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

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

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The Street Air Warming Phenomenon in a High-Rise Compact City

Atmosphere ◽

10.3390/atmos9100402 ◽

2018 ◽

Vol 9 (10) ◽

pp. 402 ◽

Cited By ~ 4

Author(s):

Xiaoxue Wang ◽

Yuguo Li ◽

Xinyan Yang ◽

Pak Chan ◽

Janet Nichol ◽

...

Keyword(s):

Thermal Comfort ◽

Air Temperature ◽

Thermal Environment ◽

Urban Climate ◽

Pollutant Dispersion ◽

Urban Morphology ◽

Air Temperatures ◽

Significant Difference ◽

Sensitivity Studies ◽

Weather Stations

The street thermal environment is important for thermal comfort, urban climate and pollutant dispersion. A 24-h vehicle traverse study was conducted over the Kowloon Peninsula of Hong Kong in summer, with each measurement period consisting of 2–3 full days. The data covered a total of 158 loops in 198 h along the route on sunny days. The measured data were averaged by three methods (direct average, FFT filter and interpolated by the piecewise cubic Hermite interpolation). The average street air temperatures were found to be 1–3 °C higher than those recorded at nearby fixed weather stations. The street warming phenomenon observed in the study has substantial implications as usually urban heat island (UHI) intensity is estimated from measurement at fixed weather stations, and therefore the UHI intensity in the built areas of the city may have been underestimated. This significant difference is of interest for studies on outdoor air temperature, thermal comfort, urban environment and pollutant dispersion. The differences were simulated by an improved one-dimensional temperature model (ZERO-CAT) using different urban morphology parameters. The model can correct the underestimation of street air temperature. Further sensitivity studies show that the building arrangement in the daytime and nighttime plays different roles for air temperature in the street. City designers can choose different parameters based on their purpose.

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Increase in Air Temperature in The Region of South Sumatra Province as the Indicator of Global Warming and the Effect on Transpiration of Lansium domesticum Corr.

10.31220/osf.io/jrf35 ◽

2019 ◽

Author(s):

Ari Sugiarto ◽

Hanifa Marisa ◽

Sarno

Keyword(s):

Global Warming ◽

Air Temperature ◽

Transpiration Rate ◽

Temperature Increase ◽

Secondary Data ◽

Temperature Data ◽

Randomized Design ◽

Maximum Air Temperature ◽

Lansium Domesticum ◽

Minimum Air Temperature

Abstract Global warming is one of biggest problems faced in the 21st century. One of the impacts of global warming is that it can affect the transpiration rate of plants that °Ccur. This study purpose to see how much increase in air temperature that occurred in the region of South Sumatra Province and to know the effect of increase in ari temperature in the region of South Sumatra Province on transpiration rate of Lansium domesticum Corr. This study used a complete randomized design with 9 treatments (22.9 °C, 23.6 °C, 24.6 °C, 26.3 °C, 27 °C, 27.8 °C, 31.7 °C, 32.5 °C, and 32.9 °C) and 3 replications. Air temperature data as secondary data obtained from the Meteorology, Climatology and Geophysics Agency (MCGA) Palembang Climatology Station in South Sumatra Province. The measurement of transpiration rate is done by modified potometer method with additional glass box. The data obtained are presented in the form of tables and graphs. Transpiration rate (mm3/g plant/hour) at temperture 22.9 °C = 4.37, 23.6 °C = 7.03, 24.6 °C = 8.03, 26.3 °C = 10.11, 27 °C = 13.13, 27.8 °C = 17.87, 31.7 °C = 23.21, 32.5 °C= 25.45 and 32.9 °C= 27.24. At the minimum air temperature in the region of South Sumatra Province there is increase in air temperature of 1.5 °C, average daily air temperature increase 1.3 °C and maximum air temperature increase 1.2 °C.

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Aumento das temperaturas extremas na cidade do Rio de Janeiro e o desvio ocasionado durante um evento de El Niño intenso (Thermal change in the city of Rio de Janeiro and the deviation caused during an intense El Niño event)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v12.4.p1291-1301 ◽

2019 ◽

Vol 12 (4) ◽

pp. 1291

Author(s):

Henderson Silva Wanderley ◽

Ronabson Cardoso Fernandes ◽

André Luiz De Carvalho

Keyword(s):

