Statistical Forecasting Models Research Articles

Meridional Propagation of the 30- to 60-day Variability of Precipitation in the East Asian Subtropical Summer Monsoon Region: Monitoring and Prediction

ABSTRACTThe meridional propagation of the 30- to 60-day intraseasonal variability (ISV) of precipitation in the East Asian subtropical summer monsoon (EASSM) region and its monitoring and prediction are investigated in the current study. Based on a multivariate empirical orthogonal function (MV-EOF) analysis of precipitation and relative vorticity at 700 hPa in East Asia, a bivariate index referred to as the EASSM-ISV index is designed using the two leading MV-EOF modes, with the objective of real-time monitoring of the 30- to 60-day variability of precipitation in the EASSM region. It is found that this index, with its eight phases, can explain the meridional propagation of the 30- to 60-day ISV in precipitation and circulation in the EASSM region. Based on a singular value decomposition technique, a statistical forecast model is developed in which the EASSM-ISV indices from the preceding five pentads are used to predict the indices in five pentads in the future. Meanwhile, the indices are used to predict the meridional propagation of the 30- to 60-day precipitation anomaly in the EASSM region. This model thus provides a useful tool for intraseasonal prediction of precipitation during the rainy season in China.

장마 강수를 위한 앙상블 통계 예측 모델 개발

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

Critical parameter estimates for earthquake forecast using PI migration

Parameters usually play a key role in statistical forecasting models and should be carefully determined because the physics of the system links to the statistical model through the parameters. To investigate the relations between parameters and pattern informatics (PI) migration, we developed a series of retrospective analyses. The results show that two parameters (i.e., cut magnitude and change interval) are essential factors of calculating PI migration. The cut magnitude is a lower cutoff magnitude applied to the catalog at the start of the analysis, and the result of the analysis shows that the PI migration hot spots are mostly distributed around the earthquakes with magnitude larger than target magnitude when the cut magnitude is 3.2–3.4 in most study regions. The change interval is a time span prior to the large event that we assumed to be the duration of the preparation process. In the retrospective analysis, the ability of PI migration hot spot to hit the target earthquake varies with change interval and the change interval that make PI migration hot spot to hit most target earthquakes varies with the study region. By using a retrospective analysis, we determined the optimal parameters for each study region, generating PI migration maps to show potential locations.

Changed relationships between the East Asian summer monsoon circulations and the summer rainfall in eastern China

In previous statistical forecast models, prediction of summer precipitation along the Yangtze River valley and in North China relies heavily on its close relationships with the western Pacific subtropical high (WPSH), the blocking high in higher latitudes, and the East Asian summer monsoon (EASM). These relationships were stable before the 1990s but have changed remarkably in the recent two decades. Before the 1990s, precipitation along the Yangtze River had a significant positive correlation with the intensity of the WPSH, but the correlation weakened rapidly after 1990, and the correlation between summer rainfall in North China and the WPSH also changed from weak negative to significantly positive. The changed relationships present a big challenge to the application of traditional statistical seasonal prediction models. Our study indicates that the change could be attributed to expansion of the WPSH after around 1990. Owing to global warming, increased sea surface temperatures in the western Pacific rendered the WPSH stronger and further westward. Under this condition, more moisture was transported from southern to northern China, leading to divergence and reduced (increased) rainfall over the Yangtze River (North China). On the other hand, when the WPSH was weaker, it stayed close to its climatological position (rather than more eastward), and the circulations showed an asymmetrical feature between the stronger and weaker WPSH cases owing to the decadal enhancement of the WPSH. Composite analysis reveals that the maximum difference in the moisture transport before and after 1990 appeared over the western Pacific. This asymmetric influence is possibly the reason why the previous relationships between monsoon circulations and summer rainfall have now changed.

Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth's rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1- to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.

