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.
Corresponding Author(s):
PAN Xiaoduo,Email:panxiaoduo@lzb.ac.cn
引用本文:
. Dynamic downscaling of near-surface air temperature at the basin scale using WRF–a case study in the Heihe River Basin, China[J]. Frontiers of Earth Science, 2012, 6(3): 314-323.
Xiaoduo PAN, Xin Li, Xiaokang SHI, Xujun HAN, Lihui LUO, Liangxu WANG. Dynamic downscaling of near-surface air temperature at the basin scale using WRF–a case study in the Heihe River Basin, China. Front Earth Sci, 2012, 6(3): 314-323.
Chen D, Achberger C, R?is?nen J, Hellstr?m 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 doi: 10.1007/s00376-006-0006-5
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 doi: 10.1029/2002JD003118
4
Hanssen-Bauer I, Achberger C, Benestad R E, Chen D, F?rland E J (2005). Statistical downscaling of climate scenarios over Scandinavia. Clim Res , 29(3): 255–268 doi: 10.3354/cr029255
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 doi: 10.1016/S0022-1694(02)00138-5
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 doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
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 doi: 10.1016/j.jhydrol.2005.03.033
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 doi: 10.1016/j.jhydrol.2006.08.006
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 doi: 10.1016/j.jhydrol.2007.12.020
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 doi: 10.1029/2008JD011590
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 doi: 10.1002/joc.617
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 doi: 10.1029/2004JD005434
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 doi: 10.2151/jmsj.85.369
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. www.mmm.ucar.edu/ wrf/users/docs/user_guide/ARWUsersGuide.pdf (accessed June 2008)
22
Uppla S M, K?llberg 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 doi: 10.1256/qj.04.176
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 doi: 10.1175/1520-0442(1997)010<2514:TSODTT>2.0.CO;2
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 doi: 10.1016/S0022-1694(01)00538-8
