Representation of meteorological data series for application in theoretical models

2003 ◽  
Author(s):  
Sergei Y. Zolotov ◽  
Alexander V. Buldakov
Keyword(s):  
Meteorological Data ◽  
Theoretical Models ◽  
Data Series

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

scholarly journals Análise comparativa da velocidade do vento e da temperatura do ar, entre dados gerados por reanálises meteorológicas e dados observacionais na região de Minas Gerais

2018 ◽  
Vol 40 ◽  
pp. 20
Author(s):  
Mauren Lucila Marques de Morais Micalichen ◽  
Nelson Luís da Costa Dias
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Meteorological Data ◽  
Reanalysis Data ◽  
Minas Gerais ◽  
Data Series ◽  
Continuous Series ◽  
Observation Data ◽  
Alternative Source ◽  
Wind Speed Data

The use of alternative sources of meteorological data has become increasingly common, making it possible to evaluate areas with no long or continuous series of meteorological data. In this context, the main objective of this study is to evaluate the performance of data series from the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) for the state of Minas Gerais and verify the possible use of them in the absence of data observations of air temperature and wind speed. The analyzes were performed by comparing observation data from 17 meteorological stations and reanalysis data of the CFSR and CFSV2 models. From the results of the statistical analysis, it is observed that the air temperature reanalysis data presented a good performance in the region of study. However, wind speed data show a weak correlation. These results show that the air temperature data from these reanalyses have the potential to be used as an alternative source of data. Further studies are suggested regarding the use of wind speed data from these reanalyses.

Climate of Moscow at the end of Little Ice Age

2021 ◽  
Author(s):  
Mikhail A. Lokoshchenko
Keyword(s):  
Little Ice Age ◽  
Meteorological Data ◽  
Total Error ◽  
Thermal Conditions ◽  
Ice Age ◽  
Data Series ◽  
Accuracy Of Measurements ◽  
Meteorological Observations ◽  
June July ◽  
First Time

<p>Better understanding of current climate changes needs a full knowledge about regional specific of thermal conditions at the end of Little Ice Age. So, the earliest available meteorological data are important. First regular daily qualitative meteorological observations were taken in Moscow city from 1657 to 1675. Episodic short series of instrumental measurements were made there for the first time in 1731; regular daily measurements started in 1779 when one of Mannheim network stations was founded in Moscow.</p><p>         All known old data series of the air temperature T measurements in Moscow since 1779 were collected and analyzed. Mannheim station existed there from 1779 to 1797 but average values of T are available from issues of Ephemerides Societatis Meteorologicae Palatinae only for the period 1779–1792. High accuracy of measurements at Mannheim network is confirmed by high correlation co-efficient between monthly-averaged T values in Moscow and at closest stations (Warsaw and St. Petersburg): up to 0.82-0.84 on separate months.</p><p>         Different methodical questions (unknown location of the station, unknown conditions of thermometer installation, its height and shading, an accuracy of its calibration, etc.) were studied. As a result it was found that the most probable error due to thermometer installation close to the northern building wall is ±0.1÷0.2 ºС; the error of daily-averaged T due to unknown height of measurements is ±0.1 ºС; the calibration accuracy in Mannheim was about ±0.1 ºС. Thus, a total error of T on average of a day in the 18<sup>th</sup> century was not higher than ±0.3÷0.4 ºС. Probably it was even less because separate components of the error may be multidirectional. For the first time mean-annual T in Moscow was received for 1783, and the most probable values were estimated for 1784 and 1785 using the data of the closest Mannheim station (Saint-Petersburg) for separate months with data gaps. The end of Little Ice Age manifeted at extremely low minimal values of T: up to –31 ˚R (–38.8 ˚С) in December 17<sup>th</sup>, 1788. However, thermal conditions from June to September changed only a bit since the 18<sup>th</sup> century till nowadays (differences are not statistically significant with the 0.95 confidence probability).  </p><p>         Later measurements in Moscow were renewed since 1808 and broken again in August of 1812 due to Napoleon’s invasion and terrible Moscow fire. For the first time unknown data series of everyday measurements which were made by Ivan Lange in 1816–1817 were found and studied. As is known the famous 1816 ‘Year Without a Summer’ was noted almost all over the World by extremely cold summer as probable result of Mount Tambora eruption in 1815. Nevertheless, it was found that summer of 1816 in Moscow was comparatively cool but not extremely cold: monthly-averaged T there was 15.7, 17.3 and 14.5 ˚С in June, July and August, respectively, and 15.8 ˚С on average of the summer. Thus, 1816 occupies only 27<sup>th</sup> place among the coldest summers in the city during 216 years.</p><p>         Author is thankful to the memory of his late PhD student Ekaterina L. Vasilenko.</p>

