Contributions of Lake-Effect Periods to the Cool-Season Hydroclimate of the Great Salt Lake Basin

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1 KRISTEN N. YEAGER,* W. JAMES STEENBURGH, AND TREVOR I. ALCOTT Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

(Manuscript received 14 March 2012, in ﬁnal form 9 August 2012)

ABSTRACT

Although smaller lakes are known to produce lake-effect precipitation, their influence on the precipitation climatology of lake-effect regions remains poorly documented. This study examines the contribution of lake- effect periods (LEPs) to the 1998–2009 cool-season (16 September–15 May) hydroclimate in the region surrounding the Great Salt Lake, a meso-b-scale hypersaline lake in northern Utah. LEPs are identified sub- jectively from radar imagery, with precipitation (snow water equivalent) quantified through the disaggregation of daily (i.e., 24 h) Cooperative Observer Program (COOP) and Snowpack Telemetry (SNOTEL) observations using radar-derived precipitation estimates. An evaluation at valley and mountain stations with reliable hourly precipitation gauge observations demonstrates that the disaggregation method works well for estimating precipitation during LEPs. During the study period, LEPs account for up to 8.4% of the total cool-season precipitation in the Great Salt Lake basin, with the largest contribution to the south and east of the Great Salt Lake. The mean monthly distribution of LEP precipitation is bimodal, with a primary maximum from October to November and a secondary maximum from March to April. LEP precipitation is highly variable between cool seasons and is strongly influenced by a small number of intense events. For example, at a lowland (mountain) station in the lake-effect-precipitation belt southeast of the Great Salt Lake, just 12 (13) events produce 50% of the LEP precipitation. Although these results suggest that LEPs contribute modestly to the hydroclimate of the Great Salt Lake basin, infrequent but intense events have a profound impact during some cool seasons.

1. Introduction that affect mineral industries, shoreline and aquatic ecosystems, natural resource management, and trans- Lake-effect precipitation is a potentially important portation (Arnow 1980; Gwynn 1980; Mohammed and component of the water cycle near large inland bodies of Tarboton 2011; see also information from the U.S. water, including the Great Salt Lake (GSL) of northern Geological Survey Utah Water Science Center avail- Utah. As a terminal lake within a closed hydrologic basin, able online at http://ut.water.usgs.gov/greatsaltlake/). the GSL serves as a collector and integrator of climate Lake-effect precipitation (primarily snow) occurs variability and change (Lall and Mann 1995; Lall et al. over northern Utah several times per year (Steenburgh 1996; Mohammed and Tarboton 2011). Imbalances be- et al. 2000; Alcott et al. 2012) and contributes to the GSL tween lake inflows, which are dominated by surface-water water budget through direct precipitation on the lake runoff (66%) and direct precipitation on the lake (31%), and the buildup of a mountain snowpack that drives and outflows, which consist entirely of evaporation, cause much of the surface-water runoff within the GSL basin, changes in lake level, area, and composition (e.g., salinity) which serves as the primary water resource for 400 000 people in Salt Lake City (Salt Lake City Department of Public Utilities 1999). Lake-effect precipitation also * Current affiliation: NOAA/National Weather Service Fore- provides a path for water recycling (Eltahir and Bras cast Office, Cleveland, Ohio. 1 Current affiliation: NOAA/National Weather Service West- 1996) because evaporation from the lake contributes to ern Region Headquarters, Salt Lake City, Utah. a portion of the water mass that falls as precipitation (Onton and Steenburgh 2001) and eventually returns as surface-water runoff. Corresponding author address: Dr. W. James Steenburgh, Dept. of Atmospheric Sciences, University of Utah, 135 South 1460 East, Lake-effect precipitation can contribute to substantial Rm. 819, Salt Lake City, UT 84112. snow accumulations over northern Utah. Carpenter E-mail: [email protected] (1993) describes a lake-effect snowstorm that produced

DOI: 10.1175/JAMC-D-12-077.1

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with the year defined by the ending calendar year), with autumn and spring peaks in frequency separated by a midwinter minimum. Radar reflectivities indicate that lake-effect precipitation is greatest to the south and east of the GSL and is most common in the overnight and early-morning hours (Steenburgh et al. 2000; Steenburgh and Onton 2001; Onton and Steenburgh 2001). No previous study quantifies how much precipitation is produced seasonally during lake-effect periods (LEPs) around the GSL or, to our knowledge, similar meso- b-scale (20–200 km; Orlanski 1975) bodies of water in other regions. Such estimates have been made, however, for larger bodies of water such as the Laurentian Great Lakes by using a variety of approaches (e.g., Changnon 1968; Eichenlaub 1970; Braham and Dungey 1984; Scott and Huff 1996, 1997). For example, Changnon (1968) compares snowfall amounts near the climatological up- wind (western) and downwind (eastern) shorelines of Lake Michigan, Eichenlaub (1970) examines how much snow is produced during periods when the synoptic conditions are favorable for lake effect, and Braham and Dungey (1984) and Scott and Huff (1996, 1997) calculate the enhancement relative to an estimate of non-lake- effect precipitation obtained by interpolating precipitation amounts from outside the lake-effect snowbelts. Scott and Huff (1996, 1997) estimate that lake effect more than doubles the mean wintertime snowfall east of Lake

FIG. 1. GSL basin, subbasins, and topography Superior and yields increases of 90% southeast of Lake (following inset scale). Huron, 35% east of Lake Michigan, and 40% east of Lakes Erie and Ontario. Complex topography strongly inﬂuences precipitation up to 69 cm of snow in the Salt Lake Valley and 102 cm around the GSL and precludes the application of rela- in the adjacent Wasatch Mountains (see Fig. 1 for geo- tively simple approaches like those described above. graphic locations). In an analysis of the so-called 22– The GSL is oriented from northwest to southeast, with 27 November 2001 Hundred-Inch Storm, Steenburgh the Wasatch Mountains to the east and the Oquirrh and (2003) attributed 1.45 cm of the snow water equiva- Stansbury Mountains to the south (Fig. 1). The GSL has lent(SWE)thatfellatSaltLakeCityInternational an average surface area of ;4400 km2, making it much Airport (KSLC) and 5.54 cm of the SWE that fell at smaller than Lake Ontario (;19 000 km2), which is the Alta–Collins (CLN) observing station in the Wasatch the smallest of the Laurentian Great Lakes. The hy- Mountains to two lake-effect periods. Beyond potential drologic basin of the GSL spans four states (Utah, impacts on water resources, lake-effect snowstorms help Wyoming, Nevada, and Idaho) and encompasses a total 2 to fuel Utah’s $1.2 billion yr 1 ski industry and reputation area of 89 000 km2. Because of the small contribution of for the ‘‘Greatest Snow on Earth’’ (Steenburgh and groundwater from the west desert, however, the basin has Alcott 2008; Gorrell 2011). an effective area of 55 000 km2 (Lall and Mann 1995; Previous studies illustrate the environmental condi- Great Salt Lake Information System 2011). The lower- tions, seasonality, and spatial distribution of the GSL elevation basins and valleys receive 10–65 cm of pre- effect, which occurs during cold-air outbreaks when cipitation (SWE) annually, whereas much larger amounts localized sensible heating and latent heating over the (100–1301 cm) fall in the mountains. relatively warm water lead to the development of pre- In this research, we develop and apply a technique cipitating moist convection (Carpenter 1993; Steenburgh to estimate the contribution of precipitation (SWE) et al. 2000; Steenburgh and Onton 2001; Alcott et al. produced during LEPs to the 1998–2009 cool-season 2012). Alcott et al. (2012) identify an average of 13 GSL- hydroclimate of the Great Salt Lake basin. The tech- effect periods per cool season (16 September–15 May, nique uses high-frequency radar-derived precipitation

