Toward snowfall retrieval over land by combining satellite and in situ measurements

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi: /2009jd012307, 2009 Toward snowfall retrieval over land by combining satellite and in situ measurements Yoo-Jeong Noh, 1 Guosheng Liu, 2 Andrew S. Jones, 1 and Thomas H. Vonder Haar 1 Received 23 April 2009; revised 18 August 2009; accepted 10 September 2009; published 31 December [1] Although snowfall is an important component of global precipitation in extratropical regions, satellite snowfall estimate is still in an early developmental stage, and existing satellite remote sensing techniques do not yet provide reliable estimates of snowfall over higher latitudes. Toward the goal of developing a global snowfall algorithm, in this study, a Bayesian technique has been tested for snowfall retrieval over land using highfrequency microwave satellite data. In this algorithm, observational data from satelliteand surface-based radars and in situ aircraft measurements are used to build the a priori database consisting of snowfall profiles and corresponding brightness temperatures. The retrieval algorithm is applied to the Advanced Microwave Sounding Unit-B data for snowfall cases that occurred over the Great Lakes region, and the results are compared with the surface radar data and daily snowfall data collected from National Weather Service stations. Although the algorithm is still at an ad hoc stage, the results show that the satellite retrievals compare well with surface measurements in the early winter season, when there is no accumulated snow on ground. However, for the late winter season, when snow constantly covers the ground, the snowfall retrievals become very noisy and show overestimation. Therefore, it is concluded that developing methods to efficiently remove surface snow cover contamination will be the major task in the future to improve the accuracy of satellite snowfall retrieval over land. Citation: Noh, Y.-J., G. Liu, A. S. Jones, and T. H. Vonder Haar (2009), Toward snowfall retrieval over land by combining satellite and in situ measurements, J. Geophys. Res., 114,, doi: /2009jd Introduction [2] With the advancement of satellite remote sensing technology in recent decades, satellite measurements have provided useful information of cloud cover and precipitation that are critical for improving weather forecasting, hydrology, and climate research. However, compared to the many achievements in satellite rainfall estimation [e.g., Kummerow et al., 2001; Ferraro et al., 2005], snowfall estimation from space is still at an early stage; so far, it has mainly been obtained from snow gauges and surface radars. [3] Some recent studies [e.g., Weng and Grody, 2000; Katsumata et al., 2000; Bennartz and Petty, 2001; Zhao and Weng, 2002; Bennartz and Bauer, 2003; Kongoli et al., 2003] have provided the essential basis and demonstrated the potential for snowfall retrievals utilizing high-frequency microwave measurements (>89 GHz). Katsumata et al. [2000] showed that snow clouds over ocean can reduce upwelling brightness temperatures up to 15 K at 89 GHz 1 Department of Defense Center for Geosciences/Atmospheric Research, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA. 2 Department of Meteorology, Florida State University, Tallahassee, Florida, USA. Copyright 2009 by the American Geophysical Union /09/2009JD frequency while they are hardly detectable at frequencies lower than 37 GHz. Bennartz and Petty [2001] and Bennartz and Bauer [2003] pointed out that high-frequency channels (>100 GHz) are very sensitive to scattering by precipitationsized particles and that a channel 150 GHz contains useful information for identification and retrieval of frozen precipitation. On the basis of sensitivity studies using a radiative transfer model, Noh and Liu [2004] also showed that there is little response of microwave signals at 37 GHz to snowfall rate variation, whereas brightness temperatures at 89 and 150 GHz show large decreases as snowfall rates (and ice water paths) increase, especially at 150 GHz. For this reason, the Advanced Microwave Sounding Unit-B (AMSU-B) onboard NOAA satellite series has been used in satellite snowfall detection and measurement. AMSU-B has two high-frequency window channels at 89 and 150 GHz and three water vapor channels at ± 1, ± 3, and ± 7 GHz [Zhao and Weng, 2002]. Skofronick-Jackson et al. [2004] presented a physical method to retrieve snowfall over land using AMSU-B data. Noh et al. [2006] and Kim et al. [2008] developed the snowfall retrieval algorithms on the basis of Bayesian methods over ocean and over land, respectively. [4] Active microwave measurements from space that recently became available also increase our capabilities for more accurate snowfall estimates. CloudSat [Stephens et al., 2002], launched in April 2006 with 94 GHz cloud profiling radar, provides an excellent opportunity to directly observe 1of15

