JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D09102, doi:10.1029/2011JD016979, 2012

Integration of remote-sensing data with WRF to improve -effect precipitation simulations over the Great 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 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

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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 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 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.

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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. (a) Lake Superior, (b) Lake Michigan, (c) Lake Huron, (d) Lake Erie, and (e) Lake Ontario. employed to interpolate the 8-daily MODIS daytime and satellite observations as well as from model output. The nighttime data to 6-hourly intervals for the lake surface uncertainty in ice concentration of this LIC data set is esti- boundary conditions of WRF. The gridded ice analysis (i.e., mated to be between 5% and 10%, according to Partington LIC) obtained from the National Ice Center (NIC) [Fetterer et al. [2003]. The LIC data have multiday intervals (3 or and Fowler, 2006] has a resolution of 2.5 km from 4 days), which were interpolated into 6-hourly data for use December 2003 to February 2007 and of 1.8 km from in WRF. December 2007 to February 2008. This LIC data set is a [9] Because the MODIS LST and NIC LIC data sets come composite of the measurements from in situ, ship, and from different sources, to maintain data consistency the

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Figure 3. Monthly precipitation (mm/month) averaged over the period of 2003–2008. Observations: (a) December, (b) January, and (c) February; MLST_Ice: (d) December, (e) January, and (f) February; NLST_Ice: (g) December, (h) January, and (i) February; NLST_Ice minus MLST_Ice: (j) December, (k) January, and (l) February. Black hatchings indicate the 90% confidence level (CL) based upon Student’s t-test. following correction scheme between these two data sets Tw ¼ 273:16 K; Ti ¼ 273:16; when LST ¼ 273:16; ð5Þ was built:

Ti ¼ 273:16 K; Tw ¼ ðÞLST LIC 273:16 =ðÞ1 LIC ; LST ¼ 273:16 K; when LST < 273:16 K and LIC when LST > 273:16 K: ð6Þ ¼ 0ðÞ pure water ; ð2Þ

¼ : ; > : ¼ ðÞ: [11] For these three equations LIC is always greater than 0 LST 273 16 K when LST 273 16 K and LIC 1 pure ice and less than 1.0, while in all the other cases (i.e., either pure ð Þ 3 water or pure ice) Tw or Ti is equal to LST. [10] In addition, when ice and water coexisted within a [12] For observed precipitation, we used the University of model grid cell (0 < LIC < 1.0), the heat fluxes were calcu- Delaware monthly global gridded data set [Legates and Willmott, 1990] available from 1950 to 2008 at a 0.5 lati- lated with the water and ice surface temperatures separately. In this case, the LST for the grid cell remained the same, but tude 0.5 longitude resolution. The 32-km NARR data the water and ice surface temperatures (Tw and Ti, respec- were used for the lateral boundary and initial conditions for tively) were calculated based on the following equations: WRF (except for LST and LIC over the Great Lakes). The fields of the NARR data were treated as observations, Tw ¼ 273:16 K; Ti ¼ ðÞLST ðÞ1 LIC 273:16 =LIC; based on the NARR’s previous evaluation [Mesinger et al., when LST < 273:16 K; ð4Þ 2006]. For model evaluation, surface and sounding

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RMSE of precipitation ranges from 19.6 to 26.7 mm/month. The Thompson MPS [Thompson et al., 2008] produces the lowest RMSE (19.6 mm/month). In the end, with all the above 17 WRF tests, the Grell-Devenyi CPS and Thompson MPS are chosen for exploring lake-effect precipitation over the Great Lakes. Other key physics schemes used in the simulations are: the CLM3.5 land surface model, the Bou- geault and Lacarrère planetary boundary layer (PBL) scheme [Bougeault and Lacarrère, 1989], and the Dudhia shortwave radiation [Dudhia, 1989] plus the Rapid Radiative Transfer Model scheme [Mlawer et al., 1997]. [15] The simulation domain is centered at 45.2 N, 85 W with a 10 km horizontal grid spacing (Figure 1). The grid points of dimension are 190 150, with 35 vertical layers topped at the 100-hPa level. The simulations cover the cold Figure 4. Spatial correlation coefficients of monthly pre- season from 15 November through 28 February (29 for leap cipitation between simulations and observations during the years) for the period of 2003–2008, reinitialized each year at winters (December, January, February) from 2003 to 2008. 0000 UTC on 15 November. The last 17 days of November (Red line represents the correlation coefficients between were treated as spin-up and discarded. We chose five geo- MLST_Ice and observations; blue line represents the corre- graphical regions encompassing the downwind shores of lation coefficients between NLST_Ice and observations.) lake-effect areas, outlined by the five boxes in Figure 1, The results are all significant at the 95% confidence level ’ where significant lake-effect precipitation often occurs (CL) based upon Student s t-test. [Norton and Bolsenga, 1993]. observations from meteorological stations at Buffalo, New York were utilized (location indicated in Figure 1 with a black dot).

