Evaluation of WRF and HadRM Mesoscale Climate Simulations over the United States Pacific Northwest

Evaluation of WRF and HadRM Mesoscale Climate Simulations over the United States Pacific Northwest Yongxin Zhang, Valérie Dulière, Philip Mote, and Eric P Salathé Jr Climate Impacts Group, Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, U.S.A Abstract This work compares the WRF (Weather Research and Forecasting) simulations at 36km and 12-km resolution and HadRM (Hadley Center Regional Model) simulations at 25-km resolution with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at HCN (Historical Climate Network) stations over the United States Pacific Northwest for 2003 The WRF runs were driven by the NCEP/NCAR reanalysis data while the HadRM runs were drive by the NCEP-DOE (Department of Energy) reanalysis data The Tmax simulated by WRF and HadRM compares well with the observations Mainly warm biases of Tmax are noted in the WRF simulations along the coast of Oregon and Washington with cold biases in the interior during autumn and winter The HadRM simulations show mainly warm biases in the interior during the same time period During summer, the distribution of Tmax biases shows large variability while during spring, cold biases of Tmax are dominant in both model simulations The simulated Tmin compares reasonably well with the observations although not as well as Tmax for both models Warm biases of Tmin prevail in both model simulations when averaged both annually and seasonally The model biases of Tmax come mainly from the large-scale driving data while the model biases of Tmin originate mainly from the regional models The temporal correlation between simulated and observed daily precipitation is relatively low However, the correlation increases steadily for longer averaging times Large improvement in the temporal correlation of precipitation is evident in the 12-km WRF simulations when compared to the 36-km WRF simulations and 25-km HadRM simulations Model overestimation of the observed precipitation is indicated in both model simulations when averaged annually A large variability in the spatial distributions of the normalized seasonal biases of precipitation is evident in both model simulations Differences in the simulated temperature and precipitation between the two models can be largely attributed to differences in the largescale driving data Introduction The United States Pacific Northwest is characterized by mountainous terrain and intricate land-sea contrasts (Fig 1) resulting in a host of fine-scale weather systems such as sea and land breezes, rain shadows, and downslope windstorms, that define the local weather and climate (Mass 2008) In a warming climate, such fine-scale weather systems can significantly alter the local temperature and precipitation trends (Salathé et al 2008) and are essential to consider in climate simulations and climate change assessment at regional and local scales Global models are generally able to resolve the large-scale weather systems that affect the Pacific Northwest but not the fine-scale processes associated with the local terrain To capture these smaller features, a more realistic representation of the local complex terrain and the heterogeneous land surfaces is needed (Mass et al 2002; Salathé et al 2008) Therefore, the use of limited-area regional climate models with horizontal resolutions on the order of tens of kilometers is crucial Recently, there have been increasing efforts over the Pacific Northwest in using limited-area mesoscale models for downscaling reanalysis data (Leung et al 2003a, b) and global climate model simulations (Salathé et al 2008) to study regional climate and climate change Much of the work has been involved with the fifth-generation Pennsylvania State University – National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) (Grell et al 1993) As NCAR has been phasing out MM5 and has released the state-of-the-art, next-generation Weather Research and Forecasting (WRF) model (http://www.wrf-model.org/index.php), it is timely to switch from the MM5-based to the WRF-based mesoscale climate modeling and examine its performance over the Pacific Northwest HadRM (Hadley Center Regional Model) is another limitedarea regional climate model widely used worldwide as part of the PRECIS (Providing REgional Climates for Impacts Studies) system, which was developed at the Hadley Center of the United Kingdom Met Office The PRECIS system can be easily applied to any area of the globe to generate detailed climate change projections Both the WRF and HadRM models are coupled land-atmosphere modeling systems that can be used to add fine-scale information to the large-scale projections of global climate models What differs between these two models is that WRF, like MM5, was designed for short-term weather forecasts and refinements have to be applied in the model in order to perform long-term climate simulations (Salathé et al 2008) In this study, we apply the WRF-based and HadRM modeling systems over the Pacific Northwest in downscaling reanalysis data (Kalnay et al 1996; Kanamitsu et al 2002) for 2003 The reanalysis data incorporate all the available observations at