scholarly journals Predicting spatiotemporally-resolved air temperature over Sweden from satellite data using an ensemble model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhihao Jin ◽  
Yiqun Ma ◽  
Lingzhi Chu ◽  
Yang Liu ◽  
Robert Dubrow ◽  
...  
2022 ◽  
Vol 204 ◽  
pp. 111960
Author(s):  
Zhihao Jin ◽  
Yiqun Ma ◽  
Lingzhi Chu ◽  
Yang Liu ◽  
Robert Dubrow ◽  
...  

Author(s):  
Marcos Ruiz-Álvarez ◽  
Francisco Alonso-Sarría ◽  
Francisco Gomariz-Castillo

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.


2010 ◽  
Vol 56 (198) ◽  
pp. 735-741 ◽  
Author(s):  
Lora S. Koenig ◽  
Dorothy K. Hall

AbstractCurrent trends show a rise in Arctic surface and air temperatures, including over the Greenland ice sheet where rising temperatures will contribute to increased sea-level rise through increased melt. We aim to establish the uncertainties in using satellite-derived surface temperature for measuring Arctic surface temperature, as satellite data are increasingly being used to assess temperature trends. To accomplish this, satellite-derived surface temperature, or land-surface temperature (LST), must be validated and limitations of the satellite data must be assessed quantitatively. During the 2008/09 boreal winter at Summit, Greenland, we employed data from standard US National Oceanic and Atmospheric Administration (NOAA) air-temperature instruments, button-sized temperature sensors called thermochrons and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument to (1) assess the accuracy and utility of thermochrons in an ice-sheet environment and (2) compare MODIS-derived LSTs with thermochron-derived surface and air temperatures. The thermochron-derived air temperatures were very accurate, within 0.1 ± 0.3°C of the NOAA-derived air temperature, but thermochron-derived surface temperatures were ∼3°C higher than MODIS-derived LSTs. Though surface temperature is largely determined by air temperature, these variables can differ significantly. Furthermore, we show that the winter-time mean air temperature, adjusted to surface temperature, was ∼11°C higher than the winter-time mean MODIS-derived LST. This marked difference occurs largely because satellite-derived LSTs cannot be measured through cloud cover, so caution must be exercised in using time series of satellite LST data to study seasonal temperature trends.


2020 ◽  
Author(s):  
Wei Wang ◽  
Cheng Liu ◽  
Lieven Clarisse ◽  
Martin Van Damme ◽  
Pierre-François Coheur ◽  
...  

Abstract. Atmospheric ammonia (NH3) plays an important role in the formation of fine particulate matter, leading to severe environmental degradation and human health issues. In this work, ground-based FTIR observations are used to obtain the total columns and vertical profiles of atmospheric NH3 at a measurement site in Hefei, China, from December 2016 to November 2018. The spatial distribution and temporal variation, seasonal trend, emission sources and potential sources of NH3 are analyzed. The time series of ammonia columns show that FTIR observations captured the seasonal cycle of NH3 over the two years of measurement, with a 22.14 % yr-1 annual increase rate over the Hefei site. We used IASI satellite data to compare with the FTIR data, and the correlation coefficients (R) between the two datasets are 0.86 and 0.78 for IASI-A and IASI-B, respectively. The results demonstrate the IASI data are in broad agreement with our FTIR data. To examine the contribution of traffic to NH3 columns, we analyze the relationship of NH3 columns with CO surface concentrations. NH3 columns show high correlation (R = 0.77) with CO concentrations in summer, indicating that the elevated NH3 columns are partly caused by urban emissions from vehicles. Further, high correlation of NH3 columns with air temperature is obvious from their diurnal variation during the observation period. In addition, the clear correlation between NH3 columns and air temperature in spring and autumn over Hefei, suggests that agriculture was indeed the main source of ammonia in spring and autumn. Furthermore, the back trajectories of air masses calculated by the HYSPLIT model confirmed that agriculture was the dominant source of ammonia in spring, autumn and winter, while urban anthropogenic emissions contributed to the high level of NH3 in summer over the Hefei site. The potential source areas influencing the NH3 columns were distributed in the local area of Hefei, the northern part of Anhui province, as well as Shangdong, Jiangsu and Henan provinces. This study helps to identify the emission sources and potential sources that contribute to NH3 columns over Hefei, a highly populated and polluted area. This is the first time that ground-based FTIR remote sensing of NH3 columns and comparison with satellite data are reported in China.


1991 ◽  
Vol 43 (4) ◽  
pp. 195-203 ◽  
Author(s):  
V. Caselles ◽  
M. J. L�pez Garc�a ◽  
J. Meli� ◽  
A. J. P�rez Cueva

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