Time Series Of Meteorological Variables Research Articles

A semi-parametric hourly space–time weather generator

Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space–time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space–time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50–20 000 km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.

Comparison of Artificial Neural Network and Regression Models for Filling Temporal Gaps of Meteorological Variables Time Series

Continuous meteorological variable time series are highly demanded for various climate related studies. Five statistical models were tested for application of temporal gaps filling in time series of surface air pressure, air temperature, relative air humidity, incoming solar radiation, net radiation, and soil temperature. A bilayer artificial neural network, linear regression, linear regression with interactions, and the Gaussian process regression models with exponential and rational quadratic kernel were used to fill the gaps. Models were driven by continuous time series of meteorological variables from the ECMWF (European Centre for Medium-range Weather Forecasts) ERA5-Land reanalysis. Raw ECMWF ERA5-Land reanalysis data are not applicable for characterization of specific local weather conditions. The linear correlation coefficients (CC) between ERA5-Land data and in situ observations vary from 0.61 (for wind direction) to 0.99 (for atmospheric pressure). The mean difference is high and estimated at 3.2 °C for air temperature and 3.5 hPa for atmospheric pressure. The normalized root-mean-square error (NRMSE) is 5–13%, except for wind direction (NRMSE = 49%). The linear bias correction of ERA5-Land data improves matching between the local and reanalysis data for all meteorological variables. The Gaussian process regression model with an exponential kernel based or bilayered artificial neural network trained on ERA5-Land data significantly shifts raw ERA5-Land data toward the observed values. The NRMSE values reduce to 2–11% for all variables, except wind direction (NRMSE = 22%). CC for the model is above 0.87, except for wind characteristics. The suggested model calibrated against in situ observations can be applied for gap-filling of time series of meteorological variables.

Application of Data Science for the reconstruction of time series of meteorological variables in the Islas del Rosario (Colombian Caribbean), between the years 2013-2021

This study reviews two time series of meteorological variables measured by an automatic station located in Islas del Rosario (Colombian Caribbean), belonging to the Network for Measurement of Oceanographic Parameters and Marine Meteorology (RedMpomm) of the General Maritime Directorate (Dimar). The time series correspond to data of air temperature and wind magnitude in the period 2013-2021, which present some missing values. The objective of the study was to develop a model that would automatically reconstruct missing values in the time series, using the advantages of data science to complete information with estimated values. The importance of obtaining reconstructed series lies in having more solid databases to be used in the research and academic work carried out by Dimar. The methodology developed consisted of the use of imputation of medians from existing data on dates and times associated with missing values, all this through the use of data lags and complementary information such as periodicity relationships on the data set. The results showed that it was possible to implement a reliable methodology capable of estimating the most appropriate value to complete the different time series, which constitutes a first approximation for reconstruction of meteorological data.

Recent trends in atmospheric evaporative demand in Southwest Iran: implications for change in drought severity

This study evaluates trends in climatic variables, atmospheric evaporative demand (AED) and meteorological drought between 1988 and 2018 over the southwest of Iran, where the Karkheh Basin is located. For this purpose, we used monthly time series of meteorological variables (precipitation, air temperature, wind speed and sunshine duration). The AED was estimated by the FAO-56 Penman-Monteith equation and drought severity by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). There have been significant changes in the climate over the study domain, including a large increase in air temperature and wind speed and a decrease in relative humidity, which have driven a positive trend in the AED. The SPEI for the region has fallen, with the decrease being larger than the magnitude of change observed with the SPI. This suggests an increase in drought severity mostly associated with the rise in AED. The implications for water availability and climate change processes in the region are discussed.

The temporal characteristics of the lag-response relationship and related key time points between ambient temperature and hand, foot and mouth disease: A multicity study from mainland China