Unauthenticated | Downloaded 10/08/21 02:13 AM UTC FEBRUARY 2013 Y E A G E R E T A L . 343 estimates to produce an hourly precipitation dataset intervals, and 4) partitioning of SWE amounts into lake- from gauge-based daily (24 h) observations, which can effect and non-lake-effect periods. then be used to partition the observed precipitation a. Construction of a serially complete precipitation into lake-effect and non-lake-effect periods. Although gauge dataset our results suggest that LEPs contribute modestly to the cool-season hydroclimate of the GSL basin, infre- The serially complete precipitation gauge dataset uses quent but intense events have a profound influence daily SWE observations from the National Weather during some cool seasons. Service (NWS) Cooperative Observer Program (COOP) and Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) stations (Fig. 2a). 2. Data and methods COOP and SNOTEL data were obtained from the Utah LEP SWE estimates were produced for the 1998–2009 Climate Center at Utah State University and the NRCS cool seasons. These cool seasons include 128 LEPs website, respectively. Most COOP stations are in valley (Table 1), which were identified by Alcott et al. (2012) locations, with citizen weather volunteers providing on the basis of visual inspection of the ‘‘KMTX’’ manual SWE measurements at 0.01-in. (0.25 mm) res- Weather Surveillance Radar-1988 Doppler (WSR- olution from an 8-in. diameter (20.3 cm) precipitation 88D) radar-reflectivity imagery (see Fig. 1 for KMTX gauge (NWS 1989; Daly et al. 2007). Most of the pre- location).1 The identification method used by Alcott cipitation gauges are unshielded, with two known ex- et al. (2012) follows that of Laird et al. (2009) and defines ceptions in the study area: KSLC and the Ogden Pioneer LEPs as periods of at least 1 h during which precipi- Power House (NWS 1989; S. Summy, NWS, 2011, per- tation features 1) were coherent and quasi-stationary sonal communication). In addition, if the precipitation with a distinct connection to the lake, 2) were shallow gauge measurement at KSLC is questionable, a manual and distinguishable from large, transitory synoptic fea- measurement is taken and the observation record is ad- tures, and 3) exhibited increasing depth and/or intensity justed accordingly (S. Summy, NWS, 2011, personal in the downwind direction. communication). Since the time of the observation is not Generation of LEP SWE estimates follows the ap- consistent throughout the COOP network (varying from proach of Wu¨ est et al. (2010) who disaggregated daily 1400 to 0700 UTC for the stations used in this study), the precipitation gauge observations using radar-derived times were obtained from the National Climatic Data SWE estimates to produce an hourly SWE dataset for Center (NCDC) to enable more accurate disaggregation Switzerland. Radar-derived SWE estimates have high of the daily SWE amounts. temporal resolution (typically every 6–10 min) but suffer SNOTEL stations are located primarily in the moun- from low absolute accuracy, especially in complex terrain tains and provide automated hourly and daily accu- (e.g., Westrick et al. 1999; Rasmussen et al. 2001). Daily mulated SWE measurements from a large storage precipitation gauge observations provide greater abso- precipitation gauge at 0.10-in. (2.54 mm) resolution lute accuracy but lack the temporal resolution needed to (Hart et al. 2004; NRCS 2011). The precipitation gauge isolate the SWE produced during LEPs, which are typi- is approximately 12 in. (30.5 cm) in diameter, has an cally less than 24 h in duration and frequently cross the Alter shield around the orifice to reduce wind effects boundaries of observing periods. Therefore, we use the on catchment, and contains antifreeze to melt frozen temporally resolved radar-derived SWE estimates to precipitation and oil to prevent evaporation (Serreze disaggregate the daily precipitation gauge observations et al. 1999; Wallis et al. 2007; R. Julander, NRCS, 2010, into hourly intervals, a procedure that preserves the daily personal communication). This design makes the SWE totals and enables the separation of accumulated SNOTEL precipitation gauge more accurate for frozen SWE into lake-effect and non-lake-effect periods. The precipitation than a conventional tipping-bucket gauge, method involves four steps: 1) construction of a serially but foreign objects falling into the gauge and thermal complete precipitation gauge dataset, 2) calculation of expansion and contraction of the aluminum cylinder hourly radar-derived SWE estimates, 3) disaggregation can produce false precipitation fluctuations (Kuligowski of daily precipitation gauge observations into hourly 1997; R. Julander, NRCS, 2010, personal communication). The thermal expansion and contraction combined with low (2.54 mm) data resolution limit the accuracy of the hourly accumulations. Therefore, we opt to disag- 1 This is one fewer cool season than the number investigated by gregate the daily data. Alcott et al. (2012), who extended their climatological dataset to Precipitation gauges provide a direct measurement at include 2010 during the course of this project. a discrete point but are susceptible to systematic and

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TABLE 1. LEP onset and ending times and evaluation stations. The latter identify the subset of LEPs used in section 3 for the evaluation of the disaggregation technique at KSLC (105 total LEPs) and CLN (78 total LEPs). Coverage is not complete for each station because of missing or unavailable radar or hourly precipitation data. Blank indicates that missing or unavailable radar (as indicated by footnote) or hourly precipitation data precluded evaluation of the disaggregation technique at both sites.

Lake-effect period Onset time (UTC) Ending time (UTC) Evaluation stations 1 2336 11 Oct 1997 0028 13 Oct 1997 KSLC 2 0339 24 Oct 1997 2041 24 Oct 1997 KSLC 3 0219 24 Dec 1997 0800 24 Dec 1997 KSLC 4 1318 30 Jan 1998 0503 31 Jan 1998 KSLC 5 2240 11 Feb 1998 0532 12 Feb 1998 KSLC 6a 0536 27 Feb 1998 2032 27 Feb 1998 7 0426 4 Mar 1998 1626 4 Mar 1998 KSLC 8 1507 29 Mar 1998 1721 29 Mar 1998 KSLC 9 0325 30 Mar 1998 2028 30 Mar 1998 KSLC 10 0958 2 Apr 1998 1956 2 Apr 1998 KSLC 11 0504 7 Apr 1998 1856 7 Apr 1998 KSLC 12 0339 8 Apr 1998 1653 8 Apr 1998 KSLC 13b 0356 15 Apr 1998 1214 15 Apr 1998 14 0428 4 Oct 1998 1421 4 Oct 1998 KSLC 15 1318 16 Oct 1998 2330 16 Oct 1998 KSLC 16 0603 3 Nov 1998 1815 3 Nov 1998 KSLC 17 0403 6 Nov 1998 0807 6 Nov 1998 KSLC 18 0328 9 Nov 1998 1009 10 Nov 1998 KSLC 19 1309 19 Nov 1998 1701 19 Nov 1998 KSLC 20 0419 5 Dec 1998 2300 5 Dec 1998 KSLC, CLN 21 1959 6 Dec 1998 2108 7 Dec 1998 KSLC, CLN 22 0141 20 Dec 1998 0822 20 Dec 1998 KSLC 23 1055 20 Dec 1998 0355 21 Dec 1998 24 0853 10 Feb 1999 0920 11 Feb 1999 KSLC, CLN 25 1114 5 Mar 1999 1640 5 Mar 1999 KSLC, CLN 26 0247 12 Mar 1999 1244 12 Mar 1999 KSLC, CLN 27 0610 2 Apr 1999 1528 2 Apr 1999 KSLC, CLN 28 0256 3 Apr 1999 1139 3 Apr 1999 KSLC, CLN 29 0323 4 Apr 1999 0953 4 Apr 1999 KSLC, CLN 30 0930 10 Apr 1999 1503 10 Apr 1999 KSLC 31 0540 5 May 1999 1550 5 May 1999 KSLC 32 0642 29 Oct 1999 1623 29 Oct 1999 KSLC 33 1620 21 Nov 1999 1759 21 Nov 1999 KSLC, CLN 34 2315 21 Nov 1999 2246 23 Nov 1999 KSLC, CLN 35 0459 3 Dec 1999 1025 3 Dec 1999 KSLC, CLN 36 2230 10 Dec 1999 1328 11 Dec 1999 KSLC, CLN 37 0210 14 Dec 1999 1354 14 Dec 1999 KSLC 38 1454 2 Jan 2000 2136 2 Jan 2000 KSLC, CLN 39 1037 3 Jan 2000 1731 3 Jan 2000 KSLC, CLN 40 0018 6 Jan 2000 1024 6 Jan 2000 KSLC, CLN 41 1147 10 Mar 2000 1933 10 Mar 2000 KSLC, CLN 42a 0928 15 Apr 2000 1425 15 Apr 2000 43 0647 19 Apr 2000 1004 19 Apr 2000 KSLC, CLN 44 0939 24 Apr 2000 1331 24 Apr 2000 KSLC, CLN 45 0754 11 May 2000 1558 11 May 2000 KSLC 46 0905 12 May 2000 1421 12 May 2000 KSLC 47a 0732 31 Oct 2000 1414 31 Oct 2000 48b 1420 1 Nov 2000 2126 1 Nov 2000 49b 0944 5 Nov 2000 1857 5 Nov 2000 50b 1722 6 Nov 2000 2042 6 Nov 2000 51 0657 9 Nov 2000 2023 9 Nov 2000 KSLC 52 1000 10 Nov 2000 1734 10 Nov 2000 KSLC 53 1408 13 Nov 2000 2229 13 Nov 2000 KSLC, CLN 54 2332 15 Nov 2000 1935 16 Nov 2000 KSLC, CLN 55 0536 9 Apr 2001 1615 9 Apr 2001 KSLC, CLN

