Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements Over Mountainous Regions in Western United States

remote sensing

Article Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States

Yixin Wen 1,*, Ali Behrangi 1, Bjorn Lambrigtsen 1 and Pierre-Emmanuel Kirstetter 2,3

1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA; [email protected] (A.B.); [email protected] (B.L.) 2 Advanced Radar Research Center, University of Oklahoma, Norman, OK 73019, USA; [email protected] 3 NOAA/National Severe Storms Laboratory, Norman, OK 73019, USA * Correspondence: [email protected]; Tel.: +1-818-354-2538

Academic Editors: Yudong Tian, Ken Harrison, Magaly Koch and Prasad S. Thenkabail Received: 9 September 2016; Accepted: 24 October 2016; Published: 1 November 2016

Abstract: Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using ground-validation datasets. A comprehensive evaluation of the Multi-Radar/Multi-Sensor (MRMS) snowfall products and Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) precipitation products is conducted using the Snow Telemetry (SNOTEL) daily precipitation and Snow Water Equivalent (SWE) datasets. Severe underestimations are found in both radar and satellite products. Comparisons are conducted as functions of air temperature, snowfall intensity, and radar beam height, in hopes of resolving the discrepancies between measurements by remote sensing and gauge, and finally developing better snowfall retrieval algorithms in the future.

Keywords: QPE; SWE; weather radar; GPM

1. Introduction Snowfall is a dominant type of precipitation in high latitudes [1], and is important in the analysis of regional and global water and energy budgets [2,3]. Snow can also cause hazardous weather and impact ﬂood and drought through delaying the precipitation–runoff process, and thus is important for water resources management. In a warming climate, changes of snowfall characteristics (e.g., intensity, duration, frequency, and density) may also substantially impact albedo and glacier dynamics. For large-scale weather monitoring and global climate studies, snowfall observations from remote sensing techniques have become highly desirable. There are two major types of remote-sensing snow measurement techniques: (1) ground-based weather radars; and (2) meteorological satellites. Ground weather radar has proven its value to the nation since the installation of the Weather Surveillance Radar-1988 Doppler (WSR-88D) Next Generation Weather Radar (NEXRAD) network. Based on data measured by the NEXRAD network, the Multi-Radar/Multi-Sensor (MRMS) is currently a real-time test bed comprising high-resolution (1 km, 2 min) liquid and solid precipitation products [4]. The MRMS system uses model surface analyses to segregate snow from rain at the surface. If the RAPid refresh (RAP) surface dry bulb temperature is less than 2 ◦C, and surface wet bulb temperature is less than 0 ◦C, the surface precipitation type is set to snow. The MRMS radar-only snowfall rates are