Prediction of short and medium term PM10 concentration using artificial neural networks

2019 ◽  
Vol 30 (2) ◽  
pp. 414-436 ◽  
Author(s):  
Elaine Schornobay-Lui ◽  
Eduardo Carlos Alexandrina ◽  
Mônica Lopes Aguiar ◽  
Werner Siegfried Hanisch ◽  
Edinalda Moreira Corrêa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Models ◽  
Urban Areas ◽  
Meteorological Data ◽  
High Volume ◽  
Data Series ◽  
Content Type ◽  
Artificial Neural ◽  
Narx Model

Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

scholarly journals Dynamics Of Seasonal Patterns In Geochemical, Isotopic, And Meteorological Records Of The Elbrus Region Derived From Functional Data Clustering

2020 ◽  
Vol 13 (3) ◽  
pp. 110-116
Author(s):  
Gleb A. Chernyakov ◽  
Valeria Vitelli ◽  
Mikhail Y. Alexandrin ◽  
Alexei M. Grachev ◽  
Vladimir N. Mikhalenko ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Ice Core ◽  
Data Series ◽  
Alignment Algorithm ◽  
Annual Variations ◽  
Temperature And Precipitation ◽  
Decadal Scale ◽  
Isotope Record ◽  
Central Caucasus ◽  
Instrumental Period

A nonparametric clustering method, the Bagging Voronoi K-Medoid Alignment algorithm, which simultaneously clusters and aligns spatially/temporally dependent  curves,  is applied to study various data series from the Elbrus  region (Central Caucasus). We used the algorithm to cluster annual curves obtained by smoothing of the following synchronous data series: titanium concentrations in varved (annually laminated) bottom sediments of proglacial  Lake Donguz-Orun;  an oxygen-18 isotope record in an ice core from Mt. Elbrus; temperature and precipitation observations with a monthly resolution from Teberda and Terskol meteorological stations. The data of different types were clustered independently. Due to restrictions concerned with the availability of meteorological data, we have fulfilled the clustering procedure separately for two periods: 1926–2010 and 1951–2010. The study is aimed to determine whether the instrumental period could be reasonably divided (clustered)  into several sub-periods using different climate and proxy time series; to examine the interpretability of the resulting borders of the clusters (resulting time periods); to study typical patterns of intra-annual variations of the data series. The results of clustering suggest that the precipitation and to a lesser degree titanium decadal-scale data may be reasonably grouped, while the temperature and oxygen-18 series are too short to form meaningful clusters; the intercluster boundaries show a notable degree of coherence between temperature and oxygen-18 data, and less between titanium and oxygen-18 as well as for precipitation series; the annual curves for titanium and partially precipitation data reveal much more pronounced intercluster  variability than the annual patterns of temperature and oxygen-18 data.

scholarly journals Delta Change Method with Cyclic Covariate Generalized Extreme Value Model for Downscaling Extreme Rainfall

2019 ◽  
Vol 8 (2S2) ◽  
pp. 158-161
Keyword(s):  
Climate Model ◽  
Meteorological Data ◽  
Global Climate Model ◽  
Seasonal Effects ◽  
Annual Rainfall ◽  
Multiple Time Scales ◽  
Data Series ◽  
P Value ◽  
Multiple Time ◽  
Delta Change