2 Figure 1. Algorithm strategy of snowfall retrievals. the vertical structure of clouds and precipitation from space. Liu [2008] presented the first report of the global distribution of snow clouds including differences of vertical structures over ocean and land in both hemispheres by analyzing 1 year of CloudSat data. [5] In retrieving snowfall rates using microwave observations, one of the most difficult challenges is to discriminate snowfall signatures in the atmosphere from surface features. Unlike ocean, land surface emissivity is greatly variable and more complex because of surface snow accumulation. Nevertheless, some recent studies have indicated the potential of retrieving snowfall over land. For example, Yan et al. [2008] studied the sensitivity of brightness temperatures at frequencies greater than 50 GHz to ice water path, effective diameter, shape of particles, and surface emissivity. They found that the model-simulated brightness temperatures at 150 GHz over fresh snow surface decrease by 51.6 K (1.6 K at 50.3 GHz and 17.5 K at 89 GHz), and the depression becomes greater for clouds with larger ice particles. For a blizzard case over the northeastern United States, Kim et al. [2008] showed that the 183-GHz water vapor channels, especially those closer to the water vapor absorption line center, could mask the surface influence, whereas the 89 GHz channel showed greater uncertainties in distinguishing signatures of snow in the atmosphere from surface features such as lakes, rivers, and snow on the ground. [6] The goal of this study is to examine the performance of an overland Bayesian snowfall retrieval algorithm that uses observations at high microwave frequencies. In particular, the dependence of the algorithm s performance on surface snow cover conditions is investigated. First, we attempt to detect snowfall signatures over land. Second, we try to quantitatively assess the measurements of snowfall rates. Note that we only focus on the snow/ice clouds that have significantly strong scattering signatures (not light snowfall events). The target region is the Great Lakes and surrounding areas. A radiative transfer model [Liu, 1998] is used to build the a priori database of the Bayesian retrieval algorithm that connects satellite brightness temperatures (T B ) to snowfall rates. For computing the scattering properties of the nonspherical ice particles and snowflakes, the discrete dipole approximation (DDA) method was adopted [Liu, 2004; Noh et al., 2006; Kim et al., 2008]. The fundamental framework of this over-land algorithm comes from our early study of snowfall retrieval over ocean near Japan [Noh et al., 2006]. However, in this study we took advantage of having richer and newer data obtained from satellite, aircraft, and surface observations. For example, CloudSat radar data and aircraft observational data obtained during the Canadian CloudSat/CALIPSO Validation Project/ Tenth Cloud Layer Experiments (C3VP/CLEX-10) conducted over southern Ontario are used to improve the representativeness of the Bayesian database. In addition, surface radar data from the Next Generation Weather Radar (NEXRAD) system are also used. To diversify the database, atmospheric sounding information from the Weather Research and Forecasting (WRF) model is added in radiative transfer modeling. Because surface types vary greatly during winter in the Great Lakes region, the NOAA National Environmental Satellite Data and Information Service (NESDIS) Microwave Land Emissivity Model (MEM) [Weng et al., 2001] is employed to improve the calculation of microwave surface emissivity. 2. Bayesian Retrieval Algorithm [7] We adopt basically the same structure of the Bayesian algorithm used for snowfall retrieval over ocean described by Noh et al. [2006]. On the basis of Bayes theorem, the best estimate of x (snowfall rate profiles), given the observations y 0, may be approximated as [e.g., Olson et al., 1996; Evans et al., 2002] ^E ðxy j 0 Þ ¼ X j T expf 0:5 y x 0 y s x j ðo þ SÞ 1 y 0 y s x j g j ; ^A ð1þ where y s (x) represents the simulation of brightness temperatures and O and S are the observation and simulation error covariance matrices, respectively. The normalization factor is ^A ¼ X j T expf 0:5 y 0 y s x j ðo þ SÞ 1 y 0 y s x j g: ð2þ In the present study, the error covariance matrices, O and S, are set similar to those of Olson et al. [1996] and Seo et al. 2of15