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TABLE 1. (Continued)

Lake-effect period Onset time (UTC) Ending time (UTC) Evaluation stations 56 0638 22 Apr 2001 1430 22 Apr 2001 KSLC, CLN 57 0519 24 Oct 2001 0811 24 Oct 2001 KSLC 58b,c 0905 23 Nov 2001 0441 24 Nov 2001 59b,c 1633 25 Nov 2001 1635 27 Nov 2001 60b 0736 28 Nov 2001 1555 28 Nov 2001 61 0808 4 Dec 2001 1720 4 Dec 2001 KSLC, CLN 62 1059 11 Dec 2001 2350 11 Dec 2001 KSLC, CLN 63 0142 12 Dec 2001 1632 12 Dec 2001 KSLC, CLN 64 1436 15 Dec 2001 2346 15 Dec 2001 KSLC, CLN 65 0553 16 Jan 2002 1751 16 Jan 2002 KSLC 66 1518 18 Jan 2002 2120 18 Jan 2002 KSLC 67 1308 14 Mar 2002 1726 14 Mar 2002 KSLC, CLN 68 0737 15 Mar 2002 1038 15 Mar 2002 KSLC 69 0813 16 Apr 2002 1303 16 Apr 2002 KSLC, CLN 70 0913 29 Oct 2002 1830 29 Oct 2002 KSLC 71 0018 30 Oct 2002 0652 30 Oct 2002 KSLC, CLN 72 1121 11 Nov 2002 1945 11 Nov 2002 KSLC, CLN 73 0245 6 Apr 2003 0436 6 Apr 2003 KSLC, CLN 74 0505 7 Apr 2003 1414 7 Apr 2003 KSLC, CLN 75a 1445 30 Oct 2003 0118 31 Oct 2003 76 0431 1 Nov 2003 0847 1 Nov 2003 KSLC 77 0316 2 Nov 2003 1818 2 Nov 2003 78 1505 22 Nov 2003 1330 23 Nov 2003 KSLC, CLN 79 1640 27 Dec 2003 0145 28 Dec 2003 KSLC, CLN 80 2005 3 Jan 2004 0806 4 Jan 2004 KSLC, CLN 81 1032 21 Apr 2004 1638 21 Apr 2004 KSLC, CLN 82 1135 29 Apr 2004 1827 29 Apr 2004 KSLC, CLN 83a 0321 12 May 2004 1422 12 May 2004 84 0711 13 May 2004 1144 13 May 2004 KSLC 85 1022 31 Oct 2004 0710 1 Nov 2004 KSLC 86 1241 20 Nov 2004 1946 20 Nov 2004 KSLC, CLN 87 0926 30 Mar 2005 2158 30 Mar 2005 KSLC, CLN 88 0210 27 Nov 2005 2126 27 Nov 2005 KSLC, CLN 89 0603 14 Dec 2005 0955 14 Dec 2005 KSLC, CLN 90 0009 16 Jan 2006 2044 16 Jan 2006 CLN 91 0456 16 Feb 2006 2146 16 Feb 2006 KSLC, CLN 92 1018 12 Mar 2006 1744 12 Mar 2006 KSLC, CLN 93 1551 17 Apr 2006 1758 18 Apr 2006 KSLC, CLN 94 1630 20 Sep 2006 0026 21 Sep 2006 KSLC 95 1121 22 Sep 2006 2100 22 Sep 2006 KSLC, CLN 96 1611 17 Oct 2006 2209 17 Oct 2006 KSLC, CLN 97 0641 29 Nov 2006 1908 29 Nov 2006 CLN 98 1059 2 Dec 2006 1944 2 Dec 2006 KSLC, CLN 99 1042 5 Jan 2007 1742 5 Jan 2007 KSLC, CLN 100 2257 11 Jan 2007 0317 12 Jan 2007 CLN 101 0317 24 Feb 2007 1552 24 Feb 2007 KSLC, CLN 102 0157 30 Sep 2007 0411 30 Sep 2007 KSLC, CLN 103 0431 18 Oct 2007 1125 18 Oct 2007 CLN 104 0137 21 Oct 2007 1340 21 Oct 2007 KSLC, CLN 105 0110 21 Nov 2007 0443 21 Nov 2007 106 1111 28 Nov 2007 1710 28 Nov 2007 KSLC, CLN 107 0212 2 Dec 2007 0834 2 Dec 2007 KSLC, CLN 108 0315 14 Dec 2007 0402 15 Dec 2007 CLN 109 0226 21 Dec 2007 2333 21 Dec 2007 KSLC, CLN 110 0703 27 Dec 2007 0011 28 Dec 2007 KSLC, CLN 111 2119 15 Jan 2008 1947 16 Jan 2008 CLN 112 0853 4 Feb 2008 0545 5 Feb 2008 KSLC, CLN 113 0502 26 Feb 2008 0838 26 Feb 2008 KSLC, CLN

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TABLE 1. (Continued)