the time of processing in order to best represent the 6-hourly large-scale state of the atmosphere Reanalysis fields are particularly suitable for driving regional climate models and for model validation Here, we present validation results based on comparisons to station observations over the Pacific Northwest from the Historical Climate Network (HCN; Karl et al 1990) Our purpose is to examine the models performance in reproducing station observations at various time scales This work is organized in the following Section contains a brief description of the models Experimental design is discussed in section Section compares model simulations with the observations Major conclusions and discussions are presented in section Model Description 2.1 WRF Model The WRF model is a state-of-the-art, next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs (http://www.wrf-model.org) It is a non-hydrostatic model, with several available dynamic cores as well as many different choices for physical parameterizations suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers The dynamic cores in WRF include a fully mass- and scalarconserving flux form mass coordinate version The physics package includes microphysics, cumulus parameterization, planetary boundary layer (PBL), land surface models (LSM), longwave and shortwave radiation (Skamarock et al 2006) In this work, the microphysics and convective parameterizations used were the WRF Single-Moment 5-class (WSM5) scheme (Hong et al 2004) and the Kain-Fritsch scheme (Kain and Fritsch 1993), respectively The WSM5 microphysics explicitly resolves water vapor, cloud water, rain, cloud ice, and snow The Kain-Fritsch convective parameterization utilizes a simple cloud model with moist updrafts and downdrafts that includes the effects of detrainment and entrainment The land-surface model used was the NOAH (National Centers for Environmental Prediction - NCEP, Oregon State University, Air Force, and Hydrologic Research Lab) LSM 4-layer soil temperature and moisture model with canopy moisture and snow cover prediction (Chen and Dudhia 2001) The LSM includes root zone, evapotranspiration, soil drainage, and runoff, taking into account vegetation categories, monthly vegetation fraction, and soil texture The PBL parameterization used was the YSU (Yonsei University) scheme (Hong and Pan 1996) This scheme includes counter-gradient terms to represent heat and moisture fluxes due to both local and non-local gradients Atmospheric shortwave and longwave radiations were computed by the NCAR CAM (Community Atmospheric Model) shortwave scheme and longwave scheme (Collins et al 2004), respectively The design of the WRF-based mesoscale climate modeling over the Pacific Northwest follows that of the MM5-based mesoscale climate modeling described in Salathé et al (2008) Basically, similar refinements as in the MM5 setup are made to the WRF configuration in order to perform long simulations and fully represent the climate system response to climate change forcing Firstly, nudging is applied to the outermost regional model domain from the forcing fields in order to prevent possible drift of regional model solution from that of the driving global climate model over long term Nudging relaxes the regional model simulations for wind, temperature, and moisture towards the driving global climate model simulations The inner nested domains are not nudged, allowing the mesoscale model to freely develop fine-scale features Secondly, WRF is modified so that soil temperatures vary at the lower boundary in accordance with the evolving surface temperatures predicted by the model 2.2 HadRM Model HadRM (Jones et al 2004) is the third-generation Hadley Center regional climate model (HadRM3H) It is a limited-area, high-resolution version of the atmospheric general circulation model HadAM3H which is itself an improved version of the latest atmospheric component of the atmosphere-ocean coupled general circulation model (HadCM3; Gordon et al 2000; Johns et al 2003) HadRM is a hydrostatic version of the fully primitive equations It includes dynamical flow, clouds and precipitation, radiative processes, land surface and deep soil An interactive atmospheric sulfur cycle is also available but was not used in this study The latitude-longitude grid is rotated in HadRM so that the equator lies inside the region of interest to obtain quasi-uniform grid box area over that region The available horizontal resolutions are 0.44°× 0.44° and 0.22°× 0.22° For the purpose of this study, we chose the higher resolution which corresponds to a minimum resolution of ~ 25 km at the equator of the rotated grid HadRM was released as part of the PRECIS package This package also includes software to allow display and processing of the model output data (http://precis.metoffice.com) The PRECIS package is flexible, easy to use and computationally inexpensive It can be applied over the U.S Pacific Northwest to provide detailed climate information for regional climate studies and climate impact assessment Experimental Design WRF was set up by using multiple nests at 108 km, 36 km, and 12 km horizontal grid spacing (Fig 1a) The outermost WRF domain covers nearly the