Forecasting High Frequency Data: An ARMA-Soft RBF Network Model for Time Series

In the article we alternatively develop forecasting models based on the Box-Jenkins methodology and on the neural approach based on classic and fuzzy logic radial basis function neural networks. We evaluate statistical and neuronal forecasting models for monthly platinum price time series data. In the direct comparison between statistical and neural models, the experiment shows that the neural approach clearly improve the forecast accuracy. Following fruitful applications of neural networks to predict financial data this work goes on. Both approaches are merged into one output to predict the final forecast values. The proposed novel approach deals with nonlinear estimate of various radial basis function neural networks.

A next generation approach to estimating customer demand via willingness to pay

Price-driven passenger behavior has been addressed in many different ways over the last several years. These approaches tend to utilize the availability of classes (or more generally, availability of choice sets) to isolate passenger’s buying behavior and address spiral down/fare dilution. The new approach will instead examine only the products purchased and the prices paid, to isolate passenger priceable demand behavior. In this article, we will cover the concepts and the underlying statistical forecast model and show examples of the willingness to pay method in practice.

New Predictors and a Statistical Forecast Model for Mei-Yu Onset Date in the Middle and Lower Reaches of the Yangtze River Valley

Abstract The prediction of mei-yu onset date (MOD) in the middle and lower reaches of the Yangtze River valley (MLYRV) is an important and challenging task for those making seasonal climate predictions in China. In this paper, the atmospheric and oceanic conditions in the preceding winter and spring related to MOD are analyzed. It is found that the MOD is associated with the intensity of the Ural high and the East Asian trough in high latitudes, with the intensity of the upper-level westerly jet in middle latitudes, and with the contrast of land–sea temperature and pressure in the preceding winter and spring, which are proxies for the intensity of the East Asian winter monsoon (EAWM). It is suggested that the intensity of the EAWM is the most crucial factor affecting the MOD. Years with an early MOD usually correspond to strong EAWMs in the preceding winter, and vice versa. The EAWM can affect the MOD by influencing the East Asian summer monsoon (EASM) through tropical ocean–atmosphere and tropical–extratropical interactions. Based on the above analysis, a physics-based statistical forecast model is established using multivariable linear regression techniques. The hindcast of MOD during the 13 yr from 1998 to 2010 is carried out to evaluate the performance of this forecast model. The MOD can be predicted successfully in 8 out of the 13 yr. The forecast model predicts the MOD in the years with strong mei-yu intensity more accurately than in those with weak mei-yu intensity, especially for cases of extreme flooding. This is useful in the prevention of flooding disasters.

Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger.

Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea.Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels.Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January–May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data.Results: At the national level, a model using early incidence in December and averaged November–December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41).Conclusions: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic.Citation: Pérez García-Pando C, Stanton MC, Diggle PJ, Trzaska S, Miller RL, Perlwitz JP, Baldasano JM, Cuevas E, Ceccato P, Yaka P, Thomson MC. 2014. Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger. Environ Health Perspect 122:679–686; http://dx.doi.org/10.1289/ehp.1306640

Spatio-temporal drought forecasting within Bayesian networks

Spatial variations of future droughts across the Gunnison River Basin in CO, USA, are evaluated in this study, using a recently developed probabilistic forecast model. The Standardized Runoff Index (SRI) is employed to analyze drought status across the spatial extent of the basin. The runoff generated at each spatial unit of the basin is estimated by a distributed-parameter and physically-based hydrologic model. Using the historical runoff at each spatial unit, a statistical forecast model is developed within Bayesian networks. The forecast model applies a family of multivariate distribution functions to forecast future drought conditions given the drought status in the past. Given the runoff in the past (January–June), the forecast model is applied in estimating the runoff across the basin in the forecast period (July–December). The main advantage of the forecast model is its probabilistic features in analyzing future droughts. It develops conditional probabilities of a given forecast variable, and returns the highest probable forecast along with an assessment of the uncertainty around that value. Bayesian networks can also estimate the probability of future droughts with different severities, given the drought status of the predictor period. Moreover, the model can be used to generate maps showing the runoff variation over the basin with the particular chance of occurrence in the future. Our results indicate that the statistical method applied in this study is a useful procedure in probabilistic forecast of future droughts given the spatio-temporal characteristics of droughts in the past. The techniques presented in this manuscript are suitable for probabilistic drought forecasting and have potential to improve drought characterization in different applications.