Lake-effect period Onset time (UTC) Ending time (UTC) Evaluation stations 114 0120 16 Mar 2008 1805 16 Mar 2008 KSLC, CLN 115 1454 29 Mar 2008 2003 29 Mar 2008 CLN 116 0326 10 Apr 2008 0934 10 Apr 2008 KSLC, CLN 117 0435 11 Apr 2008 1801 11 Apr 2008 KSLC, CLN 118 0955 16 Apr 2008 1632 16 Apr 2008 CLN 119 0600 20 Apr 2008 0936 20 Apr 2008 KSLC, CLN 120 0419 1 May 2008 1625 1 May 2008 KSLC, CLN 121 2106 11 Oct 2008 0623 12 Oct 2008 KSLC, CLN 122 0623 12 Oct 2008 1340 13 Oct 2008 KSLC, CLN 123 0553 5 Nov 2008 0348 6 Nov 2008 KSLC, CLN 124 0153 14 Dec 2008 1210 14 Dec 2008 KSLC, CLN 125 0311 26 Dec 2008 0726 26 Dec 2008 KSLC, CLN 126 1034 26 Dec 2008 0928 27 Dec 2008 KSLC, CLN 127 1409 27 Feb 2009 1701 27 Feb 2009 KSLC, CLN 128 0046 23 Mar 2009 0711 24 Mar 2009 KSLC, CLN a Missing level-III radar data. b Missing level-III and level-II radar data. c Part of the Hundred-Inch Storm described by Steenburgh (2003). random errors (Kuligowski 1997; Sieck et al. 2007; issues and infrequent maintenance due to their remote Vasiloff et al. 2007). Systematic errors include under- location can result in errors. COOP observations are catch during high winds, which likely averages ;10%– subject to observer bias, which may include under- 15% for SNOTEL gauges (Rasmussen et al. 2011) and reporting of light events and overreporting of events higher percentages for unshielded COOP gauges. Such divisible by 0.05 and 0.1 in. (Daly et al. 2007). errors are not accounted for in our statistics but are NRCS performs several levels of quality control on discussed where relevant in the paper. SNOTEL obser- the daily SNOTEL observations, including manual in- vations are otherwise generally reliable, but mechanical spection by a hydrologist, before archiving them on their

FIG. 2. Surface stations used in the study (open circles indicate COOP, 3 symbols mark SNOTEL, the asterisk is KSLC, and the ﬁlled diamond indicates CLN). Blue indicates stations that are located to the southeast of GSL that were used in Fig. 9, and red indicates stations that were eliminated from the analysis because of full blockage of the KMTX 0.58 radar scan. (a) Terrain background (as in Fig. 1). (b) Radar beam blockage (following inset scale).

Unauthenticated | Downloaded 10/08/21 02:13 AM UTC FEBRUARY 2013 Y E A G E R E T A L . 347 website (R. Julander, NRCS, 2010, personal communi- because of the wide variation in COOP station observing cation; NRCS 2011). Although this does not eliminate times, only surrounding stations that report within 3 h of all sources of error, we have assumed that the data are the missing-data station are used in the SWE estimate. reliable, and we perform no additional quality con- The SNOTEL records are more complete than the trol.Tohelptolimitsomeoftheproblemswith COOP records, with no more than 9 days of missing COOP observations, we use only COOP stations that SWE observations at any station. Since SNOTEL sta- report nearly continuously during the study period. tions report accumulated SWE, we assume that no SWE Here, nearly continuously means missing less than fell on these missing days if there was no change in ac- 290 (;10%) of all possible daily SWE observations cumulated SWE between the preceding and following and less than 13 (;10%) of all possible daily SWE days. Otherwise we use an estimate that is based on the observations during LEPs. These criteria limit our application of the normal-ratio method described above analysis to the most frequently reporting COOP sta- to surrounding SNOTEL stations. tions, which we hope reduces issues related to ob- b. Calculation of hourly radar-derived SWE server bias and identifies the most reliable stations. We estimates also examined all daily SWE amounts of $50 mm and eliminated five that were clearly erroneous as deduced Estimating precipitation rate from radar reflectivity from manual checks of surrounding observations. typically involves the use of an empirically derived For COOP stations that meet the criteria above, we power-law (i.e., Z–R) relationship of the form used the normal-ratio method (Paulhus and Kohler 1952; Young 1992; Eischeid et al. 2000) to estimate SWE on Z 5 aRb , days with missing or erroneous data. First, we calculate 2 the climatological linear correlation coefficient between where Z is the radar-reflectivity factor (mm6 m 3), R is 2 the observed daily SWE at the missing data station and the rainfall rate (mm h 1), and a and b are constants surrounding COOP stations. This is done by month to (Doviak and Zrnic 1993; Rinehart 2004). The optimal account for seasonality in the spatial distribution of SWE. Z–R relationship varies with storm type, precipitation

Then, we compute a weight Wi for each surrounding type, and location (Rinehart 2004; Doviak and Zrnic station i from 1993; Rasmussen et al. 2003). For estimating precipitation rates during snow, previous studies have derived r2(n 2 2) W 5 i i , an analogous Z–S relationship (where S is the SWE i 2 2 1 ri rate), with a ranging from 40 to 3300 and b ranging from 0.88 to 2.2 (Gunn and Marshall 1958; Ohtake and Henmi where ri is the correlation coefficient between station i 1970; Sekon and Srivastava 1970; Carlson and Marshall and the missing data station and ni is the number of days 1972; Puhakka 1975; Fujiyoshi et al. 1990; Rasmussen used to calculate the correlation coefficient [Eischeid et al. 2003; Warning Decision Training Branch 2011). et al. 2000, their Eq. (1); Young 1992]. We then calculate For KMTX, Vasiloff (2001) recommends Z 5 75S2,which the SWE estimate M from provides a nearly one-to-one linear fit between storm- total radar estimates and precipitation gauge observa- N 5 å tions in the GSL basin, although considerable variability M wisi , i51 exists in the quality of fit from station to station. The NWS Warning Decision Training Branch currently rec- 2 where N is the number of surrounding stations, si is the ommends Z 5 40S for snowstorms in the Intermountain SWE observation at station i, and wi is the relative West. We have used the Vasiloff (2001) relationship, al- weight at surrounding station i, as given by though both relationships yield the same results since the disaggregation process is sensitive only to the exponent W w 5 i . b and not to the coefficient a. For convenience, we apply i N å this relationship to all LEPs, although a small fraction Wi i51 likely produced rain in the lower elevations. Reflectivity values come from the lowest elevation In calculating M, we use only the four most highly scan (;0.58) of the KMTX radar. For each scan, the correlated surrounding stations (i.e., N 5 4) since maximum radar reflectivity in a nine-pixel stencil cen- Eischeid et al. (2000) found that the inclusion of more tered on each COOP and SNOTEL station is identified than four stations does not significantly improve the esti- and converted to a SWE rate. The nine-pixel stencil mate and, in some cases, may actually degrade it. Further, helps to minimize the effects of wind displacement of the

