scholarly journals Three novel copula-based bias correction methods for daily ECMWF air temperature data

2017 ◽  
Author(s):  
Fakhereh Alidoost ◽  
Alfred Stein ◽  
Zhongbo Su ◽  
Ali Sharifi
Keyword(s):  
Air Temperature ◽  
Bias Correction ◽  
Weather Forecast ◽  
Correction Method ◽  
Temperature Data ◽  
Weather Data ◽  
Conditional Quantiles ◽  
New Methods ◽  
Conditional Copula ◽  
Weather Stations

Abstract. Data retrieved from global weather forecast systems are typically biased with respect to measurements at local weather stations. This paper presents three copula-based methods for bias correction of daily air temperature data derived from the European Centre for Medium-range Weather Forecasts (ECMWF). The aim is to predict conditional copula quantiles at different unvisited locations, assuming spatial stationarity of the underlying random field. The three new methods are: bivariate copula quantile mapping (types I and II), and a quantile search. These are compared with commonly applied methods, using data from an agricultural area in the Qazvin Plain in Iran containing five weather stations. Cross-validation is carried out to assess the performance. The study shows that the new methods are able to predict the conditional quantiles at unvisited locations, improve the higher order moments of marginal distributions, and take the spatial variabilities of the bias-corrected variable into account. It further illustrates how a choice of the bias correction method affects the bias-corrected variable and highlights both theoretical and practical issues of the methods. We conclude that the three new methods improve local refinement of weather data, in particular if a low number of observations is available.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals <i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method"

2010 ◽  
Vol 17 (3) ◽  
pp. 269-272 ◽  
Author(s):  
S. Nicolay ◽  
G. Mabille ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Air Temperature ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Weather Forecast ◽  
Temperature Data ◽  
Temperature Time Series ◽  
Climatic Data ◽  
Statistical Validation ◽  
Confidence Levels ◽  
Temperature Time

Abstract. Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.

scholarly journals A physically based method for correcting temperature data measured by naturally ventilated sensors over snow

2001 ◽  
Vol 47 (159) ◽  
pp. 665-670 ◽  
Author(s):  
Martin Arck ◽  
Dieter Scherer
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Measurement Errors ◽  
Correction Method ◽  
Temperature Data ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Low Wind Speed ◽  
Snowmelt Period ◽  
Physically Based

AbstractDuring the snowmelt period in 1998, air-temperature data were acquired at 1 min intervals using different measurement systems as part of a field campaign in the Kärkevagge, Swedish Lapland. A comparison reveals that temperatures from naturally ventilated sensors exceed temperatures from aspirated sensors by as much as 6.2 K. Errors in temperature are closely connected to high values of upwelling shortwave radiation and are larger in periods of low wind speed. Measurement errors result from the instantaneous radiation conditions and propagate over the next measurements due to slow response time of the naturally ventilated sensor. A physically based method is developed for correcting temperature data influenced by radiation errors, which requires additional measurements of wind speed and upwelling shortwave radiation. Coefficients of the correction formula are automatically determined from the erroneous temperature data, so the method is independent of accurate air-temperature measurements. The high quality of the correction method could be validated by accurate psychrometer measurements. One of the most important applications is the computation of sensible-heat fluxes from snow-covered surfaces during the snowmelt period using the bulk-aerodynamic method, which is greatly improved by the new correction method.

scholarly journals Os fatores geoecológicos, geourbanos e o clima urbano de Iporá-GO: uma análise a partir do Método de Correlação Linear

2018 ◽  
Vol 11 (3) ◽  
pp. 77
Author(s):  
Washington Silva Alves ◽  
Zilda De Fátima Mariano
Keyword(s):  
Statistical Analysis ◽  
Spatial Variation ◽  
Statistical Methods ◽  
Air Temperature ◽  
Urban Climate ◽  
Temperature Data ◽  
Absolute Minimum ◽  
El Sistema ◽  
Weather Stations ◽  
Maximum Air Temperature

