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Evaluating Seasonal Orographic Precipitation in the Interior Western SEPTEMBER 2017 J I N G E T A L . 2541 Evaluating Seasonal Orographic Precipitation in the Interior Western United States Using Gauge Data, Gridded Precipitation Estimates, and a Regional Climate Simulation XIAOQIN JING AND BART GEERTS Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming YONGGANG WANG Department of Geosciences, Texas Tech University, Lubbock, Texas CHANGHAI LIU National Center for Atmospheric Research, Boulder, Colorado (Manuscript received 31 March 2017, in final form 12 July 2017) ABSTRACT There are several high-resolution (1–12 km) gridded precipitation datasets covering the interior western United States. This study cross validates seasonal orographic precipitation estimates from the Snowpack Telemetry (SNOTEL) network; the national hourly multisensor precipitation analysis Stage IV dataset (NCEP IV); four gauge-driven gridded datasets; and a 10-yr, 4-km, convection-permitting Weather Research and Forecasting (WRF) Model simulation. The NCEP IV dataset, which uses the NEXRAD network and precipitation gauges, is challenged in this region because of blockage and lack of low-level radar coverage in complex terrain. The gauge-driven gridded datasets, which statistically interpolate gauge measurements over complex terrain to better estimate orographic precipitation, are challenged by the highly heterogeneous, weather-dependent nature of precipitation in complex terrain at scales finer than can be resolved by the gauge network, such as the SNOTEL network. Gauge-driven gridded precipitation estimates disagree in areas where SNOTEL gauges are sparse, especially at higher elevations. The WRF simulation captures wintertime orographic precipitation distribution and amount well, and biases over specific mountain ranges are identical to those in an independent WRF simulation, suggesting that these biases are at least partly due to errors in the snowfall measurements or the gridding of these measurements. The substantial disagreement between WRF and the gridded datasets over some mountains may motivate reevaluation of some gauge records and in- stallation of new SNOTEL gauges in regions marked by large discrepancies between modeled and gauge- driven precipitation estimates. 1. Introduction hydrology, agriculture, forestry, and ecology (Mote et al. 2005; Bales et al. 2006; Ebert et al. 2007; Barnett et al. The interior western United States (IWUS)1 is mostly 2008; Rasmussen et al. 2011). Most of the precipitation arid, but is also home to the headwaters of several major over the IWUS falls as snow over its mountains (Daly river systems, for example, the Colorado, Missouri, and et al. 1994). QPE is especially challenging in complex Snake Rivers (Woodhouse 2004). There is much interest terrain (e.g., Liu et al. 2011), and gauge-based snowfall in quantitative precipitation estimation (QPE) in this rate estimation is more uncertain than rain rate estima- region, in a range of disciplines including meteorology, tion (Rasmussen et al. 2012). In the mountainous IWUS, the Snowpack Telemetry (SNOTEL) network, operated by the Natural Resources Conservation Service (NRCS), 1 In this study, IWUS includes south Montana, east Idaho, has been used as a reference in many studies to evaluate Wyoming, Utah, Colorado, north Arizona, and north New Mexico. model output (e.g., Liu et al. 2011; Gutmann et al. 2012) and serves as the basis for gridded precipitation estimates Corresponding author: Xiaoqin Jing, [email protected] over mountains. However, SNOTEL only provides point DOI: 10.1175/JHM-D-17-0056.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/04/21 04:28 AM UTC 2542 JOURNAL OF HYDROMETEOROLOGY VOLUME 18 measurements of precipitation, and the gauge density developed to study the precipitation climatology (e.g., is low compared to that in highly populated or agricul- Lin and Mitchell 2005; Hou et al. 2014). Datasets de- tural regions. Several different techniques have been veloped using ground-based scanning weather radars, developed to provide more complete precipitation dis- such as the National Centers for Environmental Pre- tribution maps, including ‘‘terrain aware’’ interpolation diction hourly multisensor precipitation analysis Stage IV techniques using gauge measurements as forcing data (NCEP IV) dataset (Lin and Mitchell 2005), are quite (e.g., Daly et al. 1994), space-based and ground-based suitable to study the precipitation distribution at high remote sensing retrievals (e.g., Lin and Mitchell 2005), spatial resolution (4 km) over relatively flat terrain, such and numerical model simulations (Liu et al. 2017). But as in the central and eastern United States. However, the the relative