Unauthenticated | Downloaded 10/08/21 02:13 AM UTC 348 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 52 snow from the elevated radar scan to the ground-level gauge total over all 24 h (i.e., the same SWE amount will observing station (Doviak and Zrnic 1993). SWE rates be recorded for each hour). For this reason, 14 stations are converted into SWE amounts over each interval, lying in areas with full radar beam blockage, as de- defined as the time between radar scans, by taking the termined by following the method of Wood et al. (2003), average SWE rate of the surrounding radar scans and were eliminated (Fig. 2b), leaving 55 for analysis. multiplying it by the interval. Hourly SWE amounts are d. Partitioning of SWE amounts based on the integration of the SWE amounts calculated for all intervals during a given hour. This process is The disaggregated hourly SWE amounts are then completed for all hours during all observing days that partitioned into LEPs and non-LEPs on the basis of the include an LEP. onset and ending times of each LEP (Table 1). Since it To minimize storage space and processing time, we is not possible to separate the precipitation produced primarily use radar data archived in level-III [also by synoptic, orographic, and lake-effect processes and known as Next Generation Weather Radar Information there are some LEPs during which these processes op- Dissemination Service (NIDS)] format (Baer 1991), erate in concert (Steenburgh et al. 2000; Alcott et al. which has a reflectivity resolution of 5 dBZ and a spatial 2012), the LEP SWE may overestimate the total lake- resolution of 1831 km. The level-III data are typically effect precipitation, as discussed where appropriate later available every 6–10 min, but there are reporting gaps. in this paper. SWE amounts during radar outages of 3 h or less are estimated in the same manner as above, by taking the 3. Evaluation of disaggregation method average SWE rate of the scans surrounding the outage and multiplying it by the length of the outage. For lake- To evaluate the accuracy of the disaggregation method, effect periods with radar outages that are greater than we compare disaggregated estimates of hourly and total 3 h (9.4% of all lake-effect periods; Table 1), hourly SWE during LEPs with reliable hourly precipitation SWE amounts are estimated using level-II data, which gauge observations at two surface stations: KSLC and have the same spatial resolution (1831 km) as level III CLN. The disaggregated estimates derive from daily but a much higher data resolution (0.5 dBZ; Crum et al. (0000–0000 UTC) SWE accumulations summed from 1993). If level-II radar data are also missing (5.5% of all the hourly observations. This enables a direct evaluation lake-effect periods; Table 1), hourly SWE amounts are that is not possible at stations with only daily data. estimated by interpolating 3-h SWE rates from the KSLC is a manually augmented Automated Surface North American Regional Reanalysis (Mesinger et al. Observing System station operated by the Salt Lake City 2006). NWS Forecast Office at an elevation of 1288 m in the Salt Lake Valley (Fig. 2a). During the first part of the c. Disaggregation of daily precipitation gauge study period, the station was equipped with a heated observations tipping bucket, which was replaced with an all-weather Using the hourly radar-derived SWE estimates, the precipitation accumulation gauge (AWPAG) in July of disaggregated hourly precipitation gauge SWE Gt is 2004 (Groisman et al. 1999; Greeney et al. 2007; NWS calculated from 2011). The heated tipping bucket measures hourly SWE at a resolution of 0.01 in. (0.25 mm) and works by E G 5 t G , melting frozen precipitation before it enters the tipping t 24 d å apparatus (Groisman et al. 1999). The AWPAG weighs Et t51 accumulated SWE at a resolution of 0.01 in. (0.25 mm) and includes a wind shield to reduce undercatch (Greeney where Et is the radar-derived hourly SWE estimate, Gd et al. 2007; Tokay et al. 2010). KSLC data were obtained is the daily precipitation gauge observation, and t is the from NCDC. hour [Wu¨ est et al. 2010, their Eq. (1)]. This disaggre- CLN is a midmountain (2945 m) station in the gation is performed for all days that include an LEP. Wasatch Mountains southeast of KSLC (Fig. 2a). Hourly The disaggregation method significantly reduces quan- SWE is measured at a resolution of 0.01 in. (0.25 mm) titative biases produced by radar-derived SWE estimates using a shielded 8-in. weighing gauge that contains anti- (e.g., Doviak and Zrnic 1993; Vasiloff 2001; Rasmussen freeze and a circulating device to minimize snow buildup et al. 2003) but does not completely eliminate them on gauge walls. The snow safety staff at Alta Ski Area (Wuëst et al. 2010), as discussed in section 3. Further, in collected and provided the CLN data. areas with complete radar beam blockage, the disaggre- The hourly SWE observations from KSLC and CLN gation will completely smooth the daily precipitation were not subjected to quality control. We concentrate

Unauthenticated | Downloaded 10/08/21 02:13 AM UTC FEBRUARY 2013 Y E A G E R E T A L . 349 the evaluation on a subset of LEPs that coincide with days with complete hourly data coverage at each station (Table 1). The coverage at KSLC is largely complete (105 out of 128 possible LEPs), but at CLN the LEPs available for evaluation begin in December of 1998 and are conﬁned mainly to November–April, which are the months when the ski area is in operation (78 LEPs).2 We evaluate the accuracy of the disaggregation technique using scatterplots, frequency distributions, and three metrics—the mean bias error, mean absolute error, and total percent error:

N 5 1 å 2 mean bias error Ei Oi , N i51

N 5 1 å j 2 j mean absolute error Ei Oi , and N i51

N å 2 Ei Oi 5 total percent error 5 i 1 , N å Oi i51 where E is the disaggregated estimate, O is the observed value, N is the number of data pairs, and i is the hour. Figure 3a presents a scatterplot of hourly disaggregated versus observed SWE at KSLC during LEPs. There are 1199 hourly estimates, with a correlation of 0.75. The hourly estimates have some errors that appear to be quasi-randomly distributed, but with a tendency for underestimation for hours with high observed SWE. Figure 4a, however, shows that the total disaggregated SWE at KSLC for the LEPs has a much higher correlation (0.92) with observed amounts. Although this partly reflects the quasi-randomness of the hourly errors, longer time periods are inherently more likely to yield a better fit. For example, the disaggregation technique would yield a perfect estimate for a 24-h LEP that coincides FIG. 3. Hourly disaggregated vs observed SWE (mm) during with the period of the daily precipitation gauge data. LEPs at (a) KSLC and (b) CLN. The solid and dashed lines indicate one-to-one and regression lines, respectively. Consistent with these results, the mean absolute and bias errors for disaggregated LEP SWE at KSLC are 0.46 and 20.02 mm, respectively. The small bias error is reflected The scatterplot of hourly disaggregated versus ob- in the bias-error frequency distribution, which is quasi served SWE at CLN during LEPs exhibits similar scatter normal with limited skew (Fig. 5a). but has a slightly larger correlation (0.76) than is seen for KSLC (Fig. 3b). Like for KSLC, there is some tendency to underestimate high SWE hours, but, when integrated for the LEPs, the agreement between the total disaggregated and observed SWE is very good (0.99 corre- 2 Because the LEP coverage is incomplete, the hourly SWE lation; Fig. 4b). The mean absolute and bias errors for observations are only used only for the evaluation of the disag- 2 gregation technique. The climatology presented in section 4 is disaggregated LEP SWE are 1.04 and 0.77 mm, re- based on disaggregated daily data from KSLC and does not include spectively. The bias-error frequency distribution is quasi CLN because of its more limited observing period. normal, but with a larger negative skew than at KSLC

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FIG. 5. Frequency distribution of disaggregated LEP SWE bias error (mm) at (a) KSLC and (b) CLN. The bins are based on ranges of two-thirds. CLN has two outlying bias errors of 26.56 and 29.21 mm (not shown). Inset abbreviations include mean bias error (MBE), mean absolute error (MAE), and total percent error (TPE).

all storms given the wide variety of hydrometeors and FIG. 4. Disaggregated vs observed LEP SWE (mm) at (a) KSLC precipitation processes (Doviak and Zrnic 1993; and (b) CLN. The solid and dashed lines indicate one-to-one and Rasmussen et al. 2001). Also contributing to Z–S errors regression lines, respectively. are issues related to the overshooting of shallow storms, evaporation and sublimation below the lowest-elevation radar scans, incomplete beam filling, and bright banding (Fig. 5b), illustrating that the disaggregation method (Doviak and Zrnic 1993; Vasiloff 2001; Rasmussen et al. tends to underestimate LEP SWE at CLN. 2003; Wuëst et al. 2010). For example, if the radar over- Hourly disaggregation errors stem from several sour- shoots a storm for a portion of the day, the disaggregation ces. The first is representativeness error arising from will underestimate the precipitation rate during that pe- differences between the volume (1831 km) and point riod and overestimate it during the remainder of the day measurements made by the radar and precipitation (Wuëst et al. 2010). Likewise, if the precipitation seen by gauge, respectively. In some instances, the radar-derived the radar evaporates or sublimates before reaching the SWE might not be representative of the SWE falling at gauge, the disaggregation will overestimate the precipi- a point beneath the radar volume (e.g., Kitchen and tation rate during that period. Both of these types of Blackall 1992; Habib et al. 2004). errors are common at valley stations like KSLC because The second is the use of a single Z–S relationship, the average altitude of the center of the KMTX 0.58 radar which cannot represent actual precipitation rates in scan is ;1500 m above the valley floor (Wood et al.

