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1.
This paper proposes a simple class of threshold autoregressive model for purpose of forecasting daily maximum ozone concentrations in Southern California. Linear time series model has been widely considered in environmental modeling. However, this class of models fails to capture the nonlinearity in ozone process and the complexity of meteorological interactions with ozone. In this article, we used the threshold autoregressive models with two classes of regimes; periodic and meteorological regimes. Days in week were used for the periodic regimes and the regression tree method was used to define the regimes as a function of meteorological variables. As the reference model we used the autoregressive model with lagged ozone and various lagged meteorological variables as the covariates. The proposed models were applied to a 3-year dataset of daily maximum ozone concentrations obtained from five monitoring stations in San Bernardino County, CA and their forecast performances were evaluated using an independent year-long dataset from the same stations. The results showed that the threshold models well capture the nonlinearity in ozone process and remove the nonstationarity in model residuals. The threshold models outperformed the non-threshold autoregressive models in day-ahead forecasts. The tree-based model showed slightly better performance than the periodic threshold model.  相似文献   

2.
Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short‐term variations, which were obtained by subtracting the seasonal components from water temperature time‐series. The first three models, a multiple regression, a second‐order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root‐mean‐square error (RMSE) for these models varied between 0·53 and 1·70 °C and the second‐order autoregressive model provided the best results. A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0·51 °C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

3.
— This paper examines the spatial and temporal distributions of the mixing height, ventilation coefficient (defined as the product of mixing height and surface wind speed), and cloud cover over the eastern United States during the summer of 1995, using the high-resolution meteorological data generated by MM5 (Version 1), a mesoscale model widely used in air quality studies. The ability of MM5 to simulate the key temporal and spatial features embedded in the time series of observations of temperature, wind speed, and moisture is assessed using spectral decomposition methods. Also, mixing heights estimated from the MM5 outputs are compared with those derived from observations at a few locations where data with high temporal resolution are available in the Northeast. In addition, the uncertainties associated with the estimation of the evolution of the boundary layer during the morning time are examined. The results indicate that nighttime mixing heights averaged <200?m, rising to 1 km by 10 EST, and to about 2.5?km in the afternoon. Ventilation coefficients followed a similar diurnal pattern, increasing from 500?m2/s at night?to 15,000?m2/s in the afternoon; the increase due to the growing mixing height and increasing surface wind speeds. Spatial variability of these parameters was relatively small (coefficient of variation=0.25) at?night and in the afternoon when conditions were quasi-stationary, but increased (to 0.5) during morning?and evening hours when mixing heights and wind speeds were changing rapidly. Analyses of surface ozone observations from about 400 sites throughout the eastern United States indicate that days with numerous stations reporting surface ozone concentrations in excess of 80 ppb (i.e., “high ozone” days) generally had less daytime cloud cover, lower surface wind speeds, higher mixing heights, and lower ventilation coefficients than did comparable “low ozone” days. Such meteorological features are consistent with a synoptic anticyclone centered over the mid-south region (Kentucky, Tennessee). Low ozone days were characterized by more disturbed weather conditions (low pressure systems, fronts, greater cloud cover, and precipitation events). Ozone observations at two elevated platforms (～400?m agl) in Garner, NC, and Chicago, IL, indicated that ozone concentrations aloft were about 40% larger on “high ozone” days than on “low ozone” days. On average, high levels of ozone persist aloft for about 2 to 3 days. Strong vertical mixing in the daytime can bring this pool of upper-level ozone downward to augment surface ozone production. Since ozone can be transported downwind several hundred kilometers from its source region over this time scale, depending on upper-level winds, effective ozone control strategies must take into consideration spatial scales ranging from local to regional, and time scales of the order of several days.  相似文献   

4.
This paper presents the verification results of nowcasts of four continuous variables generated from an integrated weighted model and underlying Numerical Weather Prediction (NWP) models. Real-time monitoring of fast changing weather conditions and the provision of short term forecasts, or nowcasts, in complex terrain within coastal regions is challenging to do with sufficient accuracy. A recently developed weighting, evaluation, bias correction and integration system was used in the Science of Nowcasting Olympic Weather for Vancouver 2010 project to generate integrated weighted forecasts (INTW) out to 6 h. INTW forecasts were generated with in situ observation data and background gridded forecasting data from Canadian high-resolution deterministic NWP system with three nested grids at 15-, 2.5- and 1-km horizontal grid-spacing configurations. In this paper, the four variables of temperature, relative humidity, wind speed and wind gust are treated as continuous variables for verifying the INTW forecasts. Fifteen sites were selected for the comparison of the model performances. The results of the study show that integrating surface observation data with the NWP forecasts produce better statistical scores than using either the NWP forecasts or an objective analysis of observed data alone. Overall, integrated observation and NWP forecasts improved forecast accuracy for the four continuous variables. The mean absolute errors from the INTW forecasts for the entire test period (12 February to 21 March 2010) are smaller than those from NWP forecasts with three configurations. The INTW is the best and most consistent performer among all models regardless of location and variable analyzed.  相似文献   