Remote Sens. 2016, 8, 904; doi:10.3390/rs8110904 www.mdpi.com/journal/remotesensing Remote Sens. 2016, 8, 904 2 of 11 obtained by applying Z = 75 S2.0 to the mosaicked reflectivity field pixel by pixel, where Z represents the radar reflectivity (mm6·m−3) and S denotes the snowfall rate (mm·h−1). The meteorological satellites are other sources to provide snowfall measurements. With decades of observations from NASA and other space agencies, satellite precipitation retrieval algorithms have been developed. The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) has provided Quantitative Precipitation Estimates (QPE) at 3 h and 0.25◦ resolution since January 1998,Remote without Sens. 2016, 8 discriminating, 904 precipitation phases. Since 2014, the Integrated Multi-satellitE2 of 11 Retrievals for GPM (Global Precipitation Measurement) (IMERG) product has provided merged global snowfall rates are obtained by applying Z = 75 S2.0 to the mosaicked reflectivity field pixel by pixel, where precipitationZ represents from a constellationthe radar reflectivity of precipitation-relevant (mm6·m−3) and S denotes the satellites snowfall rate and (mm·h global−1). surface precipitation ◦ 1 ◦ ◦ gauge analysesThe [ 5meteorological] at 0.1 resolution satellites are every other sources2 h within to provide 65 snowfallN and measurements. 65 S. Importantly, With decades it ingests the retrievalsof from observations the latest from version NASA and of other the space Goddard agencies, Profiling satellite precipitation Algorithm retrieval (GPROF2014) algorithms have [6], which is been developed. The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation designed to improve retrievals of light rain and snowfall compared to the earlier versions, which often Analysis (TMPA) has provided Quantitative Precipitation Estimates (QPE) at 3 h and 0.25° resolution missed majoritysince January of these 1998, precipitation without discriminating types [1 precip,7]. IMERGitation phases. has a Since new data2014, the field—probability Integrated Multi- of liquid precipitation—whichsatellitE Retrievals helps for identify GPM (Global precipitation Precipitation types. Measurement) (IMERG) product has provided Both radarmerged and global satellite precipitation snow from estimates a constellation need of verification precipitation-relevant and refinement satellites and from global ground-validation surface precipitation gauge analyses [5] at 0.1° resolution every ½ h within 65°N and 65°S. Importantly, it datasets. To date, much effort has gone into evaluating precipitation retrievals by comparing with ingests the retrievals from the latest version of the Goddard Profiling Algorithm (GPROF2014) [6], ground observationswhich is designed (e.g., to [8 improve–12]), while retrievals few of studieslight rain have and snowfall investigated compared the to errors the earlier in snowfall versions, retrievals. Kirstetter etwhich al. (2015)often missed [13] majority performed of these a quantitativeprecipitation types evaluation [1,7]. IMERG of has the a MRMSnew data Reflectivity-Snowfield—probability Water Equivalentof (SWE)liquid precipitation—which relationship, and helps noted identify significant precipitation underestimationtypes. relative to the precipitation Both radar and satellite snow estimates need verification and refinement from ground-validation gauges in Hydrometeorological Automated Data System (HADS). However, measurement errors datasets. To date, much effort has gone into evaluating precipitation retrievals by comparing with for snowground of HADS observations gauges (e.g., frequently [8–12]), while rangefew studies from have 20% investigated to 50%, the errors due in to snowfall undercatch retrievals. in windy conditionsKirstetter [14]. Havinget al. (2015) reliable [13] performed and consistenta quantitative surface-basedevaluation of the MRMS reference Reflectivity-Snow snow measurements Water is importantEquivalent in product (SWE) comparison relationship, and and noted validation. significant underestimation In the western relative United to the precipitation States, a gauges network called in Hydrometeorological Automated Data System (HADS). However, measurement errors for snow Snowpack Telemetry (SNOTEL) was designed to provide snow measurements from high snow of HADS gauges frequently range from 20% to 50%, due to undercatch in windy conditions [14]. accumulationHaving regions reliable (Figure and consistent1). The surface-based basic SNOTEL reference station snow measurements provides Snow is important Water in Equivalent product (SWE) data via acomparison pressure-sensing and validation. snow In pillow. the western The dailyUnited snowfall States, a network accumulation called Snowpack (SA) can Telemetry be derived from the daily increment(SNOTEL) was of thedesigned SWE to values, provide based snow onmeasurem the differenceents from high in cumulative snow accumulation values regions between day N (Figure 1). The basic SNOTEL station provides Snow Water Equivalent (SWE) data via a pressure- and N−1. The SNOTEL station also collects data on snow depth, all-season precipitation accumulation, sensing snow pillow. The daily snowfall accumulation (SA) can be derived from the daily increment and air temperatureof the SWE values, with dailybased maximums,on the difference minimums, in cumulative and values averages between [ 15day]. N SNOTEL and N−1. SWEThe records, along withSNOTEL other station station also measurements collects data on have snow been depth used, all-season in many precipitation studies. Foraccumulation, example, and SNOTEL air daily minimumtemperature with observations daily maximums, are minimums directly,ingested and averages by [15]. PRISM SNOTEL [16] SWE and records, Daymet along [17 with], two widely other station measurements have been used in many studies. For example, SNOTEL daily minimum used gridded climate products. SNTOEL SWE records have been used to examine seasonal aspects of temperature observations are directly ingested by PRISM [16] and Daymet [17], two widely used western Unitedgridded States climate precipitation products. SNTOEL [18]. SWE Forecast records centers have been (e.g., used Colorado to examine Basin seasonal River aspects Forecast of Center) use SNOTELwestern daily United SA measurements States precipitation to [18]. provide Forecast water centers supply (e.g., Colo forecastsrado Basin and River other Forecast services Center) to the public. In this study,use SNOTELSNOTEL daily daily SA SA measurements measurements to provide are usedwater assupply in situ forecasts daily and snowfall other services intensity to the reference to public. In this study, SNOTEL daily SA measurements are used as in situ daily snowfall intensity evaluate radar snowfall products. reference to evaluate radar snowfall products.

Figure 1. The locations of 788 Snowpack Telemetry (SNOTEL) stations in the western mountainous Figure 1. Theregion, locations with terrain of 788elevation Snowpack in the background. Telemetry The (SNOTEL) red dots denote stations SNOTEL in the stations western located mountainous at region, withPacific terrain Time Zone. elevation The blue in dots the denote background. SNOTEL stations The red located dots at denoteMountain SNOTEL Time Zone. stations located at Paciﬁc Time Zone. The blue dots denote SNOTEL stations located at Mountain Time Zone. Remote Sens. 2016, 8, 904 3 of 11

The objective of this study is to use the best ground observations (SNOTEL snow pillows) to evaluate daily snowfall accumulation from MRMS and IMERG. In Section2, we discuss the datasets used in this study. Section3 introduces the method for matching the dataset. Section4 presents comprehensive evaluation results of all datasets. A summary follows in the last section.

