Liquid Water in Snowing Clouds: Implications for Satellite Remote Sensing of Snowfall

Transcription

1 Liquid Water in Snowing Clouds: Implications for Satellite Remote Sensing of Snowfall Yu Wang a,b, Guosheng Liu b, Eun-Kyoung Seo c, and Yunfei Fu a a. School of Earth and Space Science, University of Science and Technology of China, Hefei, China b. Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA c. Department of Earth Sciences, Kongju National University, Korea Corresponding Author Address: Guosheng Liu Department of Earth, Ocean and Atmospheric Science Florida State University 1017 Academic Way, 404 Love Building Tallahassee, FL (850) (850) (fax) gliu@fsu.edu Manuscript submitted to Atmospheric Research - Special Issue of Perspectives on Precipitation Science Revised April 2012

2 Abstract To study the impact of cloud liquid water on passive microwave snowfall remote sensing, we analyzed 4 years of liquid water path data retrieved from microwave radiometer on Aqua satellite that are collocated with CloudSat snowfall observations. Results showed that cloud liquid water commonly occurs in snowing clouds (2-m air temperature lower than 2 C); about 72% of these clouds have a retrieved liquid water path greater than 0. The mean liquid water path for all snowing clouds is about 74 g m -2, higher for horizontally extended clouds (70~100 g m -2 ) and lower for isolated (~50 g m -2 ). There is a clear tendency that snowing clouds are less likely to contain liquid water as 2- m air temperature decreases. However, the variation of the mode values of liquid water path with 2-m air temperature seems to be cloud type dependent, particularly for colder environment with 2-m air temperature lower than 263 K. On average, larger values of liquid water path occur when near-surface radar reflectivity ranges from -10 to 0 dbz, corresponding to relatively weak snowfall of 0.02 to 0.15 mm h -1, rather than to the heaviest snowfall observed. The impact of cloud liquid water on passive microwave satellite remote sensing of snowfall has been investigated using radiative transfer simulations. It is concluded that for frequencies higher than 80 GHz the brightness temperature warming caused by cloud liquid water emission has a similar magnitude to the brightness temperature cooling caused by snowflakes' scattering. Therefore, while ice scattering is the primary signature for retrieving snowfall, it is equally important to take into account the impact by cloud liquid water when developing snowfall retrieval algorithms using high-frequency satellite observations. 1

3 Keywords: Cloud liquid water; snowing cloud; microwave remote sensing 2

4 1. Introduction The presence of supercooled water in clouds colder than -20 C is not uncommon (Rogers and Yau, 1989). In fact, aircraft observations occasionally found liquid water drops in clouds as cold as -40 C [see, for example, Pruppacher and Klett (1997)]. Based on estimates from satellite lidar observations, Hu et al. (2010) recently concluded that "at high latitudes, more than 95% of low-level clouds with temperatures between 40 C and 0 C are water clouds". Therefore, there is no surprise to find appreciable cloud liquid water in a cloud from which precipitation reaches to the ground in the form of snow (hereafter, called snowing cloud). However, to satellite remote sensing of snowfall, the presence of cloud liquid water has profound importance, particularly for retrieving snowfall using passive microwave measurements. At microwave (including sub-millimeter) wavelength, the imaginary part of the dielectric constant of ice is several orders lower than that of liquid water; the emission of microwave energy by ice and snow particles is often negligible. Therefore, the primary signature for snowfall remote sensing by passive microwave measurements is the reduction of upwelling radiative energy caused by the scattering due to ice particles. In other words, heavier snowfall commonly corresponds to lower brightness temperatures received by satellite microwave radiometers (Bennartz and Bauer, 2003; Noh et al., 2006; Skofronick-Jackson and Johnson, 2011). However, cloud liquid water absorbs and emits measurable microwave energy. If it situates within or above the snow and ice cloud layer, which observations show being often the case (e.g., Carey et al., 2008), the cloud liquid water will increase the upwelling microwave energy, therefore, mask the ice scattering signature generated by snowfall (Liu and Curry, 2003; Smith, 2010). The degree of 3

5 masking depends on the magnitude of cloud liquid water amount, as well as its vertical position in the clouds. For remote sensing of snowfall using passive microwave observations, it is, therefore, important to quantify liquid water amount in snowing clouds (Kulie et al., 2010), although characterizing cloud liquid water has its own rights for studying the microphysical processes in solid precipitation formation (e.g., Del Genio et al., 1996). Nevertheless, except for some aircraft in situ and ground-based remote sensing measurements, our knowledge to liquid water characteristics in snowing clouds is very limited. In this study, we attempt to conduct a detailed study of cloud liquid water in snowing clouds using satellite observations, including the magnitude, frequency distribution, spatial variation, and its relation to other geophysical variables. In order to simultaneously estimate cloud liquid water and snowfall rate, we will use satellite data from Aqua AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) and CloudSat CPR (Cloud Profiling Radar). Radar reflectivity from CPR has been used to derive snowfall vertical profiles (Liu, 2008a), and the AMSR-E brightness temperatures will be used to retrieve cloud liquid water path over ocean (e.g., Liu and Curry, 1993; Greenwald et al., 1993; Weng and Grody, 1994; Lin and Rossow, 1994; Wentz, 1997). Currently, there are two cloud liquid water data products archived by NASA, i.e., the AMSR-E liquid water path based on Wentz and Meissner (2000) and CloudSat liquid water content based on Austin and Stephens (2001) and Austin et al. (2009). However, neither of the two retrievals is suitable for this study. The Wentz and Meissner (2000) algorithm has been developed with warm clouds in mind; it always assumes that cloud temperature is higher than 273 K (more specifically, it assumes that 4