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2003). When the radar beam intersects a melting layer it causes high-reflectivity returns, resulting in false precipitation intensity peaks in the disaggregation (Doviak and Zrnic 1993; Wuëst et al. 2010). Incomplete beam filling can result in an underestimation of precipitation in the disaggregation as the radar is only partially sampling the storm. These quantitative errors appear to largely cancel when integrating over long periods of time, resulting in a small total percent error of 21.5% and 211.7% at KSLC and CLN, respectively. Thus, we conclude that the method works reliably for estimating climatological LEP SWE totals.

4. Results a. Cool-season mean and variability To provide spatial context for the results for individual stations, Fig. 6 shows the frequency (%) of radar reflectivities $ 10 dBZ (an approximate threshold for accumulating snow; Steenburgh et al. 2000) during the 128 LEPs. If one neglects the large localized maxima produced by vehicle traffic along Interstate Highways 15, 80, and 215 (Slemmer 1998), the frequencies are greatest to the south and east of the GSL, indicating that these areas should receive more LEP SWE. Outside of this region, there are also maxima over the Stansbury Mountains and the Wasatch Mountains southeast of Utah Lake (see Fig. 1 for locations). The maximum over the Stansbury Mountains largely reflects orographic precipitation that occurs during LEPs since FIG. 6. Frequency of occurrence of radar reflectivity that is lake effect occurs infrequently to the southwest of the $10 dBZ (%, following inset scale) during LEPs. Great Salt Lake (see section 4c). As discussed by Alcott et al. (2012), precipitation produced by non-lake-effect amounts at the Snowbird (SBDU1; 60.4 mm)3 and Dry processes, including orographic precipitation, occurs in Fork (DRFU1; 60.1 mm) SNOTEL stations in the concert with lake effect 38% of the time. The maximum Wasatch and Oquirrh Mountains, respectively (Fig. 7). over the Wasatch Mountains southeast of Utah Lake High LEP SWE is also found at upper-elevation stations likely also results from orographic precipitation. The on the northern slope of the Uinta Mountains, in the terrain in this area has a southwest–northeast orienta- Stansbury Mountains, and in the Wasatch Mountains tion, which is orthogonal to flow from the northwest, the southeast of Utah Lake. High LEP SWE in the latter most common wind direction during LEPs (Alcott et al. two mountain regions likely reflects concomitant oro- 2012). It could also be related to lake effect produced by graphic precipitation or lake effect generated by Utah Utah Lake, which has been observed by local forecasters Lake. In the case of the station on the northern slope of but, to our knowledge, remains undocumented in the the Uinta Mountains, however, an analogous maximum peer-reviewed literature. These examples illustrate that is not evident in the frequency of radar reflectivities of the LEP SWE statistics presented here include some $10 dBZ because of partial beam blockage (cf. Figs. 6 precipitation produced by non-lake-effect processes and 7). Given the lack of radar coverage, it is unclear if and, at some locations, lake effect produced by Utah Lake. Consistent with the frequency of radar reflectivities of 3 SBDU1 is ;2 km west of CLN. As discussed in section 3, CLN $10 dBZ, the mean cool-season LEP SWE is greatest at is not used for the climatology because of its limited period of stations to the south and east of the GSL, with the largest record.

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FIG. 7. Mean cool-season LEP SWE (in millimeters, following the FIG. 8. Mean cool-season LEP fraction (in percent, following the inset scale) at stations in the GSL basin. Maximum is 60.4 mm. inset scale) at stations in the GSL basin. Maximum is 8.4%. stations southeast of the Utah Lake. Although the the high LEP SWE reflects orographic precipitation greatest LEP SWE occurs at SBDU1 in the Wasatch during LEPs, disaggregation errors, or observational Mountains east of the Salt Lake Valley, the highest errors. Given that this site is well east of the GSL, the LEP fractions are found in the Oquirrh Mountains and high LEP SWE is likely not representative of a large the Wasatch Mountains southeast of Utah Lake (cf. lake-effect contribution. Figs. 7 and 8). This occurs because SBDU1 receives more Despite being located in the region with the highest precipitation during non-lake-effect periods given the frequency of radar reflectivities of $10 dBZ,some diversity of storms and flow directions that affect that COOP stations in the Salt Lake and Tooele Valleys portion of the Wasatch Mountains (Dunn 1983). In con- south and east of the GSL observe much less LEP SWE trast, the Oquirrh Mountains and Wasatch Mountains than do SNOTEL stations in the adjacent mountains. southeast of Utah Lake are climatologically drier, and This discrepancy partly reflects orographic effects. The therefore LEP SWE contributes to a greater fraction of fraction of mean cool-season SWE produced during the cool-season precipitation. LEPs (hereinafter the LEP fraction) helps to adjust for It appears, however, that measurement bias is an ad- the inherent SWE gradient between valley and moun- ditional contributor to the low LEP SWE at some valley tain stations and provides a more spatially coherent COOP stations. Figure 9 shows the frequency distribu- map of the mean contribution of LEPs to cool-season tion of LEP fraction for COOP and SNOTEL stations SWE (Fig. 8). LEP fractions above 5% occur at many south and east of the GSL (see Fig. 2 for station loca- stations south and east of the GSL, including those in tions). LEP fractions average 3.4% at COOP stations and the Oquirrh Mountains, Salt Lake Valley, and Wasatch 5.0% at SNOTEL stations, and there are three COOP Mountains east of the Salt Lake Valley, as well as two stations with an LEP fraction of ,1.5%, which is

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FIG. 9. Frequency distribution of LEP SWE fraction at COOP (black) and SNOTEL (gray) stations southeast of the GSL (indicatedbyblueinFig.2). inconsistent with the uniformly high frequency of radar reflectivities of $10 dBZ in this region (Fig. 6). Further- more, the KSLC COOP station, which features a shielded precipitation gauge and manually augmented observations, has the highest lake-effect fraction (5.8%) of the COOP stations. Since LEP precipitation falls primarily as snow, the low LEP fraction at many COOP stations likely reflects undercatch of snowfall by unshielded precipitation gauges. LEP SWE varies greatly between cool seasons, as il- lustrated by two stations in the LEP SWE maximum southeast of the GSL, KSLC, and SBDU1. KSLC, which is located at 1288 m in the Salt Lake Valley, has a mean cool-season LEP SWE of 16 mm, with a range of 3.9– 36.6 mm (Fig. 10a). The corresponding LEP fraction FIG. 10. Cool-season LEP SWE (mm; solid black line) and mean is 5.8%, with a range of 2.9%–14.5%. At SBDU1, fraction (%; dashed gray line) at (a) KSLC and (b) SBDU1. Black which is located at 2938 m in the Wasatch Mountains, the asterisk and gray triangle indicate the LEP SWE and fraction, LEP SWE mean is much larger, 60.4 mm, with a range of respectively, after removal of LEPs during the Hundred-Inch 13.6–127.4 mm (Fig. 10b). The larger value reflects oro- Storm. graphic precipitation enhancement at SBDU1 during LEPs. The LEP fraction mean and range at SBDU1, however, are 5.1% and 1.4%–11.6%, respectively, which The first LEP produced 9.7 and 27.3 mm at KSLC and are comparable to but slightly smaller than those ob- SBDU1, respectively, and the second LEP produced served at KSLC. 20.8 and 80.1 mm, the latter being the largest LEP Both KSLC and SBDU1 exhibit a prominent peak in SWE observed at each site during the study period LEP SWE and fraction during the 2002 cool season (Figs. 11a,b). Removing the SWE produced by these when two intense LEPs occurred during the 22–27 4 two LEPs reduces the 2002 LEP fraction to 2.7% and November 2001 Hundred-Inch Storm (Steenburgh 2003). 1.9% at KSLC and SBDU1, respectively (gray triangles in Figs. 10a and 10b). Cumulative distribution functions of LEP SWE at KSLC and SBDU1 suggest that lake- 4 Each of the two LEPs during the Hundred-Inch Storm includes effect events as large as the second LEP during the an intense midlake band period and portions of the preceding and Hundred-Inch Storm are extremely infrequent (,1% following postfrontal periods identified by Steenburgh (2003). probability of occurrence during the 12 cool-season study

