1. Downscaling NLDAS-2 daily maximum air temperatures using MODIS land surface temperatures
- Author
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Tabassum Z. Insaf, William L. Crosson, and Mohammad Z. Al-Hamdan
- Subjects
010504 meteorology & atmospheric sciences ,Land surface temperature ,0211 other engineering and technologies ,02 engineering and technology ,Atmospheric sciences ,01 natural sciences ,Remote Sensing ,Data assimilation ,Medicine and Health Sciences ,Surface Energy ,Public and Occupational Health ,Geographic Areas ,Multidisciplinary ,Energy demand ,Geography ,Applied Mathematics ,Simulation and Modeling ,Physics ,Temperature ,Surface Temperature ,Condensed Matter Physics ,Optical Equipment ,Air temperature ,Physical Sciences ,Engineering and Technology ,Medicine ,Seasons ,Algorithms ,Research Article ,Urban Areas ,Environmental Monitoring ,Downscaling ,Surface Properties ,Science ,Materials Science ,Material Properties ,Equipment ,Research and Analysis Methods ,Cities ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Advection ,Elevation ,Prisms ,13. Climate action ,Earth Sciences ,Spatial ecology ,Environmental science ,Mathematics - Abstract
We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation.
- Published
- 2020