3 Table 1. NEXRAD Stations for Five Selected Snowfall Cases Date NEXRAD Station a 3 Nov 2006 KCLE, KTYX 2 Dec 2006 KCLE, KAPX 19 Jan 2007 KCLE, KBUF 22 Jan 2007 KBUF 18 Feb 2007 KTYX a KCLE, Cleveland, Ohio; KTYX, Fort Drum, New York; KAPX, Gaylord, Michigan; KBUF, Buffalo, New York. [2007] as follows. The error covariance matrix, S, has no contribution because the model simulation, y s (x), is assumed to be true. The observation error variances are set equal to the instrument error variances with an assumption of zero-mean Gaussian-distributed noise with a standard deviation of 1.5 K at all channels except for 0.6 K at 150 and 183 ± 7 GHz channels. Because of the lack of information on the correlation of errors between channels, only the diagonal terms of the matrix O are estimated here, and off-diagonal terms are set to zero. The matrix (O + S) 1 for any channel in the model database is inversely proportional to the value of the diagonal term of the error variance. [8] Figure 1 schematically depicts the approach we use for the development of the snowfall retrieval algorithm over land including the a priori database constructed by using various observations. The snowfall retrieval algorithm is applied to the AMSU-B. The retrieved result (snowfall rate profiles) is a weighted average of all possible solutions under various atmospheric environment conditions in a database. In the present study, to avoid retrieval biases that happen when the probability density function of the variable is heavily skewed, we applied two remedies recommended by Seo et al. [2007] through their Bayesian retrieval study of ice water path. That is, clear-sky data points are also included in constructing the a priori database, and a narrower radius of influence by assigning smaller error variances (from s to 0.5s) is applied in searching possible solutions to a given observation than those used by Noh et al. [2006]. 3. The a Priori Database [9] The most critical component of a Bayesian retrieval algorithm is the a priori database that links satellite brightness temperatures with snowfall rates. In this study, a database consisting of a large number of pairs of brightness temperatures and corresponding snowfall profiles is built by Figure 2. CloudSat (a and b) CPR reflectivity and (c and d) LWC products on (left) 2 December 2006 and (right) 22 January of15

4 Figure 3. Examples of the vertical cross sections of NEXRAD reflectivity data (dbz) processed by NCAR SPRINT/CEDRIC in KTYX (Fort Drum, New York) (a) at 0200 UTC on 3 November 2006 and (b) at 0703 UTC on 18 February radiative transfer modeling [Liu, 1998; Noh et al., 2006] with inputs from real observations. For actual snow events, various observation data from satellite, aircraft, and surface radar are principally used as input in the radiative transfer model, while some supplementary data inputs are also obtained from numerical model simulations. Details of data sets used to construct the a priori database are as follows Snowfall Profiles CloudSat Cloud Profiling Radar Data [10] For the vertical structure of snow clouds, data from the 94 GHz cloud profiling radar (CPR) onboard CloudSat are used; CPR measures the vertical structure of clouds and precipitation from space and provides profiles of these properties with a vertical resolution of 240 m. CloudSat 2B-GEOPROF products (see edu/ for more details) are used to obtain snowfall profiles. The radar reflectivity from CPR for five selected snowfall cases (Table 1) is converted to snowfall rate using two Z e S relationships shown in (3) and (4), which were derived using DDA (see B. T. Draine and P. J. Flatau, User guide for the discrete dipole approximation code DDSCAT (ver. 5a10), 2000, available at http//arxiv.org/abs/astro-ph/ ) simulations for two [Noh, 2006] and three [Liu, 2008] different snowflake shapes. The DDA results are also used in the radiative transfer model. Z e ¼ 38:06S 1:057 Z e ¼ 11:56S 1:25 ; where Z e is the equivalent radar reflectivity in mm 6 m 3 and S is the snowfall rate in mm h 1. In addition, CloudSat 2B-CWC-RO (R. Austin, Level 2B radar-only ð3þ ð4þ 4of15

