NSSL Multi-Radar Multi-Sensor System

Journal of Hydrology xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol

Research papers

Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL Multi-Radar Multi-Sensor System Sheng Chen a,b,c,⇑, Yang Hong c,d,⇑, Mark Kulie e, Ali Behrangi f, Phillip M. Stepanian g, Qing Cao h, Yalei You i, Jian Zhang j, Junjun Hu k, Xinhua Zhang l a

School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou, Guangdong 510275, China Advanced Radar Research Center & School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA d Dept. of Hydraulic Engineering, Tsinghua University, Beijing, China e Department of Atmospheric and Oceanic Sciences and Space Science and Engineering, Center, University of Wisconsin-Madison, Madison, WI, USA f Jet Propulsion Laboratories, California Institute of Technology, Pasadena, California, USA g School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA h Research and Innovation Division, Enterprise Electronics Corporation, Norman, OK 73072, USA i CMNS-Earth System Science Interdisciplinary Center, M-Square Research Park, Maryland, USA j National Severe Storms Laboratory (NOAA/NSSL), Norman, Oklahoma, USA k School of Computer Science, University of Oklahoma, Norman, OK 73072, USA l State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan 610065, China b c

a r t i c l e

i n f o

Article history: Received 4 August 2015 Received in revised form 28 June 2016 Accepted 29 July 2016 Available online xxxx This manuscript was handled by K. Georgakakos, Editor-in-Chief, with the assistance of Ana P. Barros, Associate Editor Keywords: CloudSat NEXRAD Radar Snowfall

a b s t r a c t The latest global snowfall product derived from the CloudSat Cloud Profiling Radar (2C-SNOW-PROFILE) is compared with NOAA/National Severe Storms Laboratory’s Multi-Radar Multi-Sensor (MRMS/Q3) system precipitation products from 2009 through 2010. The results show that: (1) Compared to Q3, CloudSat tends to observe more extremely light snowfall events (<0.2 mm/h) and snowfall rate (SR) between 0.6 to 1 mm/h, and detects less snowfall events with SR between 0.2–0.5 mm/h. (2) CloudSat identifies 69.40% of snowfall events detected by Q3 as certain snow and 10% as certain mixed. When possible snow, possible mixed, and certain mixed precipitation categories are assumed to be snowfall events, CloudSat has a high snowfall POD (86.10%). (3) CloudSat shows less certain snow precipitation than Q3 by 26.13% with a low correlation coefficient (0.41) with Q3 and a high RMSE (0.6 mm/h). (4) With Q3 as reference, CloudSat underestimates (overestimates) certain snowfall when the bin height of detected snowfall events are below (above) 3 km, and generally overestimates light snowfall (<1 mm/h) by 7.53%, and underestimates moderate snowfall (1–2.5 mm/h) by 42.33% and heavy snowfall (P2.5 mm/h) by 68.73%. (5) The bin heights of most (99.41%) CloudSat surface snowfall events are >1 km high above the surface, whereas 76.41% of corresponding Q3 observations are low below 1 km to the near ground surface. This analysis will provide helpful reference for CloudSat snowfall estimation algorithm developers and the Global Precipitation Measurement (GPM) snowfall product developers to understand and quantify the strengths and weaknesses of remote sensing techniques and precipitation estimation products. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction

⇑ Corresponding authors at: School of Atmospheric Sciences, Sun Yat-sen University, 135 West Xingang Road, Guangzhou, Guangdong 510275, China. Tel.: +86 20-8411-1286 (S. Chen); Advanced Radar Research Center, University of Oklahoma, 120 David L. Boren Blvd., Suite 4610, Norman, OK 73072, USA. Tel.: +1 405-325-3644 (Y. Hong). E-mail addresses: [email protected] (S. Chen), [email protected] (Y. Hong).

Frozen precipitation plays an important role in global hydrologic processes, especially in climatologically colder regions. When considering precipitation at middle and high latitudes, as well as high altitudes, snowfall can represent a significant contribution to precipitation frequency and amounts. Advances in satellite remote sensing technology make global-scale estimates of snowfall feasible. However, current passive microwave and

http://dx.doi.org/10.1016/j.jhydrol.2016.07.047 0022-1694/Ó 2016 Elsevier B.V. All rights reserved.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

2

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

infrared-based precipitation retrievals struggle to quantify precipitation over frozen surfaces. Retrievals made over such ground surfaces tend to result in significant precipitation underestimates, especially at higher latitudes (Behrangi et al., in press). The National Aeronautics and Space Administration’s cloud observation satellite (NASA CloudSat) is the first satellite that provides sufficient sensitivity and spatial coverage to estimate snowfall at a quasi-global scale (Liu, 2008; Liu and Seo, 2013; Wood et al., 2013b). This capability is made possible by the CloudSat spaceborne active Cloud Profiling Radar (CPR; Stephens et al. (2008)), and its large latitudinal coverage (81°S–81°N). The CloudSat CPR operates at W-band (94 GHz) and is particularly sensitive to smaller hydrometeors, especially compared to other operational active space-borne sensors (Cao et al., 2014; Cao and Qi, 2014; Qi et al., 2013a). For example, the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) operates at Ku-band (13.8 GHz), and orbits between 3°S and 3°N, while the Global Precipitation Measurement (GPM) mission carries a Dual-frequency (Ku- and Ka-band) Precipitation Radar (DPR) with latitudinal coverage between 65°S and 65°N. Comparatively, the higher radar frequency with increased sensitivity and quasi-global coverage of the CloudSat CPR possess the potential to significantly improve estimates of global snowfall. A few recent studies have compared CloudSat CPR retrievals to independent data sources. Hudak et al. (2008) evaluated the CloudSat precipitation occurrence algorithm with data from the Canadian C-band radar network and found that CloudSat has excellent skill scores for precipitation occurrence detection. Hiley et al. (2011) analyzed the uncertainty of CloudSat snowfall retrievals from both microphysical and methodological standpoints, and compared snowfall estimates with ground-based measurements in Canada, reporting variable location-dependent results. Smalley et al. (2013) compared precipitation occurrence from the NCEP Stage IV QPE Product with CPR estimates, and found that the CPR observes precipitation considerably more frequently than the Stage IV. This increase in frequency is especially noticeable in northern states where frozen precipitation is prevalent in the cold season, and is likely due to the decreased precipitation detection capabilities of Stage IV when the near-surface air temperature drops below 0 °C. Furthermore, since Stage IV has a horizontal sample size of 4.7 km and temporal sampling rate of one hour, there are significant differences in spatial and temporal resolution between these measurements and CloudSat observations. The potential for improving snowfall estimates using the CloudSat CPR is clear, but characterizing the performance of CPR retrievals requires independent observations for collocated comparisons. The Multi-Radar Multi-Sensor (MRMS/Q3) system was developed by NOAA’s National Severe Storms Laboratory to provide high spatiotemporal resolution radar and precipitation products (Qi et al., 2011; Vasiloff et al., 2007; Zhang et al., 2011a, 2014a,c; Zhang and Qi, 2010). The development of MRMS/Q3 was a result of the upgrade of the NOAA Next-Generation National Mosaic and Multi-sensor QPE system (NMQ/Q2) to accommodate the polarimetric upgrade of United States Weather Surveillance Radar-1988 Doppler (WSR-88D) network in August of 2013 (Zhang et al., 2014a). Additionally, NOAA/NCEP’s Rapid Update Cycle (RUC) atmospheric model was replaced with the hourly analyses of the Rapid Refresh (RAP; http://rapidrefresh.noaa.gov) in MRMS (Zhang et al., 2014b). As a result of these changes, MRMS/ Q3 generates high spatiotemporal resolution two-dimensional (2D) precipitation products as well as three-dimensional (3D) reflectivity products at 1-km grid spacing every 5 min before Aug. 2013 and at 1-km grid spacing every 2 min after Aug. 2013. Such high-resolution QPE products offer an ideal reference for evaluating and validating satellite-based precipitation products (Amitai et al., 2009; Chen et al., 2013a,b; Kirstetter et al., 2012).