2. Datasets

2.1. SNOTEL SWE Measurements We utilized snow observations from 788 SNOTEL stations located in the western mountainous region (Figure1) from March 2014 to September 2015 (to temporally match IMERG data availability). SNOTEL stations are fully automated, collecting data on snowpack SWE, snow depth, all-season precipitation accumulation, and air temperature. SWE is measured by snow pillow ﬁlled with an antifreeze solution [15]. As snow accumulates, the weight of the snowpack forces the solution into a manometer column inside the instrument shelter. The height change of the manometer is monitored by a pressure transducer and is converted to SWE in inches. Like the snow pillows, SNOTEL gauges also work on the same manometer/pressure transducer principle. The precipitation gauges measure all phases of precipitation, while snow pillows only measure solid precipitation. The snow pillow and precipitation gauges are both hourly sensors. However, due to the wind effect and sensor issues, the hourly SWE and precipitation data may not be reliable compared to daily data. To provide a robust and reliable reference for evaluation, we matched radar and satellite data to daily SNOTEL measurements. The daily SNOTEL measurements are transmitted at local midnight for the previous day. We created daily SA based on the difference in cumulative values between day N and N–1, and conducted a quality control procedure on the SNOTEL dataset following the same one described in [15] to remove the outliers. More detailed information on SNOTEL data can be found in [15].

2.2. MRMS Snow Accumulation The MRMS is a research system integrating radar, rain gauge, satellite, and numerical weather prediction (NWP) model data and generates automated, seamless national 3D radar mosaic and multi-sensor quantitative precipitation estimates [4]. The MRMS system uses two criteria to identify snowfall. First, to avoid Bragg scattering [19,20], radar reflectivity must exceed 5 dBZ. Second, the surface temperature and wet bulb temperature from hourly temperature model analyses must be lower than 2 ◦C and 0 ◦C, respectively. For each grid identified as falling snow, the empirical relation Z = 75 S2 is applied to convert radar reflectivity to snowfall rate. Then, the instantaneous snowfall rate is accumulated to 24-h snow amount. MRMS ingests quality-controlled hourly rain gauges from the Hydrometeorological Automated Data System (HADS) [21]. Most of the HADS gauges are tipping-bucket type, and are incapable of measuring frozen precipitation properly, even when heated (e.g., [14]). When surface wet-bulb temperature (WBT) is at or below 0 ◦C, gauge data are considered unreliable and are labeled “frozen” to avoid contaminating radar measurements. So, the MRMS snow accumulation data are measured from radar only without gauge measurements incorporated.

