Creating air temperature models for high- elevation desert areas using machine learning

Massoud Forooshani; Alexander Gegov; Nick Pepin; Mo Adda

doi:10.32890/jcia2023.2.1.1

Authors

Massoud Forooshani University of Portsmouth, United Kingdom
Alexander Gegov Technical University of Sofia, University of Portsmouth, United Kingdom
Nick Pepin University of Portsmouth, United Kingdom
Mo Adda University of Portsmouth, United Kingdom

DOI:

https://doi.org/10.32890/jcia2023.2.1.1

Keywords:

air temperature, desert, Kilimanjaro, machine learning, surface temperature

Abstract

The standard way to measure the air temperature (Ta) as the key variable in climate change studies is at 2m height above the surface at a fixed location (weather station). In contrast, the surface temperature (Ts) can be measured by satellites over large areas. Estimation of Ta from Ts is one potential way of overcoming shortages due to uneven or irregular distributions of weather stations. However, whether this is successful has not been assessed in high-elevation regions. This is particularly important in high-elevation regions. In this study, we estimate Ta in the high-elevation desert zone of Kilimanjaro (>4500m) using four models (five models including the benchmark model) with unique sets of inputs using five machine learning (ML) algorithms. Note that different combinations of Ta and Ts were tested as inputs to evaluate the potential of Ts as a proxy for Ta. The Root Mean Square Error (RMSE) for each model was compared with a benchmark model and ranked according to their RMSE. Similarly, models and algorithms were ranked in terms of reliability and consistency. Correspondingly, results were compared with the benchmark model. Three models out of four outperformed the benchmark model in the consistency ranking, while two out of four models outperformed the benchmark model in the reliability ranking. Therefore, ML algorithms are efficient tools for estimating Ta from Ts in this high-elevation desert environment. However, models using Ts only as inputs were not as accurate as models that used Ta from an earlier time period as one of the inputs. This highlights the amount of de-coupling between Ta and TS at high elevations, which provides a challenge for using Ts alone as a proxy for Ta in this zone.

References

Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., & Santos, A. (2012). Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124, 108–121. https://doi.org/10.1016/j.rse.2012.04.024

Bonissone, P. (n.d.). Adaptive Neural Fuzzy Inference Systems (ANFIS): Analysis and Applications. https://www.researchgate.net/profile/Alireza-Soloukdar/post/What-references-do-you-recommend-me-to-learn-ANFIS-for-forecasting-of-Organizational-systems-in-filed-of-Management/attachment/59d629f9c49f478072e9c800/AS%3A272527505059840%401441987028751/download/anfis.rpi04.pdf

Brieman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth Inc, 67.

Colombi, A., De Michele, C., Pepe, M., Rampini, A., & Michele, C. D. (2007). Estimation of daily mean air temperature from MODIS LST in Alpine areas. EARSeL eProceedings, 6(1), 38–46.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018

Hayati, M., & Mohebi, Z. (2007). Application of artificial neural networks for temperature forecasting. World Academy of Science, Engineering and Technology, 28(2), 275–279.

Hachem, S., Duguay, C., & Allard, M. (2012). Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain. The Cryosphere, 6(1), 51–69. https://doi.org/10.5194/tc-6-51-2012

Hemp, A. (2009). Climate change and its impact on the forests of Kilimanjaro. African Journal of Ecology, 47, 3–10. https://doi.org/10.1111/j.1365-2028.2008.01043.x

Hemp, A. (2005). Climate change-driven forest fires marginalize the impact of ice cap wasting on Kilimanjaro. Global Change Biology, 11(7), 1013–1023. https://doi.org/10.1111/j.1365-2486.2005.00968.x

Jang, J.-D., Viau, A., & Anctil, F. (2004). Neural network estimation of air temperatures from AVHRR data. International Journal of Remote Sensing, 25(21), 4541–4554. https://doi.org/10.1080/01431160310001657533

Jang, J.-S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. doi: 10.1109/21.256541

Kumari, K. A., Boiroju, N. K., Ganesh, T., & Reddy, P. R. (2012). Forecasting surface air temperature using neural networks. International Journal of Mathematics and Computer Applications Research, 3, 65–78.

Kumar, P. (2012). Minimum weekly temperature forecasting using ANFIS. Computer Engineering and Intelligent Systems, 3(5), 1–6.

Knime Analytics Platform version 4.3.4 (4.3.4). (2020). [Computer software]. KNIME AG.

Lin, C.-J., & Chang, C. (2001). LIBSVM: a library for support vector machines, 2001. Software Available at 10(1961189.1961199).