BackgroundPrevious studies have thoroughly elucidated the exposure-response relationship between ambient temperature and hand, foot, and mouth disease (HFMD), whereas very little concern has been to the lag-response relationship and related key time points. ObjectivesWe aimed to clarify the temporal characteristics of the lag-response relationship between ambient temperature and HFMD and how they may vary spatially. MethodsWe retrieved the daily time series of meteorological variables and HFMD counts for 143 cities in mainland China between 2009 and 2014. We estimated the city-specific lag-response curve between ambient temperature and HFMD and related key time points by applying common distributed lag nonlinear models (DLNM) and Monte Carlo simulation methods. Then, we pooled the city-specific estimates by performing a meta-regression with the city-specific characteristics as meta-predictors to explain the potential spatial heterogeneity. ResultsWe found a robust lag pattern between temperature and HFMD for different levels of temperatures. The temporal change of risk obtained its maximum value on the current day but dropped sharply thereafter and then rebounded to a secondary peak, which implied the presence of a harvesting effect. By contrast, the estimation of key time points showed substantial heterogeneity, especially at high temperature (the I2 statistics ranged from 47% to 80%). With one unit increase in the geographic index, the secondary peak would arrive 0.37 (0.02, 0.71) days later. With one unit increase in the economic index and climatic index, the duration time of the lag-response curve would be lengthened by 0.36 (0.1, 0.62) and 0.92 (0.54, 1.29) days, respectively. ConclusionOur study examined the lag pattern and spatial heterogeneity of the lag-response relationship between temperature and HFMD. Those findings gave us new insights into the complex association and the related mechanisms between weather and HFMD and important information for weather-based disease early warning systems.

GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

Algorithm for finding the moment of a trend change in the time series of meteorological variables

Algorithm for finding the moment of a trend change in the time series of meteorological variables

Comparison of manual and automatic daily sunshine duration measurements at German climate reference stations

Abstract. Precise quantification of climate change depends on long time series of meteorological variables. Such time series should be as homogeneous as possible but some changes of measurement conditions cannot be prevented. At German climate reference stations, parallel measurements are used to analyze the effects of changes in measurement systems for example for the transition from manual to automatic instruments. These parallel measurements aim to identify measurement uncertainties and to analyze the comparability of measurement systems to investigate the homogeneity. In this study, we investigate daily sunshine duration. Traditionally, manual measurements of daily sunshine duration are taken with Campbell-Stokes sunshine recorders. For automatic measurements the SONIe or SCAPP instrument is used. The different measurement principles (glass sphere and photodiode) cause systematic differences between the observations. During summer, values for manual observations are larger especially in case of frequent alternations between sunny and cloudy conditions. Furthermore, the standard deviation of the differences between the two measurement systems is larger during summer because of the greater day length. To adjust the automatic measurements a linear regression model is suggested based on parallel measurements from 13 climate reference stations in Germany. To validate the regression coefficients, a leave-one-out cross validation was performed (by leaving out data of individual stations). The regression coefficients (derived from different sets of stations) are similar, thereby indicating a robust data set for the estimation of the linear model. With this method we want to prevent breaks in long time series of daily sunshine duration caused by the transition from manual to automatic instruments.

Homogeneity revisited: analysis of updated precipitation series in Turkey

Homogeneous time series of meteorological variables are necessary for hydrologic and climate studies. Dependability of historical precipitation data is subjected to keen evaluation prior to every study in water resources, hydrology, and climate change fields. This study aims to characterize the homogeneity of long-term Turkish precipitation data in order to ensure that they can be reliably used. The homogeneity of monthly precipitation data set was tested using the standard normal homogeneity test, Buishand test, Von Neumann ratio test, and Pettitt test at the 5% significance level across Turkey. Our precipitation records including the most updated observations, extracted from 160 meteorological stations, for the periods 1974–2014 were analyzed by all the four homogeneity tests. According to the results of all tests, five out of 160 stations have an inhomogeneity. With regard to our strict confirmation rule, 44 out of 160 stations are said to be inhomogeneous since they failed from at least one of the four tests. The breaks captured by the Buishand and Pettitt tests usually tend to appear in the middle of the precipitation series, whereas the ability of standard normal homogeneity test is in favor of identifying inhomogeneities mostly at the beginning or at the end of the records. Our results showed that 42 out of 44 inhomogeneous stations passed all the four tests after applying a correction procedure based on the double mass curve analysis. Available metadata was used to interpret the detected inhomogeneity.

Verification of the MeteoExpert Nowcasts

The MeteoExpert system was operational at the Main Operations Center of Sochi-2014 Olympic and Paralympic Games. The MeteoExpert provided information support round the clock for forecasters, referees and organizers of the Olympic and Paralympic Games via MeteoExpert web site and FROST-2014 web site. The MeteoExpert generated pointwise time series of meteorological variables for five Olympic venues in mountain cluster with four hour lead time and update cycle of ten minutes. They were based on local observations including automatic weather stations (AWS) and Doppler weather radar, and numerical atmospheric boundary layer (ABL) model. Nowcasts have been verified against actual observations at the sites where automatic weather stations exist.