2.3. IMERG Starting in March 2014, GPM Level III product IMERG synergizes three mainstream algorithms, such as the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System (PERSIANN-CCS, [22,23]) and the Climate Prediction Center Morphing-Kalman Filter (CMORPH-KF, [24]) Lagrangian time interpolation scheme to provide high resolution products (30-min and 0.1◦). Importantly, it ingests the retrievals from the latest version of the Goddard Profiling Algorithm (GPROF, [6]). The retrievals from the latest version of the GPROF2014 are used to estimate precipitation rate from a constellation of microwave sensors, and it is expected to show higher skill in retrieving light rain and snowfall compared to the earlier versions. IMERG also uses the Remote Sens. 2016, 8, 904 4 of 11 monthly Global Precipitation Climatology Center (GPCC; [25]) for bias correction. Three products are in Remote Sens. 2016, 8, 904 4 of 11 the 30-min product suite [5]: Early Run (latency ~6 h), Late Run (latency ~18 h), and Final Run (latency ~4(latency months). ~18 In h), this and study, Final all Run available (latency Final ~4 months). Run products In this (from study, March all available 2014 to Final September Run products 2015) are used(from [5]. March The precipitation 2014 to September with (precipitationCal) 2015) are used and[5] The without precipitation gauge calibration with (precipitationCal) (precipitationUncal) and arewithout both evaluated gauge calibration in this study. (precipitationUncal) Besides the precipitation are both estimates,evaluated IMERGin this half-hourlystudy. Besides data the also includesprecipitation a new estimates, data field, IMERG Probability half-hourly of liquid data precipitation, also includes to a provide new data the field, phase Probability of the precipitation of liquid (i.e.,precipitation, liquid, solid, to orprovide mixed). the Thisphase data of the field precipitat adoptsion a scheme (i.e., liquid, developed solid, or by mixed). [26], which This data is based field on differentadopts geophysicala scheme developed parameters by [26], (NASA which GPM is based IMERG on Algorithm different geophysical Theoretical Basisparameters Document). (NASA GPM IMERG Algorithm Theoretical Basis Document). 3. Matching MRMS and IMERG with SNOTEL Snow Measurements 3. Matching MRMS and IMERG with SNOTEL Snow Measurements SNOTEL observations are point measurements of snow, so we will take an approach of comparingSNOTEL these observations data with the are snowfall point measurements by radar and satelliteof snow, at theso we grid will point take nearest an approach to the station. of Onecomparing complicating these factor data with to match the snowfall MRMS andby radar IMERG and with satellite the SNOTELat the grid measurements point nearest to is thatthe station. SNOTEL stationsOne complicating do not use UTC factor time. to Thematch SNOTEL MRMS stationsand IM reportERG with daily the data SNOTEL at midnight measurements local standard is that time, whichSNOTEL is 7 h stations after the do 0000 not UTCuse UTC if located time. inThe the SNOTEL Mountain stations Time Zonereport and daily 8 h data in the at Pacificmidnight Time local Zone (Figurestandard1). So, time, to matchwhich withis 7 h groundafter the measurements, 0000 UTC if located precipitation in the Mountain from the Time last 18Zone h (Mountain and 8 h in Timethe ZonePacific for example;Time Zone 17 (Figure h if Pacific 1). So, Time to match Zone) with of remoteground sensingmeasurements, estimates precipitation was combined from the with last that 18 of theh first(Mountain 6 h (7 h Time in Pacific Zone Time for example; Zone) of 17 the h nextif Paci day’sfic Time estimates. Zone) of remote sensing estimates was combined with that of the first 6 h (7 h in Pacific Time Zone) of the next day’s estimates. Since we focus on snow measurements in this study, we only use matchups with daily maximum Since we focus on snow measurements in this study, we only use matchups with daily maximum temperature less than 0 ◦C to ensure that daily SA are exclusively from solid precipitation. Figure2 temperature less than 0 °C to ensure that daily SA are exclusively from solid precipitation. Figure 2 shows results from the comparison of snow pillow daily measurements and precipitation gauge shows results from the comparison of snow pillow daily measurements and precipitation gauge daily daily measurements on snowing days. The colored data-density scatterplot in Figure2 indicates that measurements on snowing days. The colored data-density scatterplot in Figure 2 indicates that there thereis good is good overall overall agreement, agreement, with witha relative a relative bias of bias 8.31% of 8.31%and a andcorrelation a correlation coefficient coefficient of 0.90. ofThe 0.90. Theunderestimation underestimation of ofgauge gauge measurements measurements compared compared to tosnow snow pillow pillow is the is the fact fact that that the the gauges gauges often often underestimateunderestimate frozen frozen precipitation, precipitation, particularlyparticularly in windy conditions, conditions, because because the the snowflakes snowflakes drift drift awayaway [27 [27].]. The The undercatch undercatch can can exceed exceed 50%50% inin blizzardblizzard conditions [28]. [28]. The The SNOTEL SNOTEL pillow-sensors pillow-sensors areare better better at measuringat measuring the snowthe snow than than precipitation precipitat gauges.ion gauges. For solidFor solid precipitation precipitation comparison comparison studies, snowstudies, pillow snow measurements pillow measurements are used. are used.

Figure 2. Scatterplot with colored data density of daily snowfall accumulation (SA) of the snowpack Figure 2. Scatterplot with colored data density of daily snowfall accumulation (SA) of the snowpack measured by snow pillows and total precipitation measured by gauges. All points were selected from measured by snow pillows and total precipitation measured by gauges. All points were selected from days with maximum air temperature lower than 0 °C to limit the comparison to mainly snowfall. CC: days with maximum air temperature lower than 0 ◦C to limit the comparison to mainly snowfall. correlation coefficient; MAE: mean absolute error; RMSE: root-mean-squared error. CC: correlation coefﬁcient; MAE: mean absolute error; RMSE: root-mean-squared error.

Remote Sens. 2016, 8, 904 5 of 11 Remote Sens. 2016, 8, 904 5 of 11

ToTo calculatecalculate thethe probabilityprobability ofof snowfall,snowfall, wewe simplysimply useuse unityunity minusminus thethe probabilityprobability ofof liquidliquid precipitation.precipitation. Figure Figure 33 indicatesindicates thatthat forfor snowingsnowing daysdays identiﬁedidentified byby SNOTELSNOTEL daily daily maximum maximum temperature,temperature, IMERGIMERG mostlymostly (more(more thanthan 80%)80%) showsshows probabilityprobability ofof snowfallsnowfall greatergreater than than 95%. 95%.

FigureFigure 3.3. TheThe histogramhistogram ofof probabilityprobability ofof snowfallsnowfall inin snowingsnowing daysdays (identiﬁed(identified fromfrom SNOTELSNOTEL dailydaily maximummaximum temperaturetemperature lower lower than than 0 ◦ C).0 °C). IMERG: IMERG: Integrated Integrated Multi-satellitE Multi-satellitE Retrievals Retrievals for GPM for (Global GPM Precipitation(Global Precipitation Measurement). Measurement).