5.
In this paper we consider a Linear Regression Model with a design matrix that fits the periodic structure of a time series. As a consequence, the residuals are very often autocorrelated. The main problem is that residual autocorrelation does not necessarily entail error autocorrelation. To analyse the effects of selecting different formulations to accommodate the autocorrelation in the residuals, we consider two seemingly different ways to deal with this problem: the Linear Regression Model with the error terms following an Autoregressive Stationary Process and the Partial Adjustment Model. We study the equivalence between the two formulations. We go over the problem of estimating the parameters and, especially, of making inferences in this framework. After parameter estimation, we analyse the adequacy of the models. We demonstrate that the issue of selecting the most appropriate model to capture the autocorrelation in the residuals is, in this context, a kind of an artefact since the main results concerning the fitted values and forecasting features are the same. These modelling procedures are applied to the Portuguese coastal upwelling data and we compare the estimated models.  相似文献   

6.
The measured ozone pollution peak in the atmosphere of Mexico City region was considered in order to study the effect of a non-stationary mean of the sampled data in geostatistics interpolation methods. With this objective the local mean value of the sampled data was estimated through a linear regression analysis of their values on the monitoring station’s coordinates. The residuals obtained by removing the data trend are considered as a set of stationary random variables. Several interpolation methods used in geostatistics, such as inverse distance weighted, kriging, and artificial neural networks techniques were considered. In an effort to optimize and evaluate its performance, we fit interpolated values to sampled data, obtaining optimal values for the parameters defining the used model, that means, the values of the parameters that give the lowest mean RMSE between the interpolated value and measured data at 20 stations at 1500 hours for a set of 21 days of December 2001, which was chosen as the training set. The training set is conformed by all the days in December 2001 excepting the days (3,6,9,12,...,27,30) which were considered as the testing set. Once the optimal model is obtained, it is used to interpolate the values at the stations at 1500 hours for the testing days. The RMSE between interpolated and measured values at monitoring stations was also evaluated for these testing values and is shown as a percentage in Table 2. These values and the defined generalization parameter G, can be used to evaluate the performance and the ability of the models to predict and reproduce the peak of ozone concentrations. Scatter plots for testing data are presented for each interpolation method. An interpretation of the ozone pollution levels obtained at 1500 hours at December 21 was given using the wind field that prevailed in the region 1 h before the same day.  相似文献   

7.
Simple linear regression models have been widely employed in the analysis of suspended‐sediment concentration (SSC) time series from glacierized catchments, although they have many limitations. This paper builds regression models which address these shortcomings and permit inferences concerning the controls on suspended‐sediment transfer from a glacier at 78°N in the Svalvard archipelago. A bivariate regression model, deterministically predicting SSC from discharge alone, explained less than 15 per cent of the variance in SSC. A multivariate model, incorporating additional potentially explanatory variables, offered little improvement. Diurnal hysteresis in the data gives rise to quasi‐autocorrelation in the residual series from regression models. This was effectively removed by incorporating dummy diurnal variables into the multivariate model. The presence of a first‐order autoregressive, stochastic process gives rise to true autocorrelation in the residual series from regression models. This was accommodated by incorporating an ARIMA (1,0,0) term into a multivariate autoregression model. The model‐building process yielded a systematic progression in the explanation of variance in SSC, stripping away pattern in the autocorrelation function of the residual series; mean model error was reduced from 54 per cent to 6 per cent. The dependence of SSC on the magnitude of discharge is weak and highly variable, whereas the dependence of current SSC on recent values of SSC, revealed through the stochastic term, is an order of magnitude greater and relatively constant during the melt season. The dominant control on SSC throughout the melt season is therefore short‐term sediment availability. The simple and largely unchanging stochastic process generally responsible for generating the observed SSC series implies a simple and unchanging glacier drainage system. Copyright © 1999 John Wiley & Sons, Ltd.  相似文献   

