Cited by 142Meteorological Phenomena and SimulationsEnvironmental Monitoring and Data ManagementClimate variability and models

The DOI matches its Crossref record.

Elevation correction of ERA-Interim temperature data in complex terrain

Open access
Gao, L., Bernhardt, M., Schulz, K.·2012·Hydrology and Earth System Sciences

Published version of a preprint

Abstract

Abstract. Air temperature controls a large variety of environmental processes, and is an essential input parameter for land surface models, for example in hydrology, ecology and climatology. However, meteorological networks, which can provide the necessary information, are commonly sparse in complex terrains, especially in high mountainous regions. In order to provide temperature data in an adequate temporal and spatial resolution for local scale applications a new elevation correction method has been developed that is able to downscale 3-hourly ERA-Interim temperature data. The scheme is based on model internal vertical lapse rates derived from different ERA-Interim pressure levels and has been validated for twelve meteorological stations in the German and Swiss Alps. The method was also compared with two other statistical, lapse rate based correction approaches. The results indicate that the use of model internal ERA-Interim lapse rates can significantly improve the downscaling performance when compared to the standard procedure of using fixed lapse rates.

Overview

Integrity signals

Registry

Crossref · 10.5194/hess-16-4661-2012

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 40

  1. Barry, R. G.: Mountain Climatology and Past and Potential Future Climatic Changes in Mountain Regions – a Review, Mt Res. Dev., 12, 71–86, 1992. · DOI
  2. Bernhardt, M. and Schulz, K.: SnowSlide: A simple routine for calculating gravitational snow transport, Geophys. Res. Lett., 37, L11502, https://doi.org/10.1029/2010gl043086, 2010. · DOI
  3. Berrisford, P., Kallberg, P., Kobayashi, S., Dee, D., Uppala, S., Simmons, A. J., Poli, P., and Sato, H.: Atmospheric conservation properties in ERA-Interim, Q. J. Roy. Meteorol. Soc., 137, 1381–1399, https://doi.org/10.1002/Qj.864, 2011. · DOI
  4. Blandford, T. R., Humes, K. S., Harshburger, B. J., Moore, B. C., Walden, V. P., and Ye, H. C.: Seasonal and synoptic variations in near-surface air temperature lapse rates in a mountainous basin, J. Appl. Meteorol. Clim., 47, 249–261, https://doi.org/10.1175/2007jamc1565.1, 2008. · DOI
  5. Bolstad, P. V., Swift, L., Collins, F., and Regniere, J.: Measured and predicted air temperatures at basin to regional scales in the southern Appalachian mountains, Agr. Forest Meteorol., 91, 161–176, 1998. · DOI
  6. Cullen, R. M. and Marshall, S. J.: Mesoscale Temperature Patterns in the Rocky Mountains and Foothills Region of Southern Alberta, Atmos. Ocean, 49, 189–205, https://doi.org/10.1080/07055900.2011.592130, 2011. · DOI
  7. Dee, D. P.: Bias and data assimilation, Q. J. Roy. Meteorol. Soc., 131, 3323–3343, https://doi.org/10.1256/Qj.05.137, 2005. · DOI
  8. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/Qj.828, 2011. · DOI · Paper page
  9. DeGaetano, A. T. and Belcher, B. N.: Spatial interpolation of daily maximum and minimum air temperature based on meteorological model analyses and independent observations, J. Appl. Meteorol. Clim., 46, 1981–1992, https://doi.org/10.1175/2007JAMC1536.1, 2007. · DOI
  10. Dodson, R. and Marks, D.: Daily air temperature interpolated at high spatial resolution over a large mountainous region, Clim. Res., 8, 1–20, 1997. · DOI
  11. Gruber, S.: Derivation and analysis of a high-resolution estimate of global permafrost zonation, The Cryosphere, 6, 221–233, https://doi.org/10.5194/tc-6-221-2012, 2012. · DOI · Paper page
  12. Hamlet, A. F. and Lettenmaier, D. P.: Production of temporally consistent gridded precipitation and temperature fields for the continental United States, J. Hydrometeorol., 6, 330–336, 2005. · DOI
  13. Haylock, M. R., Hofstra, N., Tank, A. M. G. K., Klok, E. J., Jones, P. D., and New, M.: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008jd010201, 2008. · DOI
  14. Ishida, T. and Kawashima, S.: Use of Cokriging to Estimate Surface Air-Temperature from Elevation, Theor. Appl. Climatol., 47, 147–157, 1993. · DOI
  15. Kunkel, E. K.: Simple Procedures for Extrapolation of Humidity Variables in the Mountainous Western United States, J. Climate, 2, 656–669, 1989. · DOI
  16. Laughlin, G. P.: Minimum Temperature and Lapse-Rate in Complex Terrain – Influencing Factors and Prediction, Arch. Meteor. Geophy. B, 30, 141–152, 1982. · DOI
  17. Liston, G. E. and Elder, K.: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet), J. Hydrometeorol., 7, 217–234, 2006. · DOI
  18. Liston, G. E., Hiemstra, C. A., Elder, K., and Cline, D. W.: Mesocell Study Area Snow Distributions for the Cold Land Processes Experiment (CLPX), J. Hydrometeorol., 9, 957–976, https://doi.org/10.1175/2008jhm869.1, 2008. · DOI
  19. Lundquist, J. D. and Cayan, D. R.: Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California, J. Geophys. Res.-Atmos., 112, D11124, https://doi.org/10.1029/2006jd007561, 2007. · DOI
  20. Mahrt, L., Vickers, D., Nakamura, R., Soler, M. R., Sun, J. L., Burns, S., and Lenschow, D. H.: Shallow drainage flows, Bound.-Lay. Meteorol., 101, 243–260, 2001. · DOI