5 Figure 4. Vertical profiles of (left) air temperature-dew point and (right) liquid-ice water content from Convair-580 aircraft observation on 22 January 2007 during C3VP/CLEX-10. cloud water content (2B-CWC-RO) process description document, CloudSat Data Processing Center, 2007, available at go=list&path=/2b-cwc-ro) data are used to provide liquid water content (LWC) information in the radiative transfer modeling. CloudSat CPR reflectivity and LWC on 2 December 2006 and 22 January 2007 are represented in Figure 2. Note that the columns having radar reflectivity greater than 0 dbz are chosen as snowfall profiles herein, except for the ones classified as liquid-only in 2B-CLDCLASS data (Z. Wang, CloudSat Project 2B- CLDCLASS interface control document algorithm version 5.3, 2007, available at edu/dataspecs.php?prodid=11&pvid=200) where ice water content (IWC) equals zero and only LWC exists. Note that we did not correct the radar reflectivity profiles for attenuation. Although this correction is certainly an area for future improvement, we do not think it affects the accuracy of our study in a meaningful way. According to a recent study by Matrosov and Battaglia [2009], the attenuation effect, even for radar reflectivities greater than dbz, is only a few dbz after multiple scattering offsets some of the attenuation NEXRAD Data [11] To increase the number of snowfall profiles in the database, surface radar data in the Great Lakes region from the NEXRAD system are also used. NEXRAD Level II reflectivity data for five snowfall cases were obtained from the National Climatic Data Center (NCDC) data archive for the four stations listed in Table 1. The data are first interpolated into the Cartesian grids using the National Center for Atmospheric Research (NCAR) radar data interpolation system [Mohr and Vaughan, 1979; Mohr et al., 1981]. The horizontal and vertical resolutions of the processed data are 1 km (with grids) and 200 m (up to 12 km), respectively. Profile data within 100 m of the radar station center do not have enough data points in the vertical direction and were therefore excluded. Examples of the processed radar reflectivity distributions are shown in Figure 3. The processed reflectivity is then converted to snowfall rate using the following Z e S relationships for WSR-88D radar that were empirically derived by Super and Holroyd [1996, 1998] for snowfall cases in the Great Lakes region: Z e ¼ 180:0S 2:0 Z e ¼ 330:0S 2:2 : 3.2. Sounding and Liquid Water Information In Situ Field Measurements [12] Aircraft observations during the following field experiments are also used as input for radiative transfer modeling. The Canadian CloudSat/CALIPSO Validation ð5þ ð6þ 5of15

6 Figure 5. Air temperature and dew point profiles observed by the balloon-sonde system from the Centre of Atmospheric Research Experiments (CARE) site (a) at 1800 UTC on 2 December 2006 and (b) at 0400 UTC on 22 January Project (C3VP) is an extensive validation project of satellite products conducted by the Meteorological Service of Canada as part of the international CloudSat program ( focusing on validating measurements and retrieved products from the CloudSat and the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. The Cloud Layer Experiment (CLEX) is part of an ongoing research effort funded by the Department of Defense s Center for Geosciences/ Atmospheric Research to observe and characterize the microphysical properties, dynamics, and morphology of midlevel, mixed phase clouds [Fleishauer et al., 2002; Carey et al., 2008]. These field experiments were jointly conducted during the winter season over southern Ontario and the surrounding areas. [13] Liquid water and sounding (pressure, temperature, and humidity) information from in situ probes and remote sensing instruments onboard the National Research Council of Canada s Convair-580 aircraft during C3VP/CLEX-10 are used as input to simulate brightness temperatures using a radiative transfer model. Figure 4 shows sample vertical profiles of temperature dew point and LWC-IWC measured from 0455 to 0907 UTC on 22 January 2007 for the entire flight track. Seven sounding measurements by the balloon sonde system from the Centre for Atmospheric Research Experiments during C3VP/CLEX-10 are also used. Two examples of temperature and dew point profiles are shown in Figure 5. All of the C3VP/CLEX-10 sounding data are used in the radiative transfer model simulations, with multiple sounding inputs matching each CloudSat or NEXRAD snowfall profile WRF Simulations [14] The WRF model [Gallus and Bresch, 2006] is also used to increase the number of sounding data points in the database. Because a model simulation of snow events, particularly those with parameters such as vertical distributions of hydrometeors and precipitation amount, still includes many uncertainties, only temperature and humidity results are used in this study. The Advanced Research WRF dynamic core (version 2.2) was run with 9 km horizontal grid spacing and grid sizes. Thompson s cloud microphysics scheme [Thompson et al., 2004], the Yonsei University planetary boundary layer scheme [Hong et al., 2006], a simple cloud-interactive radiation scheme [Dudhia, 1989], and a rapid radiative transfer model longwave radiation [Mlawer et al., 1997] scheme were employed, and the convective scheme was turned off. The National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model output was used for initialization and lateral boundary conditions. Simulations for five cases listed in Table 1 were integrated for 36 or 48 h. Temperature profiles selected from the WRF simulations for 22 January 2007 and 18 February 2007 are represented in Figure 6. The simulated hourly precipitation was examined by comparing it to surface radar data. From the comparisons, sounding data from the simulations within ±2 h were 6of15