2.2. Model Experiments [13] Three WRF experiments were conducted for the Great Lakes region, forced respectively with three sets of surface boundary conditions: (1) MODIS LST and NIC LIC; (2) NARR LST and NIC LIC; (3) MODIS LST assuming lakes with open water (i.e., without ice cover). Hereafter, we name these experiments as (1) MLST_Ice, (2) NLST_Ice, and (3) MLST_NoIce (in which M denotes MODIS and N denotes NARR; see Table 1). The MLST_Ice and NLST_Ice experiments were designed to aid in understanding the impact of LST on lake-effect precipitation, as well as to evaluate the NARR LST that has been broadly used in weather and climate modeling for the Great Lakes region. The comparison between the MLST_Ice and MLST_NoIce experiments serves to further our understanding of the role of LIC on lake-effect precipitation. [14] The choices of microphysics schemes (MPSs) and cumulus parameterization schemes (CPSs) in WRF were determined according to the precipitation root mean square error (RMSE) over the entire simulation domain. WRF version 3.2 includes 5 CPSs and 12 MPSs. The first set of WRF tests were performed for December 2003 with all 5 CPSs with an arbitrarily selected MPS, the Morrison scheme [Morrison et al., 2009]. The results show that the RMSE of Figure 5. The box and whisker plots of the ME and RMSE precipitation ranges from 20.1 to 20.7 mm/month between of the simulated monthly precipitation (mm/month) for the the 5 CPSs, and the Grell-Devenyi ensemble CPS [Grell and winters (December, January, February) for the period of Dévényi, 2002] generates the lowest RMSE (20.1 mm/ 2003–2008. (a and c) For the entire domain. (b and d) For month) and is selected for the rest of simulations. These tests the lake-effect domains. The thick horizontal line in each also show that the RMSE range is very minor, implying that box, the top and bottom edges of the box, and the upper convection is not a dominant process that controls precipi- and lower whiskers represent the ME (Figures 5a and 5b) tation over the Great Lakes during the winter. The second set and RMSE (Figures 5c and 5d) for each of the five years. of WRF tests were performed also for December 2003 with The upper (lower) whisker shows the maximum (minimum) all 12 MPSs with the Grell-Devenyi CPS, where the resulting value, and the red point represents the mean value.

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Figure 6. (a, c, and e) Monthly mean of the NIC ice fraction and (b, d, and f) monthly precipitation (%) difference between MLST_Ice and MLST_NoIce [(MLST_Ice -MLST_NoIce)/MLST_Ice] for the win- ters from 2003 to 2008.

2.3. Composite Analysis numerical experiments conducted over the [16] Typical lake-effect events at Buffalo were selected for [Onton and Steenburgh, 2001] indicated that a 2 C increase a composite analysis with the following method. First, (decrease) in LST results in 32% more (24% less) lake-effect hourly observations at Buffalo were used to identify pre- precipitation. Here, we first examined LST averaged over cipitation events that lasted at least 6 h, regardless of mag- the individual lakes of the Great Lakes to delineate the dif- nitude. Then, radar reflectivity images from the National ferences between the two sets of LST boundary conditions Climatic Data Center (NCDC) were used to select lake- (prior to the simulations). The grids of the individual lakes effect events where the reflectivity developed over Lake Erie were determined by land use types provided by the United and Lake Ontario and onto the downwind shores but did not States Geological Survey (USGS). Figure 2 depicts the appear to be associated with large-scale synoptic conditions. monthly mean differences between NARR LST and MODIS This step was derived from the lake-effect radar morphology LST from December 2003 to February 2008. In general, over these winter months, NARR LST tends to be warmer developed by Liu and Moore [2004]. Once a typical lake- effect event over Buffalo was identified, its life cycle was than MODIS LST by about 3 C, with an exception in Feb- divided into two stages: (1) the initial stage as the first time ruary 2004 when NARR LST is cooler than MODIS LST over Lake Erie (Figure 2d). The maximum difference precipitation was recorded at the Buffalo station, and (2) the demise stage as the last time precipitation was recorded between these two LST data sets reached as high as 8.6 Cin during the event. These events and their total precipitation December 2005 over Lake Erie (Figure 2d). are listed in Table 2. [18] During winter, LSTs are normally (but not necessar- ily) warmer than the air temperature near the surface. An intuitive expectation is that a higher LST creates greater 3. Results instability when the overlying air mass is colder, which may 3.1. The Role of LST lead to higher precipitation. To examine this, monthly pre- cipitation simulated from MLST_Ice and NLST_Ice, as well [17] Past studies have found that LST has a considerable as observations, is shown in Figure 3 for the 5-year average. effect on the development of lake-effect snowstorms In terms of the geographical distribution of precipitation, the [Hjelmfelt, 1990; Kristovich and Laird, 1998]. For instance,

