Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin

Abstract

Abstract:To obtain long term accurate high resolution precipitation for the Heihe River Basin (HRB), Weather Research and Forecasting (WRF) model simulations were performed using two different initial boundary conditions, with nine microphysical processes for different analysis parameterization schemes. High spatial-temporal precipitation was simulated from 2000 to 2013 and a suitable set of initial, boundary, and micro parameters for the HRB was evaluated from the Heihe Watershed Allied Telemetry Experimental Research project and Chinese Meteorological Administration data at hourly, daily, monthly, and annual time scales using various statistical indicators. It was found that annual precipitation has gradually increased over the HRB since 2000. Precipitation mostly occurs in summer and is higher in monsoon-influenced areas. High elevations experience winter snowfall. Precipitation is higher in the eastern upstream area than in the western upstream, area; however, the converse occurs in winter. Precipitation gradually increases with elevation from 1000 m to 4000 m, and the maximum precipitation occurs at the height of 3500-4000 m, then the precipitation slowly decreases with elevation from 4000 m to the top over the Qilian Mountains. Precipitation is scare and has a high temporal variation in the OPEN ACCESS Remote Sens. 2015, 7 9231 downstream area. Results are systematically validated using the in situ observations in this region and it was found that precipitation simulated by the WRF model using suitable physical configuration agrees well with the observation over the HRB at hourly, daily, monthly and yearly scales, as well as at spatial pattern. We also conclude that the dynamic downscaling using the WRF model is capable of producing high-resolution and reliable precipitation over complex mountainous areas and extremely arid environments. The downscaled data can meet the requirement of river basin scale hydrological modeling and water balance analysis.

Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products

“…As described in Pan et al [35], after comparing 9 different microphysical parameters sensitive to analysis and 2 initial and boundary conditions with other fixed parameters, one set of physical configurations was selected as the most suitable for simulating rainfall in the HRB. Because both rainfall and snowfall belong to the same moisture variable, the WRF-ARW model physical configuration in this study is the same as that used by Pan et al [35]: the Kessler scheme [36] was used as the Microphysics parameterization, the Kain-Fritsch Scheme [37] as the Cumulus parameterization, the Yonsei University scheme [38] as Planetary Boundary parameterization, the Noah LSM [39] as the land-surface parameterization, the Dudhia scheme [40] as the shortwave radiative parameterization, and the Rapid Radiative Transfer Model (RRTM) scheme [41] as the longwave radiative parameterization. In our research, WRF-ARW was initialized by real boundary conditions using the National Centers for Environmental Prediction's (NCFP) Final (FNL) Operational Global Analysis data from the Global Forecast System, which has a resolution of 1 • × 1 • .…”

“…Two-way nested computational domains of 44 × 56 × 27 and 120 × 130 × 27 grid points were set with horizontal resolutions of 0.25 • and 0.05 • , respectively. The center longitude is 100 E and the center latitude is 40.28 N. For more detailed information about the WRF-ARW configuration in this study, we refer readers to Pan et al [35].…”

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

“…Errors are larger at higher wind speeds, at which the gauge-observed rainfall rate is generally lower than the actual rainfall rate by 2-18%. Therefore, these rainfall data have been corrected according to the method used by Pan et al [8]. Note that the rain gauge observations have inherent uncertainties, which include random and systematic errors due to wind, wetting, evaporation, and splashing.…”

“…Errors are larger at higher wind speeds, at which the gauge-observed rainfall rate is generally lower than the actual rainfall rate by 2-18%. Therefore, these rainfall data have been corrected according to the method used by Pan et al [8].…”

“…Regional climate models provided initialization and boundary conditions by global climate models yield much finer spatial resolutions based on 1-50 km spatial resolution. However, precipitation is considered the most difficult parameter to simulate in numerical models because our understanding of precipitation phenomena is not sophisticated enough [7][8][9]. Because of the considerable randomness of precipitation at both the spatial and temporal scales, the quality of precipitation data simulated using atmospheric models is far worse than the quality of other atmospheric elements, especially over complex terrains.…”