7 Figure 6. Temperature profiles sampled from the WRF simulations (a) at 0700 UTC on 22 January 2007 and (b) at 1800 UTC on 18 February chosen as radiative times transfer model input. In radiative transfer modeling, the WRF sounding with the minimum distance from CloudSat- or NEXRAD-derived snowfall profile was used as input Microwave Land Surface Emissivity [15] One of the biggest challenges in snowfall retrieval studies over land, especially for those areas with variable surface conditions in winter such as the Great Lakes area, is how to treat the microwave land surface emissivity. Although the microwave surface emissivity over ocean can be estimated as a function of a few variables such as sea surface temperature, wind speed, and salinity, land surface emissivity is highly variable and complex, making it more problematic to use microwave data over land than over ocean [Jones et al., 2004]. [16] In an effort to mitigate this problem, the NOAA NESDIS Microwave Land Emissivity Model [Weng et al., 2001] is employed to determine the microwave land emissivity at high microwave frequencies in radiative transfer modeling. Its accuracy at the microwave window regions has been verified by earlier work [Ruston et al., 2008], although the emissivity for snow covered and sea ice areas remains problematic. In this study, the MEM uses data from AMSU-A, AMSU-B, and the U.S. Air Force Weather Agency s near real-time Agricultural Meteorology Analysis Model (AGRMET) (see data format handbook for AGRMET at FORMAT_HANDBOOK.pdf). AGRMET data provide the detailed information on land surface characteristics such as soil temperature, soil moisture, land-sea mask, and surface elevation that are essential components to estimate land emissivity at microwave frequencies. The MEM and all of its input data sets are processed within the Data Processing and Error Analysis System (DPEAS), a modular computing environment [Jones and Vonder Haar, 2002]. The AMSU-B data are remapped using a bilinear interpolation routine onto a regularized grid for collocated processing in the DPEAS. Two cases of the estimated land surface emissivity at 0900 UTC on 3 November 2006 and at 1100 UTC on 22 January 2006 are presented in Figure 7, showing a great variety of surface emissivity values in the domain. Further investigation will be necessary to improve the land surface emissivity, especially over snow surfaces, as indicated by Yan et al. [2008], although it will not be discussed in the present study. For each case, the emissivity value is applied to the radiative transfer calculation as input for the closest snowfall profile. Note the emissivities at three 183 GHz frequencies were assumed to be the same as that at 150 GHz. Lake ice is not considered, and no fractional emissivity adjustment [Temimi et al., 2008] is used at this time. Future work should incorporate changes so that the water features can be diagnosed adequately Radiative Transfer Modeling [17] The a priori database is constructed through radiative transfer model simulations using various combinations of the aforementioned observations and WRF model data. The 7of15