Recent changes and drivers of the atmospheric evaporative demand in the Canary Islands

Abstract. We analysed recent evolution and meteorological drivers of the atmospheric evaporative demand (AED) in the Canary Islands for the period 1961–2013. We employed long and high-quality time series of meteorological variables to analyse current AED changes in this region and found that AED has increased during the investigated period. Overall, the annual ETo, which was estimated by means of the FAO-56 Penman–Monteith equation, increased significantly by 18.2 mm decade−1 on average, with a stronger trend in summer (6.7 mm decade−1). In this study we analysed the contribution of (i) the aerodynamic (related to the water vapour that a parcel of air can store) and (ii) radiative (related to the available energy to evaporate a quantity of water) components to the decadal variability and trends of ETo. More than 90 % of the observed ETo variability at the seasonal and annual scales can be associated with the variability in the aerodynamic component. The variable that recorded more significant changes in the Canary Islands was relative humidity, and among the different meteorological factors used to calculate ETo, relative humidity was the main driver of the observed ETo trends. The observed trend could have negative consequences in a number of water-depending sectors if it continues in the future.

ANÁLISES CLIMÁTICAS: O FILTRO HODRICK-PRESCOTT APLICADO AOS ÍNDICES ATMOSFÉRICOS DA OSCILAÇÃO SUL E DA OSCILAÇÃO DO ATLÂNTICO NORTE

A existência de ciclos ou periodicidades, aparentes nas séries temporais longas de variáveis meteorológicas, auxilia no entendimento dos processos físicos que controlam o clima e os ciclos identificados, eventualmente, servem como prognosticadores do clima regional. Com essa motivação, foi aplicado o filtro de Hodrick-Prescott (HP) às séries dos índices de Oscilação Sul (IOS) e Oscilação do Atlântico Norte (OAN) no período 1900-2010. O filtro HP, aplicado a uma série temporal longa, gera duas novas séries, uma contendo os sinais da tendência e a outra, os da parte cíclica/aleatória da variável. Posteriormente, cada uma das séries foi submetida à análise das transformadas de ondeletas de Morlet para identificação dos ciclos aparentes. A série da tendência dos IOS apresentou ciclos de 6, 9, 17 e 28 anos, o primeiro associado ao Ciclo El Niño/La Niña, os dois seguintes possivelmente ao Ciclo Nodal Lunar (Ciclo Saros) de 18 anos e o último, à Oscilação Decadal do Pacífico (ODP). A série da tendência de OAN apresentou ciclos de 6, 9 e 33 anos, os dois últimos possivelmente relacionados ao Ciclo Saros. Os coeficientes de correlação linear entre a série cíclica/aleatória do IOS, adiantado de 6 meses, e a precipitação no Brasil apresentaram valores altos, de até 0,72 e -0,67 para áreas do Norte e Sul do País, respectivamente, no período 1948-1999 (52 anos), com nível de significância superior a 99%. Esse período cobre as duas fases da ODP. Considerando que os modelos de clima ainda não apresentam destreza adequada para previsão regional no Brasil, sugere-se que a previsão de clima, utilizando a série cíclica/aleatória do IOS adiantado de até 6 meses, seja explorada e, eventualmente, validada.

Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional climate models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction (BC) of RCM temperature and precipitation for Finland is carried out using different versions of the distribution based scaling (DBS) method. The DBS-adjusted RCM data are used as input of a hydrological model to simulate changes in discharges of four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS-adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period 1961–2000 and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

Development of a model for forecasting of PM10 concentrations in Salamanca, Mexico

Salamanca has been ranked as one of the most polluted cities in Mexico. The industry in the area led to a major economic development and rapid population growth in the second half of the twentieth century. The concerning registered pollutants are sulfur dioxide (SO2) and particles in the order of 10 micrometers or less (PM10). The prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. This work presents a forecasting model to predict average concentration of PM10 for the next 24 hours. The proposed model uses a combination of Multilayer Perceptron Neural Network and clustering algorithm. The source database used contains historical time series of meteorological variables and concentrations of PM10 collected in three different stations in Salamanca. The clustering algorithms have been implemented in order to find relationships between PM10 and meteorological variables. These relationships will help us to get additional information that will be used in the prediction model. The proposed model was compared, for accuracy and validation purposes, with a simple Multilayer Perceptron and a multiple Linear Regression. The performance estimation is determined using the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The obtained results show the importance of this set of meteorological variables in the prediction of pollutant concentrations.