8.
A World Weather Research Programme (WWRP) project entitled the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) was developed to be associated with the Vancouver 2010 Olympic and Paralympic Winter Games conducted between 12 February and 21 March 2010. The SNOW-V10 international team augmented the instrumentation associated with the Winter Games and several new numerical weather forecasting and nowcasting models were added. Both the additional observational and model data were available to the forecasters in real time. This was an excellent opportunity to demonstrate existing capability in nowcasting and to develop better techniques for short term (0–6 h) nowcasts of winter weather in complex terrain. Better techniques to forecast visibility, low cloud, wind gusts, precipitation rate and type were evaluated. The weather during the games was exceptionally variable with many periods of low visibility, low ceilings and precipitation in the form of both snow and rain. The data collected should improve our understanding of many physical phenomena such as the diabatic effects due to melting snow, wind flow around and over terrain, diurnal flow reversal in valleys associated with daytime heating, and precipitation reductions and increases due to local terrain. Many studies related to these phenomena are described in the Special Issue on SNOW-V10 for which this paper was written. Numerical weather prediction and nowcast models have been evaluated against the unique observational data set now available. It is anticipated that the data set and the knowledge learned as a result of SNOW-V10 will become a resource for other World Meteorological Organization member states who are interested in improving forecasts of winter weather.  相似文献   

9.
This paper examines spatial variations of urban growth patterns in Chinese cities through a case study of Dongguan, a rapidly industrializing city characterized by a bottom-up pattern of development based on townships. We have employed both non-spatial and spatial logistic regression models to analyze urban land conversion. The non-spatial logistic regression has found the significance of accessibility, neighborhood conditions and socioeconomic factors for urban development. The logistic regression with spatially expanded coefficients significantly improves the orthodoxy logistic regression with lower levels of spatial autocorrelation of residuals and better goodness-of-fit. More importantly, the spatial logistic model reveals the spatially varying relationship between urban growth and its underlying factors, particularly the local influence of environment protection and urban development policies. The results of the spatial logistic model also provide clear clues for assessing environmental risks to take the local contexts into account.  相似文献   

10.
Short-term fluctuations superimposed on the diurnal variations of surface ozone recorded at Poona during 1969–1970 are discussed.While there is a net production of ozone during electrical discharges in a thunder cloud, the surface ozone recorder often registered a decrease in surface ozone concentration. This decrease coincided with updraughts generated during the formation of a thunderstorm. Similar sharp increases in ozone were observed with downdraughts. In cases of lightning without the development of a thunderstorm over the station, an increase in ozone density was observed just after the first lightning discharge.Apart from the fluctuations associated with thunderstorms in summer, sharp fluctuations in density were also noticed during winter, in the mornings. Abrupt falls in ozone occur with the formation of a stable layer near the ground at night and a sudden surge after the breaking up of the layers in the morning. The changes in ozone are, however, much more pronounced than those in temperature and wind and this striking correlation between surface ozone, surface air temperature and wind provides a unique tool for the study of low-level temperature inversions, their establishment and destruction.  相似文献   

11.
In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict drought in the Büyük Menderes river basin using SPI as drought index. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking. In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable results up to 2 months ahead. The stochastic models developed for the Büyük Menderes river basin can be employed to predict droughts up to 2 months of lead time with reasonably accuracy.  相似文献   

12.
This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0–6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.  相似文献   

13.
River temperature models play an increasingly important role in the management of fisheries and aquatic resources. Among river temperature models, forecasting models remain relatively unused compared to water temperature simulation models. However, water temperature forecasting is extremely important for in-season management of fisheries, especially when short-term forecasts (a few days) are required. In this study, forecast and simulation models were applied to the Little Southwest Miramichi River (New Brunswick, Canada), where water temperatures can regularly exceed 25–29°C during summer, necessitating associated fisheries closures. Second- and third-order autoregressive models (AR2, AR3) were calibrated and validated using air temperature as the exogenous variable to predict minimum, mean and maximum daily water temperatures. These models were then used to predict river temperatures in forecast mode (1-, 2- and 3-day forecasts using real-time data) and in simulation mode (using only air temperature as input). The results showed that the models performed better when used to forecast rather than simulate water temperatures. The AR3 model slightly outperformed the AR2 in the forecasting mode, with root mean square errors (RMSE) generally between 0.87°C and 1.58°C. However, in the simulation mode, the AR2 slightly outperformed the AR3 model (1.25°C < RMSE < 1.90°C). One-day forecast models performed the best (RMSE ~ 1°C) and model performance decreased as time lag increased (RMSE close to 1.5°C after 3 days). The study showed that marked improvement in the modelling can be accomplished using forecasting models compared to water temperature simulations, especially for short-term forecasts.

