JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D09102, doi:10.1029/2011JD016979, 2012 Integration of remote-sensing data with WRF to improve lake-effect precipitation simulations over the Great Lakes region Lin Zhao,1,2 Jiming Jin,1,2 Shih-Yu Wang,2,3 and Michael B. Ek4 Received 5 October 2011; revised 20 March 2012; accepted 20 March 2012; published 1 May 2012. [1] In this study, remotely sensed lake surface temperature (LST) and lake ice cover (LIC) were integrated into the Advanced Research Weather Research and Forecasting (WRF) model version 3.2 to evaluate the simulation of lake-effect precipitation over the Great Lakes region. The LST was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), while the LIC was obtained from the National Ice Center (NIC). WRF simulations for the Great Lakes region were performed at 10 km grid spacing for the cold season from November 2003 through February 2008. Initial and lateral boundary conditions were provided by the North American Regional Reanalysis (NARR). Experiments were carried out to compare winter precipitation simulations with and without the integration of the satellite data. Results show that integration with MODIS LST and NIC LIC significantly improves simulation of lake-effect precipitation over the Great Lakes region by reduced latent heat and sensible heat fluxes. A composite analysis of lake-effect precipitation events further reveals that more accurately depicted low-level stability and vertical moisture transport forced by the observation-based LST and LIC contribute to the improved simulation of lake-effect precipitation. Citation: Zhao, L., J. Jin, S.-Y. Wang, and M. B. Ek (2012), Integration of remote-sensing data with WRF to improve lake-effect precipitation simulations over the Great Lakes region, J. Geophys. Res., 117, D09102, doi:10.1029/2011JD016979. 1. Introduction Sousounis and Fritsch, 1994; Ballentine et al., 1998; Kristovich and Laird, 1998; Sousounis and Mann, 2000; Liu [2] The Great Lakes exert significant influence on weather and Moore, 2004]. Modeling investigation by Lavoie [1972] and climate in the region, especially on the downwind shores suggested that the air temperature difference between the during the cold season. From late fall to winter, when arctic lake surface and the 850-hPa level is the most important air masses sweep down, considerable temperature differ- factor in triggering lake-effect precipitation. Wilson [1977] ences between the water surface and the overlying air often pointed out that when the observed 850-hPa level tempera- trigger lake-effect precipitation. This well-known lake effect ture is 7 C colder than that of the lake surface, downwind of snowstorms enhances annual precipitation by as much as precipitation increased significantly. Ice cover also has an 200% over that in nearby areas without the lake effect impact on precipitation through modifying surface evapora- influence [Scott and Huff, 1996]. tion and stability in the lower atmosphere, weakening the [3] Past studies have investigated a broad range of aspects lake-effect precipitation. Based on model results, Niziol et al. of lake-effect precipitation over the Great Lakes, including [1995] found that lake ice cover (LIC) greatly reduces surface climatology [Norton and Bolsenga, 1993; Ellis and heat and moisture fluxes. By comparing observations with Leathers, 1996] and detailed physical processes of individ- the Colorado State University mesoscale model [Pielke, ual events [Braham and Kelly, 1982; Hjelmfelt, 1990; 1974], Laird and Kristovich [2004] found that simulations of lake-effect precipitation are slightly improved when real- istic LIC is included in the model. Based on aircraft mea- surements, Gerbush et al. [2008] found that LIC results in a 1Department of Watershed Sciences, Utah State University, Logan, reduction in both surface sensible and latent heat fluxes, and Utah, USA. the reduction significantly influences the development of 2Department of Plants, Soils, and Climate, Utah State University, Logan, Utah, USA. lake-effect snowstorms [Cordeira and Laird, 2008]. 