4.4. Results Results and and Discussion Discussion

4.1.4.1. Daily SnowfallSnowfall AccumulationAccumulation ComparisonsComparisons WeWe selectselect fourfour statisticalstatistical indicesindices forfor comparisoncomparison ofof MRMSMRMS andand IMERGIMERG snowsnow dailydaily accumulationaccumulation withwith SNOTELSNOTEL snow snow pillow pillow daily daily measurements. measurements. The The Relative Relative Bias Bias (RB; (RB; in percent)in percent) is used is used to assess to assess the systematicthe systematic bias ofbias estimations. of estimation Correlations. Correlation Coefficient Coefficient (CC) is (CC) used is to used assess to the assess agreement the agreement between estimatesbetween estimates and gauge and observations. gauge observations. The mean The absolute mean error absolute (MAE) error measures (MAE) the measures average the magnitude average ofmagnitude the error, whileof the the error, root-mean-squared while the root-mean-squared error (RMSE) quantifies error (RMSE) the average quantifies error magnitude,the average giving error moremagnitude, weight giving to larger more errors. weight to larger errors. FigureFigure4 4shows shows results results from from the the quantitative quantitative comparison comparison ofof daily daily SA. SA. It It suggests suggests that that the the quantificationquantification of snow snow rate rate in in mountainous mountainous areas areas remains remains a challenging a challenging task task for forweather weather radars radars and andsatellites. satellites. The color-density The color-density scatter scatter plots and plots the and hist theogram histogram in Figure in 4 Figure indicate4 indicate that all three that allproducts three productsfrom radar from and radar satellite and have satellite severe have underesti severemation, underestimation, with relative with biases relative worse biases than worse~−71%. than The ~IMERG−71%. precipitationCal The IMERG precipitationCal shows slightly shows better slightly bias ratio better (−71.83%) bias ratio compared (−71.83%) to MRMS compared (−76.33%) to MRMS and (IMERG−76.33%) precipitationUncal and IMERG precipitationUncal (−82.35%). Correlation (−82.35%). Coefficient Correlation of MRMS Coefficient is about 0.52, of MRMS which isis aboutbetter 0.52,than whichIMERG is precipitationCal better than IMERG (CC = precipitationCal 0.20) and precip (CCitationUncal = 0.20)and (CC precipitationUncal= 0.15). The discrepancies (CC = could 0.15). Thebe due discrepancies to large variability could be duein snowflake to large variability shape, den in snowflakesities, and shape, fall velocities densities, [29]. and The fall velocitiesdiscrepancies [29]. Thecould discrepancies also be related could to alsorandom be related factors, to randomsuch as different factors, such spatial as different representativeness spatial representativeness and electronic andmiscalibration. electronic miscalibration.It is more important It is more to importantidentify and to identifyresolve andthe resolvediscrepancies the discrepancies from systematic from systematicnonrandom nonrandom effects such effects as varying such as varyingsnowflake snowflake density densitywith temperature, with temperature, snowfall snowfall intensity, intensity, and andrange-dependent range-dependent error error in radar in radar measurements measurements caused caused by by inhomogeneous inhomogeneous Vertical Vertical Profile Profile ofof ReflectivityReflectivity (VPR).(VPR). TheseThese potentialpotential nonrandomnonrandom factorsfactors areare elucidatedelucidated inin thethe followingfollowing sections.sections.

Remote Sens. 2016, 8, 904 6 of 11 Remote Sens. 2016, 8, 904 6 of 11

(a) (b)