Resumo O objetivo desse trabalho consistiu em analisar a influência dos fatores geoecológicos e geourbanos no padrão da temperatura do ar máxima e mínima absoluta em Iporá-GO, por meio do método estatístico de correlação linear. Os fundamentos teóricos e metodológicos pautaram-se no sistema clima urbano de Monteiro (2003), com ênfase no subsistema termodinâmico. Os fatores geoecológicos (hipsometria, exposição de vertente, vegetação urbana e hidrografia) e geourbanos (densidade de construção e o uso do solo urbano), foram georreferenciado com auxílio dos softwares ArcGis 9.0, Spring 5.3 e Surfer 9.0. Os dados de temperatura do ar foram coletados entre outubro de 2012 e outubro de 2013, em intervalos de 30 minutos, com termohigrômetros (modelo HT-500) e estações meteorológicas automáticas distribuídos em seis pontos da área urbana e rural de Iporá. Posteriormente, os dados foram organizados em planilhas de cálculos para análise estatística. Os resultados demonstraram que os fatores geoecológicos e geourbanos citados foram decisivos na variação espacial da temperatura do ar máxima e mínima absoluta em Iporá.Palavras-chave: Climatologia, Cidade, Clima Urbano AbstractThe objective of this study is to analyze the influence of geoecological factors and geourbanos the standard maximum air temperature and absolute minimum in Iporá-GO, by means of statistical methods of correlation linear. The theoretical and methodological foundations guided in the urban climate system Monteiro (2003), with emphasis on thermodynamic subsystem. The geoecological factors (hipsometria, slop exposure, urban and Hydrography vegetation) and geourban (building density and the use of urban land), were georeferenced with the help of software ArcGIS 9.0, Sprint 5.3 and Surfer 9.0. The air temperature data were collected between October 2012 and October 2013, in 30-minute intervals, with hygrometer term (HT-500 model) and automatic weather stations distributed in six points of the urban and rural Iporá. Later, the data were organized into spreadsheets for statistical analysis. The results showed that geoecological mentioned factors and geourbanos were decisive in the spatial variation of the temperature of the air and maximum absolute minimum in Iporá.Keywords: Climatology, City, Urban Climate ResumenEl objetivo de este estudio fue analizar la influencia de los factores geoecológicos y geourbanos en el patrón de la temperatura máxima y mínima absoluta del aire en Iporá-GO, a través de lo método estadístico de correlación lineal. Los fundamentos teóricos y metodológicos se basan en el sistema de clima urbano de Monteiro (2003), con énfasis en el subsistema termodinámico. Los factores geoecológicos (hipsometría, hebras de exposición, hidrografía y vegetación urbana) y geourbanos (densidad de edificación y uso del suelo urbano) fueron georeferenciados con la ayuda del software ArcGIS 9.0, Spring 5.3 y Surfer 9.0. Los datos de temperatura del aire se recogieron entre octubre 2012 y octubre 2013, en intervalos de 30 minutos, con termohigrômetros (modelo HT-500) y estaciones meteorológicas automáticas distribuidas en seis puntos de las zonas urbanas y rurales. Posteriormente, los datos se organizaron en las hojas de cálculo para el análisis estadístico. Los resultados mostraron que los factores geoecológicos y geourbanos citados fueron decisivos en la variación espacial de la temperatura máxima y mínima absoluta del aire en Iporá.Palavras clave: Climatología, Ciudad, Clima Urbano 

A comment on temperature measurement at automatic weather stations in Australia

2019 ◽  
Vol 69 (1) ◽  
pp. 172
Author(s):  
G. P. Ayers
Keyword(s):  
Air Temperature ◽  
Thermal Inertia ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Data Sets ◽  
Frequency Distributions ◽  
Measurement Systems ◽  
Weather Stations ◽  
The Difference ◽  
Temperature Records