Statistical modelling of the main features of the Artemisia pollen season in Wrocław, Poland, during the 2002–2011 time period

The aim of this article is to present statistical forecasting models concerning the dynamics of Artemisia pollen seasons in Wroclaw, including the start and end, the date of maximum pollen concentration and seasonal pollen index (SPI). For statistical evaluation, use was made of aerobiological and meteorological data from the last 10 years (2002–2011). Based on this data, agroclimatic indicators, i.e. crop heat units (CHUs), were determined for various averaging periods. The beginning of the Artemisia pollen season in the studied time period, on average, took place on 23 June. Its length usually varied between 26 and 45 days, and maximum daily concentrations occurred between 31 July and 18 August. It was found that the beginning of the pollen season depends, above all, on the values of CHUs and photothermal unit (PTU) (p < 0.05) in the period from March to June, for various thermal thresholds. The date of maximum daily concentration correlates with sunshine duration, PTU and air temperature for June and July (p < 0.05). On the other hand, SPI is connected with thermal variables, i.e. average, maximum and minimum air temperatures and CHUs and heliothermal unit (HTU) for July (p < 0.05) and the beginning of spring. Based on the correlation analysis and the chosen variables, regression models for the beginning date of Artemisia pollen season and SPI were prepared, which were then verified by using leave-one-out cross-validation. A better fit between modelled and actual values was found for the analysis concerning the season start date than for the SPI.

Comments on: Space-time wind speed forecasting for improved power system dispatch

I would first like to thank the authors for a well-written paper that illustrates the benefit of collaborating across disciplines. This type of work is very important for renewable energy to become integrated into mainstream utility systems, and the authors have clearly demonstrated how advanced statistical forecasting models can be incorporated into optimization models for scheduling power while simultaneously minimizing operating costs. As the percentage of a country’s total energy needs that are met with variable renewable energies, such as wind, increases, the potential to disrupt the stability of the utility to deliver uninterrupted power also increases (Marquis et al. 2011). Therefore, having good short-term wind forecasts will be even more important in the coming years, and the development of such models and testbeds as done in this paper by Zhu et al. (2014), hereafter referred to as ZGGX, will be highly relevant. In this comment on the ZGGX paper, I would like to focus my attention on the Rotating Regime-switching Space–Time Diurnal (RRSTD) model. In the following Sect. 2, novel aspects of this model will be discussed, and then potential difficulties in fitting the model will be identified. The RRSTD is a generalization of the RSTD model developed by Gneiting et al. (2006) and is also related to the TDD model developed by Hering and Genton (2010). ZGGX show that the RRSTD is better than both persistence and autoregressive models, but they have not compared their results to the RRSTD or the TDDmodels. ZGGX also state that “... the RRSTDmodel has the potential to improve forecasts further if more information on monthly wind patterns

Climate risk management for water in semi–arid regions

Background: New sources of hydroclimate information based on forecast models and observational data have the potential to greatly improve the management of water resources in semi-arid regions prone to drought. Better management of climate-related risks and opportunities requires both new methods to develop forecasts of drought indicators and river flow, as well as better strategies to incorporate these forecasts into drought, river or reservoir management systems. In each case the existing institutional and policy context is key, making a collaborative approach involving stakeholders essential. Methods: This paper describes work done at the IRI over the past decade to develop statistical hydrologic forecast and water allocation models for the semi arid regions of NE Brazil (the “Nordeste”) and central northern Chile based on seasonal climate forecasts. Results: In both locations, downscaled precipitation forecasts based on lagged SST predictors or GCM precipitation forecasts exhibit quite high skill. Spring-summer melt flow in Chile is shown to be highly predictable based on estimates of previous winter precipitation, and moderately predictable up to 6 months in advance using climate forecasts. Retrospective streamflow forecasts here are quite effective in predicting reductions in water rights during dry years. For the multi-use Oros reservoir in NE Brazil, streamflow forecasts have the most potential to optimize water allocations during multi-year low-flow periods, while the potential is higher for smaller reservoirs, relative to demand. Conclusions: This work demonstrates the potential value of seasonal climate forecasting as an integral part of drought early warning and for water allocation decision support systems in semi-arid regions. As human demands for water rise over time this potential is certain to rise in the future.