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FIG. 12. Cumulative distribution function of LEP SWE at KSLC (black line) and SBDU1 (gray line).

b. Monthly mean and variability At most stations, the mean monthly LEP SWE exhibits a bimodal distribution (Figs. 15a–i) with a primary peak in the autumn (October–November), a secondary peak in the late winter or early spring (March–April), and an intermediate winter minimum (January–February). This bimodal distribution resembles that of the monthly frequency of LEPs (Table 1; Alcott et al. 2012). At KSLC, maxima in LEP SWE occur in November FIG. 11. Frequency distribution of LEP SWE (mm) at (a) KSLC and (b) SBDU1. Gray dashed line shows the 75th percentile. and April (5.0 and 2.3 mm, respectively) and are sepa- Vertical dotted lines marked with ‘‘break’’ indicate a break in the rated by a January minimum (0.4 mm; Fig. 16a). SBDU1 numbering on the x axis. Largest event in (a) and (b) is the second exhibits a less pronounced bimodal distribution with LEP LEP during the 22–27 Nov 2001 Hundred-Inch Storm. SWE more heavily skewed toward the autumn months (Fig. 16b). The autumn maximum occurs in November (22.1 mm), and a less prominent secondary maximum period; Fig. 12). Nevertheless, infrequent but intense occurs in March (5.4 mm). The winter minimum occurs in LEPs have a profound impact on the overall climatology. February (3.0 mm). The higher mean monthly LEP SWE A mere 12 (25) LEPs at KSLC and 13 (32) LEPs at at SBDU1 relative to KSLC reﬂects orographic en- SBDU1 account for 50% (75%) of the total SWE pro- hancement. At both locations, the November maximum duced during the 128 LEPs included in the 12 cool-season is ampliﬁed by the two LEPs during the Hundred-Inch climatology (Figs. 13a,b). Storm. Removing the SWE produced during these two The importance of infrequent but intense LEPs, which LEPs reduces the mean November LEP SWE to 2.4 and may occur in isolation or possibly in sequence during 13.1 mm at KSLC and SBDU1, respectively (black as- episodes with highly favorable lake and atmospheric terisks in Figs. 16a,b). conditions (e.g., the Hundred-Inch Storm), is further c. Environmental conditions demonstrated by the maximum monthly LEP SWE during the study period (Fig. 14a). The maximum monthly We examined the environmental conditions during LEP SWE exceeds 54 mm at several stations in the LEPs by accumulating the hourly LEP SWE into 12-h Oquirrh and Wasatch Mountains south and east of the windows centered on KSLC upper-air sounding times Great Salt Lake and 27 mm at lower-elevation stations in (0000 and 1200 UTC). Because of missing sounding the northern Salt Lake Valley. These amounts are com- data, this analysis incorporates 92% of the LEP hours parable to or larger than the mean cool-season LEP SWE included in the 12-cool-season climatology discussed at most stations (Fig. 14b), indicating that episodes with above. one or more intense LEPs play a dominant role in the The 700-hPa wind direction is typically used to an- lake-effect hydroclimate of the region. ticipate the location of lake-effect storms and affects

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maximized at 3258, is underscored by the substantially lower fraction of LEP SWE for 2408,D # 2708,3608 (north) , D # 308,and308,D # 608 (Figs. 17a,e,f), although a lower frequency of favorable synoptic conditions during such ﬂow regimes may also contribute. A lake2700-hPa temperature difference DT of at least 168C has historically been used as a necessary but not sufﬁcient condition for the GSL effect (Steenburgh et al. 2000). Alcott et al. (2012) recently found, however, that LEPs during winter frequently feature lower DT values (as low as 12.48C), whereas during the spring DT values are higher. They developed a seasonally varying relationship for the minimum DT required for lake ef-

fect, DTmin, that is based on a best-ﬁt curve applied to the minimum DT observed during LEPs in each month:

D 5 : 2 2 : 1 : 8 Tmin 0 000 642 5d 0 152d 21 35 ( C),

where d is the number of days since 15 September. They

then deﬁned DTexcess as the difference between the observed DT during an LEP and DTmin (i.e., DTexcess 5DT 2 DTmin). A DTexcess that is $0 indicates that the seasonally varying threshold is met or exceeded. Figure 18 shows that at most stations the fraction of

LEP SWE is largest for 28 # DTexcess , 48C, followed by 48 # DTexcess , 68C. The LEP SWE fraction is somewhat lower for 08 # DTexcess , 28C and DTexcess $ 68C. Lower LEP SWE for large DTexcess may seem counterintuitive but likely reﬂects a combination of factors. First, the climatological frequency of cold-air intrusions that yield

large DTexcess values is low. Second, the development of moist convection requires not only instability, but also moisture and a mechanism to lift parcels to their level of free convection (Doswell 1987; Johns and Doswell 1992). For moisture, Alcott et al. (2012) found that the

mean 850–700-hPa relative humidity RH850–700 provided FIG. 13. Total accumulated LEP SWE (mm) vs individual LEP the strongest discrimination between soundings with and SWE (mm) at (a) KSLC and (b) SBDU1. Upper and lower hori- zontal gray dashed lines indicate 50% and 25% of total accumu- without GSL-effect precipitation. lated LEP SWE, respectively. Figure 19 presents the fraction of LEP SWE at KSLC

and SBDU1 as a function of DTexcess and RH850–700 the fetch across the GSL (Carpenter 1993; Steenburgh (based on intervals of 18C and 5%, respectively). At et al. 2000; Alcott et al. 2012). Steenburgh et al. (2000) KSLC, the majority of LEP SWE occurs when 0 # showed that the frequency of radar reﬂectivities of DTexcess # 88CandRH850–700 $ 70% (Fig. 19a). Sim- $10 dBZ during LEPs is greatest when the 700-hPa ilar results are found at SBDU1, although some LEP wind direction is 3008–3608 (see their Fig. 16) but did not SWE is produced at lower (60%–70%) RH850–700 examine the SWE. Figure 17 presents the fraction of values (Fig. 19b). At KSLC and SBDU1, the absolute

LEP SWE produced for various 700-hPa wind directions maximum at DTexcess ;3.58CandRH850–700 ’ 95% D. At most stations, the fraction of LEP SWE is greatest largely reﬂects the environmental conditions during one for 3008,D # 3308, followed by 2708,D # 3008 and 12-h period with high precipitation rates during the 3308,D # 3608, respectively (Figs. 17b–d). For com- Hundred-Inch Storm. Secondary maxima at other times parison, Alcott et al. (2012) found that the majority reﬂect other intense LEPs. LEP SWE occurring when

(;70%) of LEPs occur when the 700-hPa wind direc- DTexcess , 08C is a result of two LEPs that developed after tion is 3008–3608. The importance of fetch, which is the 700-hPa temperatures fell dramatically following

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FIG. 14. (a) Maximum monthly LEP SWE (in millimeters, following the inset scale) at stations in the GSL basin during the study period. Maximum is 119 mm. (b) Ratio of largest monthly LEP SWE to the mean cool-season LEP SWE (%, following inset scale) at stations in the GSL basin. Maximum is 665%.