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Figure 7. Cross sections of monthly mean vertical velocity (February) over (b) Lake Superior and (c) Lake Erie. (a) Orange and black lines on Great Lakes map indicate the positions of cross sections for Lake Superior and Lake Erie, respectively. The lower boundary in Figures 7b and 7c is the terrain.

two simulations agree with each other, but their precipitation [19] To examine quantitatively whether MLST_Ice magnitudes are noticeably different (Black hatchings are improves the precipitation simulation as compared to significant at 90% confidence level; Figures 3j, 3k, and 3l). NLST_Ice, we evaluated their mean error (ME) and RMSE: For example, in December over central Lake Superior, the precipitation difference reached a maximum of 42 mm/ XN ME ¼ 1 ðÞP O ; ð Þ month (Figure 3j). Meanwhile, when compared to observa- N i i 7 tions, both simulations show a systematic bias of over- i¼1 "# predicted precipitation, such as on the southern shores of XN 1=2 Lake Superior (Figures 3a, 3b, 3d, 3e, 3g, and 3h). However, RMSE ¼ 1 ðÞP O 2 ; ð Þ N i i 8 around Lakes Huron, Erie, and Ontario, the MLST_Ice i¼1 experiment produces less precipitation than NLST_Ice, making it closer to the observations. In eastern Lake Ontario, where N is the number of grid points for evaluation, and P the simulated precipitation is distinctly greater than the and O represent the model results and observations, respec- observations in both experiments. From the terrain map of tively. To facilitate comparisons, we interpolated model WRF (not shown), the elevation of Lake Ontario is 75 m grids to those of the observed precipitation. Both ME and while the nearest mountain to the east of the lake is 500 m. RMSE (Figure 5) were computed for (a) the entire domain Therefore, the overestimated precipitation in eastern Lake and (b) the lake-effect areas combined, calculated grid by Ontario may also be attributed to orographic lifting that is grid between simulations and observations with monthly too strong, as was documented in Niziol et al. [1995]. data. Here, ME indicates bias in the monthly magnitude of Such an orographic effect has also been found over Lake precipitation (perfect = 0), while RMSE quantifies bias in Michigan [Hjelmfelt, 1992]. Regarding the spatial pattern the precipitation variation (perfect = 0). Although ME fluc- of the precipitation differences, Figure 4 shows the spatial tuates month to month, the mean values (red dots) suggest correlation between the simulations and the observations. that MLST_Ice produces consistently less error in the pre- The correlation indicates that while both simulations capture cipitation magnitude than NLST_Ice, especially over the the major pattern of observed precipitation, MLST_Ice lake-effect domains (Figure 5b). There, the maximum dif- appears to perform slightly yet consistently better in terms of ference between the two experiments occurs in December, spatial agreement. with a mean value of 6.2 mm/month in MLST_Ice and 16.4

