Wind induced errors in precipitation measurements result in systematic deficits, which are particularly large when the precipitation falls as snow combined with high wind speed. The actual magnitude of the deficit can amount to more than half of the true precipitation. In order to correct for these deficits, newly developed statistical models for solid and mixed precipitation are presented; together with a correction model for liquid precipitation presented earlier, a comprehensive system of correction models is now available. Statistical errors on the corrections are estimated.
[1] Weather radar-based quantitative precipitation estimation (QPE) is in principle superior to the areal precipitation estimated by using rain gauge data only, and therefore has become increasingly popular in applications such as hydrological modeling. The present study investigates the potential of using multiannual radar QPE data in coupled surface watergroundwater modeling with emphasis given to the groundwater component. Since the radar QPE is partly dependent on the rain gauge observations, it is necessary to evaluate the impact of rain gauge network density on the quality of the estimated rainfall and subsequently the simulated hydrological responses. A headwater catchment located in western Denmark is chosen as the study site. Two hydrological models are built using the MIKE SHE code, where they have identical model structures expect for the rainfall forcing: one model is based on rain gauge interpolated rainfall, while the other is based on radar QPE which is a combination of both radar and rain gauge information. The two hydrological models are inversely calibrated and then validated against field observations. The model results show that the improvement introduced by using radar QPE data is in fact more obvious to groundwater than to surface water at daily scale. Moreover, substantial negative impact on the simulated hydrological responses is observed due to the cut down in operational rain gauge network between 2006 and 2010. The radar QPE based model demonstrates the added value of the extra information from radar when rain gauge density decreases; however it is not able to sustain the level of model performance preceding the reduction in number of rain gauges.Citation: He, X., T. O. Sonnenborg, J. C. Refsgaard, F. Vejen, and K. H. Jensen (2013), Evaluation of the value of radar QPE data and rain gauge data for hydrological modeling, Water Resour. Res., 49,[5989][5990][5991][5992][5993][5994][5995][5996][5997][5998][5999][6000][6001][6002][6003][6004][6005]
The Danish Meteorological Ins tute operates a radar network consis ng of fi ve C-band Doppler radars. Quan ta ve precipita on es ma on (QPE) using radar data is performed on a daily basis. Radar QPE is considered to have the poten al to signifi cantly improve the spa al representa on of precipita on compared with rain-gauge-based methods, thus providing the basis for be er water resources assessments. The radar QPE algorithm called ARNE is a distance-dependent areal es ma on method that merges radar data with ground surface observa ons. The method was applied to the Skjern River catchment in western Denmark where alterna ve precipita on es mates were also used as input to an integrated hydrologic model. The hydrologic responses from the model were analyzed by comparing radar-and ground-based precipita on input scenarios. Results showed that radar QPE products are able to generate reliable simula ons of stream fl ow and water balance. The poten al of using radar-based precipita on was found to be especially high at a smaller scale, where the impact of spa al resolu on was evident from the stream discharge results. Also, groundwater recharge was shown to be sensi ve to the rainfall product selected. Radar QPE appears to have unprecedented poten al in op mizing precipita on input to distributed hydrologic models and thus model predic ons.Abbrevia ons: CAPPI, Constant Al tude Plan Posi on Indicator; DMI, Danish Meteorological Ins tute; MFB, mean fi eld bias; QPE, quan ta ve precipita on es ma on.Weather radar is a sophisticated remote sensing instrument that measures the refl ectivity of objects in a given volume of the atmosphere. Research in the use of weather radar based quantitative precipitation estimation (radar QPE) in hydrologic applications has increased in recent years mainly due to the increasing demand for more refi ned spatial and temporal resolution of rainfall products for modeling purposes. Many meteorological institutes around the world, including the Danish Meteorological Institute (DMI), have considered using radar QPE as an important supplement to the conventional rain gauge rainfall products (Klazura and Imy, 1993;Fulton et al., 1998;Golding, 2000;Harrison et al., 2000;Tabary, 2007;Tabary et al., 2007). Operational radar QPE is a complex system that involves several elements including hardware design, signal processing, image analysis, data quality control, uncertainty analysis, and database organization. Due to the fact that each radar operation has its unique climatologic conditions, geographical terrain, and economic capacity, however, agreement on a standard procedure in the development of radar QPE products can hardly be reached.Despite the lack of standard procedures in implementation, the basic principles of radar precipitation estimation are well known and have been discussed in many textbooks (Batton, 1973;Collier, 1989;Rinehart, 1997). Radar emits and measures electromagnetic waves backscattered by raindrops; these electromagnetic waves are directly related to refl ectivity. Th e relat...
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