6 cloud temperature is between sea surface temperature and 273 K). Since snowing clouds almost always have a temperature lower than 273 K, the assumption in the algorithm will be generally broken. On the other hand, the CloudSat algorithm retrieves liquid water content from radar reflectivity based on a temperature-dependent "liquid-ice partition". This partition assumes that radar returns are solely due to ice particles when air temperature is lower than -20 C while contribution by liquid water drops increases from 0 to 100% as air temperature increases from -20 C to 0 C. Aircraft in situ observations showed that in a mixed-phase cloud liquid water layer commonly exists at the top portion (in other words, colder region) of the cloud (e.g., Carey et al., 2008), which contradicts the assumption used in the CloudSat liquid water retrieval algorithm. Additionally, liquid water retrieval is not conducted for clouds with "precipitation", which leaves substantial portion of snowing clouds without liquid water retrieval in the archived CloudSat data product. For the purpose of this study, we conducted liquid water path retrieval using combined AMSR-E and CloudSat data, based on a modified version of the Liu and Curry (1993) algorithm. The algorithm primarily utilizes brightness temperature at 36.5 GHz of AMSR-E while using CloudSat to determine clear-sky regions and using observations at low frequency AMSR-E channels to assess no-liquid background radiation for 36.5 GHz. A more detailed description of the algorithm will be given in next section. Using retrieved liquid water path, we have studied cloud liquid water characteristics and their relation to other geophysical variables; those results are described in Section 3. The impact of cloud liquid water on retrieving snowfall when using high-frequency passive 5

7 microwave observations is discussed in Section 4. Conclusions of this study are given in Section Data and Cloud Liquid Water Retrieval Method The study is primarily based on 4 years (from 15 June 2006 to 31 June 2010) of collocated Aqua AMSR-E and CloudSat CPR data; both satellites are in the A-Train formation with Aqua ahead of CloudSat by only about 1 minute, allowing good temporal collocation of data from sensors onboard the two satellites (Stephens et al., 2002). The AMSR-E is a conically scanning radiometer measuring horizontally- and verticallypolarized radiation at 55 Earth incidence angle at the following 6 frequencies: 6.9, 10.7, 18.7, 23.8, 36.5 and 89 GHz. Brightness temperature data from these channels are resampled and collocated into different spatial resolutions (Ashcroft and Wentz, 2006); the Level-2A data used in this study have a spatial resolution of 21 km and include channels at 18.7, 23.5, 36.5 and 89 GHz. The accuracy of the brightness temperatures is 1 K or better (Imaoka et al., 2010). While the swath covered by AMSR-E is 1445 km, we only use pixels at the swath center in this study for better collocating them with CloudSat CPR observations. Additionally, for liquid water retrievals, data collected over land and sea ice are excluded. Daily sea ice concentration data based on AMSR-E observations are obtained from National Snow and Ice Data Center (Cavalieri et al., 2004). The CPR is a 94-GHz nadir-looking radar that measures the power backscattered by clouds and precipitation as a function of distance from the radar. Its minimum detectable radar reflectivity factor was designed to be -26 dbz at pre-launch although analysis of in-orbit observational data has shown that the sensitivity is better than the 6

8 specification at about -30 dbz. The standard CloudSat product 2D-GEOPROF (Release version 4) (Mace, 2007) is used, which includes radar reflectivity in 150 bins in the vertical with a bin size of about 240 m. The footprint size of radar reflectivity profiles is 1.4 km cross- and 2.5 km along-track. The bin that corresponds to surface return is also specified in the product. To remove surface-contaminated data, data of the lowest 3 bins (~0.75 km) were excluded in the data analysis. In the following, the snowfall rate derived from the radar reflectivity at the 4th bin is called near-surface snowfall for over-ocean observations. Two-meter (2-m) air temperature from ancillary data product ECMWF-AUX (Partain, 2007) was used to determine whether surface precipitation is rainfall or snowfall. The ECMWF-AUX contains atmospheric variables from European Centre Medium range Weather Forecasts (ECMWF) model analysis, interpolated to the location of CPR bins. Using multiyear shipboard and ground-based weather reports, Liu (2008a) found that the transition between snow and rain commonly occurs within the air temperature range of 4 C to -1 C, with the 50% transition probability occurring at 2 C. Based on this result, Liu (2008a) has used 2-m air temperature of 2 C in ECMWF analysis to distinguish between raining and snowing conditions. The same threshold will be used in this study as well. Liquid water path retrievals have been conducted for AMSR-E nadir pixels that are determined by 2-m air temperature to be under snowing conditions, i.e., lower than 2 C. The retrieval algorithm is based on Liu and Curry (1993), with some modifications to take advantage of the availability of CloudSat radar observations. Based on Liu and Curry (1993), liquid water path, LWP, can be expressed as: 7

