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Information environment,market-wide sentiment and IPO initial returns:Evidence from analyst forecasts before listing

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China Journal of Accounting Research 2015年第3期8卷 193-211页

作者： Hongjun Zhu Cheng Zhang He Li Shimin Chen Institute of Accounting and Finance Shanghai University of Finance and Economics Post-doctoral Research Station of Bank of Communications Post-doctoral Research Station of Renmin University of China School of Accountancy Shanghai University of Finance and Economics China Europe International Business School

Measuring the information environment of firms using analyst(price)forecast bias and forecast dispersion before listing,we empirically examine the interactive influence of the information environment and market-wide sentiment on the initial returns of initial public offerings(IPOs).We find the smaller the analyst forecast bias/dispersion,the lower the effect market-wide sentiment has on IPO initial returns.This finding indicates that information asymmetry is a basic reason for noise trading occurs and demonstrates the positive effect of financial analysts during IPOs.In addition,the effect of analyst forecasts is more pronounced during periods of rising markets and when IPO prices are not regulated.

关键词： Market-wide sentiment Analyst forecasts IPO initia

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Mandatory IFRS adoption and management forecasts: The impact of enforcement changes

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China Journal of Accounting Research 2019年第1期12卷 33-61页

作者： Zhaoyang Gu Jeff Ng Albert Tsang School of Accountancy CUHK Business SchoolThe Chinese University of Hong Kong Schulich School of Business York University

We examine how concurrent enforcement changes affect the positive relationship between mandatory IFRS adoption and firms’ voluntary disclosure. We show that the increase in the issuance of management forecasts after IFRS adoption is smaller for firms from IFRS-mandating countries with concurrent enforcement changes than for those from countries without such changes. We find no difference in the increase of forecast informativeness between firms from IFRS-mandating countries without concurrent enforcement changes and firms from non-IFRS-mandating countries; however, firms domiciled in IFRS-mandating countries with concurrent enforcement changes exhibit a significantly smaller increase in forecast informativeness. Our findings suggest that better IFRS enforcement distinctly weakens (strengthens) the positive effect of IFRS adoption on voluntary (mandatory) disclosure.

关键词： Management forecasts Voluntary disclosure Mandatory IFRS adoption Changes in enforcement Information environment

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