A recent study by Chen et al. (2015a) details the high probability of detection (POD) of Q3 for snow (77%) and rain (94%) when compared to surface precipitation reports collected by citizen scientist observations from the meteorological Phenomena Identification Near the Ground (mPING) project (Elmore et al., 2014). With such good performance demonstrated by Q3 in detecting precipitation, particularly snowfall, comparisons with CloudSat retrievals can better characterize the snowfall detection performance of the CPR. In this study, the performance of CloudSat CPR retrievals for detection and estimation of snowfall is compared to Q3 estimates over the contiguous United States (CONUS). The comparison is performed with respect to precipitation rate, phase, and type. A secondary objective is to provide a methodology that can be used to evaluate future space-borne snowfall products. The paper is organized as follows. Section 2 describes the datasets used in this study. Section 3 compares the performance of CloudSat and Q3 in detecting and quantifying snowfall precipitation from 2009 through 2010, with deep investigation using a snowstorm case that occurred on 7 December 2009. A discussion on the relative uncertainties of CloudSat and Q3 estimates is presented in Section 4, and conclusions are provided in Section 5.

2. Data sources and spatiotemporal matching CloudSat snowfall estimates are obtained from the most recent version (P_R04) of the 2C-SNOW-PROFILE product (Wood et al., 2013b). The CloudSat precipitation flags can be obtained from the current version (P2_R04) of 2C-PRECIP-COLUMN (Haynes et al., 2014), and the vertical reflectivity is obtained from recent version (P_R04) of 2B GEOPROF product (Marchand et al., 2008). The 2C-SNOW-PROFILE product incorporates multiple sources of information from the CPR, passive microwave sensors, and numerical forecast model output to identify and quantify snowfall. This snowfall product provides surface snowfall rate (SR), as well as vertical profiles of SR, particle size distributions, and snow water content. Additionally, uncertainty estimates for these variables are provided for each pixel. The CloudSat CPR cannot reliably measure reflectivity near the surface because of ground clutter contamination. As a result, the 2C-SNOW-PROFILE algorithm estimates snow properties using a truncated reflectivity profile, terminated above the surface, and then uses the estimated snow properties in the bottom-most portion of the profile to estimate the surface SR (Wood et al., 2013b). The truncation forms a blind zone, which extends about 1 km above the surface over land (Norin et al., 2015). In this framework, the mixed-layer model is used to incorporate temperature and humidity information from ECMWF-AUX to model the melting of snow down to the surface layer. The computational steps in the 2C-SNOW-PROFILE algorithm used to calculate the SR are as follows: (1) determine if snowfall is present at the surface by using the variables from 2C-PRECIP-COLUMN, or the near surface reflectivity, cloud mask from 2B-GEOPROF, and temperatures from ECMWF-AUX; (2) locate a snow layer with the near-surface reflectivity, cloud mask from 2B-GEOPROF, and temperature profile from ECMWF-AUX; (3) assign a priori expected values, uncertainties, and initial values to the snow size distribution parameters in each snowcontaining radar bin; (4) retrieve a profile of snow size distribution parameters and their uncertainties using the radar reflectivity profile in the snow layer; (5) calculate a profile of SR, snow water content, and their uncertainties for the snow layer.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

3

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

Several issues should also be considered to properly interpret CPR observations and products: (1) At the frequencies used by CPR (i.e., 94 GHz), scattering by precipitation-sized particles generally does not follow the Rayleigh approximation, and attenuation of the radar beam by hydrometeors and gases may be significant. Under these conditions and assuming single scattering, the effective reflectivity factors are computed as a function of range from the CPR. (2) Gaseous attenuation is predominantly due to water vapor, with the two-way attenuation by water vapor in tropical atmospheres approaching 5 dB (Stephens et al., 2002). This contribution to attenuation is far less for snowfall events. For example, Hiley et al. (2011) reports
0.5 dB two-way attenuation by water vapor for a typical deep mid-latitude snowfall event. The snowfall retrieval algorithm uses estimates of the two-way gaseous attenuation provided by the 2B-GEOPROF product to correct the 2B-GEOPROF reflectivity before the retrieval is performed. (3) Multiple scattering partially offsets the substantial attenuation by frozen hydrometeors (Matrosov and Battaglia, 2009). (4) Due to attenuation from liquid hydrometeors, the CloudSat snowfall retrieval will not be applied to profiles that contain liquid cloud and rain. Although supercooled liquid water may also attenuate the radar beam, the CPR cannot identify its existence in the presence of precipitation-sized hydrometeors. More details can be found in CloudSat documentation by CIRA (2008) and Wood et al. (2013b). The Q3 products used in this study include the 1 km/5 min radar-only product, the precipitation type product, and threedimensional (3D) reflectivity product for the 7 December 2009 case study presented in Section 3. Q3 uses a set of algorithms to classify precipitation into five categories (snow, hail, convective rain, stratiform rain, and tropical/warm rain) based on both radar parameters and model analysis data from NOAA/NCEP’s RUC (Benjamin et al., 2004; Qi et al., 2013b,c; Xu et al., 2008; Zhang et al., 2011a). The data flow of the snow identification and quantification algorithm is as follows. First, the hybrid scan reflectivity (i.e. the radar’s lowest-elevation reflectivity that clears the surface) must exceed 5 dBZ. Second, when the surface temperature is below 2 °C and the surface wet bulb temperature is below 0 °C, the precipitation is classified as snow. The precipitation classification algorithm is applied to each 1-km grid cell and an empirical relationship Z = 75R2.0 is applied to any cells identified as snow to estimate snow liquid water equivalent rate (R, mm/h). More details on the Q3 snowfall product can be found in (Zhang et al., 2011b). Despite the benefits of ground-based remote sensing, Q3 has several limitations of its own. Specifically, NEXRAD has difficulty detecting light snow as a result of relatively lower sensitivity com-

1km 1km 1.4km

5min

These spatiotemporal matching criteria were applied to the observations by CloudSat and Q3 from January of 2009 through December of 2010. In total, there are 2187576 valid data pairs during the entire study period from 2009 to 2010. Fig. 1b shows the cumulative occurrence distribution of certain snowfall events detected both by CloudSat and Q3 as a function of the approximate radar bin height associated with surface snowfall events classified by each dataset. It is noted that there is a large discrepancy in the heights of observed snowfall events between Q3 and CloudSat. For instance, in the snowfall events detected by Q3, 77.10% have heights less than 1 km, 97.58% have heights less than 2 km. In contrast, CloudSat only detected 0.59% of snowfall events with heights less than 1 km, and 83.45% within 1 km to 3 km. The elevated heights associated with CloudSat are not surprising since a large fraction of the collocated CloudSat/Q3 snowfall dataset occurs over continental regions, (recall that CloudSat’s lowest usable radar bin exceeds 1 km over land). Since the Q3 product has lower precipitation estimate reliability when the Q3 hybrid scan reflectivity

80 60

CloudSat Q3

40

(b)

0

80

CloudSat Q3

60

(c)

40 20

20

1.7 km Q3

1km> HSRH>0 km

100

Percent (%)

A

Percent (%)

(a)

(1) the time difference between the two measurements is must less than 2.5 min; (2) the central location of the CPR footprints must fall within the Q3 grids; and (3) both the CPR and Q3 must have valid records (i.e. no missing data).