Figure 4. The color-density scatterplots of daily SA from SNOTEL and (a) Multi-Radar/Multi-Sensor Figure 4. The color-density scatterplots of daily SA from SNOTEL and (a) Multi-Radar/Multi-Sensor (MRMS); (b) IMERG with gauge calibration; (c) IMERG without gauge calibration; and (d) Histograms of (MRMS); (b) IMERG with gauge calibration; (c) IMERG without gauge calibration; and (d) Histograms SNOTEL, MRMS, IMERG precipitationCal and precipitationUncal daily SA. of SNOTEL, MRMS, IMERG precipitationCal and precipitationUncal daily SA. 4.2. Evaluation with Temperature 4.2. Evaluation with Temperature Liquid rain has a relatively constant density. However, snow density can vary greatly, making itLiquid difficult rain to estimate has a relatively particles’ constant fall speed, density. and thus However, snowfall snow intensity density [30].can Since vary snow greatly, density making and it difficultfall tospeed estimate are particles’temperature-dependent fall speed, and [31,32], thus snowfall an evaluation intensity [of30 ].remote Since snowsensing density snowfall and fall speedmeasurements are temperature-dependent as a function of temperature [31,32], an is evaluation studied. of remote sensing snowfall measurements as a functionFigure of temperature 5a compares is daily studied. SA from SNOTEL with MRMS and IMERG as a function of near- surfaceFigure 5aira comparestemperature daily measured SA from by SNOTEL SNOTEL withair-te MRMSmperature and sensor. IMERG Mean as a daily function SA measured of near-surface by SNOTEL snow pillows increases with temperature due to the increasing density. This is consistent air temperature measured by SNOTEL air-temperature sensor. Mean daily SA measured by SNOTEL with previous studies reporting a nonlinear increase in snow density with increasing temperature snow pillows increases with temperature due to the increasing density. This is consistent with previous over range of −15 °C–0 °C (e.g., [31,33]). Garrett et al. (2014) [32] found a 37% reduction in the mean studiesvalue reporting of snowfall a nonlineardensity with increase temperature in snow decreasing density from with −3.5 increasing °C to −17.3 temperature°C. Previous wind over tunnel range of ◦ ◦ −15studiesC–0 (e.g.,C (e.g., [34,35]) [31, 33have]). shown Garrett that et the al. denser (2014) snow [32] particles found a accelerate 37% reduction the falling in thespeed, mean resulting value of ◦ ◦ snowfallin significant density withaugmentation temperature of snow decreasing mass flux. from −3.5 C to −17.3 C. Previous wind tunnel studies (e.g., [34,Both35]) MRMS have shown and IMERG that theunderestimate denser snow snow particles compared accelerate to SNOTEL the fallingacross all speed, −14 °C resulting to 0 °C in significanttemperature augmentation bins. SNOTEL of snow SA shows mass a flux. large increase at temperatures warmer than −7 °C, but MRMS andBoth IMERG MRMS do and not IMERGclearly show underestimate this increase snow (Figure compared 5a). The to difference SNOTEL between across allremote−14 sensing◦C to 0 ◦C temperatureproducts bins.and SNOTEL SNOTEL becomes SA shows larger a large as temperature increase at increases temperatures and approaches warmer than 0 °C.− 7 ◦C, but MRMS and IMERG do not clearly show this increase (Figure5a). The difference between remote sensing products and SNOTEL becomes larger as temperature increases and approaches 0 ◦C. Remote Sens. 2016, 8, 904 7 of 11 Remote Sens. 2016, 8, 904 7 of 11

Remote Sens. 2016, 8, 904 7 of 11

(a) (b)

Figure 5. (a) Daily SA from SNOTEL, MRMS, and IMERG as a function of air temperature; Figure 5. (a) Daily SA from SNOTEL, MRMS, and IMERG as a function of air temperature; (b) The (b) The relative bias of MRMS and IMERG daily SA·[(MRMS − SNOTEL)/SNOTEL × 100%] as a relative bias of MRMS and IMERG daily SA·[(MRMS − SNOTEL)/SNOTEL × 100%] as a function of function of air temperature.(a) (b) air temperature. Figure 5. (a) Daily SA from SNOTEL, MRMS, and IMERG as a function of air temperature; Figure(b) The 5b relative compares bias theof MRMS products and using IMERG RB daily with SA·[(MRMS SNOTEL SA − SNOTEL)/SNOTEL as reference. MRMS × 100%] shows as lowera skillFigure thanfunction5b comparessatellite of air temperature.precipitationUncal the products using for RBtemperatures with SNOTEL below SA − as6 reference.°C, but higher MRMS skill shows at warmer lower skill thantemperatures. satellite precipitationUncal The lower temperature for temperatures may be associat belowed− 6with◦C, high but higherelevation; skill e.g., atwarmer places on temperatures. top of mountains.Figure The 5b comparesradar beam the blockage products and using overshooting RB with SNOTEL issues areSA aslikely reference. to occur MRMS in high shows elevation lower The lower temperature may be associated with high elevation; e.g., places on top of mountains. locations.skill than IMERG satellite precipitationCal precipitationUncal always for temperatureshas higher skill below than − 6MRMS °C, but at higherall temperatures. skill at warmer This The radar beam blockage and overshooting issues are likely to occur in high elevation locations. suggeststemperatures. that gauge The lowercalibration temperature is effective. may be associated with high elevation; e.g., places on top of IMERGmountains. precipitationCal The radar alwaysbeam blockage has higher and overshooting skill than MRMS issues atare all likely temperatures. to occur in Thishigh suggestselevation that gauge4.3.locations. calibration Evaluation IMERG with is effective. SnowprecipitationCal Intensity always has higher skill than MRMS at all temperatures. This suggests that gauge calibration is effective. 4.3. EvaluationFigure with6a shows Snow negative Intensity errors of snowfall estimates with increasing snow intensity measured by SNOTEL. Figure 6b shows that all snowfall estimates (MRMS, IMERG precipitationCal and 4.3. Evaluation with Snow Intensity precipitationUncal)Figure6a shows negative have best errors performance of snowfall when estimatesdaily SA is withwithin increasing 0–5 mm/day. snow The intensityproducts show measured by SNOTEL.largerFigure negative Figure 6a biasshows6 bfor showsnegative snow rates that errors greater all of snowfall snowfall than 10 mm/day.es estimatestimates This with (MRMS, suggests increasing IMERGthat snow both intensityprecipitationCalsatellite and measured radar and precipitationUncal)retrievalsby SNOTEL. have Figure better have 6bperforma best shows performance ncethat in all light snowfall whensnowfall estimates daily events SA (MRMS, an isd within do notIMERG 0–5 show mm/day.precipitationCal improved The skill productsand at showhigherprecipitationUncal) larger intensities. negative bias have for best snow performance rates greater when than daily 10 SA mm/day. is within This0–5 mm/day. suggests The that products both satellite show and radarlarger retrievals negative have bias better for snow performance rates greater in than light 10snowfall mm/day. This events suggests and do that not both show satellite improved and radar skill at retrievals have better performance in light snowfall events and do not show improved skill at higher intensities. higher intensities.

