REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses
In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-08-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/10/2905/2017/gmd-10-2905-2017.pdf |
| Summary: | In mountain areas, the use of coarse-grid reanalysis data for driving
fine-scale models requires downscaling of near-surface (e.g., 2 m high) air
temperature. Existing approaches describe lapse rates well but differ in how
they include surface effects, i.e., the difference between the simulated 2 m
and upper-air temperatures. We show that different treatment of surface
effects result in some methods making better predictions in valleys while
others are better in summit areas. We propose the downscaling method REDCAPP
(REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially
variable magnitude of surface effects. Results are evaluated with
observations (395 stations) from two mountain regions and compared with three
reference methods. Our findings suggest that the difference between
near-surface air temperature and pressure-level temperature (Δ<i>T</i>)
is a good proxy of surface effects. It can be used with a spatially variable
land-surface correction factor (LSCF) for improving downscaling results,
especially in valleys with strong surface effects and cold air pooling during
winter. While LSCF can be parameterized from a fine-scale digital elevation
model (DEM), the transfer of model parameters between mountain ranges needs
further investigation. |
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| ISSN: | 1991-959X 1991-9603 |