Methods Of Statistical Dynamics Research Articles

Comparison of probabilistic forecasts of extreme precipitation for a global and convection‐permitting ensemble and hybrid statistical–dynamical method based on equatorial wave information

AbstractRecent work has demonstrated that skilful hybrid statistical–dynamical forecasts of heavy rainfall events in Southeast Asia can be made by combining model forecasts of the phases and amplitudes of Kelvin, Rossby, and westward‐moving Rossby gravity waves with climatological rainfall statistics conditioned on these waves. This study explores the sensitivity of this hybrid forecast to its parameter choices and compares its skill in forecasting extreme rainfall events in the Philippines, Malaysia, Indonesia, and Vietnam to that of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The hybrid forecast is found to outperform both the global and convection‐permitting ensemble in some regions when forecasting the most extreme events; however, for less extreme events, the ensemble is found more skilful. A weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill of all for almost all definitions of extreme event and in most regions. To quantify the influence of errors in the predicted wave state on the skill of the hybrid forecast, the skill of a hypothetical best‐case forecast was also calculated using reanalysis data to specify the wave amplitudes and phases. This best‐case forecast indicates that errors in the forecasts of all wave types reduce the skill of hybrid forecast; however, the reduction in skill is largest for Kelvin waves. The skill in convection‐permitting models is greater than for global models in the regions where Kelvin waves dominate, but the added value of limited‐area high‐resolution forecasts is hampered by the poor representation of Kelvin waves in the parent global model.

Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision-makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict and mitigate the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change. However, errors in air quality forecasts limit their value in decision-making processes. Thus, increasing the accuracy of air quality forecasts is of significant importance. In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate daily 48-h fine particulate matter (PM2.5) forecasts for the Contiguous United States (CONUS) domain for June 1st through September 29th (major wildfire season) during 2015–2021. We conduct CMAQ offline simulations using meteorological inputs generated by the NOAA’s Unified Forecast System (UFS) numerical weather prediction model. We analyze the performance of the CMAQ PM2.5 and UFS meteorological forecasts over seven years of simulations for Environmental Protection Agency (EPA)-defined ten regions using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs better in the eastern CONUS with the lowest Root Mean Square Error (RMSE) (10–30 μg/m3) while in the west, where wildfires are prevalent, the model has the highest RMSE of up to 120 μg/m3. To address these substantial model errors, we advance the field by applying Analog Ensemble (AnEn), a state-of-the-art statistical-dynamical method, to enhance PM2.5 forecast accuracy, with a particular focus on wildfire events, when producing an accurate forecast is highly challenging. We further refine this approach by incorporating a Carbon Monoxide-FIRE (CO-FIRE) tracer to precisely monitor fire smoke. In parallel to our objective and for the first time, we introduce two novel predictors, i.e., a CO−FIRE tracer in the model, and the maximum observed PM2.5 concentrations from the previous day (PM2.5−preday−max), and evaluate their potential significance in improving forecast accuracy, which aligns with the primary goal of our study. Despite the challenges of using AnEn for wildfires, we demonstrate that it has the potential to improve the CMAQ model forecast over the CONUS. We find that AnEn decreases the model RMSE by up to 25%, including additional 7% and 15% reduction by CO−FIRE and PM2.5−preday−max predictors, respectively, at different forecast lead times. In addition, the correlation between AnEn forecasts and observations is 20%–40% higher than that between CMAQ and observations. The Mean Bias Error (MBE) for AnEn forecasts is consistent and approximately −0.5 μg/m3 whereas CMAQ MBE varies between −1 and +1 μg/m3 between 0–48 forecast hours. AnEn significantly improves the PM2.5 forecast results during its highest episodes. During the initial days of wildfires, AnEn performs similarly to CMAQ. However, it soon catches up and decreases the error significantly.

A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea

Societal concerns about air quality in East Asia are still growing despite country-level efforts to reduce air pollution emissions. In coping with this growing concern, the government and the public demand a longer‑lead forecast of air quality to ensure sufficient response time until society prepares for countermeasures such as a temporary reduction of specific emission sources. Here we propose a novel method that produces skillful seasonal forecasting of wintertime (December to February) PM10 concentration over South Korea. The method is based on the idea that climate condition and air quality have co-variability in the seasonal time scales and that the state-of-art seasonal prediction model will benefit air quality forecasting. More specifically, a linear regression model is constructed to link observed winter PM10 concentration and climate variables where the predicted climate variables were furnished from NCEP CFSv2 forecast initialized during autumn. In this case, climate variables were selected as predictors of the model because they are not only physically related to air quality but also ‘predictable’ in CFS hindcast. Through analysis of retrospective forecasts of 20 winters for the period 2001–2020, we found this model shows statistically significant skill for the seasonal forecast of wintertime PM10 concentration.

