Abstract
Surface air temperature (Ta) is important for a wide variety of applications that require real-time monitoring of environmental change; yet, few studies have attempted to deliver such products at a resolution appropriate for local-scale analysis. We investigated the feasibility of producing hourly Ta maps in near real time at a spatial grid resolution of 80 m across the state of Tasmania (area: 68,401 km2), Australia. We first assimilate 267 non-telemetered logger recording sites for data recorded in the 1-year period from September 2013 to 2014 and statistically calibrate them to 43-telemetered Australian Bureau of Meteorology (BoM) weather station sites for real-time application. This was evaluated in the following year using held back recordings in addition to evaluating regression trees (RT) and thin plate splines (TPS) to interpolate the hourly Ta estimates. For real-time operational mapping, the system was fully automated in the R programming language and hosted on a cloud-based computing platform to assess performance over a 7-day period in February 2020. The calibration procedure yielded accurate results with the root mean square error (RMSE) ranging between 1.33 °C in summer and 1.29 °C in winter. The TPS interpolation method was optimal in summer with an RMSE of 1.35 °C compared to RT with 1.39 °C; however, RT performed better in winter with an RMSE of 1.34 °C compared to 1.42 °C. The mapping system was capable of producing spatial outputs within the hour of the BoM observations becoming available with the TPS interpolation proving to be more efficient at producing outputs in a timelier manner.
Similar content being viewed by others
References
Aadhar S, Mishra V (2017) High-resolution near real-time drought monitoring in South Asia. Scientific Data 4:170145. https://doi.org/10.1038/sdata.2017.145
Australian Bureau of Statistics (2018) Australian Bureau of Statistics webiste. (Australian Government). http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/4671.0main+features172012. Accessed 10/08/2018
Australian CliMate Development Team (2016) Australian CliMate: Climate analysis for decision makers. University of Southern Queensland, Australia. https://climateapp.net.au/.
Böhner J, Antonić O (2009) Land-surface parameters specific to topo-climatology. Developments in Soil Science 33:195–226
Bureau of Meteorology (2015) Bureau of Meteorology website. (Australian Government). http://www.bom.gov.au/climate/current/statement_archives.shtml. Accessed 28 August 2015
Bureau of Meteorology (2018) Bureau of Meteorology website. (Australian Government). http://www.bom.gov.au/climate/cdo/about/airtemp-measure.shtml. Accessed 11 August
Cheng AR, Lee TH, Ku HI, Chen YW (2016) Quality control program for real-time hourly temperature observation in Taiwan. Journal of Atmospheric and Oceanic Technology 33:953–976. https://doi.org/10.1175/jtech-d-15-0005.1
Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Dodson R, Marks D (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate Research 8:1–20. https://doi.org/10.3354/cr008001
Gallant JC, Dowling TI (2003) A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research 39
Gallant J, Dowling TI, Read A, Wilson N, Tickle P (2011) 1 second SRTM derived digital elevation models user guide. Geoscience Australia, Canberra, p 106
Grabs T, Seibert J, Bishop K, Laudon H (2009) Modeling spatial patterns of saturated areas: a comparison of the topographic wetness index and a dynamic distributed model. Journal of Hydrology 373:15–23
Guisan A, Weiss SB, Weiss AD (1999) GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143:107–122. https://doi.org/10.1023/a:1009841519580
Hamill TM, Bates GT, Whitaker JS, Murray DR, Fiorino M, Galarneau TJ Jr, Zhu Y, Lapenta W (2013) NOAA’s second-generation global medium-range ensemble reforecast dataset. Bulletin of the American Meteorological Society 94:1553–1565. https://doi.org/10.1175/bams-d-12-00014.1
Hijmans RJ, van Etten J (2012) raster: Geographic analysis and modeling with raster data. R package version 2.0-12. http://CRAN.R-project.org/package=raster
Hutchinson M (1991) The application of thin plate smoothing splines to continent-wide data assimilation. Bureau of Meteorology, Melbourne
Jarvis CH, Stuart N (2001a) A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part I: The selection of “guiding” topographic and land cover variables. Journal of Applied Meteorology 40:1060–1074
Jarvis CH, Stuart N (2001b) A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method. Journal of Applied Meteorology 40:1075–1084
Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software 16:309–330. https://doi.org/10.1016/S1364-8152(01)00008-1
Jenness J (2006) Topographic position index (tpi_jen.avx) extension for ArcView 3.x (v1.3a). Jenness Enterprises. http://www.jennessent.com/arcview/tpi.htm, Flagstaff, AZ
Jobst AM, Kingston DG, Cullen NJ, Sirguey P (2017) Combining thin-plate spline interpolation with a lapse rate model to produce daily air temperature estimates in a data-sparse alpine catchment. International Journal of Climatology 37:214–229. https://doi.org/10.1002/joc.4699
Jones D, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58. https://doi.org/10.22499/2.5804.003
Kidd D, Webb M, Malone B, Minasny B, McBratney A (2015) Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia. Geoderma Regional 6:7–21. https://doi.org/10.1016/j.geodrs.2015.08.005