8 Figure 7. Microwave land surface emissivity at 89 and 150 GHz calculated from the NOAA NESDIS Microwave Land Emissivity Model (MEM) (a and b) at 0900 UTC on 3 November 2006 and (c and d) at 1100 UTC on 22 January radiative transfer model used in this study solves the radiative transfer equation using the discrete ordinate method [Liu, 1998], which includes the single scattering properties of nonspherical snowflakes from the DDA calculations [Liu, 2004; Noh et al., 2006]. The model used an exponential size distribution, which is one of the most commonly applied particle size distributions for snowflakes: NðDÞ ¼ N 0 expð LDÞ: ð7þ As indicated by Weng and Grody [2000], the scattering process is very sensitive to the particle size distribution. Currently, values for the parameters N 0 and L are based on the work by Sekhon and Srivastava [1970], although it will be possible to apply observed particle size distributions from C3VP/CLEX-10 when they become available. The total number of data points in the a priori database is about 540, Applications of the Retrieval Algorithm [18] The snowfall retrieval algorithm is applied to the AMSU-B data. A snowfall case that occurred on October 2006 over Buffalo, New York, is investigated. Over the Great Lakes region, the surface is radiometrically very complex because of variable surface types and vegetation particularly in winter, which makes it especially difficult to sense snow clouds. In the present study, therefore, we use brightness temperature depressions from the background brightness temperatures (DT B = T B T B0, where T B and T B0 are the cloudy and background brightness temperatures, respectively) to detect snowfall signatures. Background T B are obtained by analyzing the histograms of AMSU-B data from November 2006 to February 2007 as shown in Figure 8. During this period, the brightness temperature that is the most frequently observed at each channel, each surface type, and each viewing angle is used as the background T B. T B within 10 bin width were averaged for each surface type considering the sampling number. To further remove false alarms, we also adopt a channel combination filter suggested by Kongoli et al. [2003], i.e., T B 183 ± 7 T B 183 ± 3 < 20 K or T B 150 T B 183 ± 3 < 40 K as potential snowfall, and T B 183 ± 7 and T B 183 ± 3 < 256 K as no snowfall but possible new snow on ground under clear weather. Note that the T B constraints we chose here for the 183 GHz channels is slightly higher than Kongoli et al. s [2003] original 255 K since this snowfall event occurred in early winter with relatively warm air temperatures that influence these water vapor channels. [19] On October 2006, an intense, but localized, early season snowstorm hit Buffalo, New York. Although 8of15

9 Figure 8. Histogram analysis to find background brightness temperatures at each frequency using 4 months of AMSU-B data from November 2006 to February the areal extent of the snowfall was small, the narrow band of very heavy snow storm that was developed off the warm waters of Lake Erie (surface temperature 17 C) under a cold front produced approximately 60 cm of heavy snow in 12 h, and over 1,000,000 people of the upstate New York area were directly impacted by the storm [Hamilton et al., 2007]. Figure 9 shows the brightness temperatures measured from AMSU-B onboard NOAA 16 satellite at 2125 UTC on 12 October Because the interaction of the microwave radiation with snow-covered surfaces requires further extensive research, here the selected snowfall event during the early winter season is an ideal case that is not greatly contaminated by accumulated snow on surface. However, scattering signatures from snow clouds are still not clear because of the variety of surface types in this region, although the 89 and 150 GHz channels are known to be less sensitive to the surface types [e.g., Bennartz and Bauer, 2003; Kongoli et al., 2003]. In particular, the 89 GHz channel shows ambiguity in detecting snowfall from land surface features. [20] The brightness temperature depressions (DT B ) (also after applying the aforementioned channel combination filter) are presented in Figure 10. Compared to Figure 9, Figure 10 shows that the snowfall signatures around the Lakes Erie and Ontario are more accurately captured in all the channels, even at 89 GHz. As Bennartz and Bauer [2003] pointed out, the 150 GHz channel contains more information to identify snowfall among all the channels. The snow cloud is also quite clearly detected at ± 7 GHz. The retrieved snowfall result at 1.5 km from the ground is shown in Figure 11 together with merged NEXRAD data from KBUF (Buffalo) and KDTX (Detroit). It is noted that NEXRAD snowfall converted by equations (5) and (6) is an average for 2 h around the satellite overpass (±1 h), and the area north of about 43 N is not covered by NEXRAD system. In Figure 11, the retrieval result captures well the main spatial pattern compared with the radar observation, and the maximum amounts also show good agreement. In particular, the location of the maximum snowfall over Buffalo greatly agrees with the radar data. [21] The second case is a snowfall event during February 2007 (Figures 12 14). A major winter storm along a strong low-level easterly jet hit the eastern United States and southeastern Canada, producing significant accumulations of snow and ice from Nebraska to Maine [Grumm and Stuart, 2007]. The greatest impact from snow was in upstate New York and northern New England, where the storm brought snow accumulations over 100 cm. It was 9of15