HSRH>0 km

100

CloudSat 7 km/s on track

pared to the CPR. This limitation is due in part to the lower frequencies (2.7–3.0 GHz) used by NEXRAD. Furthermore, the quality control model in the Q3 data flow serves to filter out weak signals (<5 dBZ) that may be under detected. Second, NEXRAD suffers from severe beam blockage in mountainous areas, especially in the western CONUS (Maddox et al., 2002), and beam overshooting and broadening increase with distance from the radar. Third, only one invariant empirical Z-R relationship (Z = 75R2.0, with R representing snow water equivalent) is recommended by National Weather Service for winter straitiform precipitation west of continental divide and for the orographic rain in the western CONUS. Conversely, CloudSat radar reflectivity to precipitation rate retrievals may vary between different CPR observations based on its optimal estimation retrieval scheme. Finally, the ground-clutter masking for Q3 increases with increasing distance from the radar, limiting the range of the NEXRAD estimates, whereas groundclutter masking in CloudSat is almost constant everywhere around the globe due to reflectivity profile truncation techniques. Since CloudSat and Q3 have different spatiotemporal resolutions, the data must be collocated prior to evaluation. The CloudSat footprint is approximately 1.4 km  1.7 km and moves at a speed of 7 km/s along its track. A time and location matching algorithm is applied to obtain instantaneous matching pairs of CloudSat CPR and Q3 observations (Fig. 1a). To match the two observations, the following criteria must be met:

1

2

3

4

5

Height of surface snowfall (km)

0

1

2

3

4

5

Height of surface snowfall (km)

Fig. 1. (a) Matching between Q3 and CloudSat footprint. The Q3 pixels with the nearest locations and time to each profile of CloudSat were selected. (b) Cumulative occurrence distribution as a function of the bin height of surface precipitation observation. (c) Conditioned Cumulative occurrence distribution as a function of the bin height of surface precipitation observations with HSRH is less than 1 km.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

4

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

Table 1 Contingency table comparing snowfall detection by Q3 and CloudSat.

CloudSat Snow Yes CloudSat Snow No

Q3 Snow Yes

Q3 Snow No

H M

F Z

height (HSRH; the height at which the Q3 product derives its precipitation rate estimate) is >1 km, a data pair quality-control filter was applied to the dataset to screen for this condition. The result of this filtering was 849389 high quality data pairs, in which 105496 pairs containing probable snowfall information. After filtering, the occurrence distributions of certain snowfall events detected by CloudSat largely retains the same distribution shape compared to the unfiltered dataset, with 0.67% of snowfall events associated with heights less than 1 km and 85.97% of snowfall events with heights ranging from 1 km to 3 km (Fig. 1c). Herein, all analyses were conducted using the high quality data pairs after filtering. To evaluate CloudSat snowfall quantification capabilities relative to Q3, the following statistical metrics were calculated pixel by pixel: the difference (Dif) between CloudSat and Q3 observations, relative difference (RD), root-mean-squared error (RMSE), and correlation coefficient (CC). RD and CC are dimensionless, while Dif and RMSE units are mm/h. To evaluate CloudSat’s snowfall detection capabilities relative to Q3, the number of hits (H), false alarms (F), and misses (M) are computed (Table 1). In Table 1 a threshold of ‘‘0” was used to distinguish between snowfall occurrence (Yes) and non-occurrence (No). By using different thresholds, more detailed comparisons of the detection skills and instrument sensitivity can be obtained. From these tabulations the traditional contingency metrics of probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are calculated using Eqs. (1)–(3):

H HþM F FAR ¼ HþF H CSI ¼ HþFþM POD ¼

ð1Þ ð2Þ ð3Þ

It should be noted that Q3 is merely a proxy of the true unknown snowfall estimates, although it is largely considered the reference dataset in this study, especially considering only the highest quality Q3 observations associated with HSHR < 1 km are used to filter the coincident Q3/CloudSat observational dataset. Ground radar snowfall estimates, such as those provided by Q3, suffer from some aforementioned inherent shortcomings that prevent the validation of satellite snowfall estimates in a strict sense. Therefore, the results presented herein identify the discrepancies between the two products as opposed to validating one against the other. 3. Results 3.1. Comparison for a heavy snow storm A heavy snow that was observed by Q3 and CloudSat at 0815 UTC on 7 December 2009 was selected for a detailed Q3/CloudSat comparison to illustrate typical differences between the respective datasets for intense snowfall conditions (Fig. 2). The Q3 dataset shows a large precipitation region with isolated moderate to heavy precipitation rates embedded within a lighter precipitation swath (Fig. 2a). The Q3 precipitation type product indicates mostly snow associated with this precipitation event based on the Q3 precipitation type criteria discussed in Section 2 (Fig. 2b). CloudSat tran-

sected this storm through and observed a relatively intense precipitation region located near 41°N/90°W (black line in Fig. 2a). As shown in Fig. 2c, CloudSat appears to have pronouncedly lower SR than Q3 in the heaviest snowfall core for this event, e.g. the areas bounded by red circles in Fig. 2a. Q3 estimated snowfall rates exceed 3 mm/h in this heavier precipitation core, while CloudSat snowfall rates plateau at about 0.8 mm h1. CloudSat, however, estimates slightly higher snowfall rates compared to Q3 outside of this heavy precipitation core between about 42–44°N (Fig. 2c). The SR comparative scatter plot for this event (Fig. 2d) shows that CloudSat and Q3 demonstrate fair agreement for snowfall observations associated with SR less than 1 mm/h. When Q3 SR exceeds 1 mm/h, however, CloudSat shows pronouncedly lower rates. Quantitatively, the CloudSat estimated 16.69% less snowfall than Q3 for this particular snow event, with a low CC (0.31) and a high RMSE (0.42 mm/h). For further comparison, Fig. 2e and f shows CloudSat and Q3 vertical profiles of reflectivity (VPRs) overlapped with the Q3 HSRH and the bin height of the CloudSat surface snowfall. Figs. 2e and f show that the heights of CloudSat surface snowfall are around 1.2–1.5 km for this case, and far higher than the Q3 HSRH that rarely exceeds 0.5 km for the entire coincident CloudSat transect. Figs. 2e and f indicate that Q3 often uses reflectivity observations that are systematically closer to the ground and thus more representative of the actual surface precipitation rate than the bin of CloudSat surface snowfall. In order to further investigate why CloudSat shows lower SR compared to Q3 in heavy snowfall regions, one CloudSat VPR at the latitude of 41.3414°was selected to compare with that of Q3 (Fig. 2g and h) in the heaviest precipitation core of the 9 December 2009 snowfall event. The Q3 SR at this latitude was estimated to be as high as 3.6 mm/h, while CloudSat’s snowfall rate estimate in the same region was less than 1 mm/h. Figs. 2g and h show both CloudSat and Q3 VPRs increasing as the observation heights decrease, although the Q3 reflectivity magnitudes are much larger than CloudSat reflectivities not affected by ground clutter at the same latitude (CloudSat’s ground clutter signature is represented by the extremely large reflectivity increase from <10 dBZ to over 30 dBZ below the 1 km level). Fig. 2i shows the Q3 average VPRs for 1.5 mm/h > SR P 1 mm/h, 2.5 mm/h > SR P 1.5 mm/h, and SR P 2.5 mm/h and illustrates that the average VPRs of moderate (2.5 mm/h > SR P 1 mm/h) and heavy (SR P 2.5 mm/h) snowfall increase as the observation heights decrease. This reflectivity trend can lead to significant SR enhancement near to the ground surface in heavy snowfall events (e.g., see Fig. 2f) that may adversely affect CloudSat’s surface snowfall estimates under such conditions. The average Q3 VPR of light snowfall (RS < 1 mm/h) also increases as the observation height decreases for this case. The reasons why CloudSat shows distinctly lower SR in heavy snow areas observed by Q3 for this event can be possibly attributed to a combination of the following factors. Firstly and primarily, the CloudSat near-surface bin used for estimating the surface SR tends to be located about 1 km above the ground surface (Fig. 2e), and the SR increases when snow particles fall through the sub1 km layer (most likely due to aggregation processes). The Q3 dataset adequately samples the sub-1 km in this event and more effectively observes the near-surface snowfall intensity. Secondly, according to the wind observation of meteorology station 3LF (located within the Q3 domain shown in Fig. 2b) of the Automated Surface Observing System (ASOS), the gust wind speed at 0524UTC on 7 December 2009 was 17.1 mph. Thus, the snow particles which were observed by CloudSat are generally much higher above those observed by Q3 and may be significantly advected by winds during snowstorms. The wind effects could introduce significant RMSE differences and lower the correlation between these two snowfall products. Other factors also need to be considered, such as the