Investigation of the damping properties of the process module for a tractor of traction class 1.4

To analyze the positive influence of the technological module on the fluctuations of the external load of the tractor, the methods of statistical dynamics are used. A dynamic model of a process module with two rotating masses is considered: the rim and tire of the drive wheel and the translational mass of the process module. Using the transfer function, the spectral functions of the output process (the horizontal component of the force on the MTZ-82 tractor) were determined when performing a machine-tractor unit (MTA) technological operation with heavy disks (BDT-7) intended for a class 3 tractor. The developed mathematical model of the technological module allowed us to construct theoretical spectral densities of the horizontal component of the force on the tractor hitch when completing it with the technological module. The use of the process module allows the frequency range of forces on the tractor hitch to be shifted to a lower zone from 0 to 2.5 Hz, excluding high-frequency components compared to the standard configuration of the tractor.

STATISTICAL SIMULATION OF FIBRE STRAIGHTENING DYNAMICS IN CARDING ON ROLLER MACHINE

The article analyses the straightening of fibres in the working areas of roller carding machines. The initial assumptions and parameters are given, which were used to develop a computer model for statistical simulation of the process, taking into account the elastic-viscous properties of fibres and their interaction with neighbouring fibres, based on the methods of statistical dynamics. The dynamics of changes in the straightening of the fibres depending on their initial straightening and the number of passes of the fibres through the working zones are studied. It is established that the partial restoration of the tortuosity of the fibres due to their elasticity does not allow for their complete straightening, regardless of the number of repeated passes of the working areas of the machine.

Improving Air Quality Predictions over the United States with an Analog Ensemble

AbstractAir quality forecasts produced by the National Air Quality Forecasting Capability (NAQFC) help air quality forecasters across the United States in making informed decisions to protect public health from acute air pollution episodes. However, errors in air quality forecasts limit their value in the decision-making process. This study aims to enhance the accuracy of NAQFC air quality forecasts and reliably quantify their uncertainties using a statistical–dynamical method called the analog ensemble (AnEn), which has previously been found to efficiently generate probabilistic forecasts for other applications. AnEn estimates of the probability of the true state of a predictand are based on a current deterministic numerical prediction and an archive of prior analogous predictions paired with prior observations. The method avoids the complexity and real-time computational expense of model-based ensembles and is proposed here for the first time for air quality forecasting. AnEn is applied with forecasts from the Community Multiscale Air Quality (CMAQ) model. Relative to CMAQ raw forecasts, deterministic forecasts of surface ozone (O3) and particulate matter of aerodynamic diameter smaller than 2.5 μm (PM2.5) based on AnEn’s mean have lower systemic and random errors and are overall better correlated with observations; for example, when computed across all sites and lead times, AnEn’s root-mean-square error is lower than CMAQ’s by roughly 35% and 30% for O3 and PM2.5, respectively, and AnEn improves the correlation by 50% for O3 and PM2.5. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

The Linear Sensitivity of the North Atlantic Oscillation and Eddy-Driven Jet to SSTs

Abstract The North Atlantic Oscillation (NAO) and eddy-driven jet contain a forced component arising from sea surface temperature (SST) variations. Due to large amounts of internal variability, it is not trivial to determine where and to what extent SSTs force the NAO and jet. A linear statistical–dynamic method is employed with a large climate ensemble to compute the sensitivities of the winter and summer NAO and jet speed and latitude to the SSTs. Key regions of sensitivity are identified in the Indian and Pacific basins, and the North Atlantic tripole. Using the sensitivity maps and a long observational SST dataset, skillful reconstructions of the NAO and jet time series are made. The ability to skillfully forecast both the winter and summer NAO using only SST anomalies is also demonstrated. The linear approach used here allows precise attribution of model forecast signals to SSTs in particular regions. Skill comes from the Atlantic and Pacific basins on short lead times, while the Indian Ocean SSTs may contribute to the longer-term NAO trend. However, despite the region of high sensitivity in the Indian Ocean, SSTs here do not provide significant skill on interannual time scales, which highlights the limitations of the imposed SST approach. Given the impact of the NAO and jet on Northern Hemisphere weather and climate, these results provide useful information that could be used for improved attribution and forecasting.

