Gap Filling and Quality Control Applied to Meteorological Variables Measured in the Northeast Region of Brazil

In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, atmospheric pressure, wind speed and insolation) from 96 stations located in the northeast region of Brazil (NEB) for the period from 1961 to 2014. We then applied tests with a quality control system (QCS) developed for the detection, correction and possible replacement of suspicious data. Both the applied gap filling technique and the QCS showed that it was possible to solve two of the biggest problems found in time series of daily data measured in meteorological stations: the generation of plausible values for each variable of interest, in order to remedy the absence of observations, and how to detect and allow proper correction of suspicious values arising from observations.

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Sistema de Controle de Qualidade para Dados Diários de Variáveis Meteorológicas (Quality Control System for Data Daily Weather Variables)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v7.5.p827-836 ◽

2015 ◽

Vol 7 (5) ◽

pp. 827

Author(s):

Rafaela Lisboa Costa ◽

Fabrício Santos Silva ◽

Pedro Vieira Azevedo

Keyword(s):

Time Series ◽

Quality Control ◽

Control System ◽

Meteorological Variables ◽

Gap Filling ◽

Weather Variables ◽

Daily Weather ◽

Daily Data ◽

Plausible Values ◽

Weather Stations

Neste trabalho utilizou-se a técnica MICE (do inglês “Multivariate Imputation by Chained Equations”) para imputação de dados diários de variáveis meteorológicas, para séries temporais provenientes de 96 estações convencionais do INMET, entre período de 1961 e 2012, para em seguida aplicar os testes de um sistema de controle de qualidade desenvolvido para detecção, correção e possível substituição de dados suspeitos. Tanto as técnicas de preenchimento de falhas utilizadas, quanto do sistema de controle de qualidade, evidenciaram ser possível solucionar dois dos maiores problemas encontrados nas séries temporais de dados diários medidos em estações meteorológicas: a geração de valores plausíveis, para cada variável de interesse, a fim de sanar a ausência de observações, como detectar e permitir a devida correção, a valores suspeitos provenientes das observações. A B S T R A C T In this work we used the technique MICE (";Multivariate imputation by Chained Equations";) for allocating daily data of meteorological variables for time series from 96 conventional stations of INMET, between 1961 and 2012, and then to apply the tests for a system of quality control developed for detection, correction and possible replacement of suspect data. Both gap filling technique used, as the system of quality control, showed to be possible to solve two major problems encountered in time series of daily data measured at weather stations: the generation of plausible values for each variable of interest, the order to remedy the lack of observations, how to detect and allow correction due to suspicious values from the observations.

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SARS-CoV-2 and the Weather: Correlation between COVID-19 and Meteorological Variables in 3 Cities in Mexico

Asian Journal of Environment & Ecology ◽

10.9734/ajee/2021/v16i430261 ◽

2021 ◽

pp. 74-91

Author(s):

Hermes Ulises Ramirez-Sanchez ◽

Alma Delia Ortiz-Bañuelos ◽

Aida Lucia Fajardo-Montiel

Keyword(s):

Wind Speed ◽

Atmospheric Pressure ◽

Ecological Study ◽

Metropolitan Areas ◽

Meteorological Factors ◽

Correlation Coefficients ◽

Weather Conditions ◽

Dew Point ◽

Meteorological Variables ◽

Infected Person

Meteorological factors such as temperature, humidity, atmospheric pressure, wind speed and direction are associated with the dispersion of the SARS-CoV-2 virus through aerosols, particles <5μm are suspended in the air being infective at least three hours and dispersing from eight to ten meters. It has been shown that a 10-minute conversation, an infected person produces up to 6000 aerosol particles, which remain in the air from minutes to hours, depending on the prevailing weather conditions. Objective: To establish the correlation between meteorological variables, confirmed cases and deaths from COVID-19 in the 3 most important cities of Mexico. Methodology: A retrospective ecological study was conducted to evaluate the correlation of meteorological factors with COVID-19 cases and deaths in three Mexican cities. Results: The correlations between health and meteorological variables show that in the CDMX the meteorological variables that best correlate with the health variables are Temperature (T), Dew Point (DP), Wind speed (WS), Atmospheric Pressure (AP) and Relative Humidity (RH) in that order. In the ZMG are T, WS, RH, DP and AP; and in the ZMM are RH, WS, DP, T and AP. Conclusions In the 3 Metropolitan Areas showed that the meteorological factors that best correlate with the confirmed cases and deaths from COVID-19 are the T, RH; however, the correlation coefficients are low, so their association with health variables is less than other factors such as social distancing, hand washing, use of antibacterial gel and use of masks.