MATLAB version R2020a. (2020). The Mathworks, Inc. Mölg, T., Hardy, D. R., & Kaser, G. (2003). Solar-radiation-maintained glacier recession on Kilimanjaro drawn from combined ice-radiation geometry modeling. Journal of Geophysical Research: Atmospheres, 10, 4731.

Mote, P. W., & Kaser, G. (2007). The shrinking glaciers of Kilimanjaro: Can global warming be blamed? The Kibo ice cap, a” poster child” of global climate change, is being starved of snowfall and depleted by solar radiation. American Scientist, 95(4), 318–325. https://doi.org/10.1511/2007.66.318

Moninger, W. R., Davis, J., Dyer, R., Kittredge, R., McArthur, R., Murphy, A. H., & Racer, I. R. (1987). Summary of the first conference on Artificial Intelligence Research in Environmental Sciences (AIRIES). Bulletin of the American Meteorological Society, 68(7), 793–800. http://www.jstor.org/stable/26225427

McCann, D. W. (1992). A neural network short-term forecast of significant thunderstorms. Weather and Forecasting, 7(3), 525–534. https://doi.org/10.1175/1520-0434(1992)007%3C0525:ANNSTF%3E2.0.CO;2

Neter, J., Kutner, M., Wasserman, W., & Nachtsheim, C. J. (1996). Applied linear statistical models, 4th edition. McGraw-Hill.

Palazzi, E., Mortarini, L., Terzago, S., & Von Hardenberg, J. (2019). Elevation-dependent warming in global climate model simulations at high spatial resolution. Climate Dynamics, 52(5–6), 2685–2702. https://doi.org/10.1007/s00382-018-4287-z

Pepin, N., Deng, H., Zhang, H., Zhang, F., Kang, S., & Yao, T. (2019). An examination of temperature trends at high elevations across the Tibetan Plateau: The use of MODIS LST to understand patterns of elevation-dependent warming. Journal of Geophysical Research: Atmospheres, 124(11), 5738-5756. https://doi.org/10.1029/2018JD029798

Pepin, N., Maeda, E. E., & Williams, R. (2016). Use of remotely sensed land surface temperature as a proxy for air temperatures at high elevations: Findings from a 5000 m elevational transect across Kilimanjaro. Journal of Geophysical Research: Atmospheres, 121(17), 9998–10. https://doi.org/10.1002/2016JD025497

Pepin, N. C. (2004). Meteorological data for 22 sites across Kilimanjaro. University of Portsmouth.

Potter, C., & Coppernoll-Houston, D. (2019). Controls on land surface temperature in deserts of Southern California derived from MODIS satellite time series analysis, 2000 to 2018. Climate, 7(2), 32. https://doi.org/10.3390/cli7020032

Shen, S., & Leptoukh, G. G. (2011). Estimation of surface air temperature over central and eastern Eurasia from MODIS land surface temperature. Environmental Research Letters, 6(4), 045206. http://dx.doi.org/10.1088/1748-9326/6/4/045206

Schizas, C. N., Michaelides, S., Pattichis, C. S., & Livesay, R. (1991). Artificial neural networks in forecasting minimum temperature (weather). 1991 Second International Conference on Artificial Neural Networks, 112–114.

Shehzadex. (2021, 27 November). Support vector machine.png. https://commons.wikimedia.org/wiki/File:Kernel_yontemi_ile_veriyi_daha_fazla_dimensiyonlu_uzaya_tasima_islemi.png

Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Henderson, K. A., Brecher, H. H., Zagorodnov, V. S., et al. (2002). Kilimanjaro ice core records: Evidence of Holocene climate change in tropical Africa. Science, 298(5593), 589–593. https://doi.org/10.1126/science.1073198

Urban, M., Eberle, J., Hüttich, C., Schmullius, C., & Herold, M. (2013). Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the pan-Arctic Scale. Remote Sensing, 5(5), 2348–2367. https://doi.org/10.3390/rs5052348

Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114(2), 449–465. https://doi.org/10.1016/j.rse.2009.10.002

Xu, Y., Knudby, A., Shen, Y., & Liu, Y. (2018). Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 345–354. doi: 10.1109/JSTARS.2017.2787191

Zhao, D., Zhang, W., & Shijin, X. (2007). A neural network algorithm to retrieve near surface air temperature from lands at ETM+ imagery over the Hanjiang River Basin, China. 2007 IEEE International Geoscience and Remote Sensing Symposium, 1705–1708.

Zhou, C., & Wang, K. (2016). Land surface temperature over global deserts: Means, variability, and trends. Journal of Geophysical Research: Atmospheres, 121(24), 14–344. https://doi.org/10.1002/2016JD025410