EDITOR M.C. Acreman ASSOCIATE EDITOR S. Huang  相似文献   

14.
Much research has been devoted to the development of numerical models of river incision. In settings where bedrock channel erosion prevails, numerous studies have used field data to calibrate the widely acknowledged stream power model of incision and to discuss the impact of variables that do not appear explicitly in the model's simplest form. However, most studies have been conducted in areas of active tectonics, displaying a clear geomorphic response to the tectonic signal. Here, we analyze the traces left in the drainage network 0.7 My after the Ardennes region (western Europe) underwent a moderate 100–150 m uplift. We identify a set of knickpoints that have traveled far upstream in the Ourthe catchment, following this tectonic perturbation. Using a misfit function based on time residuals, our best fit of the stream power model parameters yields m = 0.75 and K = 4.63 × 10‐8 m‐0.5y‐1. Linear regression of the model time residuals against quantitative expressions of bedrock resistance to erosion shows that this variable does not correlate significantly with the residuals. By contrast, proxies for position in the drainage system prove to be able to explain 76% of the residual variance. High time residuals correlate with knickpoint position in small tributaries located in the downstream part of the Ourthe catchment, where some threshold was reached very early in the catchment's incision history. Removing the knickpoints stopped at such thresholds from the data set, we calculate an improved m = 0.68 and derive a scaling exponent of channel width against drainage area of 0.32, consistent with the average value compiled by Lague for steady state incising bedrock rivers. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

15.
For water supply, navigational, ecological protection or water quality control purposes, there is a great need in knowing the likelihood of the river level falling below a certain threshold. Ensemble streamflow prediction (ESP) based on simulations of deterministic hydrologic models is widely used to assess this likelihood. Raw ESP results can be biased in both the ensemble means and the spreads. In this study, we applied a modified general linear model post‐processor (GLMPP) to correct these biases. The modified GLMPP is built on the basis of regression of simulated and observed streamflow calculated on the basis of canonical events, instead of the daily values as is carried out in the original GLMPP. We conducted the probabilistic analysis of post‐processed ESP results falling below pre‐specified low‐flow levels at seasonal time scale. Raw ESP forecasts from the 1980 to 2006 periods by four different land surface models (LSMs) in eight large river basins in the continental USA are included in the analysis. The four LSMs are Noah, Mosaic, variable infiltration capacity and Sacramento models. The major results from this study are as follows: (1) a modified GLMPP was proposed on the basis of canonical events; (2) post‐processing can improve the accuracy and reduce the uncertainty of hydrologic forecasts; (3) post‐processing can help deal with the effect of human activity; and (4) raw simulation results from different models vary greatly in different basins. However, post‐processing can always remove model biases under different conditions. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

16.
Abstract

There is a lack of consistency and generality in assessing the performance of hydrological data-driven forecasting models, and this paper presents a new measure for evaluating that performance. Despite the fact that the objectives of hydrological data-driven forecasting models differ from those of the conventional hydrological simulation models, criteria designed to evaluate the latter models have been used until now to assess the performance of the former. Thus, the objectives of this paper are, firstly, to examine the limitations in applying conventional methods for evaluating the data-driven forecasting model performance, and, secondly, to present new performance evaluation methods that can be used to evaluate hydrological data-driven forecasting models with consistency and objectivity. The relative correlation coefficient (RCC) is used to estimate the forecasting efficiency relative to the naïve model (unchanged situation) in data-driven forecasting. A case study with 12 artificial data sets was performed to assess the evaluation measures of Persistence Index (PI), Nash-Sutcliffe coefficient of efficiency (NSC) and RCC. In particular, for six of the data sets with strong persistence and autocorrelation coefficients of 0.966–0.713 at correlation coefficients of 0.977–0.989, the PIs varied markedly from 0.368 to 0.930 and the NSCs were almost constant in the range 0.943–0.972, irrespective of the autocorrelation coefficients and correlation coefficients. However, the RCCs represented an increase of forecasting efficiency from 2.1% to 37.8% according to the persistence. The study results show that RCC is more useful than conventional evaluation methods as the latter do not provide a metric rating of model improvement relative to naïve models in data-driven forecasting.