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Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico

Energies ◽

10.3390/en12122330 ◽

2019 ◽

Vol 12 (12) ◽

pp. 2330 ◽

Cited By ~ 2

Author(s):

Quetzalcoatl Hernandez-Escobedo ◽

Javier Garrido ◽

Fernando Rueda-Martinez ◽

Gerardo Alcalá ◽

Alberto-Jesus Perea-Moreno

Keyword(s):

Wind Speed ◽

Gulf Of Mexico ◽

Atmospheric Pressure ◽

Power Plants ◽

Thermal Power ◽

Wind Farms ◽

Electricity Demand ◽

Eastern Region ◽

Thermal Power Plants ◽

Northeast Region

The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively.

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Analysis on atmospheric pressure, temperature, and wind speed profiles during total solar eclipse 9 March 2016 using time series clustering

Journal of Physics Conference Series ◽

10.1088/1742-6596/771/1/012009 ◽

2016 ◽

Vol 771 ◽

pp. 012009 ◽

Cited By ~ 1

Author(s):

Lala Septem Riza ◽

Yaya Wihardi ◽

Enjang Ali Nurdin ◽

Nanang Dwi Ardi ◽

Cahyo Puji Asmoro ◽

...

Keyword(s):

Time Series ◽

Wind Speed ◽

Solar Eclipse ◽

Atmospheric Pressure ◽

Total Solar Eclipse ◽

Time Series Clustering

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Comparative evaluation of some climate references of Novolazarevskaya station and Novolazarevskaya airfield

Arctic and Antarctic Research ◽

10.30758/0555-2648-2017-0-3-27-38 ◽

2017 ◽

pp. 27-38

Author(s):

E. I. Alexandrov ◽

A. N. Prakhov

Keyword(s):

Time Series ◽

Comparative Analysis ◽

Wind Speed ◽

Comparative Evaluation ◽

Meteorological Station ◽

Climatic Parameters ◽

Air Humidity ◽

Correlation Index ◽

Daily Data ◽

Average Maximum

The results of a comparative analysis of the monthly mean of average, maximum and minimum values of air temperature, air humidity and wind speed calculated by daily data with one minute increment for the period from November 2006 to February 2012 during the months of operation of the meteorological station at the Novolazarevskaya airfield and directly at Novolazarevskaya station are given. The climatic parameters at two points of observation give high correlation index. The estimations of the trends in time series of climatic references at Novolazarevskaya station for the period 1961–2015 are also given.

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Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2018.p0900 ◽

2018 ◽

Vol 22 (6) ◽

pp. 900-906 ◽

Cited By ~ 2

Author(s):

Wonjik Kim ◽

Osamu Hasegawa ◽

◽

Keyword(s):

Neural Network ◽

Time Series ◽

Wind Speed ◽

Atmospheric Pressure ◽

Time Series Data ◽

Meteorological Data ◽

Series Data ◽

Total Solar Radiation ◽

Proposed Model ◽

Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

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The influence of meteorological variables on the development of deep venous thrombosis

Thrombosis and Haemostasis ◽

10.1160/th09-04-0214 ◽

2009 ◽

Vol 102 (10) ◽

pp. 676-682 ◽

Cited By ~ 18

Author(s):

Helen K. Brown ◽

John A. Simpson ◽

John T. Murchison

Keyword(s):

Seasonal Variation ◽

Relative Risk ◽

Wind Speed ◽

Venous Thrombosis ◽

Deep Venous Thrombosis ◽

Atmospheric Pressure ◽

Hospital Admissions ◽

Meteorological Variables ◽

Weather Variables ◽

Increased Risk

SummaryThe influence of weather on deep venous thrombosis (DVT) incidence remains controversial. We aimed to characterize the temporal association between DVT and meteorological variables including atmospheric pressure. Data relating to hospital admissions with DVT in Scotland were collected retrospectively for a 20 year period for which corresponding meteorological recordings were available. Weather variables were calculated as weighted daily averages to adjust for variations in population density. Seasonal variation in DVT and short-term effects of weather variables on the relative risk of developing DVT were assess using Poisson regression modelling. The models allowed for the identification of lag periods between variation in the weather and DVT presentation. A total of 37,336 cases of DVT were recorded. There was significant seasonal variation in DVT with a winter peak. Seasonal variation in wind speed and temperature were significantly associated with seasonal variation in DVT. When studying more immediate meteorological influences, low atmospheric pressure, high wind speed and high rainfall were significantly associated with an increased risk of DVT approximately 9–10 days later. The effect was most strikingly demonstrated for atmospheric pressure, every 10 millibar decrease in pressure being associated with a 2.1% increase in relative risk of DVT. Alterations in weather have a small but significant impact upon the incidence of DVT. DVT is particularly associated with reduction in atmospheric pressure giving weight to the hypothesis that reduced cabin pressure in long haul flights contributes to DVT. These findings have implications for our understanding of the pathogenesis of DVT.

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