Time Series Of Weather Variables Research Articles

Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators

AbstractResampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis.

Técnica alternativa de preservación de material biológico humano, implementando reactivos químicos de uso común

The estimation of the effects of climate change on water resources is of great importance in regions with arid and semiarid climates where water availability is limited, such as 034 irrigation district of Zacatecas, Mex. The reliability of the estimate depends directly on the quality of each of the time series of weather variables to use, having to have continuous records and unaltered by anthropogenic conditions. In this paper we apply nonparametric tests Standard Normal Homogeneity (SNHT for its acronym in English), and PettittBuishand to determine the record length data homogeneous maximum temperature, and precipitation Miniña each weather station that dominates the six modules surface irrigation district 034. It identifies the homogeneous period of the three time series in each irrigation module which can be used with high certainty in future studies of the effects of climate change in this area.

The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.

Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good performance in simulating time series of the snow water equivalent and snow depth. The developed weather generator (NEsted Weather Generator, NewGen) includes nested generators of annual, monthly and daily time series of weather variables (namely, precipitation, air temperature, and air humidity). The parameters of the NewGen have been adjusted through calibration against the long-term meteorological data in the European Russia. A disaggregation procedure has been proposed for transforming parameters of the annual weather generator into the parameters of the monthly one and, subsequently, into the parameters of the daily generator. Multi-year time series of the simulated daily weather variables have been used as an input to the snow model. Probability properties of the snow cover, such as snow water equivalent and snow depth for return periods of 25 and 100 years, have been estimated against the observed data, showing good correlation coefficients. The described model has been applied to different landscapes of European Russia, from steppe to taiga regions, to show the robustness of the proposed technique.

A Versatile Weather Generator for Daily Precipitation and Temperature

Stochastic daily weather generators are often used to generate long time series of weather variables to drive hydrological and agricultural models. More recently, they have also been used as a downscaling tool for studying the impacts of climate change. This article describes a versatile stochastic weather generator (WeaGETS) for producing daily precipitation, and maximum and minimum temperatures (Tmax and Tmin). WeaGETS regroups several options of other weather generators into one package, such as three Markov models to produce precipitation occurrence, four distributions to generate wet day precipitation amount, and two methods to simulate Tmax and Tmin. More importantly, a spectral correction approach is included in WeaGETS for correcting the underestimation of interannual variability, which is a problem common to all weather generators. The performance of WeaGETS is demonstrated through a comparison against two well-known weather generators (WGEN and CLIGEN) with respect to the generation of precipitation, Tmax, and Tmin for two Canadian meteorological stations. The results show that the widely used first-order Markov model is adequate for producing precipitation occurrence, but it underestimates the longest dry spell for the low-precipitation station. The higher-order models have positive effects. The mixed exponential and skewed normal Pearson III distributions are consistently better than the exponential and gamma distributions at generating precipitation amounts. The two-component mixed exponential distribution is better at representing extreme precipitation events than the other three distributions. WeaGETS is consistently better than WGEN and CLIGEN at producing Tmax and Tmin. Both WGEN and CLIGEN underestimate the monthly and interannual variances of precipitation and temperatures. However, WeaGETS successfully preserves the observed low-frequency variability and autocorrelation functions of precipitation and temperatures. Overall, WeaGETS is consistently better than the other two weather generators (WGEN and CLIGEN) for producing precipitation, Tmax, and Tmin. The Matlab freeware allows for easy modification of all routines, making it easy to add additional weather variables to simulate.

Temporal associations between weather and headache: analysis by empirical mode decomposition.

BackgroundPatients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area.Methodology/Principal FindingsThe study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts.Conclusions/SignificanceUsing EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons.

Assessment and Improvement of Stochastic Weather Generators in Simulating Maximum and Minimum Temperatures

Stochastic weather generators are commonly used to generate time series of weather variables to drive agricultural and hydrologic models. One of their most appealing features is the ability to rapidly generate the very long time series used in agricultural and hydrological impact studies. However, they also have various problems, such as the inability to represent the interannual variability of the climate system, and it is difficult for them to accurately preserve the auto- and cross-correlation of maximum and minimum temperatures (Tmax and Tmin). This research aims to merge two widely used weather generators (CLIGEN (v5.22564) and WGEN) into a hybrid method that combines the strengths of each (referred to as the conditional method) for generating Tmax and Tmin and apply an approach to correct the interannual variability of Tmax and Tmin (referred to as the spectral correction method). The results show that CLIGEN reproduced mean daily Tmax and Tmin very well. WGEN also produced mean daily Tmax reasonably well but slightly underestimated mean daily Tmin. Moreover, CLIGEN was better than WGEN at producing standard deviations of daily Tmax and Tmin. The conditional and spectral correction methods resulted in a weather generator that accurately produced means, standard deviations, and extremes of daily Tmax and Tmin. The auto- and cross-correlations of and between daily Tmax and Tmin were well reproduced and much better than those of CLIGEN- and WGEN-generated data. Moreover, the spectral correction approach successfully reproduced the observed interannual variability of Tmax and Tmin.

Generalized linear modeling approach to stochastic weather generators

Stochastic weather generators are a popular method for producing synthetic sequences of daily weather. We demonstrate that generalized linear models (GLMs) can provide a general modeling framework, allowing the straightforward incorporation of annual cycles and other covariates (e.g. an index of the El Nino-Southern Oscillation, ENSO) into stochastic weather generators. We apply the GLM technique to daily time series of weather variables (i.e. precipitation and minimum and maximum temperature) from Pergamino, Argentina. Besides annual cycles, the fit is significantly improved by permitting both the transition probabilities of the first-order Markov chain for daily precipitation occurrence, as well as the means of both daily minimum and maximum temperature, to depend on the ENSO state. Although it is more parsimonious than typical weather generators, the GLMbased weather generator performs comparably, particularly in terms of extremes and overdispersion.