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5 winter months, however, statistical significance of the precipitation difference is generally low (i.e., less than the 90% level) and the results here should be considered sug- gestive rather than conclusive. It appears that MLST_Ice generally reduces precipitation, while the decrease seems proportional to the extent of ice cover over the lake surfaces. The maximum effect of LIC occurs along the downwind shores, such as southwestern Lake Superior, northern Lake Huron, and southeastern Lake Erie, causing a precipitation reduction as large as 46% (Figure 6f). However, positive precipitation differences between MLST_NoIce and MLST_Ice are observed around eastern Lake Superior and Lake Ontario. Sections of February mean vertical velocity across Lake Superior and Lake Erie (Figure 7a; orange and black lines) depict a branch of increased ascending motion (or reduced descent) over eastern Lake Superior (Figure 7b). Compared to Lake Erie (Figure 7c), the overall descent cor- responds well with the reduced precipitation. These features suggest that ice cover may modify local and mesoscale cir- culations over the Great Lakes, complicating the precipita- tion generation process in MLST_Ice. [22] To quantify the LIC impact on precipitation, the MEs of precipitation in the entire Great Lakes domain (Figure 8a) and the lake-effect areas (Figure 8b) are shown. From December to February, the discrepancy in the mean values (red point) between MLST_Ice and MLST_NoIce gradually increases in both domains as the season progresses and ice cover develops. The maximum difference occurs over lake- effect areas in February (Figure 8b), which shows an ME of Figure 8. Same as Figure 5 but for the MLST_Ice and 5.9 mm/month in MLST_Ice and 9.5 mm/month in MLST_NoIce experiments. MLST_NoIce. The presence of ice cover on the lakes could change surface albedo [Ingram et al., 1989; Curry et al., 1995] and influence energy exchange between the water surface and the overlying air – i.e., evaporation when there mm/month in NLST_Ice. Likewise, RMSE suggests that the is no ice over the lake surface and sublimation when there is precipitation of MLST_Ice is more realistic than that of ice cover. In the WRF model, the default albedo for water is NLST_Ice, as is reflected in their mean values (Figures 5c 0.08, and for ice it is 0.98. Because we used the fractional ice and 5d); this result corresponds to the lower LST as shown option in WRF, ice cover varies between 0% and 100%, and in Figure 2. These experiments are discussed further in this results in a corresponding change in albedo. For exam- section 3.3. ple, if the ice fraction of a model grid cell is 0.7 over Lake Erie, then according to equation (1) this grid cell would have 3.2. The Role of Ice Cover an albedo of 0.71 in MLST_Ice; this change in albedo would [20] It is known that lake ice cover weakens precipitation absorb much less solar radiation as compared to the open by prohibiting upward heat and moisture fluxes from the water setting in MLST_NoIce (where the albedo is 0.08). Ice lake surface. Here, the experiments of MLST_Ice and cover also changes the method of energy transfer from the MLST_NoIce were performed to examine the effect of LIC lake surface – i.e., evaporation and sublimation, the latter inputs on lake-effect precipitation simulation. For LST, both being much slower than the former due to the lower tem- experiments used the surface boundary conditions of perature and larger latent heat of vaporization. Thus, subli- MODIS LST, owing to its better simulation results as shown mation makes transport of water vapor into the atmosphere above. more difficult; this may be linked to the overall precipitation [21] We first examined the distribution of 5-year monthly decrease revealed in Figure 6. mean NIC LIC over the Great Lakes (Figures 6a, 6c, and [23] The differences in simulated latent heat flux and 6e). In general, ice cover gradually increases from December sensible heat flux between MLST_Ice and MLST_NoIce in through February, but the development of ice cover over February are illustrated in Figure 9; their average difference individual lake surfaces is different. For instance, the ice in latent heat flux is 3.3 W/m2 with a maximum difference fraction over most of Lake Erie is more than 0.7 during of 106.8 W/m2 (Figure 9a). Clearly, ice cover decreases February, while most of Lake Ontario is ice-free (Figure 6e). water vapor flux to the atmosphere, and this lends support to To detect the impact of ice cover on precipitation, the the reduced precipitation in MLST_Ice. With ice cover, the monthly precipitation difference in percentage between most substantial decreases in latent heat flux occur near the MLST_NoIce and MLST_Ice (i.e., MLST_Ice minus lake shores, consistent with the spatial distributions of ice MLST_NoIce, then divided by MLST_Ice) is shown in cover (Figure 6e). On the other hand, decreased sensible heat Figures 6b, 6d, and 6f. Due to the small sample size of flux with more lake ice cover would result in less energetic

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Figure 9. Monthly difference in (a) the latent heat flux (W m2) and (b) the sensible heat flux (W m2) between MLST_NoIce and MLST_Ice (MLST_Ice minus MLST_NoIce) in February averaged over 2003–2008. buoyant mixing in the lake-effect convective boundary 3.3. Composite Analysis of Lake-Effect Events layer. This subsequently decreases buoyant convection that [24] In view of the discrepancy in precipitation between contributes to the shallow convective boundary layer, sup- MLST_Ice and NLST_Ice, we next examined the composite pressing the generation of precipitation (Figure 9b). The analysis of typical lake-effect precipitation events. Eleven most significant difference in the latent heat flux and sensi- such precipitation events were identified in section 2.4 and ble heat flux occurs over Lake Erie, where the ice fraction is summarized in Table 2. The 850-hPa wind, 10 m wind, 2 m the largest. This further emphasizes the role of ice cover in temperature, and precipitation differences (NLST_Ice minus affecting water vapor flux into the atmosphere and subse- MLST_Ice) are shown in Figure 10 to examine the dynamic quently, the precipitation. cause of the precipitation difference. All variables were

Figure 10. Composite of (a) NARR 850 hPa wind field, (b) 2 m temperature difference (NLST_Ice minus MLST_Ice), (c) difference in 10 m wind and divergence (NLST_Ice minus MLST_Ice), and (d) dif- ference in precipitation and 10 m wind (NLST_Ice minus MLST_Ice) for the 11 lake-effect events. The black dot shows the location of Buffalo, New York. Black hatchings indicate the 90% confidence level (CL) based upon Student’s t-test.