3Utah Climate Center, Utah State University, Logan, Utah, USA. [4] Despite these earlier efforts, the extent to which the 4Environmental Modeling Center, National Centers for Environmental combined contribution of LIC and lake surface temperature Prediction, National Weather Service, National Oceanic and Atmospheric (LST) to simulations of lake-effect precipitation has not been Administration, Camp Springs, Maryland, USA. sufficiently studied. In particular, the impact of realistic LST Corresponding Author: J. Jin, Department of Watershed Sciences, and LIC conditions on simulations of lake-effect precipita- Utah State University, 5210 Old Main Hill, Logan, UT 84341, USA. tion requires further analysis. Compared to the Moderate ([email protected]) Resolution Imaging Spectroradiometer (MODIS) observed Copyright 2012 by the American Geophysical Union. LST, positive temperature differences (warm biases) have 0148-0227/12/2011JD016979 D09102 1of12 D09102 ZHAO ET AL.: WRF LAKE D09102 Table 1. Configurations of the WRF Experiments MLST_Ice, NLST_Ice, and MLST_NoIce Experiments MLST_Ice NLST_Ice MLST_NoIce Lake Surface MODIS LST NARR LST MODIS LST Temperature (LST) Ice Concentration NIC NIC Open water water of the Great Lakes. Instead, the freezing point in the Great Lakes was adjusted to 273.16 K to reflect reality. [7] We selected the fractional ice cover option (added into WRF since version 3.1) for the simulations, since this option treats the model grid cells with an ice fraction between 0% and 100%. For a model grid cell in the lake, variables are averaged over the ice-covered and open water (i.e., without ice cover) fractions [Avissar and Pielke, 1989; Vihma, Figure 1. Domain of the WRF simulations. Boxes 1, 2, 3, 1995]: 4, and 5 over the Great Lakes region represent the lake-effect x ¼ x LIC þ x ð À LICÞ; ð Þ regions. The black dot is the location of Buffalo, New York, i w 1 1 at and near which there are one sounding station and two where x is a quantity, and the subscripts i and w refer to the additional surface stations. ice and open water components within a model grid cell, respectively. Here, 0 ≤ LIC ≤ 1.0. [8] The observed LSTs used in this study were obtained been found in the LSTs from reanalysis data such as the from MODIS. The MODIS LST product is an 8-daily North American Regional Reanalysis (NARR) [Mesinger composite, including daytime and nighttime, configured et al., 2006] (discussed later and shown in Figure 2). onto a 0.05 (5.6 km) latitude/longitude grid [Wan et al., Furthermore, LIC is unavailable from the NARR data, and 2002, 2004; Coll et al., 2005; Hook et al., 2007]. Missing the LIC data from other reanalyses [Kalnay et al., 1996; values for the Great Lakes due to clouds were replaced with Kanamitsu et al., 2002] cover only the oceans and not values derived from solving the Poisson’s equation via lakes (e.g., the Great Lakes). Such discrepancies in LST relaxation [Evans, 1998]. A simple linear method was and LIC have a strong potential to negatively impact simulated precipitation related to lake processes. [5] In this study, we used the Weather Research and Forecasting (WRF) [Skamarock et al., 2008] model version Table 2. The 11 Lake-Effect Events at Buffalo, New York, During 3.2, developed by the National Center for Atmospheric the Five Winters (December, January, February) From 2003 to Research, to simulate lake-effect precipitation over the Great 2008a Lakes. Our intention was to explore the impact of remote Initial Stage Demise Stage Total Daily sensing LST and LIC on precipitation simulations over the (UTC) (UTC) Precipitation (mm) Great Lakes region through the WRF model. The physical processes during lake-effect precipitation events were 2003/12/20 2003/12/20 1.5 1:50 16:05 examined as well. Effects of cumulus convection and 2004/01/31 2004/01/31 3.8 microphysics schemes on modeling lake-effect precipitation 1:40 13:25 have been explored by Theeuwes et al. [2010] and are not 2004/12/24 2004/12/25 10.7 our focus. The paper is arranged as follows: section 2 17:30 4:50 2005/12/05 2005/12/06 1.0 describes the model, data sets, experiment design, and 14:45 12:50 methodology, section 3 presents the results, and section 4 2005/12/21 2005/12/21 2.5 provides conclusions. 10:50 16:50 2006/12/7 2006/12/08 3.0 23:50 10:50 2006/12/27 2006/12/27 0.5 2. Model, Data Sources, and Methodology 1:50 12:50 2007/02/03 2007/02/04 5.6 2.1. Model and Data 10:50 8:50 2007/02/06 2007/02/07 1.5 [6] In this study we used the coupled WRF version 3.2 14:15 02:40 and the Community Land Model version 3.5 (CLM3.5) [Jin 2007/02/10 2007/02/10 2 et al., 2010] for the proposed simulations. This version of 0:50 11:50 2007/02/22 2007/02/23 1.0 WRF presets the water body freezing point at 271.4 K when 23:50 16:50 the fractional ice option is employed; this temperature set- a ting can be used for treating only saline water such as in The initial and demise stages indicate the first time and last time when precipitation was recoded at the Buffalo station (the black dot in Figure 1) oceans. This setting therefore is not suitable for the fresh during the event. Dates are given as yyyy/mm/dd. 2of12 D09102 ZHAO ET AL.: WRF LAKE D09102 Figure 2. The differences in the monthly LST between MODIS and NARR (NARR minus MODIS; C) for the Great Lakes during the winters (December, January, February) from 2003 to 2008. The grids of each lake were determined by land use type data from the USGS.