In meteorological data, lots of variables have annual, seasonal or diurnal cycles. These would be based on different climatic patterns in different seasons rising sea levels. The delta change approach is one of the statistical downscaling methods that used to downscale global climate model data in order to use it as a future input for hydrological models and flood risk assessment. In this work, a non-stationary GEV model with cyclic covariate structure for modelling magnitude and variation of data series with some degrees of correlation for real-world applications is proposed. All extreme events were calculated assuming that maximum annual daily precipitations follow the GEV distribution. The method makes it possible to identify and estimate the impacts of multiple time scales-such as seasonality, interdecadal variability, and secular trends-throughout the area, scale, and shape parameters of extreme sea level probability distribution. The incorporation of seasonal effects describes a huge amount of data variability, permitting the methods involved to be estimated more efficiently. Next, the technique of deltachange was implemented to the mean annual rainfall and also the regular rainfall occurrences of 5, 10, 20, 50 and 100 years of return. The capability of the proposed model will be tested to one rainfall station in Sabah. The new model suggesting improvement over the stationary model based on the p-value which is highly significant (approximate to 0). GEV model with cyclic covariate on both location and scale parameters is able to capture the seasonality factor in rainfall data. Hence, a reliable delta-change model has been developed in this study. This could produce more accurate projection of rainfall in the future

scholarly journals Monitoring Retreat of Coastal Sandy Systems Using Geomatics Techniques: Somo Beach (Cantabrian Coast, Spain, 1875–2017)

2018 ◽  
Vol 10 (9) ◽  
pp. 1500 ◽  
Author(s):  
José de Sanjosé Blasco ◽  
Manuel Gómez-Lende ◽  
Manuel Sánchez-Fernández ◽  
Enrique Serrano-Cañadas
Keyword(s):  
Laser Scanning ◽  
Meteorological Data ◽  
Tidal Range ◽  
Data Series ◽  
Global Changes ◽  
Estuarine System ◽  
Level Curve ◽  
Dune System ◽  
Human Environment ◽  
Historical Cartography

The dynamics and evolution of a coastal sandy system over the last 142 years (1875–2017) were analyzed using geomatics techniques (historical cartography, photogrammetry, topography, and terrestrial laser scanning (TLS)). The continuous beach–dune system is a very active confining sand barrier closing an estuarine system where damage is suffered by coastal infrastructures and houses. The techniques used and documentary sources involved historical cartography, digitalizing the 5-m-level curve on the maps of 1875, 1908, 1920, 1950, and 1985; photogrammetric flights of 1985, 1988, and 2001 without calibration certificates, digitalizing only the upper part of the sandy front; photogrammetric flights of 2005, 2007, 2010, and 2014, using photogrammetric restitution of the 5-m-level curve; topo-bathymetric profiles made monthly between 1988 and 1993 using a total station; a terrestrial laser scanner (TLS) since 2011 by means of two annual measurements; and the meteorological data for the period of 1985–2017. The retreat of the sandy complex was caused by winter storms with large waves and swells higher than 6 m, coinciding with periods demonstrating a high tidal range of over 100 and periods with a large number of strong storms. The retreat was 8 m between December 2013 and March 2014. The overall change of the coastline between 1875 and 2017 was approximately 415 m of retreat at Somo Beach. The erosive processes on the foredune involved the outcrop of the rock cliff in 1999 and 2014, which became a continuous rocky cliff without sands. To know the recent coastal evolution and its consequences on the human environment, the combined geomatic techniques and future TLS data series may lead to the improvement in the knowledge of shoreline changes in the context of sea level and global changes.

scholarly journals Relevance and Scale Dependence of Hydrological Changes in Glacierized Catchments: Insights from Historical Data Series in the Eastern Italian Alps