Chuvas Intensas em Santa Catarina: Análise Sinótica de um Evento Extremo e Simulação Numérica com o Modelo Atmosférico WRF

The extreme rainfall event in November 2008 in Santa Catarina is discussed in the present study. Due to the importance of the event, the objectives of this study are: i) discuss the large scale aspects of the raifall event and ii) perform a numerical simulation using WRF Model. Synoptic analysis results show that the heavy rainfall event was associated to the persistence of a blocking high during November 2008 and a cyclonic vortex in middle-levels. The analysis of time series of meteorological variables led us to identify the origin of these atmospheric disturbances and its decay, resulting in high accumulated precipitation between November 22nd and 24th. The WRF simulation results allowed the detailing of dynamical and thermodynamic conditions that contributed to the intensity of the phenomenon. Further, it allowed the identification of the organized precipitation over the coast, even though their intensity was underestimated.

A wind chart to characterize potential offshore wind energy sites

Offshore wind industry needs to improve wind assessment in order to decrease the uncertainty associated to wind resource and its influence on financial requirements. Here, several features related to offshore wind resource assessment are discussed, such as input wind data, estimation of long-term and extreme wind statistics, the wind profile and climate variations.This work proposes an analytical method to characterize wind resource. Final product is a wind chart containing useful wind information that can be applied to any offshore sites. Using long-term time series of meteorological variables (e.g. wind speed and direction at different heights), the methodology is applied to five pilot sites in different countries along European Atlantic corridor and it is used to describe and compare offshore wind behavior.

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

Discerning connectivity from dynamics in climate networks

Abstract. The bias due to dynamical memory (serial correlations) in an association/dependence measure (absolute cross-correlation) is demonstrated in model data and identified in time series of meteorological variables used for construction of climate networks. Accounting for such bias in inferring links of the climate network markedly changes the network topology and allows to observe previously hidden phenomena in climate network evolution.

Order and disorder in ecological time-series: Introducing normalized spectral entropy

Information theory and entropy measures have been extensively applied in ecology in different areas like biodiversity assessment, evolution, species interactions, spatial dynamics or landscape analysis. Ecological applications of entropy measures have been primarily focused on structural and functional complexity of systems and less attention has been paid to temporal evolution and dynamics. The aim of this paper is to present “normalized spectral entropy” (Hsn), an entropy related index able to measure part of the structural complexity of an ecological time series. Hsn quantifies the degree of order and predictability derived by the series’ power spectrum. The index sensitivity to data attributes is investigated by means of time-series surrogates of known properties (i.e., time-series length, power spectrum shape, and time-series values distribution). A procedure to calculate confidence intervals is outlined as a preliminary statistical tool to assess differences among values. Three examples of possible application are described using time series of meteorological variables, vegetation physiological responses and remote sensing images. Results show how Hsn is able to contribute to the ongoing debate on how to estimate spatio-temporal complexity of ecological systems, thus making a step forward in the proposed use of complexity as an ecological orientor.

Climate Change Impact Assessment Using K-Nearest Neighbor Weather Generator: Case Study of the Nakdong River Basin in Korea

This study presents a methodology for assessing impacts of climate change in regional-scale hydrology using the K-nearest neighbor weather generator (WG) model in combination with the outputs of global circulation models (GCMs) and hydrologic rainfall-runoff model. The Nakdong River Basin in Korea is used as a case study. The study applies a systematic approach to select the variables for the WG model from five meteorological variables available in the basin. In addition, the GCMs’ projections based on a B1 emission scenario are incorporated into the proposed WG model to reflect the impacts of climate change, consequently generating the meteorological series for 60 years. Meteorological data for historic and two climate scenarios (dry and wet) are generated. The generated time series of meteorological variables are used with the streamflow synthesis and reservoir regulation rainfall-runoff model to calculate the streamflow at 25 sub-basins. The results demonstrate that the WG models combined with the GCMs...