scholarly journals A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

2015 ◽  
Vol 7 (5) ◽  
pp. 6005-6025 ◽  
Author(s):  
Renhua Zhang ◽  
Yuan Rong ◽  
Jing Tian ◽  
Hongbo Su ◽  
Zhao-Liang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vapor Pressure ◽  
Air Temperature ◽  
Regional Scale ◽  
Surface Air Temperature

scholarly journals Estimating the Surface Air Temperature by Remote Sensing in Northwest China Using an Improved Advection-Energy Balance for Air Temperature Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jing Tian ◽  
Weizhen Wang
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Air Temperature ◽  
Driving Force ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Absolute Error ◽  
Northwest China ◽  
Absolute Deviation ◽  
Distance Weighting

To estimate the surface air temperature by remote sensing, the advection-energy balance for the surface air temperature (ADEBAT) model is developed which assumes the surface air temperature is driven by the local driving force and the advective driving force. The local driving force produces a local surface air temperature whereas the advective driving force changes it by adding an exotic air temperature. An advection factorfis defined to measure the quantity of the exotic air brought by the advection. Since thefis determined by the advection, this paper improves it to a regional scale by using the Inverse Distance Weighting (IDW) method whereas the original ADEBAT model uses a constant offfor a block of area. Results retrieved by the improved ADEBAT (IADEBAT) model are evaluated and comparison was made with the in situ measurements, with anR2(correlation coefficient) of 0.77, an RMSE (Root Mean Square Error) of 0.31 K, and a MAE (Mean Absolute Error) of 0.24 K. The evaluation shows that the IADEBAT model has higher accuracy than the original ADEBAT model. Evaluations together with at-test of the MAD (Mean Absolute Deviation) reveal that the IADEBAT model has a significant improvement.

Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

2015 ◽  
Vol 16 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Sanaz Moghim ◽  
Andrew Jay Bowen ◽  
Sepideh Sarachi ◽  
Jingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Production Model ◽  
Integral Model ◽  
Near Surface ◽  
Surface Heat Fluxes

Abstract A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

A Remote-Sensing Method of Selecting Reference Stations for Evaluating Urbanization Effect on Surface Air Temperature Trends

2011 ◽  
Vol 24 (13) ◽  
pp. 3179-3189 ◽  
Author(s):  
Yuyu Ren ◽  
Guoyu Ren
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Image Data ◽  
Temperature Series ◽  
Urbanization Effect ◽  
Temperature Reference ◽  
Weather Stations ◽  
Reference Climate

Abstract In the global lands, the bias of urbanization effects still exits in the surface air temperature series of many city weather stations to a certain extent. Reliable reference climate stations need to be selected for the detection and correction of the local manmade warming bias. The underlying image data of remote sensing retrieval is adopted in this study to obtain the spatial distribution of surface brightness temperature, and the surface air temperature reference stations are determined based on the locations of the weather stations in the remote sensing surface thermal fields. Among the 672 national reference climate stations and national basic weather stations of mainland China, for instance, 113 surface air temperature reference stations are selected for applying this method. Compared with the average surface air temperature series of the reference stations obtained by a more sophisticated method developed in China, this method is proven to be robust and applicable, and can be adopted for the evaluation and adjustment study on the urbanization bias of the currently used air temperature records of surface climate stations in the global lands.

scholarly journals Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale

2017 ◽  
Vol 56 (3) ◽  
pp. 803-814 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Jing Tian ◽  
Renhua Zhang ◽  
Weizhen Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Air Temperature ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
The Other ◽  
Linear Regression Method ◽  
Estimation Errors ◽  
Remote Sensing Methods

AbstractSurface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods—the temperature–vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)—have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1°C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.

scholarly journals Estimating urban heat island effects on near-surface air temperature records of Uccle (Brussels, Belgium): an observational and modeling study

2011 ◽  
Vol 6 (1) ◽  
pp. 27-34 ◽  
Author(s):  
R. Hamdi ◽  
H. Van de Vyver
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Urban Bias ◽  
Near Surface ◽  
Urban Warming ◽  
Urban Heat ◽  
Weather Stations