Air Temperature ◽

El Niño ◽

Urban Areas ◽

Rio De Janeiro ◽

El Nino ◽

Pearson Correlation ◽

Temperature Data ◽

Kolmogorov Smirnov ◽

The City ◽

Maximum Air Temperature

O processo de urbanização tem o potencial de alterar a característica térmica e aerodinâmica da superfície dos grandes centros urbanos, possibilitando o aumento da temperatura do ar. No entanto, a correlação da intensificação da temperatura do ar em áreas urbanas em resposta a um evento extremo de El Niño é escassa, principalmente no que se refere à cidade do Rio de Janeiro. Assim, o objetivo deste estudo visa quantificar as mudanças ocorridas na temperatura do ar (máxima e mínima) na cidade do Rio de Janeiro e o desvio ocasionado às temperaturas extremas durante um evento de El Niño intenso. Os dados de temperatura do ar utilizados referem-se às normais climatológicas nos períodos climatológicos de 1961-1990 e 1980-2010, comparados entre si, e posteriormente, comparou-se as normais climatológicas do período de 1980-2010 com as do El Niño intenso de 2015-2016. Para a análise, dados de temperatura mínima e máxima do ar em uma escala mensal foram comparados. As médias mensais das temperaturas em análise foram submetidas ao ajuste do coeficiente de correlação de Pearson, ao teste t de Student e ao teste de Kolmogorov-Smirnov. Os resultados mostraram um aumento médio na temperatura do ar mínima (máxima) de +0,66 °C e +0,73 °C (+1,21 °C e +0,90 °C), respectivamente entre os períodos climatológicos e o último período climatológico com o evento El Niño intenso, entretanto, sem diferença estatística para o aumento da média e de sua distribuição. A B S T R A C TUrbanization process has potential to change the thermal and aerodynamic characteristics of large urban centers surface, allowing the increase of air temperature. However, correlation of air temperature intensification in urban areas in response to an extreme event of El Niño is scarce, especially in relation to the city of Rio de Janeiro. Thus, the objective of this study is to quantify the changes occurred in the air temperature (maximum and minimum) in the city of Rio de Janeiro and the deviation caused to extreme temperatures during an intense event of El Niño. Data of air temperature data refer to the climatological normals in the periods of 1961-1990 and 1980-2010, and intense event of El Niño occurred in 2015-2016. For the analysis, minimum and maximum air temperature data on a monthly scale were compared. Monthly mean values of the air temperature under analysis were adjusted to the Pearson correlation coefficient, Student's t-test and Kolmogorov-Smirnov test. The results showed a mean increase in minimum (maximum) air temperature of +0.66 °C and +0.73 °C (+1.21 °C and +0.90 °C), respectively between the climatological periods and the last climatological period with the intense event of El Niño, however, with no statistical difference for the increase of the mean and its distribution.Keywords: Urban climate, ENSO, air temperature.

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

10.5194/hess-2017-93 ◽

2017 ◽

Cited By ~ 3

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.

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CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.720747 ◽

2021 ◽

Vol 9 ◽

Author(s):

Daniel Fenner ◽

Benjamin Bechtel ◽

Matthias Demuzere ◽

Jonas Kittner ◽

Fred Meier

Keyword(s):

Quality Control ◽

Air Temperature ◽

World Wide ◽

Meteorological Data ◽

Urban Climate ◽

Specific Humidity ◽

Data Sets ◽

User Friendliness ◽

Weather Stations ◽

Further Development

In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

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A comment on temperature measurement at automatic weather stations in Australia

Journal of Southern Hemisphere Earth System Science ◽

10.1071/es19010 ◽

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.

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Evaluasi Data TerraClimate Dalam Mengestimasi Suhu Udara Di Provinsi Papua Barat

Rona Teknik Pertanian ◽

10.17969/rtp.v14i1.19241 ◽

2021 ◽

Vol 14 (1) ◽

pp. 15-25

Author(s):

Baso Daeng ◽

Arif Faisol

Keyword(s):