Совершенствование системы виброзащиты операторов промышленных тракторов

When developing new models of industrial tractors, compliance with the regulatory requirements for vibration protection of operators is verified at the stage of certification tests. At this stage, development procedures are associated with significant time and material costs. The article proves the possibility to use a computational and experimental technique which allows us to determine the level and frequency content of potentially dangerous vibration sources at the deve­lopment stage; to develop computer models of the system “excitation source – frame – cab – antivibration seat – operator”; to develop proposals to comply with legislative requirements using parametric forecasting. As an example, the problem of vibration in the low frequency range of 2–14 Hz was considered. This vibration is caused by rolling of track rollers on the track chain, which lies on a yielding soil foundation. Basing on the analysis of field test results, the kinematic effect of track rollers on the tractor frame was represented as stationary random narrow-band processes. A mathematical model of the tractor was described by a set of differential equations with random input processes. Methods of statistical dynamics were used to implement the model. The simulation data of T-11 motion with different speeds are given. The results are presented as a set of transfer functions, as well as the spectral densities of changes in generalized model coordinates and accelerations at the driver’s seat. The difference between the calculation results and the experimental data does not exceed 15–20 %. The effect of elastic-viscous characteristics of vibration protection devices on the level of accelerations at the operator’s seat was analyzed. As a result, it was recommended that these characteristics should be changed in order to reduce the vertical vibration acceleration of the driver’s seat. This approach allows us to solve the problems of vibration protection of operators when designing and testing prototype models of road-building machinery. It is achieved by tuning out the system “track assembly – frame – cab – antivibration seat” of resonance phenomena.

A statistical dynamics track model of tropical cyclones for assessing typhoon wind hazard in the coast of southeast China

An improved full track model for tropical cyclones (TCs) based on statistical dynamics method is developed to estimate typhoon wind hazard in the coast of southeast China. The track model includes genesis model, movement model, and intensity model. The genesis model is established based upon an improved kernel probability density function with the biased cross-validation bandwidth. The movement model is developed based on the Beta and Advection Model (BAM). The intensity model consists of an auto-regression ocean intensity model and an inland decay model using the nearest neighbor method. Using the historical environment data, 1000 years TC tracks are simulated by the improved model and compared with features of the historical tracks in the western North Pacific (WNP) basin, the results verify that the developed model performs well. Moreover, typhoon wind hazard in the coastal areas of southeast China is estimated by the improved track model combined with a parametric wind field model. Compared with the current Chinese design code, the relative differences between estimated return period velocities and those recommended in design code are less than 13.4%. The developed model enables analyzing typhoon hazard in the context of climate change, which is our future task.

A Global Statistical–Dynamical Tropical Cyclone Wind Radii Forecast Scheme

Abstract Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical–dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s−1) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical–dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind–pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus.

Simulation of the formation of vibration loading of operator workplace of industrial tractor

The check of compliance with legislative requirements for vibration protection of operators is carried out at the stage of certification testing of new and upgrading products. At this stage, the further development is associated with high time and material costs. The article substantiates the possibility of use of a calculation and experimental method which allows to determine at the development stage the level and frequency content of potentially dangerous sources of vibration; to develop a computer model for the system «excitation source - frame - cab - antivibration seat - operator»; to make out proposals to comply with legislative requirements by means of parametric forecasting. As an example, the problem of vibration in the low frequency range of 2-14 Hz is considered. This vibration is the result of the process of rolling of track rollers on track chain that lies on the yieldable soil foundation. According to biomedical research, the resonance frequencies of critical parts of human body are in this low frequency range, which is displayed in sanitary norms. Based on the analysis of field test results, the kinematic impact of track rollers on tractor frame is represented as stationary narrow-band random processes. Mathematical model of the system is described by a set of differential equations with random input processes. The methods of statistical dynamics are used to implement the model. The simulation results are presented as a set of transfer functions. They represent the distribution of resonance frequencies of angular and heave oscillations of the separate system elements and the spectral density functions. These results allow to obtain the quantitative estimations in the form of mean-square values of the output processes. The use of this approach allows to solve the problems of vibration protection of operator at the stages of design and testing of prototypes of road-building machinery. It is achieved by tuning-out of the system «track assembly - frame - cab - antivibration seat» from resonance phenomena and by introducing some design solutions.