9 1 1 LWP ln( )cos k 1, (1) c where k is the absorption coefficient for liquid water, and a function of frequency and cloud mean temperature, c is the emissivity of the cloud, and is the incidence angle (55 for AMSR-E observations). The cloud emissivity, c, is derived from a quadratic equation as follows: and 2 a b c 0, (2) c a T b T c T B0 B c c TB 1 2 (1 ) T T B ( ) T T a c s, (3) where T B is the observed brightness temperature over the cloud, T B0 is the brightness temperature under clear-sky conditions, T c, T a, and T s are the temperatures of the cloud (mean), lower atmosphere (average temperature weighted by gas absorption coefficients) and sea surface, respectively. For this study, horizontally-polarized brightness temperature at 36.5 GHz is used for the liquid water path retrieval. It is assumed that scattering by snowflakes at the 36.5 GHz channel is negligible. T a and T s are derived from collocated ECMWF data; T c is taken to be the air temperature in the top layer of the CloudSat observed cloud, in consistence with aircraft observations that liquid water commonly found at the top portion of a mixed phase cloud (e.g., Carey et al., 2008), but disallowing T c to be lower than -20 C. Similar to Liu and Curry (1993), T B0 is first derived from brightness temperatures at 18.7, 23.8 and 36.5 GHz frequencies by regressing radiative transfer model (Liu, 1998) results under various cloudy and clear-sky conditions using multivariate regression. A further improvement to the value of T B0 in 8

10 this study takes place by using AMSR-E observed brightness temperatures at CloudSat CPR-determined clear-sky locations to remove any biases in the computed T B0. A clearsky AMSR-E pixel is defined as all CloudSat observations within the AMSR-E field of view have radar reflectivity values less than -25 dbz. Because of this improvement, systematic error (or difference) between modeled and observed brightness temperatures will be removed in our retrieval algorithm, so that it does not affect on the retrieved liquid water path. 3. Cloud Liquid Water Characteristics in Snowing Clouds Following retrieval algorithm described in the previous section, liquid water path was retrieved using data from 15 June 2006 to 31 June 2010 for pixels under conditions of 2-m air temperature being lower than 2 C. In this section, we describe the cloud liquid water characteristics for these snowing clouds. In the data analysis, we further grouped all clouds into 5 types according to their appearances in the CloudSat radar reflectivity height-distance cross sections, i.e., IS: isolated shallow clouds; ID: isolated deep clouds; ES: extended shallow clouds; ED: extended deep clouds; NP: nonprecipitating clouds. The definitions of these terms are as follows. The term "isolated" is for continuous radar returns horizontally shorter than 40 km, otherwise it is called "extended". Likewise, the term "shallow" is used for clouds with radar echo top height lower than 5 km, otherwise it is called "deep". NP is for clouds with radar returns that do not continuously attached to a precipitating (echo reaches ground) system. In Figs. 1 and 2, we show two examples of the cloud classification and liquid water path retrievals. The first example (Fig.1) is for a case on 18 July 2007, when the 9

11 CPR observed an extended shallow precipitating cloud system between 52 and 56 S and an extended deep precipitating cloud system south of 56 S, together with some isolated and nonprecipitating clouds scattered around. Liquid water path within the shallow clouds reaches 400~500 g m -2, while it within the deep cloud system varies dramatically from over 300 g m -2 at one part of the clouds to near zero at some other parts. The second example (Fig.2) is for a case on 21 August 2006 when a deep cloud system is found in the domain. An interesting feature of the deep cloud system is that large liquid water path values are found north of 52 S while near zero liquid water is observed to the south (Note that liquid water path retrieval is not performed to the south of 56.5 S because it is over land/sea ice) although CPR radar reflectivity indicates that near surface snowfall should be heavier in the southern portion of the cloud system. In other words, at least for extended deep cloud systems the largest liquid water path does not occur where the snowfall is the heaviest. 3.1 Liquid water path statistics Using the 4 years of collocated AMSR-E and CloudSat data, liquid water path for snowing clouds is retrieved and its statistics are computed. Some general statistics of the snowing clouds are shown in Table 1. The global distribution of the snowing clouds, their conditional probability to have liquid water path greater than 0, and their averaged values of liquid water path are shown in Fig. 3. In northern hemisphere, snowing clouds are most likely to occur in the storm track areas, with the maximum frequency of occurrence in the Greenland-Iceland-Norwegian Seas. In the southern hemisphere, the most likely snowing locations form a "doughnut" shape along ~65 S, circling the south pole. Within 10