Comparative evaluation of physics‐based and statistical forecasts in Northern California

AbstractWe perform a retrospective forecast test using Northern California seismicity for the period between 1980 and 2009. We compare 7 realizations of the short‐term clustering epidemic‐type aftershock sequence (ETAS) model, and 21 models combining Coulomb stress change calculations and Rate/State theory (CRS) to forecast seismicity rates in 10 day time intervals. We employ a common learning phase (1974–1980) for CRS models to ensure consistency, and we evaluate the forecasts with log likelihood statistics to detect any spatial inconsistencies and compare the total numbers of forecasts versus observed events. We find that: (1) ETAS models are better forecasters of the spatial evolution in seismicity in the near‐source region, (2) CRS models can compete with ETAS models away from the mainshock rupture, and for short periods after mainshocks, (3) CRS models with optimally oriented receiver fault planes perform better in the first few days after mainshocks, whereas mapped fault planes should be implemented for longer‐term forecasting, and (4) CRS models based on shear stress change calculations have comparable performance with Coulomb stress change models, with the benefit of lesser parameters involved in stress calculations. We conclude that physics‐based and statistical forecast models are complimentary to each other and that future forecasts should be based on statistical models for near‐source regions, and physical models for longer periods and distances. However, the realization of the CRS models involves a number of critical parameters (reference seismicity rates, regional stress field, and loading rates), which should be retrospectively tested to improve the predictive power of physics‐based models.

Real-Time Upper-Ocean Temperature Observations from Aircraft during Operational Hurricane Reconnaissance Missions: AXBT Demonstration Project Year One Results

Abstract Thousands of aircraft observations of upper-ocean thermal structures have been obtained during hurricane and typhoon research field experiments in recent decades. The results from these experiments suggest a strong correlation between upper-ocean thermal variability and tropical cyclone (TC) intensity change. In response to these results, during the Office of the Federal Coordinator of Meteorology (OFCM) 2011 Interdepartmental Hurricane Conference (IHC), the Working Group for Hurricane and Winter Storms Operations and Research (WG/HWSOR) approved a 3-yr project to demonstrate the usefulness of airborne expendable bathythermographs (AXBTs) in an operational setting. The goal of this project was to initialize and validate coupled TC forecast models and was extended to improve input to statistical intensity forecast models. During the first season of the demonstration project, 109 AXBTs were deployed between 28 July and 28 August 2011. Successes included AXBT deployment from WC-130J aircraft during operational reconnaissance missions tasked by the National Hurricane Center (NHC), real-time onboard and postflight data processing, real-time data transmission to U.S. Navy and NOAA hurricane numerical prediction centers, and near-real-time assimilation of upper-ocean temperature observations into the Naval Research Laboratory Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC) forecast model. Initial results showed 1) increased model accuracy in upper-ocean temperatures, 2) minor improvements in TC track forecasts, and 3) minor improvements in TC intensity forecasts in both coupled dynamical and statistical models [COAMPS-TC and the Statistical Hurricane Intensity Prediction Scheme (SHIPS), respectively].