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

5

Fig. 2. Various Q3 and CloudSat reflectivity and snowfall rate (SR) figures for a snow storm that occurred at 0815 UTC on 7 Dec. 2009. (a) The Q3 precipitation rates (mm/h) overlapped with CloudSat overpass (black dotted line). The red circle indicates regions of particularly heavy snowfall rates. (b) The Q3 precipitation phases (solid and liquid) distribution, and the location of a meteorology station 3LF of Automated Surface Observing System (ASOS) for wind observation (black asterisk). (c)Snowfall rate (SR) from coincident Q3 and CloudSat certain snow estimates. (d) SR scatter plot of the certain snow observations both detected simultaneously by Q3 and CloudSat. (e) CloudSat vertical profile of reflectivity (VPR) for transect shown in panel (a). (f) Profile of Q3 snowfall reflectivity derived from Q3 3D reflectivity product for the transect shown in panel (a). (g) CloudSat VPR when latitude = 41.3414 in (e). (h) Q3 Vertical reflectivity profile when latitude = 41.3414 and Q3 SR = 3.6 mm/h in panel (f). (i) Q3 average VPR for 1.5 > SR P 1, 2.5 > SR P 1.5, and SR P 2.5 mm/h for the 0815 UTC 7 Dec. 2009 snow event. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

reflectivity to snowfall rate conversions used by these different wavelength radars – especially since CloudSat’s reflectivity is strongly affected by non-Rayleigh scattering that effectively caps its reflectivity to about 20 dBZ. Finally, attenuation by cloud liquid water and large snow or graupel particles could play a role in reducing the CloudSat signal in the heaviest precipitation core. 3.2. Two-year statistics 3.2.1. Statistics of snowfall occurrence distribution Section 3.1 analyzed a sample snowfall case to highlight differences between Q3 and CloudSat snowfall rates under intense snowfall conditions. A more exhaustive multi-year analysis is presented in this section to further highlight systematic differences between the Q3 and CloudSat datasets. Recall that only coincident CloudSat and Q3 observations associated with Q3 HSRH values below 1 km are used for the multi-year statistics presented when Q3 is assumed to be the reference dataset.

Fig. 3a and b show distributions of snowfall occurrence as a function of the SR for snowfall events detected both simultaneously by CloudSat and Q3 using the subsetted dataset described in Section 2. CloudSat detected more extremely light snowfall events (<0.2 mm/h) and a much lower occurrence of light snowfall events (0.2–0.5 mm/h) and heavy snowfall events (>1.4 mm/h) relative to Q3. Specifically, CloudSat classified 31.36% of all snowfall events as extremely light snow events (<0.2 mm/h), 28.21% as light snow events (0.2–0.5 mm/h), and 4.86% as moderate and heavy snowfall events (>1.4 mm/h), with 87.22% of snowfall events classified by CloudSat as less than 1 mm/h. In contrast, Q3 cannot detect snowfall rates less than 0.2 mm/h (thus highlighting an obvious CloudSat detection advantage), and therefore detected 44.52% of all snowfall events as light (0.2–0.5 mm/h), 9.89% as moderate and heavy (>1.4 mm/h), and 78.74% of all Q3 snowfall events associated with SR less than 1 mm/h. These results suggest that the light snowfalls dominates snowfall observations by CloudSat and Q3, and CloudSat is sensitive to the smaller snowfall

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

Q3 Snow CloudSat Snow

Occurrence

(a) 4000

2000

0 0.1

0.2 0.3

0.5

1

2

3

5

100

Snowfall Rate (mm/h)

Q3 Snow CloudSat Snow

80 60

Cloudsat (mm/h)

6000

Cumulative occurrence (%)

6

(b)

40 20

0.1

0.20.3 0.5

1

2 3

5

2574

(c)

5

1716 858

0

0 0.01

3432

RD= -26.13% 10 RMSE= 0.60mm/h CC= 0.41

1 0

5

Snowfall Rate (mm/h)

10

Q3 (mm/h)

Fig. 3. (a) The occurrence distribution of snow as a function of the snowfall rate with snow events detected both simultaneously by CloudSat and Q3. (b) Cumulative occurrence distribution as a function of the snowfall rate with snow events detected both simultaneously by CloudSat and Q3. (c)Scatter plots of the CloudSat vs. Q3 for certain snowfall events both detected simultaneously by Q3 and CloudSat over CONUS.

Index

>0

1 > SR > 0

2.5 > SR P 1

P2.5

Dif (mm/h) RD (%) RMSE (mm/h) CC

0.18 26.13 0.60 0.41

0.03 7.53 0.39 0.33

0.61 42.33 0.87 0.13

2.30 68.73 2.58 0.21

Index

>0

1 > SR > 0

2.5 > SR P 1

P2.5

Dif (mm/h) RD (%) RMSE (mm/h) CC

0.18 35.37 0.60 0.41

0.24 66.61 0.53 0.37

0.19 13.82 0.87 0.07

1.79 57.72 2.13 0.05

0.6

Dif (mm/h)

0.4

(a)

0.2 0 -0.2 -0.4 100

Relative Dif (%)

50

(b)

0

-50

-100 1

CC

0.5

(c)

0

-0.5

RMSE (mm/h)

-1 1.5

(d) 1

0.5

0 75 >H 1> ≥0 H .5 1. ≥0.7 2 1. 5> 5 5> H ≥ 1. H≥1 1 75 .2 >H 5 2> ≥1 H .5 2. ≥1.7 2 2. 5> 5 5> H ≥ 2. H≥2 2 75 .2 >H 5 3 > ≥2 H .5 3. ≥2.7 2 3. 5> 5 5 H 3. >H ≥3 75 ≥3 >H .2 ≥ 5 4> 3.2 H 5 4. ≥3.7 2 4. 5> 5 5> H ≥ 4. H≥4 4 75 .2 >H 5 5> ≥4 H .5 5. ≥4.7 2 5. 5> 5 5> H≥ H 5 ≥5 .2 5

Table 2 Dif, RD, CC and RMSE of CPR for overall certain (dry) snowfall events (snowfall rate (SR) > 0), light (1 > SR > 0), moderate (2.5 > SR P 1) and heavy snowfall (SR P 2.5) with Q3 as reference. The SR unit is mm/h.

Table 3 Dif, RD, CC and RMSE of Q3 for overall certain snowfall events (snowfall rate (SR) > 0), light (1 > SR > 0), moderate (2.5 > SR P 1) and heavy snowfall (SR P 2.5) with CloudSat as reference. The SR unit is mm/h.