  21. Mahrt, L.: Variation of surface air temperature in complex terrain, J. Appl. Meteorol. Clim., 45, 1481–1493, 2006. · DOI
  22. Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themessl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation Downscaling under Climate Change: Recent Developments to Bridge the Gap between Dynamical Models and the End User, Rev. Geophys., 48, Rg3003, https://doi.org/10.1029/2009rg000314, 2010. · DOI
  23. Marshall, S. J., Sharp, M. J., Burgess, D. O., and Anslow, F. S.: Near-surface-temperature lapse rates on the Prince of Wales Icefield, Ellesmere Island, Canada: implications for regional downscaling of temperature, Int. J. Climatol., 27, 385–398, https://doi.org/10.1002/Joc.1396, 2007. · DOI · Paper page
  24. Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., and Nijssen, B.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States, J. Climate, 15, 3237–3251, 2002. · DOI
  25. Mernild, S. H., Liston, G. E., Hiemstra, C. A., Steffen, K., Hanna, E., and Christensen, J. H.: Greenland Ice Sheet surface mass-balance modelling and freshwater flux for 2007, and in a 1995–2007 perspective, Hydrol. Process., 23, 2470–2484, https://doi.org/10.1002/Hyp.7354, 2009. · DOI
  26. Minder, J. R., Mote, P. W., and Lundquist, J. D.: Surface temperature lapse rates over complex terrain: Lessons from the Cascade Mountains, J. Geophys. Res.-Atmos., 115, D14122, https://doi.org/10.1029/2009jd013493, 2010. · DOI · Paper page
  27. Mokhov, I. I. and Akperov, M. G.: Tropospheric lapse rate and its relation to surface temperature from reanalysis data, Izv Atmos. Ocean Phy.+, 42, 430–438, https://doi.org/10.1134/S0001433806040037, 2006. · DOI
  28. Mooney, P. A., Mulligan, F. J., and Fealy, R.: Comparison of ERA-40, ERA-Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland, Int. J. Climatol., 31, 545–557, https://doi.org/10.1002/Joc.2098, 2011. · DOI
  29. Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970. · DOI
  30. Nieto, H., Sandholt, I., Aguado, I., Chuvieco, E., and Stisen, S.: Air temperature estimation with MSG-SEVIRI data: Calibration and validation of the TVX algorithm for the Iberian Peninsula, Remote Sens. Environ., 115, 107–116, https://doi.org/10.1016/j.rse.2010.08.010, 2011. · DOI
  31. Pages, M. and Miro, J. R.: Determining temperature lapse rates over mountain slopes using vertically weighted regression: a case study from the Pyrenees, Meteorol. Appl., 17, 53–63, https://doi.org/10.1002/Met.160, 2010. · DOI
  32. Pepin, N. C. and Seidel, D. J.: A global comparison of surface and free-air temperatures at high elevations, J. Geophys. Res.-Atmos., 110, D03104, https://doi.org/10.1029/2004JD005047, 2005. · DOI
  33. Prihodko, L. and Goward, S. N.: Estimation of air temperature from remotely sensed surface observations, Remote Sens. Environ., 60, 335–346, 1997. · DOI
  34. Prince, S. D., Goetz, S. J., Dubayah, R. O., Czajkowski, K. P., and Thawley, M.: Inference of surface and air temperature, atmospheric precipitable water and vapor pressure deficit using Advanced Very High-Resolution Radiometer satellite observations: comparison with field observations, J. Hydrol., 213, 230–249, 1998. · DOI
  35. Regniere, J.: Generalized approach to landscape-wide seasonal forecasting with temperature-driven simulation models, Environ. Entomol., 25, 869–881, 1996. · DOI
  36. Rolland, C.: Spatial and seasonal variations of air temperature lapse rates in Alpine regions, J. Climate, 16, 1032–1046, 2003. · DOI
  37. Simmons, A. J., Willett, K. M., Jones, P. D., Thorne, P. W., and Dee, D. P.: Low-frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets, J. Geophys. Res.-Atmos., 115, D01110, https://doi.org/10.1029/2009jd012442, 2010. · DOI
  38. Stahl, K., Moore, R. D., Floyer, J. A., Asplin, M. G., and McKendry, I. G.: Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density, Agr. Forest Meteorol., 139, 224–236, 2006. · DOI
  39. Vicente-Serrano, S. M., Saz-Sanchez, M. A., and Cuadrat, J. M.: Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature, Climate Res., 24, 161–180, 2003. · DOI
  40. Wörlen, C., Schulz, K., Huwe, B., and Eiden, R.: Spatial extrapolation of agrarmeteorological variables, Agric. For. Meteorol., 94, 233–242, 1999. · DOI

Cited by

Sorted by citations

Metadata

Authors
Gao, L., Bernhardt, M., & Schulz, K.
Affiliations
Ludwig-Maximilians-Universität München (DE)
Venue
Hydrology and Earth System Sciences
Published
2012-12-17
Volume
16
Issue
12
Pages
4661-4673
ISSN
  • 1027-5606 (ISSN-L)
  • 1607-7938
OpenAlex
W2001883352
Cited by
142
References
0
Citations by year
2026: 6 · 2025: 6 · 2024: 7 · 2023: 9 · 2022: 19 · 2021: 26 · 2020: 10 · 2019: 14 · 2018: 12 · 2017: 9 · 2016: 12 · 2015: 4 · 2014: 4 · 2013: 4
FWCI
2.62
Type
journal article
Language
en
Open access
Locations
  • Hydrology and earth system sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Monitoring and Data Management
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Keywords
elevation correction, ERA-Interim reanalysis, air temperature, complex terrain, mountain meteorology, downscaling, land surface model, high mountains, Alps

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review