performances of different techniques in QPE operational weather radar network is challenged over the are not well understood, so a cross validation of different complex terrain environment of the western United precipitation datasets is necessary. States because of blockage by the first range of mountains The terrain-aware interpolation techniques have been and the inability to capture the low-level orographic widely used to study the precipitation distribution over precipitation growth zone if the lowest unblocked beam is IWUS (e.g., Daly et al. 1994; Thornton et al. 1997; Xia et al. high above the surface, which is common (e.g., Fulton 2012; Newman et al. 2015a). Daly et al. (1994) developed et al. 1998; Maddox et al. 2002; Lin and Mitchell 2005; Lin the Parameter-Elevation Regressions on Independent and Hou 2012; Smalley et al. 2014). A radar network Slopes Model (PRISM) to produce a terrain-sensitive much denser than that currently in operation in the gridded precipitation dataset, which remains widely used. IWUS would be needed to achieve NCEP IV pre- This model estimates the precipitation in areas without cipitation accuracies comparable to those over the cen- gauges using physically informed statistical relations tral/eastern United States. Space-based remote sensing between terrain and gauge precipitation. Several other has the advantage of vertical incidence and thus no gauge-driven datasets have been developed using different blockage issues. Precipitation estimates using passive statistical downscaling techniques, including Daymet remote sensing techniques cannot reveal the variable (Thornton et al. 1997), North American Land Data As- precipitation distribution over complex terrain (Ebert similation System Stage II (NLDAS II; Xia et al. 2012), et al. 2007). Active space-based radar measurements, and the continental United States ensemble gridded da- such as Global Precipitation Measurement (GPM, from tasets (CUSEG; Newman et al. 2015a). 2014 to present; Hou et al. 2014), are inadequate to study The uncertainties of the gauge-driven gridded datasets the finescale quantitative precipitation climatology now have been discussed in several previous studies (e.g., Daly because of lack of overpasses, but they could be useful in et al. 2008; Gutmann et al. 2012). For example, Daly et al. the future. (2008) tried to estimate the error of PRISM using a data Recently, numerical weather prediction (NWP) denial cross-validation method. In this method, the models with high resolution (,6 km) have been used to PRISM regression with a single gauge removed is com- study the precipitation climatology over complex ter- pared to that with all gauges in the vicinity of the gauge rain. Ikeda et al. (2010) showed that the Weather Re- site. They show that the resulting difference in annual search and Forecasting (WRF) Model at a grid spacing precipitation estimates is 20%–30% over mountains. smaller than 6 km well captures the seasonal snowfall in Daly et al. (2008) also shows PRISM precipitation has a the Colorado Rockies; the difference of cold-season larger uncertainty in winter than in summer. Gutmann precipitation between the model output and SNOTEL is et al. (2012) compared the winter precipitation estimates within 20% for 71% of the SNOTEL sites. Liu et al. from PRISM against a SNOTEL site in the eastern San (2011) pointed out that this performance is highly sen- Juan Mountains at Moon Pass. The SNOTEL gauge was sitive to the choice of cloud microphysics parameteri- installed in October 2008 and was not applied in the zation. Rasmussen et al. (2011, 2014) further confirmed PRISM used in their study. The comparison indicates that WRF, at a resolution of 4 km, captures the cold- PRISM significantly overestimates the winter pre- season precipitation distribution and amount over the cipitation (600 mm) compared to SNOTEL (232 mm) at Colorado headwaters region well, with a bias of 10%–15% that point. Recently, Henn et al. (2017) compared the compared to SNOTEL measurements. Because of precipitation trend estimates from gauge-driven gridded the good performance in simulating orographic precipi- datasets against the streamflow observations in Sierra tation over complex terrain, high-resolution WRF Nevada and suggests the gauge-driven gridded datasets simulations have been used to assess changes of oro- may have substantial uncertainty at high elevations. graphic precipitation in a changing global climate. For Other than gauge-driven gridded datasets, space-based instance, Rasmussen et al. (2011, 2014) analyzed the and ground-based remote sensing techniques have been hydrological cycle in the Colorado headwaters region Unauthenticated | Downloaded 10/04/21 04:28 AM UTC
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