The Development of a Statistical Forecast Model for Changma

Abstract Forecasting year-to-year variations in East Asian summer monsoon (EASM) precipitation is one of the most challenging tasks in climate prediction because the predictors are not sufficiently well known and the forecast skill of the numerical models is poor. In this paper, a statistical forecast model for changma (the Korean portion of the EASM system) precipitation is proposed that was constructed with three physically based predictors. A forward-stepwise regression was used to select the predictors that included sea surface temperature (SST) anomalies over the North Pacific, the North Atlantic, and the tropical Pacific Ocean. Seasonal predictions with this model showed high forecasting capabilities that had a Gerrity skill score of ~0.82. The dynamical processes associated with the predictors were examined prior to their use in the prediction scheme. All predictors tended to induce an anticyclonic anomaly to the east or southeast of Japan, which was responsible for transporting a large amount of moisture to the southern Korean Peninsula. The predictor in the North Pacific formed an SST front to the east of Japan during the summertime, which maintained a lower-tropospheric baroclinicity. The North Atlantic SST anomaly induced downstream wave propagation in the upper troposphere, developing anticyclonic activity east of Japan. Forcing from the tropical Pacific SST anomaly triggered a cyclonic anomaly over the South China Sea, which was maintained by atmosphere–ocean interactions and induced an anticyclonic anomaly via northward Rossby wave propagation. Overall, the model used for forecasting changma precipitation performed well (R = 0.85) and correctly predicted information for 16 out of 19 yr of observational data.

Evaluation of a statistical forecast model for size-fractionated urban particle number concentrations using data from five European cities

In this study we evaluated a forecast model for urban aerosol number concentrations against measurements made at five European cities: Helsinki, Stockholm, Copenhagen, Leipzig, and Athens. This model requires a learning data set with particle number concentrations, traffic densities and local meteorology. Additionally, in the forecasting process it requires the same parameters from the past week and forecasted values of weather and traffic. The model performance was tested based on the R2, index of agreement (IA), mean square error (MSE), and bias. We tested three modelling approaches: one with fixed parameterisation and two with optimisations based on either the Deviance or the Akaike Information Criterion. Based on the hourly one-day forecasts at the urban background sites the IA ranged from 0.65 to 0.80 for accumulation mode particles and from 0.68 to 0.87 for ultrafine particles. The model performance was the best for Helsinki and Stockholm and the worst for Leipzig and Copenhagen. The main reason is the more pronounced diurnal variation in Helsinki and Stockholm. Another reason is that the traffic data for Leipzig and Copenhagen were not as complete as for the other cities. The three approaches yielded similar model performances, hence we conclude that the simplest one based on a fixed parametrisation is to be preferred.

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

AbstractIn this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models, Splines and Other Traffic Variables

SummaryTraffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This paper shows how cubic splines can be used to incorporate extra variables into the LMDM in order to enhance flow forecasts. Cubic splines are also introduced into the LMDM to parsimoniously accommodate the daily cycle exhibited by traffic flows.The proposed methodology allows the LMDM to provide more accurate forecasts when forecasting flows in a real high‐dimensional traffic data set. The resulting extended LMDM can deal with some important traffic modelling issues not usually considered in flow forecasting models. Additionally, the model can be implemented in a real‐time environment, a crucial requirement for traffic management systems designed to support decisions and actions to alleviate congestion and keep traffic flowing.

A Proposal for Analyzing and Forecasting Lower-Atmospheric Undular Bores in the Western Gulf of Mexico Region

Abstract A method is presented for analyzing and forecasting the occurrence of lower-atmospheric undular bores over the western and central Gulf of Mexico (GOM) and adjacent land areas using standard operational forecasting and analysis techniques. The method is based on research that has identified a set of sufficient conditions associated with these occurrences realized by observations of the phenomena in recent years and is grounded in the theoretical understanding of undular bores developed during the last century by fluid dynamicists. The approach discusses practical approximations to the theory that allows the operational forecaster to use output from standard numerical and statistical forecast models. In addition to providing an operational method for forecasting the time and location of occurrence, the technique provides a methodology for analyzing and anticipating the strength, forward speed, and horizontal wavelength of the phenomena.