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Figure 11. Comparison of observations (purple dot) and simulations (blue and green lines) of the vertical temperature (C) and water vapor mixing ratio (g/kg) composites of the 11 lake-effect events for the initial and demise stages at the Buffalo sounding station: (a and b) temperature; (c and d) water vapor mixing ratio. averaged within the duration of each event (from initial stage lake-effect precipitation through a combination of processes to demise stage). As shown in Figure 10a, the prevailing both thermodynamic (i.e., stability over and downwind of during the lake-effect precipitation events are mainly the lakes) and dynamic (i.e., downwind convergence), in northwesterlies, consistent with the synoptic setting that addition to the mere change in water vapor fluxes. creates lake effect. Over and downwind of the Great Lakes, [25] As a further examination of the atmospheric condi- the 2 m temperature of NLST_Ice is warmer than MLST_Ice tions, we plotted in Figure 11 the vertical profiles of tem- (Figure 10b); this warming is distributed downstream and perature and water vapor mixing ratios in Buffalo. Although signifies a stronger low-level instability in NLST_Ice. the precipitation difference is not remarkable in Buffalo, the Meanwhile, surface winds converge toward the warming vertical profile did reveal a noticeable difference between regions (Figure 10c), such as southeastern Lake Superior, NLST_Ice and MLST_Ice (i.e., green and blue lines). We southeastern Lake Huron, southeastern Lake Michigan, note that the twice-daily sounding observations (0000 UTC eastern Lake Ontario, and central Lake Erie, based on the and 1200 UTC) do not align completely with the initial distribution of the convergence. The coupling of warming and demise stages of the lake-effect precipitation events; with convergence suggests a stronger lift being produced in however, these observations are the only data available to NLST_Ice. Moreover, those convergence centers are con- represent the atmospheric sounding. In general, both sistently distributed on the downwind shores, hence sup- MLST_Ice and NLST_Ice performed reasonably well in porting the increase in precipitation as shown in Figure 10d. simulating the temperature and moisture profiles for the In summary, the change in LST conditions appears to affect PBL, the observed height of which often ranges from 1 to

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Table 3. MAEs of the Vertical Temperature and Water Vapor Spinar, 2005]. Therefore, to reproduce LST more accurately Mixing Ratios From the Surface to a Height of 1.6 km for the 11 (diurnal variation), a lake model may be a good option for Selected Lake-Effect Event Simulations simulating lake-effect precipitation and regional climate. Temperature Water Vapor Mixing [28] Acknowledgments. This work was supported by the Utah Agri- ( C) Ratio (g/kg) cultural Experiment Station, the NOAA MAPP NA090AR4310195 grant, and the EPA RD83418601 grant. MLST_Ice (Initial Stage) 1.02 0.10 NLST_Ice (Initial Stage) 1.87 0.18 MLST_Ice (Demise Stage) 0.66 0.06 NLST_Ice (Demise Stage) 1.48 0.10 References Avissar, R., and R. A. Pielke (1989), A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional 3 km in the Great Lakes [Chang and Braham, 1991; meteorology, Mon. Weather Rev., 117, 2113–2136, doi:10.1175/1520- 0493(1989)117<2113:APOHLS>2.0.CO;2. Kristovich, 1993]. However, the results from MLST_Ice Ballentine, R. J., A. J. Stamm, E. E. Chermack, G. P. Byrd, and D. Schleede are apparently closer to the observations. From the surface (1998), Mesoscale model simulation of the 4–5 January 1995 lake-effect to the height of 1.6 km, where the two runs differ the most, snowstorm, Weather Forecast., 13, 893–920, doi:10.1175/1520-0434 the mean absolute error (MAE) of temperature is 1.02C (1998)013<0893:MMSOTJ>2.0.CO;2. Bougeault, P., and P. Lacarrère (1989), Parameterization of orographic (0.66 C) in MLST_Ice for the initial stage (demise stage), induced turbulence in a mesobeta scale model, Mon. Weather Rev., whereas it is 1.87 C (1.48 C) in NLST_Ice (Table 3). Within 117, 1872–1890, doi:10.1175/1520-0493(1989)117<1872:POOITI>2.0. the same layer of the atmosphere, the MAE of the mixing CO;2. Braham, R., Jr., and R. 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