12 the snowing clouds, those located in the lower latitudes are likely to contain liquid water, particularly for Southern Hemisphere, while overall some 70% of the snowing clouds have liquid water path greater than 0 (Table 1). Similarly, the mean liquid water path values are also larger in lower latitudes in Southern Hemisphere; but in the Northern Hemisphere large liquid water path values are observed at some fixed locations, such as Norwegian Sea, Japan Sea and Hudson Bay. As shown in Table 1, most of the snowing clouds contained cloud liquid water as detected by AMSR-E, with isolated deep and extended shallow clouds having the highest probability (over 80%). The mean liquid water path can reach about 100 g m -2 for extended shallow snowing clouds, while its value for isolated shallow snowing clouds is about 50 g m -2. The overall average of cloud liquid water path in all clouds is about 75 g m -2 with a large spread in distribution (standard deviation ~95 g m -2 ). On average, deep clouds (ID and ED) correspond to the heaviest snowfall by counting either any level in the vertical (MaxZ e ) or the near surface level (SurfZ e ). Note that the heaviest snowfall often does not occur at the near surface level, but slightly above. Additionally, liquid water path in isolated snowing clouds (IS and ID) could have been underestimated due to the small size of these clouds that may only partially occupy a radiometer's field of view. Shown in Fig.4 are the frequency distributions of liquid water path for different cold cloud/snowfall types (i.e., 2-m air temperature lower than 2 C). The liquid water path frequency resembles the shape of a log-normal distribution, but skewed toward the lower end (i.e., 0 g m -2 ). On the logarithmic scale, the mode of the distribution is g m -2 for the extended snowing clouds and g m -2 for the isolated snowing clouds. The distributions for the two isolated cloud types are quite similar, while between 11

13 the two extended cloud types the deep cloud has a much broader liquid water path distribution than does the shallow cloud. In fact, among all the snowing clouds, the extended deep snowing cloud has the broadest liquid water path distribution. 3.2 Cloud liquid water and vertical snowfall profiles Using backscattering cross section calculated from nonspherical ice particles and particle size distribution from several in situ measurements, Liu (2008a) has derived the following snowfall rate (S) - radar reflectivity (Z e ) relation for CloudSat CPR: 1.25 Z e 11.5S, (4) with Z e in mm 6 m -3, and S in mm h -1 (liquid water equivalent). Using this Z e -S relation, eleven snowfall rate bins are divided, and all snowing clouds are then grouped according to their near-surface snowfall rate and cloud type. The mean vertical profiles of snowfall rate for these snowing cloud groups are shown in Fig.5, and their mean values of liquid water path are given in Table 2. For shallow snowing clouds, the distinct feature of the vertical snowfall rate profiles is the sharp increase of snowfall rate as altitude lowers. While being shallow, these clouds can sometimes generate heavy snowfalls as indicated by the large values in the highest snowfall rate bin. For deep snowing clouds, the vertical variation of snowfall rate profiles is relatively gradual; the maximum snowfall rate in a vertical profile is often observed at a level high above the surface, particularly for isolated deep clouds. Liquid water path in the isolated clouds (Table 2) tends to be lower than those in the extended clouds, which may be partially due to the small scale of the isolated clouds that cannot fully fill the radiometer's field of view. It appears that there is no clear dependence of liquid water path on surface snowfall rate for the isolated clouds. 12

14 For extended clouds, liquid water path peaks at relatively light snowfall (~0.05 mm h -1 ) for shallow clouds, but at relatively heavy snowfall (~1.5 mm h -1 ) for deep clouds. The magnitude of liquid water path in the extended clouds is about twice of that in their counterpart of isolated clouds. 3.3 Relations to temperature and cloud height For each snowing cloud type, the "Contoured Frequency of liquid water path by 2-m Temperature Diagram" (CFTD) is computed using all available data. The CFTD is defined similar to the CFAD (Contoured Frequency by Altitude Diagram) of Yuter and Houze (1995), only to replace altitude by 2-m temperature and radar reflectivity by liquid water path (in logarithmic scale). The CFTDs are shown in Fig.6, together with the number of observations and the percent of LWP>0 pixels in each 2-m temperature bin. Isolated deep snowing clouds were observed only under warmer conditions (2-m temperature higher than 265 K); the percentage of pixels with LWP>0 is mostly over 80%. For other snowing cloud types, the percentage of LWP>0 pixels increases as 2-m air temperature increases, from several percent to about 80% when the 2-m temperature changes from 250 K to 275 K. From the CFTDs, it seems that the mode liquid water path, which is around 100 g m -2 (also see Fig.4), does not have a clear dependency on 2-m temperature in the temperature range of 260 to 275 K. For lower temperature range, the mode liquid water path appears to decrease for isolated shallow clouds, but increase for extended shallow and deep clouds, as 2-m temperature decreases. In other words, for those isolated shallow clouds with liquid water path greater than 0, liquid water path tends to be lower at colder ( K) environment. But the opposite is true for those 13