Two versions of 1-min air-temperature data recorded at Bureau Automatic Weather Stations (AWSs) were compared in three case studies. The aim was to evaluate the difference between 1-min data represented by a measurement at the last second of each minute, compared with an average of four or five 1-s measurements made during the minute. Frequency distributions of the difference between these two values were produced for 44 000 min in three monthly data sets, January and July 2016 and September 2017. Diurnal and seasonal changes in standard deviation of the temperature differences showed that minute-to-minute fluctuations were driven by solar irradiance as the source of turbulent kinetic energy in the planetary boundary layer. Fluctuations in the difference between the two versions of 1-min data were so small overnight in all months that minimum temperature (Tmin) was the same using both methods. In midsummer, any difference between the two values for maximum temperature (Tmax) was greatest at midday. Tmax could be up by 0.1 K higher in the 1-s data compared with Tmax averaged from four measurements in the minute, but less often than 1 min in five. A follow-up test for September 2017 at Mildura when a new Tmax record was set found the difference immaterial, with Tmax the same for the averaged or 1-s values. Thus while the two versions of 1-min air-temperature data showed fluctuating small differences, largest at midday in summer, for the 3 months studied at both sites, fluctuations were too small to cause bias in climatological air-temperature records. This accorded with a numerical experiment confirming the Bureau’s advice that thermal inertia in the AWS measurement systems ensured that its 1-s data represented averages over the prior 40–80 s, providing a 1-min average of air temperature in accord with World Meteorological Organization requirements.

scholarly journals Analysis of Long-Term Measured Exterior Air Temperature in Zilina

2018 ◽  
Vol 14 (2) ◽  
pp. 124-131 ◽  
Author(s):  
Daniela Jurasova
Keyword(s):  
Climate Change ◽  
Temperature Measurement ◽  
Air Temperature ◽  
Weather Data ◽  
Time Data ◽  
Long Time ◽  
Experimental Laboratory ◽  
Weather Stations ◽  
Selection Of

Abstract The climate change assumes nowadays on significance. Weather data may be available on various time scales – long-term, minutes, hours, days, periods, years. Measurements of air temperature are realized for a long time. Data in Slovakia are available from few weather stations of Slovak Hydrometeorological Institute (SHMI). For long-term and wide-ranging measurement of various parameters this can be complicated and expensive. This paper is focused on temperature measurement near the experimental laboratory. Estimation of daily, monthly and yearly mean temperatures is done in different ways. Results from experimental temperature measurement, in a way of selection of unusual extremes are presented. Shorter recording intervals describe the temperature courses in a more pertinent way.

scholarly journals Internet of Things Based Weather Forecast Monitoring System

2018 ◽  
Vol 9 (3) ◽  
pp. 555 ◽  
Author(s):  
Atul Kulkarni ◽  
Debajyoti Mukhopadhyay
Keyword(s):  
Weather Forecasting ◽  
Weather Forecast ◽  
Severe Weather ◽  
Weather Data ◽  
Partial Explanation ◽  
Significant Function ◽  
Weather Events ◽  
Weather Stations ◽  
Communication Methods ◽  
Cost Components

<p>Weather forecasting is a significant function in meteorology and has been one of the most systematically challenging troubles around the world.This scheme deals with the structure of a weather display method using small cost components so that any electronics hobbyist can construct it. As a replacement for using sensors to collect the weather data, the development gets the information from weather stations placed around the world through a global weather data supplier. Severe weather phenomena challengedifficult weather forecast approach with the partial explanation. Weather events have numerous parameters that are not possible to detail and compute. Growing on communication methods enables weather predictsspecialist systems to combine and share possessions and thus hybrid systems have emerged. Still, though these improvements on climate predict, these expert systems can’t be entirely reliable while weather forecast is central problem.</p>

scholarly journals Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products

2015 ◽  
Vol 16 (6) ◽  
pp. 2463-2480 ◽  
Author(s):  
Lei Ji ◽  
Gabriel B. Senay ◽  
James P. Verdin
Keyword(s):  
Data Assimilation ◽  
Air Temperature ◽  
Temperature Data ◽  
Mountainous Areas ◽  
Global Land ◽  
Land Data Assimilation ◽  
Weather Stations ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

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