0.

particles associated with lower reflectivity values and most likely observed these events more effectively than Q3. The color-density scatter plot in Fig. 3c indicates that CloudSat estimates less snowfall than Q3 by 26.13%, and again has a low CC (0.41) with Q3 and a high RMSE (
0.60 mm/h). This discrepancy may be primarily attributed to the VPRs of the moderate (1–2.5 mm/h) and heavy (P2.5 mm/h) snowfall events systematically decreasing as the observation height decreases below 1 km and highlights a Q3 observational advantage by sampling the sub-1 km layer more effectively than CloudSat. For more quantitative comparisons with Q3 as the reference dataset, Table 2 shows the CloudSat Dif, RD, CC, and RMSE for light snowfall (<1 mm/h), moderate snowfall (1–2.5 mm/h), and heavy snowfall (P2.5 mm/h). These SR categories are defined by the Society of Automotive Engineers (SAE) Ground Deicing committee (Rasmussen et al., 2001). CloudSat underestimates snowfall relative to Q3, but the degree of underestimation varies considerably with snowfall intensity. Specifically, CloudSat underestimates light snowfall by 7.53%, moderate snowfall by 42.33% and heavy snowfall by 68.73%. Also, the CloudSat has low CCs (0.33, 0.13 and 0.21, respectively) for light, moderate and heavy snowfall. Additionally, CloudSat demonstrates relatively large RMSEs (0.39 mm/h, 0.87 mm/h, and 2.58 mm/h) for light, moderate and heavy snowfall, respectively. These low CCs and larger RMSE values may be linked to aforementioned wind effects, the large differences in sensor observations, and the characteristics of heavy snowfall VPRs and highlight the difficulty comparing spaceborne and groundbased scanning radar datasets. In general, the quantitative SR differences between the two datasets are significant and distinct, especially for moderate and heavy snowfall events that are significantly underestimated by CloudSat when compared to Q3. For a balanced comparison, Table 3 also gives the statistics of Dif, RD, CC, and RMSE for Q3 with CloudSat as reference. Overall, Q3 estimates more snowfall than CloudSat by 35.37% for all certain snow events (as identified by CloudSat), overestimates light snowfall by 66.61%, and underestimates moderate (heavy) snowfall by 13.82 (57.72%). Generally, Q3 differs considerably from CloudSat with overestimation of light snowfall and underestimation of moderate and heavy snowfall when CloudSat is assumed as the reference dataset. These results suggest complex snowfall detection issues related to the dataset that is assumed to be the reference.

Height of CloudSat surface snowfall (km) Fig. 4. Dif, RD, CC, and RMSE as a function of bin height of CloudSat surface snowfall.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

7

41.45

44.23

36.76 17.24

20

40

FAR

CSI

Contingency

SR ≥5

SR ≥2 .5

60 40 20 0 >0 .0 ≥1

POD

0.

0

1>

20

NaN SR >0 1. 5> SR ≥0 .1 2. 5> SR ≥1 .5

0

(c)

80

≥8 ≥9 ≥1 0 ≥1 1 ≥1 2

49.28

40.76

40

100

≥6 ≥7

60

(b)

60

≥4 ≥5

(a)

69.40

POD (%)

percent (%)

80

≥2 ≥3

100

Snowfall Detection (%)

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

Q3 snowfall rate (mm/h)

Snowfall rate (mm/h)

Fig. 5. CloudSat snowfall contingency statistics with Q3 as reference. (a) Total contingency of CloudSat. (b) POD for extremely light snowfall (SR < 0.1 mm/h), light snowfall (1 > SR > 0.1 mm/h), moderate. (c) POD as a function of Q3 snowfall rate.

(P2.5 mm/h), and extremely heavy snow (P5 mm/h). Furthermore, Figs. 5c and 6c show that Q3 has much higher POD than CloudSat when SR is >3.5 mm/h. This implies that CloudSat and Q3 have significant differences in detecting and quantifying heavy snowfall events, with Q3 having better detectability of certain heavy snowfall events than CloudSat, and this trend may be linked to the VPR characteristics of heavy snowfall as shown in Fig. 2f and i. To further investigate the CloudSat contingency performance, Fig. 7 presents contingency (POD, FAR, and CSI) as a function of the bin height of CloudSat surface snowfall. CloudSat shows high POD (
60%) from 1 km to 2 km above the ground surface, and gradually decreases as the bin heights increase. The CSI shows a very similar trend with POD, indicating a high ratio of critically successful snowfall detection. The variation of FAR indicates that CloudSat has high FAR when bin heights are less than 0.75 km, suggesting the large uncertainty of CloudSat in detecting snowfall near to the ground surface.

3.2.2. Dif, RD, CC and RMSE as a function of bin height of CloudSat surface snowfall As shown in Fig. 1b and c, the bin height of CloudSat surface snowfall demonstrates large variation. The bin height used to estimate surface snowfall is surmised to be a primary factor that causes discrepancies between Q3 and CloudSat snowfall rate retrievals, especially over land surfaces. Fig. 4 therefore shows the Dif, RD, CC and RMSE as a function of bin height of CloudSat surface snowfall. The variations of Dif and RD suggest that CloudSat estimates less snowfall than Q3 when the bin heights are less that 3 km, indicating 99.33% of all snowfall events are underestimated by CloudSat when compared to Q3. The CC does not show large variation when the bin height ranges from 0.75 to 4.75, implying the bin height has little impact on the correlation between CloudSat and Q3 observations. As for RMSE, it shows smooth variation when the bin height varies from 1 km to 3 km, and undergoes a rapid increase from 3 km to 5 km. This indicates the large uncertainty of CloudSat snowfall observations when bin heights are >3 km.

Contingency

Snowfall rate (mm/h)

60 40 20 0 ≥8 ≥9 ≥1 0 ≥1 1 ≥1 2

SR ≥5

≥2

.5

.5 SR

≥1 R

R

5> S

CSI

5> S

FAR

1.

POD

0. 1

>S

R

≥0

>0

.1

0

(c)

80

≥6 ≥7

20.00

20.10

20

30.60

20 0

36.28

≥4 ≥5

41.45

41.16

38.79 40

100

≥2 ≥3

50.72

40

(b)

2.

percent (%)

60

POD (%)

(a)

80

60

>0 .0 ≥1

100

Snowfall Detection (%)

3.2.4. Statistics of precipitation type detection Both CloudSat and Q3 provide precipitation type products, including rain. Investigation of different precipitation type detectability offers further insights into the discrepancy and agreement between Q3 and CloudSat. Fig. 8 (Fig. 9) presents the distributions of different precipitation types detected by CloudSat (Q3) when snow is detected by Q3 (CloudSat). When Q3 is used as the reference (Fig. 8),
69.4% of all snowfall events detected by Q3 were classified as certain snowfall events by CloudSat. When considering possible snow, possible mixed, and certain mixed precipitation, CloudSat demonstrates high detectability (86.10%) of all snow events. It is noted that a marginal fraction (4.2%) of snow events are classified as rain and a considerable percentage (9.17%) of snowfall events are classified as no precipitation. For light (1.5 mm/h > SR > 0 mm/) and moderate (2.5 mm/ h > SR P 1 mm/h) snowfall, CloudSat still has a relative high snowfall POD detection, i.e. 67.91% and 75.82%, respectively. This indicates that CloudSat shows better agreement with Q3 in detecting moderate snowfall events than light snowfall. In terms of heavy