entire North American continent as well as much of the eastern Pacific Ocean and the western Atlantic Ocean The use of this large domain ensures that synoptic weather systems approaching the U.S are well represented by the time they reach the region The 36-km model domain covers the continental U.S and part of Canada and Mexico The innermost model domain is centered on the Pacific Northwest and includes the states of Washington, Oregon, and Idaho (Fig 1b) We used 31 vertical levels in the model with the highest resolution (~ 20 – 100 m) in the boundary layer The model top was fixed at 50 mb One-way nesting was applied in this work The domain of HadRM (Fig 1a) was chosen with the highest available horizontal resolution of ~ 25 km at the equator of the rotated grid The HadRM model domain covers a large part of the eastern Pacific Ocean and part of Mexico and Canada to better resolve the synoptic weather systems that affect the Pacific Northwest This model domain encompasses entirely the states of Arizona, California, Idaho, Nevada, Oregon, Utah and Washington There are 19 vertical hybrid levels in HadRM spanning from the surface to 0.5 mb The WRF and HadRM runs were initialized at 0000 UTC December 1, 2002 and ended at 0000 UTC January 1, 2004 The first one-month simulations by WRF and HadRM were regarded as model spin-up The initial and lateral boundary conditions were interpolated from the NCEP/NCAR Reanalysis Project (NNRP; R1) data (Kalnay et al 1996) for WRF and from the NCEP-DOE (Department of Energy) AMIP-II (Atmospheric Model Intercomparison Project) Reanalysis (R-2) data (Kanamitsu et al 2002) for HadRM The lateral boundary conditions were updated every six hours for both models SST was updated every six hours in WRF using the RTG_SST (Real-Time, Global, Sea Surface Temperature) analysis (ftp://polar.ncep.noaa.gov/pub/history/sst) developed and archived at NCEP In HadRM, SST was taken from a combination of the monthly HadISST (Hadley Center’s sea ice and sea surface datasets; http://badc.nerc.ac.uk/data/hadisst) and weekly NCEP observed datasets (http://www.cdc.noaa.gov/cdc/reanalysis/reanalysis.shtml) The simulations from both WRF and HadRM models were output every hour The R-2 data are an updated and processing error-fixed version of the NNRP data (Kanamitsu et al 2002) The R-2 data have been shown to improve soil moisture, snow cover and radiation fluxes over ocean when compared to the NNRP data Over the Pacific Northwest, the R-2 data are about 0.2 ~ 0.9°C warmer and about 5% wetter than the NNRP data In terms of other surface fields and upper-air fields (not shown) the differences between the R-2 and NNRP data sets are negligible Results In this section, model simulations from WRF and HadRM are compared with observations at 63 HCN stations in the states of Washington, Oregon and Idaho We select only the stations from which 80% or more of the daily precipitation and temperature measurements are available during the year 2003 and the stations whose corresponding model grid points are land grid points in the WRF and HadRM model domains The locations of these HCN stations are indicated in Fig 1b Comparisons will focus on daily maximum and minimum temperatures (Tmax and Tmin) and precipitation with averages over various time scales for the nested WRF and HadRM domains The daily maximum and minimum temperatures are found from the hourly temperature simulations A lapse rate correction of 6.5°C/km was applied to the simulated temperature to account for the differences in temperature between the elevation of the model grid point and the station elevation No lapse rate was applied to precipitation as a lapse rate over complex terrain would depend on several factors such as mountain width, buoyancy and moisture fields (Smith and Barstad 2004) as well as on winds (Esteban and Chen 2008) Comparisons between the nested WRF domains and HadRM domain are also presented for daily temperatures and precipitation 4.1 Maximum Temperature Figure shows scatter plots of the observed and simulated daily Tmax at all HCN stations combined and all days of the year Correlation coefficients between the observations and simulations are listed in Table Both WRF and HadRM simulate the observed Tmax well High correlation coefficients between the observations and simulations are noted (0.92 for WRF Domain and 0.93 for both WRF Domain and HadRM Domain) The simulated temperature at the HCN grids is virtually identical for the two nested WRF domains, with a correlation coefficient of 0.99 and it is 0.93 between the nested WRF domains and the HadRM domain One intriguing phenomenon associated with the HadRM simulations is the parabolic tendency in the scatter plots especially for Tmax smaller than 20°C (Figs 2c, e and f) This appears to be inherited from the large-scale forcing as can be inferred from the difference plot in monthly mean temperatures averaged over the U.S Pacific Northwest domain between the two driving data sets (R2 – NNRP; Fig 3) This plot shows higher Tmax in R2 than in NNRP during winter months with relatively small differences between the two driving datasets in the other months We will also show later in the model biases of seasonal