15 extended snowing clouds, although for all snowing clouds the fraction of clouds having measurable liquid water path always becomes smaller as 2-m temperature decreases. In Fig.7 are shown the mean values of liquid water path sorted by their cloud top height as determined by CloudSat CPR echo. For isolated snowing clouds, it appears no clear dependency of liquid water path on cloud top height while the maximum liquid water path occurs when cloud top height is around 2 km. For extended clouds, the liquid water path has two maxima, at cloud top height near 2 and 12 km, respectively. At the cloud top height range of 5 to 8 km, which corresponds to most of the synoptic scale cloud systems, the mean liquid water path shows little dependency on cloud top height. The mean liquid water path as a function of near-surface radar reflectivity and cloud top temperature is shown in Fig.8, separately for isolated shallow, isolated deep, extended shallow and extended deep snowing clouds. Except for isolated deep clouds, in which number of data samples appears too low to show a clear distribution pattern, liquid water path seems to show greater values when near surface radar reflectivity ranges from -10 to 0 dbz, corresponding to a weak snowfall of approximately 0.02 to 0.15 mm h -1. As shown in the cases of Figs. 1 and 2, heavy snowfalls are commonly not accompanied by large values of cloud liquid water. Additionally, the diagram shows that larger values of liquid water paths often appear at several "favorite" cloud top temperature ranges, e.g., around 0 C (extended deep clouds), -10 C (263 K, all except for isolated deep clouds), and -19 C (254 K, isolated shallow clouds). To the authors' best knowledge, this result does not seem to have an obvious explanation. 14

16 4. Implication to Satellite Snowfall Remote Sensing Radiative transfer model simulations have been performed to understand how cloud liquid water modifies brightness temperatures at the following microwave frequencies [part of Global Precipitation Measurement (GPM) Microwave Image (GMI) frequencies]: 37, 89, 166 and 183±7 GHz. The radiative transfer model of Liu (1998) has been used, in which the transfer equation in a plane-parallel atmosphere is solved by the 4-stream discrete ordinate method. The scattering properties of snow particles are computed by discrete dipole approximation, for which a tabulated database for 11 particle shapes at various frequencies has been given by Liu (2004; 2008b). The results to be shown in this section are generated using the particle shape of "dendrite snowflakes". Using other particle shapes does not change the general conclusions although the values of brightness temperature would change some. In the following figures, we show the change of the vertically-polarized brightness temperature from clear-sky (no cloud, no snowfall) values. The radiometer's viewing zenith angle is assumed to be 53, commonly used for conically scanning microwave radiometers. The modeling results for isolated shallow snowing clouds (see profiles in Fig.5) under a standard middle latitude winter atmosphere are shown in Fig.9. A 1-km deep liquid water cloud is placed between 5 to 6 km altitude with liquid water path varying from 0 to 500 g m -2, a range being likely according to Fig.4. A calm ocean surface is assumed in the calculations. At 37 GHz, reduction of brightness temperatures by snow particles is minimal, less than 5 K even for the heaviest snowfall examined. The main signature is brightness temperature increase with the increase of cloud liquid water path. At 89 GHz, the decrease of brightness temperature due to snowflake scattering and its 15

17 increase due to cloud liquid water are equally competitive; brightness temperature could have little change if snowfall rate and liquid water path varies proportionally as shown by the curve corresponding to T B -change equaling zero. At 166 GHz, the ice scattering signatures dominates; without cloud liquid water the brightness temperature can decrease by ~40 K as snowfall rate reaches to 5 mm h -1. However, cloud liquid water masks the scattering signature substantially. For example, given a snowfall rate of 3 mm h -1, the brightness temperature depression changes from 35 K at LWP=0 to 15 K at LWP=360 g m -2, the latter is equivalent to 0.7 mm h -1 snowfall rate without cloud liquid water. For mid latitude atmosphere at a radiometer's viewing of 53, the 183±7 GHz channel brightness temperature does not respond much to the cloud and snowfall in the lower atmosphere; the brightness temperature changes shown in Fig.9c ranges with 0 to 10 K. In Fig.10, we show the results for the same variation of cloud liquid water path and snowfall rate profiles (i.e., isolated shallow clouds), but under the Artic winter standard atmospheric condition. Due to less water vapor in the cold environment, compared to mid latitudes, the ice scattering signatures are much stronger in the Arctic if the snowfall rate profiles remain the same; a brightness temperature decrease of ~20 K can be seen even at 183±7 GHz when near surface snowfall rate reaches 5 mm h -1 and without the masking of cloud liquid water. Again, the masking effect by cloud liquid water to scattering signature is substantial. The severity of the masking effect depends on the relative locations between the liquid water and snow particle layers. In Fig.11, we show results for the same condition as that for Fig.9, except for replacing snowfall rate profiles of isolated shallow clouds by those of extended deep clouds. From Fig.5, it is seen that the depth of snowfall rate 16