(a) (b)

Figure 6. (a) Daily SA from SNOTEL, MRMS, and IMERG as a function of daily SA measured by SNOTEL; (b) The relative bias of MRMS and IMERG daily SA·[(MRMS − SNOTEL)/SNOTEL × 100%] (a) (b) as a function of SA measured by SNOTEL. Figure 6. (a) Daily SA from SNOTEL, MRMS, and IMERG as a function of daily SA measured by Figure 6. (a) Daily SA from SNOTEL, MRMS, and IMERG as a function of daily SA measured by SNOTEL; (b) The relative bias of MRMS and IMERG daily SA·[(MRMS − SNOTEL)/SNOTEL × 100%] SNOTEL; (b) The relative bias of MRMS and IMERG daily SA·[(MRMS − SNOTEL)/SNOTEL × 100%] as a function of SA measured by SNOTEL. as a function of SA measured by SNOTEL. Remote Sens. 2016, 8, 904 8 of 11

The possible factors causing the intensity-dependent errors for ground radar are (1) radar attenuation; and (2) Z-S relation uncertainties. Considering WSR-88D radars are S band with 10-cm wavelength, attenuation does not cause radar underestimation. The Z-S relation uncertainties are Remotediscussed Sens. 2016in many, 8, 904 studies (e.g., [36]). A variety of Z-S relations have been derived in previous studies.8 of 11 The one adopted in MRMS system (Z = 75 S2) is from the “Guidance on selecting Z-R relationships” reportedThe possiblein Radar factorsOperations causing Center the (1999). intensity-dependent This Z-S relation errors may forbe groundinappropriate radar to are use (1) for radar the attenuation;Continental andUnited (2) States Z-S relation due to uncertainties. large variability Considering in snow density. WSR-88D A new radars setare of Z-S S band relationships with 10-cm is wavelength,needed for the attenuation western United does notStates cause studied radar here. underestimation. The Z-S relation uncertainties are discussed in many studies (e.g., [36]). A variety of Z-S relations have been derived in previous studies. 4.4. Evaluation with Ground Radar Beam Height The one adopted in MRMS system (Z = 75 S2) is from the “Guidance on selecting Z-R relationships” reportedSystematic in Radar errors Operations in ground-based Center (1999). radar Thisprecip Z-Sitation relation estimation—related may be inappropriate to the toVPR use features for the Continentalcombined with United the geometric States due effects to large of the variability radar beam—can in snow density. create the A newoften-noted set of Z-S radar relationships beam height is neededdependence for the [37,38]. western Figure United 7a Statesshows studied that as here. the beam height increases as daily SA measured by ground radar decreases. However, this beam height-dependent underestimation is less significant 4.4.compared Evaluation to that with as Ground a function Radar of Beam temperature. Height SystematicIt is interesting errors to innote ground-based that IMERG radarproducts precipitation (though they estimation—related also have underestimation to the VPR problem) features combinedremain stable with with the geometricradar beam effects height. of theThis radar indica beam—cantes that as create a different the often-noted viewing angle radar from beam ground- height dependencebased radar, [satellite37,38]. Figurecan provide7a shows a compensatory that as the beam view height on measuring increases precipitation. as daily SA measuredIn fact, [39,40] by groundsuggested radar a method decreases. to enhance However, ground this beamradar-based height-dependent precipitation underestimation estimates using is reflectivity less signiﬁcant from comparedTRMM precipitation to that as a radar. function of temperature.

(a) (b)

FigureFigure 7.7. ((aa)) DailyDaily SASA fromfrom SNOTEL,SNOTEL, MRMS,MRMS, andand IMERGIMERG asas aa functionfunction ofof radarradar beambeam heightheight aboveabove groundground level;level; (b) The relative biasbias ofofMRMS MRMS and and IMERG IMERG daily daily SA SA·[(MRMS·[(MRMS − −SNOTEL)/SNOTEL SNOTEL)/SNOTEL× × 100%]100%] asas aa function of of radar radar beam beam height height above above ground ground level. level.