Air Temperature ◽

Research Unit ◽

Mean Square ◽

Data Recording ◽

Climate Research ◽

West Papua ◽

Weather Stations ◽

Papua Barat ◽

Maximum Air Temperature ◽

Minimum Air Temperature

Abstrak. Terra Climate merupakan seperangkat data iklim yang mengkombinasikan antara data WorldClim, Climate Research Unit (CRU), dan Japanese 55-year Reanalysis (JRA 55). TerraClimate menyediakan data iklim bulanan tahun 1958 – 2019 pada resolusi spasial ~4 km. Penelitian ini bertujuan untuk mengevaluasi data TerraClimate dalam mengestimasi suhu udara di Provinsi Papua Barat. Data yang digunakan pada penelitian ini adalah data TerraClimate dan data suhu udara perekaman tahun 1996 – 2019 yang diperoleh dari automatic weather stations (AWS) Rendani – Kabupaten Manokwari, AWS Jefman – Kabupaten Raja Ampat, AWS Torea – Kabupaten Fakfak, dan AWS Kaimana – Kabupaten Kaimana. Data TerraClimate dievaluasi dengan dibandingkan data AWS menggunakan metode point to pixel berdasarkan 5 (lima) parameter statistik, yaitu mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (PBIAS), dan koefisien korelasi Pearson (r). Hasil penelitian menunjukkan bahwa data TerraClimate cenderung overestimated dalam mengestimasi suhu udara minimum bulanan dan cenderung underestimated dalam mengestimasi suhu udara maksimum bulanan di Provinsi Papua Barat. Namun TerraClimate memiliki akurasi yang sangat baik dalam mengestimasi suhu udara bulanan di Provinsi Papua Barat dengan nilai ME= 0,87 oC, RMSE = 1,19 oC, RBIAS = 0,04, dan PBIAS = 3,71 dalam mengestimasi suhu udara minimum, dan ME = 0,54 oC, RMSE = 0,88 oC, RBIAS = 0,02, dan PBIAS = 1,79 dalam mengestimasi suhu udara maksimum. Disamping itu TerraClimate memiliki korelasi yang sedang terhadap data AWS nilai r = 0,40 - 0,68. Sehingga TerraClimate dapat digunakan sebagai solusi alternatif untuk penyedia data suhu udara di Provinsi Papua Barat.An Evaluation of TerraClimate Data in Estimating Monthly Air Temperature in West PapuaAbstract. TerraClimate is a climate dataset that combines WorldClim data, Climate Research Unit (CRU) data, and Japanese 55-year Reanalysis (JRA 55) data at ~4 km spatial resolution. TerraClimate provides monthly climate data from 1958 to recent years. This research aims to evaluate the TerraClimate data in estimating monthly air temperature in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are TerraClimate data and AWS data recording from 1996 to 2019 obtained from AWS Rendani – Manokwari, AWS Jefman – Raja Ampat, AWS Torea – Fakfak, and AWS Kaimana – Kaimana. TerraClimate data were evaluated using the Point to Pixel method based on 5 (five) statistical parameters i.e., mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (RBIAS), and Pearson correlation coefficient (r). The research showed that TerraClimate is overestimated in estimating monthly minimum air temperature and underestimated in estimating monthly maximum air temperature in West Papua. However, TerraClimate and has very good accuracy in estimating the monthly temperature in West Papua with ME = 0.87 oC, RMSE = 1.19 oC, RBIAS = 0.04, and PBIAS = 3.71 in estimating monthly minimum air temperature, and ME=0.54 oC, RMSE = 0.88 oC, RBIAS = 0.02, PBIAS = 1.79 in estimating monthly maximum air temperature. Besides, TerraClimate data has a moderate correlation with AWS data in estimating monthly air temperature with r= 0.40 - 0.68. Therefore, TerraClimate can be used as an alternative solution for providing air temperature data in West Papua.

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Construção de cenários futuros da temperatura máxima do ar: Capitais do Nordeste Brasileiro

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v14.4.p2427-2445 ◽

2021 ◽

pp. 2427-2445

Author(s):

Ana Carla dos Santos Gomes ◽

Maytê Duarte Leal Coutinho ◽

Fábio de Paula Viana ◽

Losany Branches Viana ◽

Sivaldo Filho Seixas Tavares ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Wind Speed ◽

Air Temperature ◽

Temperature Data ◽

Northeastern Brazil ◽

Future Scenarios ◽

General Terms ◽

Dynamic Regression Model ◽

Maximum Air Temperature

This research aims to analyze and estimate future scenarios of maximum air temperature in the capitals of northeastern Brazil, in order to highlight the importance of climate change today and in the future. For this, rainfall, wind speed, relative humidity and maximum air temperature data were used by the database meteorological activities of the National Institute of Meteorology, of the nine capitals of the northeastern region of Brazil from 1980 to 2019, and the dynamic regression technique that combines the dynamics of time series and the effect of explanatory variables.The main results showed that the dynamic regression model satisfactorily adjusted the association between meteorological variables.Trend (without lag) and seasonality (lag) functions were considered in all capitals, presenting the occurrence of different lags according to the capital and the variable. Thus, the highest temperatures among the capitals analyzed occurred in Teresina/PI and the least high, in Salvador/BA. In general terms, the optimistic scenarios (C1) presented temperature between 32.5 and 35 ºC, the pessimists (C2) between 37.5 ºC and extremes (C3) 35 and 39 ºC, evidencing that all future scenarios present danger to the population. It is expected that the results obtained can help public policies.

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GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

10.5194/essd-2021-442 ◽

2021 ◽

Author(s):

Qian He ◽

Ming Wang ◽

Kai Liu ◽

Kaiwen Li ◽

Ziyu Jiang

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Air Temperature ◽

Spatial Interpolation ◽

Gaussian Process Regression ◽

Temperature Data ◽

Air Temperatures ◽

Maximum Air Temperature ◽

Maximum Minimum

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0230.1 ◽

2015 ◽

Vol 16 (6) ◽

pp. 2463-2480 ◽

Cited By ~ 20

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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