Kuhn M, Weston S, Keefer C, Coulter N (2014) C code for Cubist by Ross Quinlan. Cubist: rule- and instance-based regression modeling. R package version 0.0.18. http://CRAN.R-project.org/package=Cubist
Lazzarini M, Marpu PR, Eissa Y, Ghedira H (2014) Toward a near real-time product of air temperature maps from satellite data and in situ measurements in arid environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7:3093–3104. https://doi.org/10.1109/JSTARS.2014.2320762
Legates DR, Willmott CJ (1990) Mean seasonal and spatial variability in global surface air temperature. Theoretical and Applied Climatology 41:11–21. https://doi.org/10.1007/bf00866198
Lin LIK (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255–268. https://doi.org/10.2307/2532051
Liu S, Su H, Tian J, Wang W (2018) An analysis of spatial representativeness of air temperature monitoring stations. Theoretical and Applied Climatology 132:857–865. https://doi.org/10.1007/s00704-017-2133-6
Mahrt L (2006) Variation of surface air temperature in complex terrain. Journal of Applied Meteorology and Climatology 45:1481–1493. https://doi.org/10.1175/jam2419.1
Minder JR, Mote PW, Lundquist JD (2010) Surface temperature lapse rates over complex terrain: lessons from the Cascade Mountains. Journal of Geophysical Research: Atmospheres 115. https://doi.org/10.1029/2009JD013493
Noi P, Degener J, Kappas M (2017) Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LST data. Remote Sensing 9:398
Nychka D, Furrer R, Paige J, Sain S (2017) fields: Tools for spatial data. R package version 10.3. https://github.com/NCAR/Fields. https://doi.org/10.5065/D6W957CT
Odeh IOA, McBratney AB, Chittleborough DJ (1995) Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67:215–226. https://doi.org/10.1016/0016-7061(95)00007-B
Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrological Processes 23:1437–1443. https://doi.org/10.1002/hyp.7266
Parton WJ, Logan JA (1981) A model for diurnal variation in soil and air temperature. Agricultural Meteorology 23:205–216. https://doi.org/10.1016/0002-1571(81)90105-9
Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30:683–691
Pouladi N, Møller AB, Tabatabai S, Greve MH (2019) Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging. Geoderma 342:85–92. https://doi.org/10.1016/j.geoderma.2019.02.019
Quinlan JR (1986) Induction of decision trees. Machine learning 1:81–106
Quinlan JR (1992) Learning with continuous classes. In: Proceedings of Australian Joint Conference on Artificial Intelligence, Singapore. World Scientific Press, pp 343-348
R Development Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria
Rolland C (2003) Spatial and seasonal variations of air temperature lapse rates in Alpine regions. Journal of Climate 16:1032–1046. https://doi.org/10.1175/1520-0442(2003)016<1032:sasvoa>2.0.co;2
Stahl K, Moore RD, Floyer JA, Asplin MG, McKendry IG (2006) Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agricultural and Forest Meteorology 139:224–236. https://doi.org/10.1016/j.agrformet.2006.07.004
Wang A, Zeng X (2013) Development of global hourly 0.5° land surface air temperature datasets. Journal of Climate 26:7676–7691. https://doi.org/10.1175/jcli-d-12-00682.1
Wang K, Sun J, Cheng G, Jiang H (2011) Effect of altitude and latitude on surface air temperature across the Qinghai-Tibet Plateau. Journal of Mountain Science 8:808–816. https://doi.org/10.1007/s11629-011-1090-2
Webb M, Hall A, Kidd D, Minasny B (2016) Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability. Theoretical and Applied Climatology 124:1145–1165. https://doi.org/10.1007/s00704-015-1461-7
Webb M, Pirie A, Kidd D, Minasny B (2018) Spatial analysis of frost risk to determine viticulture suitability in Tasmania, Australia. Australian Journal of Grape and Wine Research 24:219–233. https://doi.org/10.1111/ajgw.12314
Weiss A (2001) Topographic position and landforms analysis. In: Poster presentation, ESRI user conference. San Diego, CA
Whiteman CD, Allwine KJ, Fritschen LJ, Orgill MM, Simpson JR (1989) Deep valley radiation and surface energy budget microclimates. Part II: Energy Budget. Journal of Applied Meteorology 28:427–437. https://doi.org/10.1175/1520-0450(1989)028<0427:dvrase>2.0.co;2
Williams M, Cornford D, Bastin L, Jones R, Parker S (2011) Automatic processing, quality assurance and serving of real-time weather data. Computers & Geosciences 37:353–362. https://doi.org/10.1016/j.cageo.2010.05.010
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 11:345–354. https://doi.org/10.1109/JSTARS.2017.2787191
Zhu W, Lű A, Jia S (2013) Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sensing of Environment 130:62–73. https://doi.org/10.1016/j.rse.2012.10.034
Acknowledgments
This research has been supported by the Tasmanian Government Department of Primary Industries, Parks Water and Environment (DPIPWE) who allowed access to their temperature logger recordings database and computing infrastructure to conduct this research. This research was also supported by Tasmanian Partnership for Advanced Computing and by use of the Nectar Research Cloud. The Nectar Research Cloud is a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy (NCRIS).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Webb, M.A., Kidd, D. & Minasny, B. Near real-time mapping of air temperature at high spatiotemporal resolutions in Tasmania, Australia. Theor Appl Climatol 141, 1181–1201 (2020). https://doi.org/10.1007/s00704-020-03259-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-020-03259-4