High-resolution precipitation data derived from dynamical downscaling using the WRF model for the Heihe River Basin, northwest China

The community of climate change impact assessments and adaptations research needs regional high-resolution (spatial) meteorological data. This study produced two downscaled precipitation datasets with spatial resolutions of as high as 3 km by 3 km for the Heihe River Basin (HRB) from 2011 to 2014 using the Weather Research and Forecast (WRF) model nested with Final Analysis (FNL) from the National Center for Environmental Prediction (NCEP) and ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF) (hereafter referred to as FNLexp and ERAexp, respectively). Both of the downscaling simulations generally reproduced the observed spatial patterns of precipitation. However, users should keep in mind that the two downscaled datasets are not exactly the same in terms of observations. In comparison to the remote sensing-based estimation, the FNLexp produced a bias of heavy precipitation centers. In comparison to the ground gauge-based measurements, for the warm season (May to September), the ERAexp produced more precipitation (root-mean-square error (RMSE) = 295.4 mm, across the 43 sites) and more heavy rainfall days, while the FNLexp produced less precipitation (RMSE = 115.6 mm) and less heavy rainfall days. Both the ERAexp and FNLexp produced considerably more precipitation for the cold season (October to April) with RMSE values of 119.5 and 32.2 mm, respectively, and more heavy precipitation days. Along with simulating a higher number of heavy precipitation days, both the FNLexp and ERAexp also simulated stronger extreme precipitation. Sensitivity experiments show that the bias of these simulations is much more sensitive to micro-physical parameterizations than to the spatial resolution of topography data. For the HRB, application of the WSM3 scheme may improve the performance of the WRF model.

[1]  Q. Tang,et al.  A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China , 2014 .

[2]  Luciana L. Porfirio,et al.  Climate projections for ecologists , 2014 .

[3]  Song‐You Hong,et al.  The WRF Single-Moment 6-Class Microphysics Scheme (WSM6) , 2006 .

[4]  W. Collins,et al.  Description of the NCAR Community Atmosphere Model (CAM 3.0) , 2004 .

[5]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[6]  P. Whetton,et al.  An appraisal of downscaling methods used in climate change research , 2015 .

[7]  Pedro M. A. Miranda,et al.  WRF high resolution dynamical downscaling of ERA-Interim for Portugal , 2012, Climate Dynamics.

[8]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[9]  Govindasamy Bala,et al.  Evaluation of a WRF dynamical downscaling simulation over California , 2008 .

[10]  A. Sorteberg,et al.  Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model , 2011 .

[11]  Jiming Jin,et al.  Integrating Remote Sensing Data with WRF for Improved Simulations of Oasis Effects on Local Weather Processes over an Arid Region in Northwestern China , 2012 .

[12]  J. Dudhia,et al.  A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation , 2004 .

[13]  Haiying Li,et al.  Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, Northwest China , 2008 .

[14]  E. Kessler.  On the distribution and continuity of water substance in atmospheric circulations , 1969 .

[15]  S. Freitas,et al.  A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling , 2013 .

[16]  D. Maraun,et al.  Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user , 2010 .

[17]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

[18]  Jianqi Sun,et al.  Evaluation of a high-resolution historical simulation over China: climatology and extremes , 2015, Climate Dynamics.

[19]  F. Giorgi,et al.  Regional Dynamical Downscaling and the Cordex Initiative , 2015 .

[20]  Christian W. Dawson,et al.  The Statistical DownScaling Model: insights from one decade of application , 2013 .

[21]  Rui Mao,et al.  Modeled responses of summer climate to realistic land use/cover changes from the 1980s to the 2000s over eastern China , 2015 .

[22]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[23]  M. Manton,et al.  Estimation of wind-induced losses from a precipitation gauge network in the Australian Snowy Mountains , 2015 .

[24]  B. Rockel.  The Regional Downscaling Approach: a Brief History and Recent Advances , 2015, Current Climate Change Reports.

[25]  Jiyuan Liu,et al.  Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s , 2014, Journal of Geographical Sciences.

[26]  Honglang Xiao,et al.  Integrated study of the water–ecosystem–economy in the Heihe River Basin , 2014 .

[27]  J. Seibert,et al.  Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods , 2012 .

[28]  J. Gutiérrez,et al.  Assessing and Improving the Local Added Value of WRF for Wind Downscaling , 2015 .

[29]  Songjun Han,et al.  Cooling effect of agricultural irrigation over Xinjiang, Northwest China from 1959 to 2006 , 2013 .

[30]  Jie He,et al.  Comparison of Downscaled Precipitation Data over a Mountainous Watershed: A Case Study in the Heihe River Basin , 2014 .

[31]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[32]  Xin‐Zhong Liang,et al.  Improving cold season precipitation prediction by the nested CWRF‐CFS system , 2011 .