18 profiles are much deeper in extended deep clouds than that in isolated shallow clouds, especially for those profiles with near surface snowfall rate lower than 2 mm h -1. In this case, a substantial portion of snow particles is located above the cloud liquid water layer. Again, brightness temperature at 37 GHz receives little reduction due to the scattering by snow, and its change is dominated by cloud liquid water. The brightness temperature change at the two highest frequencies (166 and 183±7 GHz) appears rather peculiar as the largest reduction occurs at near surface snowfall rate of ~1.8 mm h -1. As shown in Fig.5d, for the last two profiles with surface snowfall rates greater than 2 mm h -1, their depth is actually shallower than that of most of the other profiles, resulting in less snow particles in the altitudes higher than 4 km. Since brightness temperatures at the two highest frequency channels, particular the 183±7 GHz channel, are largely governed by hydrometeors at high altitudes, they start to increase with the increase of snowfall rates above 2 mm h -1. This pattern of brightness temperature change further complicates the relationship between microwave signature and snowfall rate, therefore, making it difficult for snowfall retrieval. 5. Summary and Conclusions Satellite remote sensing of snowfall using passive microwave observations relies on the ice scattering signatures due to snowflakes, which generally exhibits a reduction of upwelling brightness temperature as snowfall intensifies. However, earlier studies have indicated that supercooled cloud liquid water commonly exists in snowing clouds. Unlike ice, liquid water can cause substantial absorption and emission of energy in the microwave frequencies, thus has a masking effect to the ice scattering signature 17

19 originating from snowflakes. The purpose of this study is to evaluate the magnitude and features of cloud liquid water in snowing clouds using satellite observational data, and assess its impact on upwelling brightness temperature using radiative transfer simulations. It is hoped that the results from this study will help retrieval algorithm developers alleviate algorithm's inaccuracy caused by cloud liquid water. Collocated AMSR-E, CloudSat and ECMWF analysis data over 4 years are used to identify snowfall pixels, to retrieve cloud liquid water path, and to derive vertical snowfall rate profiles. The separation of snow and rain is based on a 2-m air temperature threshold earlier developed by Liu (2008a) based on multiyear land station and shipboard present weather reports. Cloud liquid water path is retrieved from AMSR-E data using a modified version of the algorithm developed by Liu and Curry (1993); the modification is intended to take the advantage of the availability of coincident CloudSat radar data, which can identify cloud-free scenes and cloud top locations. The snowfall rate is derived from CloudSat CPR data using a radar reflectivity - snowfall rate relation developed for CloudSat CPR by Liu (2008a). Additionally, clouds are grouped into 5 types according their appearance in the CloudSat radar reflectivity distance-height cross sections. The results from the 4-year data showed that cloud liquid water commonly occurs in snowing clouds; some 70% of these clouds have a retrieved liquid water path greater than 0. The mean liquid water path for all snowing clouds is about 74 g m -2, higher for extended clouds (70~100 g m -2 ) and lower for isolated (~50 g m -2 ). The liquid water path frequency distribution resembles the shape of a log-normal distribution but skewed toward the lower end (0 g m -2 ). The mode values of liquid water path in the logarithmic scale are around 100 g m -2. There is a clear tendency that snowing clouds are 18

20 less likely to contain liquid water as 2-m air temperature decreases. However, the variation of the mode values of liquid water path with 2-m air temperature seems to be cloud type dependent, particularly for colder environment with 2-m air temperature lower than 263 K. On average, larger values of liquid water path occur when near surface radar reflectivity ranges from -10 to 0 dbz, corresponding to relatively weak snowfall of 0.02 to 0.15 mm h -1, rather than to the heaviest snowfall observed. The impact of cloud liquid water on passive microwave satellite remote sensing of snowfall has been investigated using radiative transfer simulations. The vertical profiles of snowfall rate and the magnitude of cloud liquid water path used in the simulations are based on results of this study. It is found that cloud liquid water could substantially mask the ice scattering signatures produced by falling snowflakes although the severity of the impact depends on the liquid water amount and the altitude of the cloud liquid water layer. Based on the data analysis and model simulations results, we conclude that the brightness temperature warming caused by cloud liquid water emission has a similar magnitude to the brightness temperature cooling caused by snowflakes' scattering for frequencies of 85 GHz and above. Therefore, while ice scattering is the primary signature for retrieving snowfall, it is equally important to take into account the impact by cloud liquid water when developing snowfall retrieval algorithms using high-frequency satellite observations. Acknowledgements. This research has been supported by NASA PMM grant NNX10AG76G and NASA CloudSat Grant NNX10AM30G. YW has also been supported by the Knowledge Innovation Program of the Chinese Academy of Science 19

21 (Grant No. KZCX2-EW-QN507). EKS's participation of this research has been supported by the Korea Meteorological Administration Research and Development Program under grant CATER References Ashcroft, P. and F. Wentz, AMSR-E/Aqua L2A Global Swath Spatially- Resampled Brightness Temperatures V002, Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. Austin, R. T., and G. L. Stephens, Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat: 1. Algorithm formulation, J. Geophys. Res., 106(D22), 28,233 28,242. Austin, R. T., A. J. Heymsfield, and G. L. Stephens, Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J. Geophys. Res., 114, D00A23, doi: /2008jd Bennartz, R., and P. Bauer, Sensitivity of microwave radiances at GHz to precipitating ice particles, Radio Sci., 38(4), 8075, doi: /2002rs Carey, L. D., J. Niu, P. Yang, J. A. Kankiewicz, V. E. Larson, and T. H. Vonder Haar, The vertical profile of liquid and ice water content in mid-latitude mixedphase altocumulus clouds, J. Appl. Meteor. Climatol., 47,