mean Tmax that warmer biases are noted in the HadRM simulated Tmax during winter when compared to the WRF simulations Annual mean model biases of Tmax at HCN stations are presented in Fig For both WRF Domain and Domain 3, similar but small biases on the order of -1 ~ 1ºC are identified along the coast of the Pacific Northwest while cold biases around 3ºC are noted in the interior It is possible that these cold biases are partially inherited from the largescale driving data and partially related to the modeled terrain effect as eastward moving air masses are subject to large modification by the Cascade and Rocky Mountains Cold biases fluctuating around the mean of -1.9ºC are also noted in high-resolution MM5 simulations over the Pacific Northwest driven by the NNRP data (Salathé et al 2008) The annual mean biases of Tmax in the HadRM simulations range predominantly between -1 ~ 1ºC at HCN stations and are smaller in magnitude when compared to the WRF simulations As mentioned previously, the R-2 data that drive HadRM are about 0.2 ~ 0.9°C warmer than the NNRP data (see Fig 3) This difference in large-scale driving data may account for the smaller magnitude of the Tmax biases in the HadRM simulations when compared to the WRF simulations Figure shows scatter plots of the observed and simulated monthly mean Tmax at all HCN stations combined Correlation coefficients are listed in Table Nearly a perfect relationship is noted in Fig between the observations and model simulations with a correlation coefficient of 0.97 for both models This is an appreciable improvement of the model performance over the daily time scale (see Figs and and Table 1) Table also shows the correlation coefficients of 10-day mean Tmax between the observations and model simulations The correlation coefficients change little from the 10-day mean to the monthly mean for Tmax Figure shows seasonal mean model biases of Tmax at HCN stations Model biases from both models are generally smaller than 5ºC in magnitude for all seasons During winter and autumn, the nested WRF domains exhibit predominantly warm biases along the coast of Oregon and Washington and cold biases in the interior During spring, virtually all stations show cold biases in the nested WRF domains Summer is characterized by mainly cold biases in the nested WRF simulations with scattered warm biases noted along the coast of Oregon and Washington and the southern part of Idaho For HadRM, the distributions of model biases appear similar to those of WRF except in the interior where mainly warm biases are noted in winter, summer and autumn Similar seasonal distributions in the model biases of Tmax between WRF and HadRM seem to suggest that the large-scale driving data play a role in causing the distributions This is supported by examining the model biases of Tmax in the WRF Domain at 108km resolution that exhibit similar distributions to the nested WRF domains and HadRM domain although in winter the WRF Domain shows large cold biases at more stations than the other domains (Fig 6) In our model runs nudging is applied throughout the WRF Domain and this domain basically represents the large-scale weather conditions We further performed sensitivity study by running the WRF Domain using the same initial and boundary conditions as before but without using nudging The results (not shown) exhibit rather similar distributions to the WRF Domain simulations with nudging This illustrates that the model biases of Tmax are largely inherited from the forcing data We also examined the radiation budget at surface around the time of the maximum temperature from both models We noted similar radiation budget in both models without significant differences 4.2 Minimum Temperature Figure shows scatter plots of the observed and simulated daily Tmin at all HCN stations combined Correlation coefficients between the observations and simulations are presented in Table A good correspondence is evident between the observations and model simulations with the correlation coefficients being 0.85 for both WRF domains and 0.86 for HadRM domain It is evident that the correlation coefficients of Tmin between the observations and simulations are always smaller than those of Tmax (see Table 1) Two reasons might be responsible for these discrepancies Firstly, the 108-km WRF Domain simulations show a correlation coefficient of 0.87 for minimum temperature and 0.94 for maximum temperature, suggesting that the large-scale driving data at a 6hourly temporal resolution not represent the observed daily minimum temperatures as well as the maximum temperature Secondly, WRF and HadRM may contain model deficiencies in the parameterization of nocturnal boundary layer physics and dynamics that have a significant impact on nighttime (Tmin) temperatures Figure presents the annual mean model biases of Tmin at HCN stations In contrast to Tmax, the nested WRF simulations show predominantly warm biases on the order of 0.5 ~ 2.5ºC Two stations along the coast of Washington exhibit small cold biases (-1.0 ~ -0.5ºC) in the WRF simulations, likely due to the modeled influence of marine air As in the case for Tmax, the model biases in WRF are generally smaller in