5. Conclusions It is interesting to note that IMERG products (though they also have underestimation problem)This remainstudy provides stable with a quantitative radar beam assessment height. This of snowfall indicates estimates that as afrom different ground-based viewing angleradar fromproduct ground-based MRMS and radar,satellite satellite Global canPrecipitation provide a Measurement compensatory (GPM) view onlevel measuring III product precipitation. IMERG for Inall fact,snowing [39,40 days] suggested (from 13 a methodMarch 2014 to enhance to 30 September ground radar-based 2015) using precipitation the SNOTEL estimates snow pillow using reﬂectivitymeasurements from as TRMM ground precipitation truth, snowfall radar. estimates at daily scale were evaluated, and few potential nonrandom factors causing discrepancies were tested. The main findings are summarized as follows: 5. Conclusions (1) Comparisons of daily SA indicate severe underestimations by MRMS and IMERG, with relative Thisbias studyworse provides than −71%. a quantitative IMERG precipitationCal assessment of snowfall product estimates outperforms from MRMS ground-based and IMERG radar productprecipitationUncal MRMS and satellite product Global in terms Precipitation of relative Measurementbias. (GPM) level III product IMERG for(2) all Since snowing snow daysdensity (from and 13 fallin Marchg speed 2014 are to 30temperature September dependent, 2015) using snow the SNOTELestimates snowas a function pillow measurementsof temperature as ground were truth,studied. snowfall The error estimates between at daily remote scale sensing were evaluated,instruments and and few SNOTEL potential is nonrandom factors causing discrepancies were tested. The main ﬁndings are summarized as follows: Remote Sens. 2016, 8, 904 9 of 11

(1) Comparisons of daily SA indicate severe underestimations by MRMS and IMERG, with relative bias worse than −71%. IMERG precipitationCal product outperforms MRMS and IMERG precipitationUncal product in terms of relative bias. (2) Since snow density and falling speed are temperature dependent, snow estimates as a function of temperature were studied. The error between remote sensing instruments and SNOTEL is enhanced as temperature increases and approaches 0 ◦C. This suggests that radar and satellite retrievals may not be able to capture increasing snowfall mass ﬂux caused by temperature. (3) By comparing remote sensing snow as a function of snowfall intensity, we found that remote sensing retrievals have better performance in light snowfall events. For heavy snow, the underestimation is worse. This snow intensity-associated underestimation indicates an impropriate Z–S applied in MRMS system. (4) The negative errors of radar daily snow accumulation with increasing radar beam height is demonstrated in this study, but is not the primary explanatory variable for the signiﬁcant underestimation.

Both radar and satellite show severe underestimation in QPE of snowfall. Current remote sensing products for snowfall estimates can introduce large errors into hydrology and climate studies. Further improvement in radar and satellite snowfall rate retrieval might be possible by stratifying the data by degree of crystal riming and by snowﬂake size using environmental variables (e.g., air temperature and relative humidity) and/or polarimetric radar signals.

Acknowledgments: The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship is acknowledged. Ali Behrangi was supported by NASA GRACE/GRACE-FO and NASA WEATHER awards. Author Contributions: Yixin Wen and Ali Behrangi conceived and designed the experiments; Yixin Wen performed the experiments; Yixin Wen, Ali Behrangi, Bjorn Lambrigtsen and Pierre Kirstetter analyzed the data; Yixin Wen and Ali Behrangi wrote the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Abbreviations The following abbreviations are used in this manuscript: CC Correlation Coefficient CMORPH-KF Climate Prediction Center Morphing-Kalman Filter GPCC Global Precipitation Climatology Center GPM Global Precipitation Measurement GPROF Goddard Profiling Algorithm HADS Hydrometeorological Automated Data System IMERG Integrated Multi-satellite retrievals for GPM MAE Mean Absolute Error MRMS Multi-Radar/Multi-Sensor NEXRAD Next Generation Weather Radar NWP Numerical Weather Prediction Precipitation Estimation from Remotely Sensed Information using Artificial Neural PERSIANN-CCS Networks—Cloud Classification System PrecipitationCal Precipitation with Gauge Calibration PrecipitationUncal Precipitation without Gauge Calibration QPE Quantitative Precipitation Estimates RAP Rapid Refresh RB Relative Bias RMSE Root-Mean-Squared Error SA Snowfall Accumulation SNOTEL Snow Telemetry SWE Snow Water Equivalent TMPA TRMM Multi-satellite Precipitation Analysis TRMM Tropical Rainfall Measurement Mission VPR Vertical Profile of Reflectivity WSR-88D Weather Surveillance Radar-1988 Doppler Remote Sens. 2016, 8, 904 10 of 11

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