3.2.3. Snowfall contingency statistics The total contingency statistics regarding certain snowfall detection and the scores as a function of certain SR are shown in Fig. 5. Overall, when Q3 is used as reference, CloudSat has good snowfall detectability with a high POD (69.4%) largely due to CPR’s excellent sensitivity to smaller hydrometeors in the atmosphere (Fig. 5a–c). More specifically, CloudSat shows compatible POD (40.76%, 44.23%, 36.76%) for light snowfall (0.1–1 mm/h), moderate snow (1–2.5 mm/h), and heavy snow (P2.5 mm/h), respectively. CloudSat demonstrates low POD (17.24%) for extremely heavy snow (P5 mm/h). To provide a more balanced comparison, the contingency statistics of Q3 are also given in Fig. 6 when CloudSat is used as reference. It is noted that Q3 shows an overall moderate snowfall POD (50.72%), low POD (20.10%) for extremely light snowfall (<0.1 mm/h), and a little lower but comparable snowfall PODs (38.79%, 41.16%, 36.28%) than CloudSat for light snowfall (0.1– 1 mm/h), moderate snowfall (1–2.5 mm/h), heavy snow

CloudSat snowfall rate (mm/h)

Fig. 6. Q3 snowfall contingency statistics with CloudSat as reference. (a) Total contingency of CloudSat. (b) POD for extremely light snowfall (SR < 0.1 mm/h), light snowfall (1 > SR > 0.1 mm/h), moderate. (c) POD as a function of CloudSat snowfall rate.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

8

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx 80 POD FAR CSI

40

20

0 .5 75 ≥1 25 1.5 .75 ≥2 .25 2.5 .75 ≥3 .25 .25 .75 ≥4 .25 4.5 .75 ≥5 .25 ≥0 ≥0. >H ≥1. ≥ ≥2 >H ≥3 ≥3 ≥3 >H ≥4 ≥ ≥1 >H ≥2 ≥ ≥4 >H ≥5 H 25 H H H H 25 H >H H 25 H >H H 25 >H H 25 H 1> 1. .5> .75 2> 2. .5> .75 3> 3. .5> 75> 4> 4. .5> .75 5> 5. .5> 1 5 2 3 3. 1 4 4 2

>H

Height of CloudSat surface snowfall (km) Fig. 7. CloudSat snowfall detection as a function of bin height of CloudSat surface snowfall.

snowfall, CloudSat shows a lower snow POD (58.05%), but interestingly indicates a high certain rain POD (14.74%) and high certain mixed POD (25.53%). These rain POD values are much higher than those in light and moderate snowfall, indicating that the rain/snow partitioning influences snowfall detection in both datasets. Alternatively, with CloudSat as reference, only 50.72% of all certain snowfall events were classified as snow by Q3, and 45.59% of certain snowfall events were classified as no precipitation (Fig. 9a). Q3 shows a high snowfall POD (75.32%) for moderate snowfall, indicating the good agreement between CloudSat and Q3 in detecting moderate snowfall events. For light snowfall, only 48.58% of certain snowfall events were classified as snowfall by Q3 and a very high percentage (48.70%) of certain snow were deemed as no precipitation by Q3 when compared to moderate and heavy snowfall. This indicates that the Q3 overall percentage of no precipitation was primarily derived from the high percentage of no precipitation

SR>0 mm/h

100

1>SR>0 mm/h

100

(a)

(b) Percent (%)

50

10.93 0.11

0.54

pr ec ip bl e ra pr in ob ab le ra in C er ta i Po n ra ss in ib le sn C ow er ta in Po sn ss ow ib le m ix C ed er ta in m ix ed si

o

pr ec ip ib le ra pr in ob ab le ra in C er ta i Po n ra ss in ib le sn C ow er ta in Po sn ss ow ib le m ix C ed er ta in m ix ed Po

(d)

75.82

50

15.03 4.46

0.05

50 25.53

1.81

0.24

1.37

0.30

ed

w

m

ix

sn o C

er

ta

in

ai n er t

in er C

N

o

ta i

n

pr

m

CloudSat Precip_Flag

ra

ix

ec ip

ed

ed ix ta er C

Po

ss

ib

in

le

in ta er C

m

sn ow

ow

ra Po

ss

ib le

in ta er C

sn

in

in ra

in

le

ra ab ob pr

si

bl e

pr

ec

ip

0

Po s

o

58.05

14.74

0

N

Percent (%)

Percent (%)

100

(c)

0.10

0.63

SR≥2.5 mm/h

2.5>SR≥ 1mm/h

2.49

8.63

8.02

3.62

CloudSat Precip_Flag

CloudSat Precip_Flag

100

0.14

0

ss

o N

10.00

6.70

3.97

0.12

0.11

Po s

9.17 0

67.91

50

N

Percent (%)

69.40

C

0.

sn ow

75

Po ss ib le

Percent (%)

60

during light snowfall. Additionally, this confirms the fact that CloudSat has much higher sensitivity than Q3 in detecting light snowfall events. Regarding the heavy snowfall, Q3 also displays a low snowfall POD (56.93%) and a high stratiform POD. This indicates the large percentage of liquid precipitation existing in the heavy snowfall and hints at rain/snow partitioning algorithm differences that warrant further studies. The rain/snow partitioning issue apparently causes to snowfall certain events detected by CloudSat to be classified by Q3 as stratiform (liquid) precipitation, thus suggesting a large discrepancy between CloudSat and Q3 in properly classifying hydrometeor type during some heavy snowfall events. In summary, the differences between CloudSat and Q3 can be attributed to the following primary causes: (1) SR quantification; (2) identification of precipitation type; and (3) detectability of snowfall events. Several factors may therefore lead to large discrepancies between the two datasets. First, CloudSat has a higher sensitivity than Q3 in identifying snowfall particles. This observational advantage allows CloudSat to identify more snowfall events while Q3 classifies these events as no precipitation. Second, the altitudes of near-surface snow particles observed by CloudSat can be quite different from those of Q3, and are thus susceptible to wind effects under excessively windy conditions. These height effects may also lead to CloudSat classifying the snow events detected by Q3 as no precipitation or another precipitation type if the snowflakes melt into liquid particles at the altitudes observed by Q3. Similarly, difference in SR can be due to three factors. Firstly, either disaggregation of snowflakes into small hydrometeor particles or aggregation of snowflakes to become larger snowflakes can lead to larger reflectivity values by Q3. These larger reflectivity measurements could exist even if the SR does not appreciatively change, but would lead to an overestimate of

CloudSat Precip_Flag

Fig. 8. CloudSat precipitation type detection statistics as functions of as functions of Q3 snowfall rate (a) SR > 0 mm/h; (b) light snowfall (1 mm/h > SR > 0 mm/h), (c) moderate snowfall (2.5 mm/h > SR P 1 mm/h), (d) heavy snowfall (SR P 2.5 mm/h).