magnitude along the coast than in the interior Warm biases are noted at each HCN station in the HadRM simulations (Fig 8) The biases range from 0.5 to 3.0ºC with large biases located mainly along the coast of Oregon, the northern parts of Oregon and Washington, and the southeastern part of Idaho These warm biases cannot be explained by the large-scale forcing data as we will show later that the driving data appear to contain cold biases in Tmin as indicated by the WRF Domain simulations with nudging This suggests that the model deficiency must play a role As has been mentioned before, the R-2 data are about 0.2 ~ 0.9ºC warmer than the NNRP data over the Pacific Northwest (see Fig 3) and Fig indicates that the warm biases in the HadRM simulations are about 1°C higher than in the nested WRF simulations Figure shows scatter plots of the observed and simulated monthly mean Tmin at all HCN stations combined with the correlation coefficients given in Table Nearly a linear correspondence is noticed between the observations and simulations with the correlation coefficients being 0.95 for WRF Domain and 0.94 for both WRF Domain and HadRM Domain This is an appreciable improvement of the model performance over the daily time scale (Table 1) Note also in Table that the correlation coefficients of 10-day mean Tmin between the observations and simulations are very close to those of the monthly mean Tmin Warm biases are suggested in the scatter plots especially for the HadRM simulations in that the majority of points are located above the 1:1 line (Fig 9) Figure 10 shows seasonal mean model biases of Tmin at HCN stations Warm biases on the order of ~ 5ºC prevail in both model simulations throughout the year Cold biases are noted along the coast, eastern part of Washington and Snake Plain The WRF Domain simulated Tmin exhibits predominantly cold biases throughout the year (Fig 10), in contrast to the nested WRF and HadRM simulations that show mainly warm biases during each season This discrepancy implies that the model biases of Tmin in the nested WRF and HadRM simulations cannot be attributable to the driving data, which suggests that the warm biases are likely associated with the regional models Our sensitivity study by running the WRF Domain without using nudging (not shown) exhibits mainly warm biases of Tmin during each season, similar to the nested WRF and HadRM simulations Further analyses indicated that excessive downward longwave flux at surface during nighttime appears to be responsible for the warm biases of Tmin in the simulations 4.3 Precipitation At daily time scale, a large spreading is observed in the scatter plots of the observed and model simulated precipitation (not shown) with the correlation coefficients being 0.51 for WRF Domain 2, 0.56 for WRF Domain and 0.52 for HadRM Domain (Table 1) The correlation coefficients increase steadily as daily values are averaged over increasing number of days (Fig 11) For each time scale, the observed and simulated precipitations were averaged over the specified time period and the spatial correlation for all stations and all times was computed For example, the correlation coefficients of 5-day mean precipitation are 0.69 for WRF Domain 2, 0.76 for WRF Domain and 0.71 for HadRM Domain and they become 0.78, 0.87 and 0.78 for 20-day mean precipitation The improvement in correlation over the range of the averaging days is the largest between 1day mean and 20-day mean but slow down substantially after 20-day mean (Fig 11) Averaging was not performed beyond 90 days since the correlation coefficients may become misleading beyond 90-day mean due to short-term (one-year in this case) model simulation Note that WRF Domain consistently shows the highest correlation coefficients among all the model domains due to the improved representation of the local terrain at higher resolutions Figure 12 shows scatter plots of monthly mean precipitation at all stations combined A good relationship is observed between the observations and simulations with the correlation coefficients being very close to those for 20-day mean (see Table 1) To establish how well the models capture the observed daily weather sequence spatial correlation coefficients of daily precipitation at HCN stations are illustrated in Fig 13 Relatively high correlation coefficients (≥ 0.6) are noted along the coast of Oregon and Washington and over the northern part of Washington in the simulations for both models Relatively low correlation coefficients (0.2 ~ 0.4) are identified over the southeastern part of Washington, the eastern part of Oregon and the southern part of Idaho in both models These regional differences in model performance may be related to the modeled terrain effects as air masses move across the Cascade and Rocky Mountains and are subject to larger modifications in the interior domain than the coastal areas Figure 14 shows the model biases of annual mean precipitation normalized by the observed annual mean precipitation at each HCN station Normalization was employed on precipitation in order to make meaningful comparisons between dry and wet stations The WRF simulations exhibit wet biases at almost every station The wet