Development of a Numerical Wind Atlas for South Africa

Two verified wind atlases have been developed for South Africa. The first adopted a statistical-dynamical approach and the second a novel, fully dynamical approach. We verify the atlases against an observational wind atlas generated fromthree years of data from 10 measurement masts. The statistical-dynamical method underestimates the generalized mean wind speeds at the observation sites whereas the fully dynamical method has lower biases and slightly overestimates the generalized mean wind speeds. The dynamical method captures thermally forced dynamical processes and also resolves topographically enhanced flows the statistical-dynamical method cannot.

Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method

This study conducted 24- to 72-h multi-model ensemble forecasts to explore the tracks and intensities (central mean sea level pressure) of tropical cyclones (TCs). Forecast data for the northwestern Pacific basin in 2010 and 2011 were selected from the China Meteoro- logical Administration, European Centre for Medium- Range Weather Forecasts (ECMWF), Japan Meteoro- logical Agency, and National Centers for Environmental Prediction datasets of the Observing System Research and Predictability Experiment Interactive Grand Global Ensemble project. The Kalman Filter was employed to conduct the TC forecasts, along with the ensemble mean and super-ensemble for comparison. The following results were obtained: (1) The statistical-dynamic Kalman Filter, in which recent observations are given more importance and model weighting coefficients are adjusted over time, produced quite different results from that of the super- ensemble. (2) The Kalman Filter reduced the TC mean absolute track forecast error by approximately 50, 80 and 100 km in the 24-, 48- and 72-h forecasts, respectively, compared with the best individual model (ECMWF). Also, the intensity forecasts were improved by the Kalman Filter to some extent in terms of average intensity deviation (AID) and correlation coefficients with reanalysis intensity data. Overall, the Kalman Filter technique performed better compared to multi-models, the ensemble mean, and the super-ensemble in 3-day forecasts. The implication of this study is that this technique appears to be a very promising statistical-dynamic method for multi-model ensemble forecasts of TCs.

Estimating the Sensitivity of the Atmospheric Teleconnection Patterns to SST Anomalies Using a Linear Statistical Method

Abstract The Pacific–North American (PNA) pattern and the North Atlantic Oscillation (NAO) are known to contain a tropical sea surface temperature (SST)-forced component. This study examines the sensitivity of the wintertime NAO and PNA to patterns of tropical SST anomalies using a linear statistical–dynamic method. The NAO index is sensitive to SST anomalies over the tropical Indian Ocean, the central Pacific Ocean, and the Caribbean Sea, and the PNA index is sensitive to SST anomalies over the tropical Pacific and Indian Oceans. The NAO and PNA patterns can be reproduced well by combining the linear operator with the consistent SST anomaly over the Indian Ocean and the Niño-4 regions, respectively, suggesting that these are the most efficient ocean basins that force the teleconnection patterns. During the period of 1950–2000, the NAO time series reconstructed by using SST anomalies over the Indian Ocean + Niño-4 region + Caribbean Sea or the Indian Ocean + Niño-4 region is significantly correlated with the observation. Using a cross-spectral analysis, the NAO index is coherent with the SST forcing over the Indian Ocean at a significant 3-yr period and a less significant 10-yr period, with the Indian Ocean SST leading by about a quarter phase. Unsurprisingly, the PNA index is most coherent with the Niño-4 SST at 4–5-yr periods. When compared with the observation, the NAO variability from the linear reconstruction is better reproduced than that of the coupled model, which is better than the Atmospheric Model Intercomparison Project (AMIP) run, while the PNA variability from the AMIP simulations is better than that of the reconstruction, which is better than the coupled model run.