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

9

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

SR>0 mm/h

40 2.58

o N

an d

Q3 Precip_Flag

(c)

(d) Percent (%)

73.75

60 40

g ho

ot

in

Sn ow

m or

rs ve

ve

O

C

on

tif

ct

pr

iv

ec

e

g in ot

O

ve

rs

ho

Sn ow

d ht rig

B

St

ra

tif

B

or

an

m

ip ec pr

0.90

0 ra

0.16

ip

0.09

o

0.36 0

o

28.31 13.86

11.79

St

20 13.86

56.93 50

N

Percent (%)

SR ≥ 2.5 mm/h 100

80

N

0.04

Q3 Precip_Flag

2.5>SR≥ 1mm/h 100

0.03

O

B

C

O ve

0.08

B

ve

pr

ct

iv

e

0

on

ho ot in g

rs

Sn ow

an d rig ht B

B

N

St

o

ra

pr

tif

ec

or m

ip

0

Sn ow ve rs ho ot in g C on ve ct iv e

20

0.05

rm

0.03

0.10

48.58

St ra tif o

3.50

48.70

60

ec ip

50.72

45.59 50

(b)

80

ht

Percent (%)

100

rig

(a)

100

Percent (%)

1>SR>0 mm/h

Q2 Precip_Flag

Q3 Precip_Flag

Fig. 9. Q3 precipitation type detection statistics as functions of as functions of CloudSat snowfall rate (a) SR > 0 mm/h; (b) light snowfall (1 mm/h > SR > 0 mm/h), (c) moderate snowfall (2.5 mm/h > SR P 1 mm/h), (d) heavy snowfall (SR P 2.5 mm/h).

the SR by Q3 due to its fixed Z-S relationship. Additionally, when precipitating cloud structures have high cloud bases, snowflakes could fall into a dry sub-cloud layer and evaporate or sublimate before reaching areas where ground radars scan. This last process will lead to CloudSat detecting snowfall events, while Q3 does not. Secondly, as shown in Fig. 2f and i, the heavy snowfall VPRs increases as the observation height decrease to the near surface below 1 km, which would lead to that Q3 observes higher SR. Finally, CloudSat and Q3 use different criteria to classify precipitation events. CloudSat uses the ECMWF-AUX temperature fields to judge whether CPR observations are likely to be associated with snow layers. Q3 uses a different precipitation classification algorithm from CloudSat and identifies precipitation phase using dry temperature and web bulb temperature from a different modeling source (Zhang et al., 2011a). 4. Discussion The CloudSat snowfall retrieval algorithm shows good performance in detecting light snowfall, but possibly degraded performance in detecting heavy snowfall when compared with Q3 observations. Given that the attenuation caused by the liquid and solid hydrometers is considered and corrected in the snowfall algorithm, CloudSat does not account for supercooled water attenuation that may adversely affect SR estimates under certain conditions (Wood et al., 2013a). This may be a factor limiting CloudSat’s ability to detect moderate and heavy snowfall events. Additionally, the assumed snowflake shape models in the CloudSat snowfall algorithm may be another limiting factor. It is known that modeling larger snow particles, which are present in heavier snowfall, at W-band frequency is very challenging and great uncertainties in backscatter properties of such particles exist. Also, most

(>90% of) bins of CloudSat surface snowfall are high above 1 km and may not accurately represent SRs near the ground, particularly when SRs vary considerably with bin height, especially during heavy snowfall. SR differences (underestimation and overestimation) may also be linked to the invariant Z-R relationship utilized by Q3 products and the dynamic relationship associated with CloudSat precipitation rate retrievals that vary between different CPR observations. A recent study by Chen et al. (2015b) shows that the Q3 radaronly product underestimates precipitation in winter and spring, and no evaluations of the Q3 radar-only product for snowfall events with independent gauge observations have been performed to date. Also, as described in Section 2, Q3 has its own limitations including lower sensitivity, beam blockage in mountainous areas, beam overshooting, and broadening. Furthermore, certain and mixed snowfall events were not separated in the Q3 algorithm. These limitations act to degrade Q3 snowfall measurements in light snow, mountainous regions, regions far from the radar, and situations in which the snowfall-reflectivity relationships stray considerably from the relationship utilized by Q3. Therefore, it is difficult to quantify the absolute error in CloudSat light snowfall (<0.2 mm/h) estimates using the Q3 radar-only product given its limitations. Robust, independent ground-based snowfall measurements like gauge observations and vertically-pointing radar datasets are still needed to further evaluate both the Q3 and CloudSat snowfall products, and this could help better quantifying the biases and errors present in both of these snowfall datasets. 5. Conclusions This study compares the CloudSat 2C-SNOW-PROFILE product and NOAA/NSSL Multi-Radar Multi-Sensor System (NMQ/Q3) in

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

10

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx

detecting snowfall events and quantifying snowfall rates. Precipitation flags derived from 2C-Precipitation-Column precipitation type product were used to select certain snowfall events for comparison. Horizontal and vertical structures of CloudSat snowfall products were investigated for a heavy snow storm, while overall statistics were also calculated based on two-year (2009–2010) combined snowfall observations by CloudSat and Q3. The main results from this study are summarized below: (1) Compared to Q3, CloudSat tends to observe more extremely light snowfall events (<0.2 mm/h) and SRs between 0.6 to 1 mm/h, and detects less events with SRs between 0.2 and 0.5 mm/h. (2) CloudSat identifies 69.40% of snowfall events detected by Q3 as certain snow and 10% as certain mixed. When possible snow, possible mixed, and certain mixed precipitations are assumed to be snowfall events, CloudSat has a high snowfall POD (86.10%). (3) With Q3 as reference, CloudSat underestimates (overestimates) certain snowfall than Q3 when the bin height of detected snowfall events below (above) 3 km. Overall, CloudSat shows less certain snow precipitation than Q3 by 26.13% with a low CC (0.41) with Q3 and a high RMSE (0.6 mm/h). CloudSat generally overestimates light snowfall (<1 mm/h) by 7.53%, and underestimates moderate snowfall (1–2.5 mm/h) by 42.33% and heavy snowfall (P2.5 mm/h) by 68.73%. (4) For all certain snow events detected by CloudSat, Q3 identifies 50.72% as snow, 45.59% as no precipitation, 3.5% as straitiform, 0.1% as bright band, 0.03% as overshooting, and 0.05% as convective. (5) CloudSat has large discrepancies in the height of observed surface snowfall. Most (>99% of) surface snowfall events observed by CloudSat are >1 km high above the surface, whereas 76.41% of corresponding Q3 observations are low below 1 km to the near ground surface. This study is the first attempt to compare the performance (both detection and quantitative precipitation estimation) of the CloudSat snowfall product (2C-SNOW-PROFILE) and the high spatiotemporal ground radar QPE product Q3 over CONUS. Specifically, this study investigates and addresses some of the potential errors and limitations of both CloudSat and ground radar snowfall retrievals. However, because this study utilizes the Q3 radar-only product for comparison, more robust independent ground-based snowfall measurements like gauge observations are needed to better assess the performance of both CloudSat and Q3 snowfall estimates. This comparison is particularly valuable due to the recent launch (Feb 28, 2014) of the Global Precipitation Measurement (GPM) mission, which carries a Ka/Ku band dual frequency precipitation radar and a multichannel passive microwave imager, orbiting between 65°S and 65°N. The GPM utilizes these instruments to provide quasi-global snowfall estimates, and a similar methodological framework could be readily applied to evaluate GPM snowfall products. Acknowledgements This work was supported in part by the Hydrometeorology and Remote Sensing (HyDROS) Laboratory at The University of Oklahoma, in part by the National Natural Science Foundation of China (No. 41361022 and No. 41171020), the Open Fund from State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University (No. SKHL1310 and No. SKHL1501). Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics

and Space Administration. A.B. was supported by NASA New Investigator Program (NIP) and Energy and Water Cycle Study (NEWS) awards. Thanks are given to Youcun Qi from NOAA/NSSL for his great help in VPR analysis during revision process, to Dr. Benjamin Johnson from Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center for his constructive advice for this paper in early version, and to Mr. Nicholas Carr from The University of Oklahoma for assistant proofreading early versions of this manuscript.