normalized biases in the WRF simulations are generally smaller than 100% along the coast and are larger than 150% in the interior The wet normalized biases are also larger in WRF Domain than WRF Domain at the majority of stations HadRM exhibits small model normalized biases (-50 ~ 50%) at stations along the borders of Oregon and Washington and over the northern part of Idaho with wet biases (≥ 100%) over the southern part of Idaho The model biases of seasonal mean precipitation normalized by the observed seasonal mean precipitation at each HCN station are shown in Fig 15 The WRF and HadRM simulations show a large variability in the spatial distributions of the seasonally normalized biases This reflects the strong influence of complex terrain on local precipitations During winter, spring and autumn, mainly wet biases are noted in the nested WRF simulations while in summer, more stations show dry biases when compared to the other seasons For HadRM, neither dry biases nor wet biases are dominant although wet biases are persistently identified over the southern parts of Idaho For precipitation, the WRF Domain simulations with nudging exhibit predominantly dry biases close to 100% in all seasons (Fig 15), in contrast to the nested WRF and HadRM simulations that show both wet and dry biases This implies that the precipitation in the driving data is rather small since the model biases are computed as the differences between the modeled and observed precipitation normalized by the observed precipitation Our sensitivity study by running WRF Domain without nudging shows similar distributions in model biases of precipitation to the nested WRF domains but with larger model biases when compared to the nested WRF and HadRM domains (not shown) Conclusions and Discussion Figure (a) WRF model domains with two nests (Domain 1, 2, 3) and HadRM domain, and (b) WRF innermost domain (Domain 3) and terrain height (m) Shadings in (a) represent terrain height (m) for the corresponding WRF domain Grid spacing for each domain is: WRF Domain 1, 108 km; WRF Domain 2, 36 km; WRF Domain 3, 12 km; and HadRM Domain, 25 km HCN stations in the states of Washington, Oregon and Idaho are represented by filled brown circles in (b) Figure Scatter plots of daily Tmax (°C) at HCN stations (a) observations versus WRF Domain simulations, (b) observations versus WRF Domain simulations, (c) observations versus HadRM simulations, (d) WRF Domain versus WRF Domain 3, (e) WRF Domain versus HadRM, and (f) WRF Domain versus HadRM Correlation coefficient is shown on the upper right corner of each plot Figure Monthly mean differences of Tmax, Tmin and T (°C) between the two driving data (R2-R1) averaged over the United States Pacific Northwest Dashed line represents the 0°C reference line Figure Annual mean model biases of Tmax (ºC) at HCN stations for (a) WRF Domain 2, (b) WRF Domain 3, and (c) HadRM Color and size of the filled squares correspond to the sign and magnitude of the represented model biases, respectively Cross represents a magnitude range of -0.1 ~ 0.1°C Figure Scatter plots of monthly averaged Tmax between observations and model simulations from (a) WRF Domain 2, (b) WRF Domain 3, and (c) HadRM Figure Seasonal mean model biases of Tmax (°C) for WRF Domain (a), WRF Domain (b), WRF Domain (c), and HadRM (d) Each four-panel corresponds to model biases for one season and is arranged from DJF (December-January-February) on the top to SON (September-October-November) on the bottom Color represents the magnitude of the biases Figure Same as Fig except for daily Tmin (°C) Figure Same as Fig except for Tmin (°C) Figure Same as Fig except for Tmin (°C) Figure 10 Same as Fig except for Tmin (°C) Figure 11 Correlation coefficients of precipitation between observations and model simulations on various time scales Figure 12 Scatter plots of monthly averaged precipitation between observations and model simulations from (a) WRF Domain 2, (b) WRF Domain 3, and (c) HadRM Figure 13 Correlation coefficients of daily precipitation between observations and model simulations at HCN stations for (a) WRF Domain 2, (b) WRF Domain 3, and (c) HadRM Magnitude of the correlation coefficients are represented by the size of the filled squares Figure 14 Annually averaged model biases of precipitation (mm) normalized by annual mean precipitation at each HCN station for (a) WRF Domain 2, (b) WRF Domain 3, and (c) HadRM Cross represents a magnitude range of -0.1 ~ 0.1 mm Figure 15 Same as Fig except for seasonal mean model biases of precipitation (mm) normalized by seasonal mean precipitation at each HCN station ... resolution of ~ 25 km at the equator of the rotated grid The HadRM model domain covers a large part of the eastern Pacific Ocean and part of Mexico and Canada to better resolve the synoptic weather... 1°C are noted in the HadRM simulations and along the coast of Oregon and Washington in the WRF simulations For WRF, mainly warm biases of Tmax are noted along the coast of Oregon and Washington... 36-km domain The HadRM simulations display small biases at stations along the borders of Oregon and Washington and over the northern part of Idaho with wet biases over the southern part of Idaho