10 Figure 9. Brightness temperatures at 89, 150, 183 ± 1, and 183 ± 7 GHz from AMSU-B onboard NOAA 16 at 2125 UTC on 12 October reported that there were 13 deaths and around 300,000 people without power in areas of upstate New York, and Montreal, Canada, received 53 cm snow in 1 day (NOAA NCDC Hazards information, available at noaa.gov/oa/climate/research/2007/feb/hazards.html). AMSU-B brightness temperatures and their depressions at 2114 UTC on 14 February 2007 are presented in Figure 12 and 13, respectively. This case is selected because it gives the indication of the algorithm s performance when there is snow accumulation on the ground. A significant difference is found in both snow patterns and the amounts between the retrieval and radar observations. Reviewing NEXRAD and daily weather reports reveals that the areas in the pink squares and the circled area higher than 44 N (where the NEXRAD system cannot cover, but there was no precipitation according to the daily weather report from Environment Canada) in Figure 14 are not snowfall but signatures from surfaces with snow accumulation. This result implies that many snowcovered areas are misclassified as falling snow in the current algorithm because both cause similar brightness temperature depressions. Also, the brightness temperature depressions at 150 GHz decrease by 64 K in Figure 13 while the maximum depression is only 13 K at 183 ± 1 GHz, which suggests that this snowfall is very dry and that water vapor absorption may not effectively mask the surface emission at frequencies near 183 GHz [Kim et al., 2008]. The algorithm shows inconsistency in discriminating between snowfall and deposited snow accumulations, even though only one case is presented here. [22] Figure 15 shows the averaged snowfall retrievals for November 2006 and February 2007 compared with NOAA NCDC U.S. daily snowfall data collected from National Weather Service (NWS) cooperative observer stations and NWS first-order stations ( research/snow/). Note that we ignore coastlines in Figure 15. All of the retrievals and surface observations were interpolated in grids. AMSU-B data onboard NOAA 15, 16, and 17 are utilized in the retrieval. Note that only the areas south of 44 N are considered because of data avail- 10 of 15

11 Figure 10. Same as Figure 9 but for AMSU-B brightness temperature depressions after applying a channel combination filter. ability. In the retrieval, precipitation is considered snowfall only when the surface air temperature is below 2 C based on NCEP/NCAR reanalysis data provided by the NOAA- CIRES Climate Diagnostics Center (available at During November 2006, although there is a low frequency of snowfall events, the comparison shows good agreement between the retrievals and surface observations in the pattern of snowfall that occurred in the lee of Lake Ontario, but the amounts do not agree very well. However, the difference becomes greater and more complex in pattern for February 2007, although the maxima of the areas downwind of Lake Erie and Lake Ontario shown in surface observations (Figure 15d) also appear in the retrievals. Figure 15d implies that conditions with only snow accumulation on the ground but no snowfall may have been mistakenly identified as falling snow, and nonzero snowfall rates for those conditions were included in the average. From these results, again, it is inferred that the greater amounts and the different patterns of retrieved snowfall compared to surface observations are caused by deposited snow accumulations on the ground. 5. Summary and Conclusions [23] This study describes our approach to develop a snowfall retrieval algorithm over land based on Bayes theorem using high-frequency microwave observations. The a priori database of the Bayesian algorithm is constructed utilizing a variety of actual observations from satellite, aircraft, and surface measurements over the Great Lakes region with help from model simulations. The diversity of the database is further enhanced by taking advantage of rich data from new measurement sources such as the recently launched CloudSat and the C3VP/CLEX-10 field experiment. Sounding information from the WRF simulations is also added. The relationship between brightness temperatures and snowfall rates in the database are built by radiative transfer modeling that includes detailed single scattering properties from a discrete dipole approximation 11 of 15

12 Figure 11. Comparison between (a) the retrieved snowfall rates (mm/h) at 1.5 km from the surface and (b) merged surface radar snowfall (mm/h) from NEXRAD system (KBUF (Buffalo) and KDTX (Detroit)) at 2125 UTC on 12 October Figure 12. Same as Figure 9 but for 2214 UTC on 14 February of 15