References Amitai, E., Llort, X., Sempere-Torres, D., 2009. Comparison of TRMM radar rainfall estimates with NOAA next-generation QPE. J. Meteorol. Soc. Jpn. 87, 109–118. Behrangi, A., Tian, Y., Lambrigtsen, B., Stephens, G.L., in press. What does CloudSat reveal about global land precipitation detection by other space-borne sensors? Water Resour. Res (in press). Benjamin, S.G. et al., 2004. An hourly assimilation-forecast cycle: the RUC. Mon. Weather Rev. 132 (2), 495–518. Cao, Q. et al., 2014. Snowfall detectability of NASA’s CloudSat: the first crossinvestigation of its 2c-Snow-profile product and National Multi-Sensor Mosaic QPE (NMQ) snowfall data. Prog. Electromagnet. Res. 148, 55–61. Cao, Q., Qi, Y., 2014. The variability of vertical structure of precipitation in Huaihe River Basin of China: implications from long-term spaceborne observations with TRMM precipitation radar. Water Resour. Res. 50 (5), 3690–3705. Chen, S. et al., 2015a. Using citizen science reports to evaluate estimates of surface precipitation type. Bull. Am. Meteorol. Soc. Chen, S. et al., 2013a. Evaluation and Uncertainty Estimation of NOAA/NSSL Next Generation National Mosaic QPE (Q2) over the Continental United States. J. Hydrometeorol. (2013) Chen, S. et al., 2013b. Evaluation and uncertainty estimation of NOAA/NSSL nextgeneration national mosaic quantitative precipitation estimation product (Q2) over the Continental United States. J. Hydrometeorol. 14 (4), 1308–1322. Chen, S. et al., 2015b. Intercomparison of precipitation estimates from WSR-88D Radar and TRMM measurement over continental United States. Geosci. Remote Sensing, IEEE Trans. 53 (8), 4444–4456. CIRA, 2008. CloudSat Project: A NASA Earth System Science Pathfinder Mission: CloudSat Standard Data Products Handbook, Fort Collins, CO 80523. Elmore, K.L. et al., 2014. MPING: crowd-sourcing weather reports for research. Bull. Am. Meteorol. Soc. 95 (9), 1335–1342. Haynes, J., L’Ecuyer, T., Vane, D., Stephens, G., Reinke, D., 2014. Level 2-C Precipitation column algorithm product process description and interface control document. Hiley, M.J., Kulie, M.S., Bennartz, R., 2011. Uncertainty analysis for CloudSat snowfall retrievals. J. Appl. Meteorol. Climatol. 50 (2), 399–418. Hudak, D., Rodriguez, P., Donaldson, N., 2008. Validation of the CloudSat precipitation occurrence algorithm using the Canadian C band radar network. J. Geophys. Res.: Atmospheres (1984–2012) 113 (D8). Kirstetter, P.E. et al., 2012. Toward a framework for systematic error modeling of spaceborne precipitation radar with NOAA/NSSL ground radar-based national mosaic QPE. J. Hydrometeorol. (2012) Liu, G., 2008. Deriving snow cloud characteristics from CloudSat observations. J. Geophys. Res. 113 (D8), D00A09. Liu, G., Seo, E.-K., 2013. Detecting snowfall over land by satellite high-frequency microwave observations: the lack of scattering signature and a statistical approach. J. Geophys. Res.: Atmospheres 118 (3), 1376–1387. Maddox, R.A., Zhang, J., Gourley, J.J., Howard, K.W., 2002. Weather radar coverage over the contiguous United States. Weather Forecasting 17 (4), 927–934. Marchand, R., Mace, G.G., Ackerman, T., Stephens, G., 2008. Hydrometeor detection using CloudSat-An Earth-orbiting 94-GHz cloud radar. J. Atmospheric Oceanic Technol. 25 (4), 519–533. Matrosov, S.Y., Battaglia, A., 2009. Influence of multiple scattering on CloudSat measurements in snow: a model study. Geophys. Res. Lett. 36 (12). Norin, L., Devasthale, A., L’Ecuyer, T.S., Wood, N.B., Smalley, M., 2015. Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden. Atmos. Meas. Tech. 8 (12), 5009–5021. Qi, Y., Kingsmill, D., Zhang, J., Min, J., Howard, K., 2011. Correction of radar QPE errors associated with low and partially observed bright band layers. In: Radar Conference (RADAR), 2011 IEEE. IEEE, pp. 580–585. Qi, Y., Zhang, J., Cao, Q., Hong, Y., Hu, X.-M., 2013a. Correction of radar QPE errors for nonuniform VPRs in mesoscale convective systems using TRMM observations. J. Hydrometeorol. 14 (5), 1672–1682. Qi, Y., Zhang, J., Zhang, P., 2013b. A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates. Quart. J. Royal Meteorol. Soc. 139 (677), 2233–2240. Qi, Y., Zhang, J., Zhang, P., Cao, Q., 2013c. VPR correction of bright band effects in radar QPEs using polarimetric radar observations. J. Geophys. Res.: Atmospheres 118 (9), 3627–3633. Rasmussen, R. et al., 2001. Weather support to deicing decision making (WSDDM): a winter weather nowcasting system. Bull. Am. Meteorol. Soc. 82 (4), 579–595.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047

S. Chen et al. / Journal of Hydrology xxx (2016) xxx–xxx Smalley, M., L’Ecuyer, T., Lebsock, M., Haynes, J., 2013. A comparison of precipitation occurrence from the NCEP StageIV QPE Product and the CloudSat Cloud Profiling Radar. J. Hydrometeorol. (2013) Stephens, G.L. et al., 2002. The CloudSat mission and the A-Train: a new dimension of space-based observations of clouds and precipitation. Bull. Am. Meteorol. Soc. 83 (12), 1771–1790. Stephens, G.L. et al., 2008. CloudSat mission: performance and early science after the first year of operation. J. Geophys. Res.: Atmospheres (1984–2012) 113 (D8). Vasiloff, S.V. et al., 2007. Improving QPE and very short term QPF. Bull. Am. Meteorol. Soc. 88 (12), 1899–1911. Wood, N., L’Ecuyer, T.S., Vane, D., Stephens, G., Partain, P., 2013a. Level 2C Snow Profile Process Description and Interface Control Document. Wood, N., L’Ecuyer, T., Vane, D., Stephens, G., Partain, P., 2013b. Level 2C Snow Profile Process Description and Interface Control Document. version 0, Tech. rep. http://wwwCloudSat.cira.colostate.edu/ICD/2C-SNOW-PROFILE/2C-SNOWPROFILE_PDICD_P_R04.pdf. Xu, X., Howard, K., Zhang, J., 2008. An automated radar technique for the identification of tropical precipitation. J. Hydrometeorol. 9 (5), 885–902.

11

Zhang, J. et al., 2011a. National mosaic and multi-sensor QPE (NMQ) system: description, results, and future plans. Bull. Am. Meteorol. Soc. 92, 1321–1338. Zhang, J. et al., 2011b. National Mosaic and Multi-Sensor QPE (NMQ) system: description, results, and future plans. Bull. Am. Meteorol. Soc. 92 (10), 1321– 1338. Zhang, J. et al., 2014a. Initial operating capabilities of quantitative precipitation estimation in the Multi-Radar Multi-Sensor System. In: 28th Conf. on Hydrology. Zhang, J. et al., 2014b. Initial operating capabilities of quantitative precipitation estimation in the multi-radar multi-sensor system. In: Preprints (Ed.), 28th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc, Atlanta, GA. Zhang, J., Qi, Y., 2010. A real-time algorithm for the correction of brightband effects in radar-derived QPE. J. Hydrometeorol. 11 (5), 1157–1171. Zhang, J., Qi, Y., Langston, C., Kaney, B., Howard, K., 2014c. A real-time algorithm for merging radar QPEs with rain gauge observations and orographic precipitation climatology. J. Hydrometeorol. 15 (5), 1794–1809.

Please cite this article in press as: Chen, S., et al. Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL MultiRadar Multi-Sensor System. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.07.047