Abstract. In this letter, the Brussels's urban heat island (UHI) effect on the near-surface air temperature time series of Uccle (the national suburban recording station of the Royal Meteorological Institute of Belgium) was estimated between 1955 and 2006 during the summer months. The UHI of Brussels was estimated using both ground-based weather stations and remote sensing imagery combined with a land surface scheme that includes a state-of-the-art urban parameterization, the Town Energy Balance scheme. Analysis of urban warming based on the remote sensing method reveals that the urban bias on minimum air temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The summer-mean urban bias on the mean air temperature is 0.8 °C (0.9 °C ground-based observed). The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of an ground-observational approach.

scholarly journals Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling

2019 ◽  
Vol 12 (4) ◽  
pp. 74-95 ◽  
Author(s):  
Mikhail I. Varentsov ◽  
Mikhail Y. Grishchenko ◽  
Hendrik Wouters
Keyword(s):  
Remote Sensing ◽  
Spatial Structure ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Atmospheric Model ◽  
Heat Island ◽  
Near Surface ◽  
Urban Heat

This study compares three popular approaches to quantify the urban heat island (UHI) effect in Moscow megacity in a summer season (June-August 2015). The first approach uses the measurements of the near-surface air temperature obtained from weather stations, the second is based on remote sensing from thermal imagery of MODIS satellites, and the third is based on the numerical simulations with the mesoscale atmospheric model COSMO-CLM coupled with the urban canopy scheme TERRA_URB. The first approach allows studying the canopy-layer UHI (CLUHI, or anomaly of a near- surface air temperature), while the second allows studying the surface UHI (SUHI, or anomaly of a land surface temperature), and both types of the UHI could be simulated by the atmospheric model. These approaches were compared in the daytime, evening and nighttime conditions. The results of the study highlight a substantial difference between the SUHI and CLUHI in terms of the diurnal variation and spatial structure. The strongest differences are found at the daytime, at which the SUHI reaches the maximal intensity (up to 10°С) whereas the CLUHI reaches the minimum intensity (1.5°С). However, there is a stronger consistency between CLUHU and SUHI at night, when their intensities converge to 5–6°С. In addition, the nighttime CLUHI and SUHI have similar monocentric spatial structure with a temperature maximum in the city center. The presented findings should be taken into account when interpreting and comparing the results of UHI studies, based on the different approaches. The mesoscale model reproduces the CLUHI-SUHI relationships and provides good agreement with in situ observations on the CLUHI spatiotemporal variations (with near-zero biases for daytime and nighttime CLUHI intensity and correlation coefficients more than 0.8 for CLUHI spatial patterns). However, the agreement of the simulated SUHI with the remote sensing data is lower than agreement of the simulated CLUHI with in situ measurements. Specifically, the model tends to overestimate the daytime SUHI intensity. These results indicate a need for further in-depth investigation of the model behavior and SUHI–CLUHI relationships in general.

scholarly journals DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

2021 ◽  
Author(s):  
Dan Lunt ◽  
Keyword(s):  
Boundary Conditions ◽  
Air Temperature ◽  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Model Parameters ◽  
Global Mean

<div> <div> <div>We present results from an ensemble of eight climate models, each of which has carried out simulations of theearly Eocene climate optimum (EECO, ∼50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO<sub>2</sub> boundary conditions contribute between 3 and 5<sup>o</sup>C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show in general reduced spread of global mean surface temperature response across the ensemble for a given atmospheric CO<sub>2</sub> concentration, and an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with preindustrial arises mostly from decreases in emissivity due to the elevated CO<sub>2</sub> (and associated water vapour and long-wave cloud feedbacks), whereas in terms of the meridional temperature gradient, the reduction in the Eocene is primarily due to emissivity and albedo changes due to the non-CO<sub>2</sub> boundary conditions (i.e. removal of the Antarctic ice sheet and changes in vegetation). Three of the models (CESM, GFDL, and NorESM) show results that are consistent with the proxies in terms of global mean temperature, meridional SST gradient, and CO<sub>2</sub>, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale the models lack skill. In particular, in the southwest Pacific, the modelled anomalies are substantially less than indicated by the proxies; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxiesor models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols</div> </div> </div>

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