the sounding time. In these cases, the actual DTexcess knowledge of the area of the GSL has limited influence during lake-effect precipitation was likely larger and on the LEP SWE during the forthcoming cool season, also greater than 08C. although larger lake-area variations might have a more significant influence. d. Perspectives on LEP trends and variability Alcott et al. (2012) identified a stronger relationship The area of the GSL varies dramatically on inter- between the frequency of cool-season LEPs and 500-hPa annual and interdecadal time scales (Lall and Mann trough days, defined as a day on which the 500-hPa 2 2 1995; Lall et al. 1996; Mohammed and Tarboton 2011) relative vorticity exceeds 2 3 10 5 s 1. There is negli- and could influence the frequency and magnitude of gible correlation between the amount of cool-season LEPs. In the historical record (1847–2011), the maxi- LEP SWE at KSLC (R 5 0.01) and SBDU1 (R 5 0.11) mum, average, and minimum GSL area are 8550, 4400, and the frequency of 500-hPa trough days (Figs. 20a,b), and 2460 km2, respectively. During the study period, the however. Instead, as noted previously, cool-season LEP mean cool-season area of the GSL featured an overall SWE is dominated by a small number of trough passages decline from 4500 to 3100 km2. Nevertheless, there is during which the environmental conditions enable the little correlation between standardized anomalies (i.e., development of major lake-effect storms. Although it departures from the study period mean expressed as the is recognized that both instability (i.e., DTexcess $ 0) number of the standard deviations; Grumm and Hart and moisture (i.e., RH850–700 .;60%) are needed for 2001) of mean cool-season GSL area and cool-season lake-effect storms, the factors contributing to intense LEP SWE at KSLC (R 5 0.23) and SBDU1 (R 5 0.36) events remain unclear. This lack of knowledge, com- (Figs. 20a,b). Thus, year-to-year variations in LEP SWE bined with the infrequent nature of intense events, are poorly explained by variations in GSL area during serves as a barrier to better understanding trends and the 12-cool-season study period. This suggests that variations in LEP SWE.

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FIG. 15. Monthly mean LEP SWE (in millimeters, following the inset scale) at stations in the GSL basin for (a) 16–30 September, (b) October, (c) November, (d) December, (e) January, (f) February, (g) March, (h) April, and (i) 1–15 May. Maximum at any station is 22.1 mm in November.

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FIG. 16. Mean monthly LEP SWE (mm) at (a) KSLC and (b) SBDU1. Black asterisk indicates the SWE after the removal of LEPs during the Hundred-Inch Storm described by Steenburgh (2003).

5. Summary and conclusions We have evaluated and applied a method to estimate the amount of precipitation (snow water equivalent) FIG. 17. Fraction of LEP SWE (percent, following the inset 8, # 8 produced during lake-effect periods in the region sur- scale) for 700-hPa wind directions D of (a) 240 D 270 , (b) 2708,D # 3008, (c) 3008,D # 3308, (d) 3308,D # 3608, (e) 3608 rounding the Great Salt Lake. The method follows (north) , D # 308, and (f) 308,D # 608. Maximum at any station Wu¨ est et al. (2010) and uses high-temporal-resolution is 56% at 3008,D # 3308. radar-derived SWE estimates to disaggregate daily precipitation gauge observations to hourly time resolution. By combining these two datasets, we preserve the daily precipitation gauge totals obtained from SNOTEL

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FIG. 18. Fraction of LEP SWE (percent, following the inset scale) for (a) 08 # DTexcess , 28C (149 total LEP hours), (b) 28 # DTexcess , 48C (371 h), (c) 48 # DTexcess , 68C (362 h), and (d) DTexcess $ 68C (350 h). Maximum at any station is 83% for 2 # DTexcess , 48C. and COOP stations and enable the separation of SWE into lake-effect and non-lake-effect periods. The method was applied over the 1998–2009 cool seasons (16 September–15 May, with the year deﬁned FIG. 19. Fraction of LEP SWE at (a) KSLC and (b) SBDU1 D by the ending calendar year), which encompasses 128 during the 12-yr cool-season study period as a function of Texcess LEPs, and was evaluated at valley (Salt Lake City In- and RH850–700. ternational Airport) and mountain (Alta–Collins) stations that are located in the lake-effect precipitation belt radar and precipitation gauges, respectively, the use of southeast of the GSL. At both stations the method asingleZ–S relationship for all LEPs, and radar sam- works well for estimating LEP SWE. Scatter exists in the pling issues. hourly SWE estimates, but the errors are quasi random, Analysis of the disaggregated COOP and SNOTEL and estimates for longer periods inherently yield a better data shows that the mean cool-season LEP SWE is ﬁt. The sources of these errors include differences be- greatest to the south and east of the GSL, including in tween the volume and point measurements made by the Oquirrh Mountains, Salt Lake Valley, and adjoining

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and lake effect generated by Utah Lake. Therefore, the LEP SWE likely overestimates the precipitation produced solely by the GSL effect. The fraction of cool-season SWE produced during LEPs (i.e., the LEP fraction) is also greatest to the south and east of the GSL. Although the largest LEP SWE occurs at the Snowbird SNOTEL (SBDU1) in the Wasatch Mountains east of the Salt Lake Valley, the largest LEP fractions are found in the Oquirrh Moun- tains and in the Wasatch Mountains southeast of Utah Lake, which are climatologically drier during non-lake- effect periods. Throughout the GSL basin, the LEP fraction is small, with a maximum of 8.4% in the Oquirrh Mountains. Although previous studies do not enable a direct comparison, such LEP fractions are likely much lower than are found downstream of larger bodies of water, such as the Laurentian Great Lakes. For comparison, Scott and Huff (1996, 1997) estimate that lake effect doubles the mean winter snowfall east of Lake Superior and increases snowfall east of Lake Ontario, the smallest of the Great Lakes, by 40%. At most stations the mean monthly LEP SWE exhibits a bimodal distribution with a primary peak in autumn (October–November) and a secondary peak in late winter and spring (March–April), which closely resembles the monthly frequency of LEPs described by Alcott et al. (2012). The secondary late-winter and spring maximum contrasts with the Laurentian Great Lakes, which can become partially ice covered during winter and warm slowly in the spring (e.g., Niziol et al. 1995). In contrast, the shallow (mean depth ;3 m), hypersaline GSL never freezes and thus warms rapidly during the spring. LEP SWE and fraction are highly variable among cool seasons and are strongly inﬂuenced by infrequent but intense LEPs. At KSLC and SBDU1, 50% of the total LEP SWE during the study period was produced by just 12 and 13 LEPs, respectively. The fraction of LEP SWE is greatest when the 700-hPa wind direction D is 3008, FIG. 20. Standardized anomalies of cool-season LEP SWE (gray # 8 bars), mean cool-season GSL area (solid lines), and 500-hPa trough D 330 , which roughly corresponds to the maximum days (dash–dot lines) at (a) KSLC and (b) SBDU1. fetch (3258). Multiple variables are needed to help to identify the environmental conditions that produce the most LEP SWE, including a seasonally varying lake

Wasatch Mountains. Upper-elevation SNOTEL stations 2700-hPa temperature threshold DTexcess and mean generally receive more LEP SWE than valley COOP 850–700-hPa relative humidity RH850–700. Most of the stations, reﬂecting both orographic precipitation en- LEP SWE at KSLC and SBDU1 falls when 0 # DTexcess hancement and a probable undercatch of snow at COOP # 88C and RH850–700 $ 70%, although some LEP SWE stations that use unshielded precipitation gauges. Rela- falls at SBDU1 at lower RH850–700 values. Diagnosis of tively high LEP SWE at stations in other areas, includ- the factors leading to intense events, which may be re- ing the Stansbury Mountains and Wasatch Mountains lated to poorly resolved (or understood) mesoscale phe- southeast of Utah Lake, is likely due primarily to pre- nomena such as thermally driven ﬂows (e.g., Steenburgh cipitation phenomena that sometimes occur in concert and Onton 2001; Onton and Steenburgh 2001), remains with the GSL effect, including orographic precipitation an important topic for future research.

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