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23 Liu, G., A fast and accurate model for microwave radiance calculations. J. Meteor. Soc. Japan, 76, Liu, G., Approximation of single scattering properties of ice and snow particles for high microwave frequencies. J. Atmos. Sci., 61, Liu, G., 2008a. Deriving snow cloud characteristics from CloudSat observations, J. Geophys. Res., 113, D00A09, doi: /2007jd Liu, G., 2008b. A database of microwave single-scattering properties for nonspherical ice particles. Bull. Am. Met. Soc., 89, Liu, G. and J. A. Curry, Determination of characteristic features of cloud liquid water from satellite microwave measurements. J. Geophys. Res., 98, Liu, G., and J. A. Curry, Observation and Interpretation of microwave cloud signatures over the Arctic Ocean during winter. J. Appl. Meteor., 42, Mace, G Level 2 GEOPROF product process description and interface control document algorithm version 5.3. Available from: Noh, Y.-J., G. Liu, E.-K. Seo, J. R. Wang, and K. Aonashi, Development of a snowfall retrieval algorithm at high microwave frequencies, J. Geophys. Res., 111, D22216, doi: /2005jd Partain, P., Cloudsat ECMWF-AUX auxiliary data process description and interface control document, Available from: Pruppacher, H. R., and J. D. Klett, Microphysics of Clouds and Precipitation. Reidel, Dordrecht, Holland. 714pp. 22

24 Rogers, R. R., and M. K. Yau, A Short Course in Cloud Physics. Third Edition. Butterworth-Heinemann, Oxford. 290pp. Skofronick-Jackson, G., and B. T. Johnson, Surface and atmospheric contributions to passive microwave brightness temperatures for falling snow events, J. Geophys. Res., 116, D02213, doi: /2010jd Smith, J. R., Jr., Determination of the quantity of cloud liquid water in snow clouds and its effect on masking the snow scattering signature. M.S. Thesis. Florida State University. Tallahassee, FLA. 76pp. Stephens, G. L., et al., The CloudSat mission and the A-Train. Bull. Amer. Meteor. Soc., 83, Weng, F., and N. C. Grody, Retrieval of cloud liquid water using the special sensor microwave imager (SSM/I), J. Geophys. Res., 99, 25,535 25,551. Wentz, F. J., A well-calibrated ocean algorithm for SSM/I, J. Geophys. Res., 102, Wentz, F. J., and T. Meissner, AMSR ocean algorithm, Version 2. RSS Tech. Report A-1. Remote Sensing Systems, Santa Rosa, CA, 66 pp. Available online at Yuter, S. E., and R. A. Houze Jr., Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus, part III, Vertical mass transport, mass divergence, and synthesis, Mon. Weather Rev., 123,

25 Figure Captions: Fig.1 A case on 18 July Top: Cloud type and liquid water path, Bottom: CloudSat CPR radar reflectivity. Fig.2 A case on 21 August Top: Cloud type and liquid water path, Bottom: CloudSat CPR radar reflectivity. Note: Liquid water path is not retrieved south of 56.5 S since it is over land/sea ice. Fig.3 (a) Number of snowing cloud observations (2-m temperature lower than 2 C, maximum dbz in a vertical profile greater than -22 dbz) within 2.5 x2.5 boxes, (b) Percent of observations with retrieved LWP greater than 0, (c) Mean LWP. In each panel, Northern Hemisphere is shown on the left, and Southern Hemisphere is shown on the right. The latitude range is from pole to 40 N or 40 S. Fig.4 Frequency distribution of liquid water path for various cold cloud/snow types. Note that liquid water path is in logarithmic scale. Fig.5 The variation of mean snowfall profiles as a function of near surface snowfall rate for different cloud types. The correspondence between line color and bin number (Table 2) is shown in (a). Fig.6 Contoured frequency of liquid water path by 2-m temperature for (a) IS, (b) ID, (c) ES, and (d) ED type snowing clouds. The number of pixels (solid line) and the percent of LWP>0 pixels (dashed line) in each 1-K temperature bin is also given for every cloud type. Fig.7 Mean liquid water path vs. cloud top height for (a) IS&ID, and (b) ES&ED cloud pixels. The vertical bars in the LWP plots indicate plus or minus one standard 24

