Dynamic downscaling of near-surface air temperature at the basin scale using WRF–a case study in the Heihe River Basin, China

Abstract

The spatial resolution of general circulation models (GCMs) is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model (WRF) is a next-generation, fully compressible, Euler non-hydrostatic mesoscale forecast model with a run-time hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/1 h air temperatures by daily observed data of China Meteorological Administration (CMA) stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R² between the WRF simulation and each station is more than 0.93; the absolute of meanbias error (MBE) for each station is less than 2°C; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R² is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2°C, the R² of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

Keywords

weather research and forecasting model / dynamic downscaling / surface air temperature / Heihe River Basin / Watershed Allied Telemetry Experimental Research Project

References

[1] Chen D, Achberger C, Räisänen J, Hellström C (2006). Using statistical downscaling to quantify the GCM-related uncertainty in regional climate change scenarios: a case study of Swedish precipitation. Adv Atmos Sci, 23(1): 54–60

[2] Collischonn W, Haas R, Andreolli I, Tucci C E M (2005). Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model. J Hydrol (Amst), 305(1–4): 87–98

[3] Cosgrove B A, Lohmann D, Mitchell K E, Houser P R, Wood E F, Schaake J C, Robock A, Marshall C, Sheffield J, Duan Q, Luo L, Higgins R W, Pinker R T, Tarpley J D, Meng J (2003). Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) Project. J Geophys Res, 108(D22): 1–12

[4] Hanssen-Bauer I, Achberger C, Benestad R E, Chen D, Førland E J (2005). Statistical downscaling of climate scenarios over Scandinavia. Clim Res, 29(3): 255–268

[5] Jasper K, Gurtz J, Lang H (2002). Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model. J Hydrol (Amst), 267(1–2): 40–52

[6] Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Jenne R, Joseph D (1996). The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc, 77(3): 437–471

[7] Kislter R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, Dool H, Jenne R, Fiorino M (2001). The NCEP/NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc, 82(2): 247–267

[8] Kunstmann H, Jung G, Wagner S, Clottey H (2008). Integration of atmospheric sciences and hydrology for the development of decision support systems in sustainable water management. Phys Chem Earth, 33(1–3): 165–174

[9] Kunstmann H, Stadler C (2005). High resolution distributed atmospheric-hydrologic modeling for Alpine catchments. J Hydrol (Amst), 314(1–4): 105–124

[10] Laprise R (1992). The Euler equation of motion with hydrostatic pressure as independent coordinate. Mon Weather Rev, 120(1): 197–207

[11] Leander R T, Buishand A (2007). Resampling of regional climate model output for the simulation of extreme river flows. J Hydrol (Amst), 332(3–4): 487–496

[12] Leander R, Buishand T A, van den Hurk B J J M, de Wit M J M (2008). Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output. J Hydrol (Amst), 351(3–4): 331–343

[13] Li X, Li X W, Li Z Y, Ma M, Wang J, Xiao Q, Liu Q, Che T, Chen E, Yan G, Hu Z, Zhang L, Chu R, Su P, Liu Q, Liu S, Wang J, Niu Z, Chen Y, Jin R, Wang W, Ran Y, Xin X, Ren H (2009). Watershed allied telemetry experimental research. J Geophys Res, 114(D22103): 1–19

[14] Lu G, Wu Z, Wen L, Zhang J (2006). Application of a coupled atmospheric-hydrological modeling system to real-time flood forecast. Advances in Water Science, 17(6): 847–852 (in Chinese)

[15] Michalakes J, Chen S, Dudhia J, Hart L, Klemp J, Middlecoff J, Skamarock W (2001). Development of a Next Generation Regional Weather Research and Forecast Model in developments in teracomputing. In: Zwieflhofer W, Kreitz N, eds, Proceedings of the 9th ECMWF Workshop on the Use of High Performance Computing in Meteorology. Singapore: World Scientific, 269–276

[16] Michalakes J, Dudhia J, Gill D, Klemp J, Shamarock W (1998). Design of a next-generation regional weather research and forecast model: towards teracomputing, World Scientific, River Edge, New Jersey, 117–124

[17] Michalakes J, Dudhia J, Gill D, Henderson T, Skamarock W, Wang W (2004). The weather reseach and forecast model: software architecture and performance. In: Proceedings of the 11th ECMWF Workshop on the Use of High Performance Computing in Meteorology, 25–29 October, 2004.

[18] Mpelasoka F S, Mullah A B, Heerdegen R G (2001). New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches. Int J Climatol, 21(11): 1415–1433

[19] Ngo-Duc T, Polcher J, Laval K (2005). A 53-year forcing data set for land surface models. J Geophys Res, 110(D06116): 13

[20] Onogi K, Tsutsui J, Koide H, Sakamoto M, Kobayashi S, Hatsushika H, Matsumoto T, Yamazaki N, Kamahori H, Takahashi K, Kadokura S, Wada K, Kato K, Oyama R, Ose T, Mannoji N, Taira R (2007). The JRA-25 reanalysis. J Meteorol Soc Jpn, 85(3): 369–432

[21] Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda M G, Huang X Y, Wang W, Power J G (2008). A description of the advanced research WRF Version 3. accessed June 2008)

[22] Uppla S M, KÅllberg P W, Simmons A J, Andrae U, Bechtold V D C, Fiorino M, Gibson J K, Haseler J, Hernandez A, Kelly G A, Li X, Onogi K, Saarinen S, Sokka N, Allan R P, Andersson E, Arpe K, Balmaseda M A, Beljaars A C M, Berg L V D, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins B J, Isaksen L, Janssen P A E M, Jenne R, Mcnally A P, Mahfouf J F, Morcrette J J, Rayner N A, Saunders R W, Simon P, Sterl A, Trenberth K E, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005). The ERA-40 reanalysis. Q J R Meteorol Soc, 131(612): 2961–3012

[23] Wilby R L, Hay L E, Gutowski W J Jr, Arritt R W, Takle E S, Pan Z, Leavesley G H, Clark M P (2000). Hydrological responses to dynamically and statistically downscaled climate model output. Geophys Res Lett, 27(8): 1199–1202

[24] Wilby R L, Hay L E, Leavesley G H (1999). A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado. Journal of Hydro1ogy, 225 (1–2): 67–91

[25] Winkler J A, Palutikof J P, Andresen J A, Goodess C M (1997). The simulation of daily temperature time series from GCM output: Part II: Sensitivity analysis of an empirical transfer function methodology. J Clim, 10(10): 2514–2532

[26] Yu Z, Barron E J, Yarnal B, Lakhtakia M N, White R A, Pollard D, Miller D A (2002). Evaluation of basin-scale hydrologic response to a multi-storm simulation. J Hydrol (Amst), 257(1–4): 212–225

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