13 Figure 13. Same as Figure 10 but for 2214 UTC on 14 February Figure 14. Same as Figure 11 but for the NEXRAD system (see Table 1) at 2214 UTC on 14 February The areas in the pink squares and the circled area higher than 44 N are not snowfall but signatures from surfaces with snow accumulation. 13 of 15

14 Figure 15. Averaged snowfall retrievals (mm/h) during (a) November 2006 and (c) February 2007 compared with (b and d) corresponding NOAA NCDC U.S. daily snowfall averages (mm/h). for nonspherical snowflakes. In addition, the NOAA MEM is employed to calculate the microwave land emissivity in this region. [24] The Bayesian snowfall retrieval algorithm is applied to satellite microwave AMSU-B data for snowfall events in the Great Lakes region. In order to extract snowfall scattering signatures, we used AMSU-B brightness temperature depressions from the 4 month averages of brightness temperatures over this region and applied a channel combination filter using 150, 183 ± 3, and 183 ± 7 GHz. [25] Although the algorithm is still at an ad hoc stage, the retrieved snowfall results show good agreement in spatial pattern compared to NEXRAD data for an early winter case. However, when there is significant snow accumulation over the ground for a late winter case, the current algorithm is deficient in discriminating between falling snow and snowcovered surface. The daily mean amounts in November 2006 and February 2007 are also presented. The retrievals are compared with NOAA NCDC daily snowfall observations collected from NWS stations. Again, good agreement is found in the spatial patterns for the early winter months, but the retrievals show overestimation and false alarms for the late winter month, likely due to snow accumulation over land. The results indicate that the algorithm needs to be improved for snowfall cases over snow-covered surfaces, although we have utilized an AMSU-B channel combination filter, calculated background T B, and adopted an advanced microwave land surface emissivity calculation using NOAA MEM. Therefore, it is concluded that developing methods to efficiently remove surface snow cover contamination will be the major task in the future to improve the accuracy of satellite snowfall retrieval over land. [26] Acknowledgments. The authors would like to thank to David Hudak and Peter Rodriguez (Environment Canada) for help with C3VP/ CLEX-10 data sets. CloudSat data were provided by the CloudSat Data Processing Center at the Colorado State University. Also, NEXRAD data and U.S. daily snowfall data were provided by the National Climatic Data Center, and GFS data were obtained from the NOAA National Operational Model Archive and Distribution System. This research was supported by the Department of Defense Center for Geosciences/Atmospheric Research at Colorado State University under cooperative agreement W911NF with the Army Research Laboratory. References Bennartz, R., and P. Bauer (2003), Sensitivity of microwave radiances at GHz to precipitation ice particles, Radio Sci., 38(4), 8075, doi: /2002rs Bennartz, R., and G. W. Petty (2001), The sensitivity of microwave remote sensing observations of precipitation to ice particle size distributions, J. Appl. Meteorol., 40, , doi: / (2001)040 <0345:TSOMRS>2.0.CO;2. Carey, L. D., J. Niu, P. Yang, J. A. Kankiewicz, V. E. Larson, and T. H. Vonder Haar (2008), The vertical profile of liquid and ice water content in mid-latitude mixed-phase altocumulus clouds, J. Appl. Meteorol. Climatol., 47, , doi: /2008jamc Dudhia, J. (1989), Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, , doi: / (1989)046 <3077:NSOCOD>2.0.CO;2. Evans, K. F., S. J. Walter, A. J. Heymsfield, and G. M. McFarquhar (2002), Submillimeter-wave cloud ice radiometer: Simulations of retrieval algorithm performance, J. Geophys. Res., 107(D3), 4028, doi: / 2001JD Ferraro, R. R., F. Weng, N. C. Grody, L. Zhao, H. Meng, C. Kongoli, P. Pellegrino, S. Qiu, and C. Dean (2005), NOAA operational hydrological products derived from the advanced microwave sounding unit, IEEE Trans. Geosci. Remote Sens., 43, , doi: / TGRS Fleishauer, R. P., V. E. Larson, and T. H. Voner Haar (2002), Observed microphysical structure of mid-level, mixed-phase clouds, J. Atmos. Sci., 59, , doi: / (2002)059<1779:omsomm> 2.0.CO;2. Gallus, W. A., Jr., and J. F. Bresch (2006), Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season 14 of 15

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