Editor D. Koutsoyiannis, Associate editor D. Yang

Citation Hwang, S.H., Ham, D.H., and Kim, J.H., 2012. A new measure for assessing the efficiency of hydrological data-driven forecasting models. Hydrological Sciences Journal, 57 (7), 1257–1274.  相似文献   

17.
A Bayesian framework for model order selection of auto‐regressive exogenous (ARX) models is developed and applied to actual earthquake response data obtained by the structural health monitoring system of a high‐rise building. The model orders of ARX models are selected appropriately by the Bayesian framework, and differ significantly from the optimal order estimated by AIC; in fact, in many cases AIC does not even give an optimal order. A method is also proposed for consistently selecting the same ‘genuine’ modes of interest from the whole set of modes corresponding to each of the identified models from a sequence of earthquake records. In the identification analysis based on building response records from 43 earthquakes over 9 years, the modal parameters of the first four modes in each horizontal direction are estimated appropriately in all cases, showing that the developed methods are effective and robust. As the estimates of natural frequency depend significantly on the response amplitude, they are compensated by an empirical correction so that the influence of the response amplitude is removed. The compensated natural frequencies are much more stable over the nine‐year period studied, indicating that the building had no significant change in its global dynamic characteristics during this period. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

18.
On the assimilation of total-ozone satellite data   总被引：1，自引：0，他引：1  
A two-dimensional model for advection and data assimilation of total-ozone data has been developed. The Assimilation Model KNMI (AMK) is a global model describing the transport of the column amounts of ozone, by a wind field at a single pressure level, assuming that total ozone behaves as a passive tracer. In this study, ozone column amounts measured by the TIROS Operational Vertical Sounder (TOVS) instrument on the National Oceanic and Atmospheric Administration (NOAA) polar satellites and wind fields from the Meteorological Archive and Retrieval System (MARS) archives at ECMWF have been used. By means of the AMK, the incomplete space-time distribution of the TOVS measurements is filled in and global total-ozone maps at any given time can be obtained. The choice of wind field to be used for transporting column amounts of ozone is extensively discussed. It is shown that the 200-hPa wind field is the optimal single-pressure-level wind field for advecting total ozone. Assimilated ozone fields are the basic information for research on atmospheric chemistry and dynamics, but are also important for the validation of ozone measurements.  相似文献   

19.
In glacierized catchments, meteorological inputs driving surface melting are translated into runoff outputs mediated by the glacier hydrological system: analysis of the relationship between meteorology and diurnal and seasonal patterns of runoff should reflect the functioning of that system, with the role of meltwater storage likely to be of particular importance. Daily meltwater storage is determined for a glacier at 78 °N in the Svalbard archipelago, by comparing inputs calculated from a surface energy balance model with measured outputs (proglacial discharge). Solar radiation, air temperature, wind speed and proglacial discharge are then analysed by regression and time‐series methods, in order to assess the meteorology–discharge relationship and its variation at diurnal and seasonal time‐scales. The recorded discharge time‐series can be divided into two contrasting intervals: up to early August, proglacial discharge was high and variable, mean hydrographs showed little indication of diurnal cycling, ARIMA models of discharge indicated a non‐seasonal, moving‐average generating process, and there was a net loss of meltwater from storage; from early August, proglacial discharge was low and relatively invariable, but with clearer diurnal cycles, regression models of discharge showed substantially improved correlations with air temperature and solar radiation, ARIMA models indicated a non‐seasonal, autoregressive generating process, and eventually a seasonal component, and there was a net gain in meltwater storage. The transition between the two periods is brief compared with the duration of the melt season. The runoff response to meteorology therefore lacks the strongly progressive element previously identified in mid‐latitude glacierized catchments. In particular, the glacier hydrological system only appears responsive to diurnal forcing following the depletion of the seasonal snowpack meltwater store. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

20.
We propose a method of the modeling and analysis of ionospheric parameters by combining wavelet transform with autoregression models (integrated moving average). The method makes it possible to reveal regularities in ionospheric parameters and to make forecasts on variations. Also, this method can be used to fill the gaps in ionospheric parameters, with consideration of their diurnal and seasonal variations. The method was tested on foF2 data and data on the total electron content for the regions of Kamchatka and Magadan. The models constructed for the natural variation in ionospheric parameters allowed us to analyze its dynamical mode and build a forecast with a step of up to five hours. Based on estimates for model errors, we revealed anomalies arising during periods of increased solar activity and strong earthquakes in Kamchatka.  相似文献   

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