Joint statistical-dynamical approach to decadal prediction of East Asian surface air temperature

A joint statistical-dynamical method addressing both the internal decadal variability and effect of anthropogenic forcing was developed to predict the decadal components of East Asian surface air temperature (EATs) for three decades (2010–2040). As previous studies have revealed that the internal variability of EATs (EATs_int) is influenced mainly by the ocean, we first analyzed the lead-lag connections between EATs_int and three sea surface temperature (SST) multidecadal modes using instrumental records from 1901 to 1999. Based on the lead-lag connections, a multiple linear regression was constructed with the three SST modes as predictors. The hindcast for the years from 2000 to 2005 indicated the regression model had high skill in simulating the observational EATs_int. Therefore, the prediction for EATs_int (Re_EATs_int) was obtained by the regression model based on quasi-periods of the decadal oceanic modes. External forcing from greenhouse gases is likely associated with global warming. Using monthly global land surface air temperature from historical and projection simulations under the Representative Concentration Pathway (RCP) 4.5 scenario of 19 Coupled General Circulation Models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), we predicted the curve of EATs (EATs_trend) relative to 1970–1999 by a second-order fit. EATs_int and EATs_trend were combined to form the reconstructed EATs (Re_EATs). It was expected that a fluctuating evolution of Re_EATs would decrease slightly from 2015 to 2030 and increase gradually thereafter. Compared with the decadal prediction in CMIP5 models, Re_EATs was qualitatively in agreement with the predictions of most of the models and the multi-model ensemble mean, indicating that the joint statistical-dynamical approach for EAT is rational.

Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications

AbstractThis paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.

Classical reactive scattering in a quantum spirit: improving the shape of rotational state distributions for indirect reactions in the quantum regime

For indirect triatomic chemical reactions under single-collision conditions, we propose a new implementation of the quasi-classical trajectory (QCT) approach to rotational state distributions, of particular interest in the quantum regime where only a few rotational states are available to the products. This method is directly inspired from the amendments to be introduced in classical phase space theory (PST) in order to make it in exact agreement with quantum PST. The approach is applied to the \(\hbox {D}^{+} + \hbox {H}_2\) and \(\hbox {H}^{+} + \hbox {D}_2\) reactions, and the population of the rotational ground state is found to be in much closer agreement with the exact quantum one than the one obtained by means of standard QCT calculations. The impact on the whole distribution is all the stronger as the number of available states is small. Lastly, the shape of the distribution appears to be controlled to a large extent by three factors, respectively, called parity, edge and rotational shift factors. The method is a complementary alternative to the statistical–dynamical methods, often used to study indirect processes. However, contrary to what is done in these methods, trajectories are not stopped at the entrance of the strong coupling region within our approach, thus expected to lead to reasonable predictions also for indirect processes that are not fully statistical, like those involving an osculating complex.

Technical note on a track-pattern-based model for predicting seasonal tropical cyclone activity over the western North Pacific

Recently, the National Typhoon Center (NTC) at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone (TC) track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific (WNP) basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.

A statistical–dynamical method for predicting estuary morphology

A statistical–dynamical model for estuary morphodynamics is presented and demonstrated with a case study on the Humber Estuary, UK. The model presented here is hybrid in nature where simplified process dynamics are combined with a data-driven approach. The modelling methodology uses an inverse technique to construct an unknown source function in the model-governing equation, using historic measurements of estuary bathymetry. Spatial and temporal variability of the source function was established using empirical orthogonal eigenfunction analysis. Predictions of estuary morphology are obtained by extrapolating the temporal coefficients of the EOFs, in order to construct a predicted source function which is then used in the hybrid morphodynamic equation to obtain the predicted estuary morphology. The model was applied to predict the morphological evolution of the Humber Estuary over a period of a decade. Predicted results were compared with measured bathymetry data. The results reveal that the model captured the decadal scale morphodynamic response of the estuary with a good qualitative accuracy, despite the simplified approach used in the model. The accuracy of model predictions can be greatly improved if high-frequency bathymetry data are available.

Estimating near-shore wave statistics from regional hindcasts using downscaling techniques

Several downscaling techniques, comprising fully dynamical and statistical–dynamical methods applied to near-shore local wave climate, are tested and assessed in terms of wave statistics with respect to the added value that can be achieved compared to larger scale data. The techniques are applied for the example of Helgoland, a small island in the German Bight. It was found that an improved representation could generally be obtained from all downscaling techniques by comparing the near-shore wave climate. Based on a balance between the required computer resources and the improvements achieved, it is suggested, to this end, that a dynamical–statistical approach based on high-resolution coastal wave modeling and linear regression provides the optimal choice.