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 89 ◽  
Author(s):  
Luca Carturan ◽  
Fabrizio De Blasi ◽  
Federico Cazorzi ◽  
Davide Zoccatelli ◽  
Paola Bonato ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Hydrological Regime ◽  
Late Summer ◽  
Ice Age ◽  
Data Series ◽  
Italian Alps ◽  
Rapid Reduction ◽  
Buffering Effect ◽  
Hydrological Sensitivity ◽  
Alpine Catchments

Glaciers have an important hydrological buffering effect, but their current rapid reduction raises concerns about future water availability and management. This work presents a hydrological sensitivity analysis to different climatic and glacier cover conditions, carried out over four catchments with area between 8 and 1050 km2, and with glacierization between 2% and 70%, in the Italian Alps. The analysis is based on past observations, and exploits a unique dataset of glacier change and hydro-meteorological data. The working approach is aimed at avoiding uncertainties typical of future runoff projections in glacierized catchments. The results highlight a transition from glacial to nival hydrological regime, with the highest impacts in August runoff over smaller catchments. The buffering effect of current glaciers has largely decreased if compared to the Little Ice Age, up to 75% for larger catchments, but it is still important during warm and dry summers like 2003. We confirm a non-linear relationship between glacier contribution in late summer and catchment area/percent glacierization. The peak in runoff attributable to glacier melt, expected in the next 2–3 decades on highly glacierized alpine catchments, has already passed in the study area.

scholarly journals Large-scale lysimeter site St. Arnold, Germany: analysis of 40 years of precipitation, leachate and evapotranspiration

2008 ◽  
Vol 5 (4) ◽  
pp. 2623-2656 ◽  
Author(s):  
N. Harsch ◽  
M. Brandenburg ◽  
O. Klemm
Keyword(s):  
Large Scale ◽  
Evaporation Rate ◽  
Deciduous Forest ◽  
Meteorological Data ◽  
Regional Climate ◽  
Coniferous Forest ◽  
Primary Objective ◽  
Secular Trends ◽  
Data Series

Abstract. This study deals with a lysimetrical-meteorological data series collected on the large-scale lysimeter site "St. Arnold", Germany, from November 1965 to April 2007. The particular relevance of this data rests both upon its perdurability and upon the fact that the site is comprised of a grassland basin, an oak/beech and a pine basin1. Apart from analyzing secular trends of the meteorological measurements, the primary objective of this study is the evaluation of precipitation in connection with leachate quantities and potential and actual evapotranspiration. The latter are based upon the Penman and the Penman-Monteith approaches, respectively. The main results of this survey are that, on a long-term average, the grassland basin turns more than half (53%) of its annually incoming precipitation into leachate and only 36% into water vapour, while the deciduous forest exhibits a rather balanced ratio with 37% for leachate and 44% for evapotranspiration, and the evergreen coniferous forest shows the highest evaporation rate (56%) and the lowest leachate rate (28%). Concerning these water balances, considerable differences both between basins and between seasons stand out. While summer periods exhibit high evapotranspiration rates for the forests and moderate ones for the grassland, winter periods are characterised by considerable leachate quantities for grassland and the deciduous forest and moderate ones for the coniferous forest. Following the analysis of the climatic development in St. Arnold, trends towards a milder and more humid regional climate were detected. 1According to a survey conducted by Lanthaler in 2006, only 1% of all European lysimeters are planted with forests. Leading varieties are fields (63%) and grassland (21%).

scholarly journals Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

2017 ◽  
Vol 38 (4Supl1) ◽  
pp. 2265
Author(s):  
Rodrigo Cornacini Ferreira ◽  
Rubson Natal Ribeiro Sibaldelli ◽  
Heverly Morais ◽  
Otávio Jorge Grigoli Abi Saab ◽  
José Renato Bouças Farias
Keyword(s):  
Spatial Variability ◽  
Meteorological Data ◽  
Rainfall Data ◽  
Data Series ◽  
Weather Station ◽  
Climate Data ◽  
Crop Models ◽  
Area Of Interest ◽  
Grain Productivity ◽  
Weather Stations

Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data.

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