26 deviation from the mean. The number of pixels in each cloud type is also given (lower panels). Fig.8 Mean liquid water path as a function of cloud top temperature and near surface radar reflectivity (as a proxy of snowfall rate) for (a) IS, (b) ID, (c) ES, and (d) ED cloud pixels. Note that the LWP color scales for isolated and extended clouds are different. Fig.9 Model-simulated brightness temperature changes relative to clear-sky values as a function of surface snowfall rate and liquid water path for IS snowfall rate profiles as shown in Fig. 5, when the liquid water is placed at 5-6 km layer, for (a) 37, (b) 89, (c) 166, and (d) 183±7 GHz. Over ocean cases. Mid-latitude winter standard atmosphere. Assume that satellite radiometers' viewing zenith angle is 53. Fig.10 Same as Fig.9, except for Arctic winter atmosphere. Fig.11 Same as Fig.9, except for ED snowfall rate profiles. 25

27 N Total Table 1. Statistics of Over Ocean Snowing Clouds * N LWP 0 LWP N (g m -2 ) Total LWP (g m -2 ) MaxZ e (dbz) SurfZ e (dbz) T 2m ( C) IS 4,732,498 75% ID 12,944 83% ES 10,510,966 84% ED 11,628,358 67% ALL 26,884,766 75% * N Total : total number of observations (pixels), N LWP>0 : number of observations that have retrieved liquid water path greater than 0, LWP: mean liquid water path, LWP : standard deviation of liquid water path, MaxZ e : the maximum value in the mean radar reflectivity factor profile, SurfZ e : mean of radar reflectivity factors near the surface, T 2m : mean of 2- m air temperatures. 26

28 Table 2. Number of observations (1st line) and mean liquid water path (2nd line, in g m -2 ) in each near surface snowfall rate bin for various snowing cloud types Bin No. Snowfall Range (mm h -1 ) >5.12 IS ID ES ED ALL 2,251, , , , , , , , , , , , , , , , , ,985, ,727, ,663, ,487, ,170, , , , , , , ,248, , , , ,183, ,509, ,674, ,053, , , , ,489, ,539, ,930, ,737, ,571, ,451, ,221, ,313, , , ,

29 Fig.1 A case on 18 July Top: Cloud type and liquid water path, Bottom: CloudSat CPR radar reflectivity.

30 Fig.2 A case on 21 August Top: Cloud type and liquid water path, Bottom: CloudSat CPR radar reflectivity. Note: Liquid water path is not retrieved south of 56.5 S since it is over land/sea ice.

31 (a) Number of snowing cloud observations (b) Percent of observations with LWP>0 (c) Mean LWP Fig.3 (a) Number of snowing cloud observations (2-m temperature lower than 2 C, maximum dbz in a vertical profile greater than -22 dbz) within 2.5 x2.5 boxes, (b) Percent of observations with retrieved LWP greater than 0, (c) Mean LWP. In each panel, Northern Hemisphere is shown on the left, and Southern Hemisphere is shown on the right. The latitude range is from pole to 40 N or 40 S.

32 Frequency (%) IS ID ES ED NP LWP (gm -2 ) Fig.4 Frequency distribution of liquid water path for various cold cloud/snow types. Note that liquid water path is in logarithmic scale.

33 (a) Isolated Shallow Clouds (b) Isolated Deep Clouds (c) Extended Shallow Clouds (d) Extended Deep Clouds Fig.5 The variation of mean snowfall profiles as a function of near surface snowfall rate for different cloud types. The correspondence between line color and bin number (Table 2) is shown in (a).

34 (a) Isolated Shallow Clouds (b) Isolated Deep Clouds (c) Extended Shallow Clouds (d) Extended Deep Clouds Fig.6 Contoured frequency of liquid water path by 2-m temperature for (a) IS, (b) ID, (c) ES, and (d) ED type snowing clouds. The number of pixels (solid line) and the percent of LWP>0 pixels (dashed line) in each 1-K temperature bin is also given for every cloud type.

35 (a) Isolated Shallow and Deep Clouds (b) Extended Shallow and Deep Clouds Fig.7 Mean liquid water path vs. cloud top height for (a) IS&ID, and (b) ES&ED cloud pixels. The vertical bars in the LWP plots indicate plus or minus one standard deviation from the mean. The number of pixels in each cloud type is also given (lower panels).

36 (a) Isolated Shallow Clouds (b) Isolated Deep Clouds (c) Extended Shallow Clouds (d) Extended Deep Clouds Fig.8 Mean liquid water path as a function of cloud top temperature and near surface radar reflectivity (as a proxy of snowfall rate) for (a) IS, (b) ID, (c) ES, and (d) ED cloud pixels. Note that the LWP color scales for isolated and extended clouds are different.

37 Fig.9 Model-simulated brightness temperature changes relative to clear-sky values as a function of surface snowfall rate and liquid water path for IS snowfall rate profiles as shown in Fig. 5, when the liquid water is placed at 5-6 km layer, for (a) 37, (b) 89, (c) 166, and (d) 183±7 GHz. Over ocean cases. Mid-latitude winter standard atmosphere. Assume that satellite radiometers' viewing zenith angle is 53.

38 Fig.10 Same as Fig.9, except for Arctic winter atmosphere.

39 Fig.11 Same as Fig.9, except for ED snowfall rate profiles.