Abstract
We present a method for enhancing the spatial resolution of 2 m temperature (T2m) estimates. The method is based on operational forecast data supplied by the European Centre for Medium Range Weather Forecast. From the hourly and monthly average 2-meter temperatures a vertical gradient is determined by linear fitting to the temperature data in larger areas of 1º x 1º or 2° x 2° . Validation against data from more than 8000 meteorological stations worldwide shows that the estimates of annual average temperature at these points becomes significantly more accurate when applying the vertical gradients to correct the local temperature estimates to the elevation of the stations. When the elevation difference between forecast and station is larger than 300 m, the overall mean absolute deviation of the individual stations bias values decreases from 3.44 to 1.02 º C and the root mean square deviation decreases from 4.11 to 1.42 ºC. The gradients have also been applied to the ERA-Interim reanalysis data and the validation results are similar. The vertical temperature gradients will be useful for studies in many fields, including renewable energy and the study of energy performance of buildings.
Highlights
Many technical applications need data on outdoor air temperature at near-ground level, often called2-meter temperature from the standard height of meteorological measurements
Building on an idea proposed by Kunz et al [23], who obtained a vertical gradient of surface air temperature for a region (Switzerland) by linear regression using data from a number of ground stations located at different elevations, we propose to calculate the time-varying temperature gradient using the high-resolution forecast data available from European Centre for Medium Range Weather Forecast (ECMWF) by applying a linear regression procedure
Using the same criteria |Zs − Zf | < 100 m, the number of non-SYNOP stations is 3133. For these stations we found gMBD = +0.006 ◦ C, while gMAB = 0.54 ◦ C and gRMSD = 0.72 ◦ C
Summary
Many technical applications need data on outdoor air temperature at near-ground level, often called2-meter temperature from the standard height of meteorological measurements. The availability of 2 m-temperature measurements varies strongly: while some regions have a rather dense network of measurement stations, other regions in the world have very few. If the 2 m-temperature varies strongly over short distances, which happens especially if there are large variations in elevation, even measurement stations a short distance away may not be representative of local conditions. Meteorological models are a possible tool to overcome the problem of regions with very sparse data both through reanalysis and operational products, either on a regional or global scale. Reanalysis products such as European Centre for Medium Range Weather Forecast (ECMWF)
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