PUBLICATIONS. Journal of Geophysical Research: Atmospheres

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1 PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Key Point: A snowfall algorithm from passive microwave measurements has been developed Correspondence to: C. Kongoli, cezar.kongoli@noaa.gov Citation: Kongoli, C., H. Meng, J. Dong, and R. Ferraro (2015), A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements Application to ATMS, J. Geophys. Res. Atmos., 120, , doi:. Received 11 AUG 2014 Accepted 1 JAN 2015 Accepted article online 9 JAN 2015 Published online 11 MAR 2015 A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements Application to ATMS Cezar Kongoli 1,2, Huan Meng 2, Jun Dong 1, and Ralph Ferraro 2 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA, 2 National Environmental Satellite, Data and Information Service, National Oceanic Atmospheric Administration, Camp Springs, Maryland, USA Abstract This paper presents a snowfall detection algorithm over land from high-frequency passive microwave measurements. The algorithm computes the probability of snowfall using logistic regression and the principal components of the seven high-frequency brightness temperature measurements at Atmospheric Technology Microwave Sounder (ATMS) channel frequencies 89 GHz and above. The oxygen absorption channel 6 (53.6 GHz) is utilized as temperature proxy to define the snowfall retrieval domain. Ground truth surface meteorological data including snowfall occurrence were collected over Conterminous U.S. and Alaska during two winter seasons in and Statistical analysis of the in situ data matched with ATMS measurements showed that in relatively warmer weather, snowfall tends to be associated with lower high-frequency brightness temperatures than no snowfall, and the brightness temperatures are negatively correlated with measured snowfall rate. In colder weather conditions, however, snowfall tends to occur at higher microwave brightness temperatures than no-snowfall, and the brightness temperatures are positively correlated with snowfall rate. The brightness temperature decrease and the negative correlations with snowfall rate in warmer weather are attributed to the scattering effect. It is hypothesized that the scattering effect is insignificant in colder weather due to the predominance of lighter snowfall and emission. Based on these results, a two-step algorithm is developed that optimizes snowfall detection over these two distinct temperature regimes. Evaluation of the algorithm shows skill in capturing snowfall in variable weather conditions as well as the remaining challenges in the retrieval of lighter and colder snowfall. 1. Introduction Snowfall detection and measurement from space represent highly difficult problems in modern hydrometeorology [Levizzani et al., 2011]. Detection of snowfall is more challenging than that of rainfall due to the presence of snow cover on the ground that further complicates the separation between atmospheric snowfall and snow cover contributions to the satellite-observed brightness temperature [Kongoli et al., 2003] and the diverse nature of weather systems at higher latitudes [ESA, 2004]. The retrieval of light snow events becomes increasingly important at higher latitudes since light snow is significant in the middle to high latitudes where frontal and stratiform precipitation systems are dominant. In addition, unlike raindrops that are (nearly) spheres with a known density of 1 g cm 3,snowflakes are highly nonspherical, and their density varies with particle size and shape [Liu, 2008]. Unlike rainfall, detection of snowfall utilizing visible and/or infrared satellite measurements has not been accomplished since it is very difficult to distinguish between precipitating and nonprecipitating high-latitude clouds during winter. Satellite high-frequency passive microwave measurements on the other hand have been utilized for snowfall detection [Liu and Curry, 1997; Staelin and Chen, 2000; Chen and Staelin, 2003; Kongoli et al., 2003; Skofronick-Jackson et al., 2004, 2012; Munchak and Skofronick-Jackson, 2013; Noh et al., 2006; Liu and Seo, 2013]. Progress has only been made recently when high passive microwave instruments such as the Advanced Microwave Sounding Unit (AMSU) began flying on board polar-orbiting satellites. Staelin and Chen [2000] and Chen and Staelin [2003] were the first to present a global precipitation detection method that is also valid for snowfall by utilizing opaque atmospheric channels: the AMSU-A oxygen absorption channel 5 at 53.6 GHz and the AMSU-B or Microwave Humidity Sounder (MHS) water vapor absorption channels near 183 GHz. The underlying assumption for using these channels in combination is that precipitating ice hydrometeors decrease high-frequency brightness temperatures due to scattering and KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1918

2 that this scattering signal can be detected in sufficiently opaque atmospheric conditions, corresponding to 53.6 GHz brightness temperatures at K and above. Surussavadee et al. [2012] describe a follow-on global precipitation algorithm applied to the Atmospheric Technology Microwave Sounder (ATMS) measurements considering retrievals only when the brightness temperature at ATMS channel 6 (AMSU-A channel 5 at 53.6 GHz) is above 242 K. Kongoli et al. [2003] presented an operational snowfall detection algorithm using AMSU-A and AMSU-B/MHS measurements, generated at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration (NOAA) [Ferraro et al., 2005]. Detection of snowfall is based on a decision tree classification scheme that utilizes the AMSU channels in the microwave window, water vapor and oxygen absorption regions. Measurement at the AMSU-A oxygen absorption channel 5 is used only as a proxy for atmospheric temperature. The algorithm is applicable when the brightness temperature at this channel is at or above 245 K. A set of statistically derived criteria involving all five of AMSU-B/MHS channels are employed to detect snowfall, and filter out false snowfall triggered by snow on the ground. Again, the assumption in Kongoli et al. [2003] method is that for warmer and more opaque atmospheres, scattering by snowfall-sized ice particles decreases the brightness temperatures at the high-frequency channels, and this scattering effect can be detected using opaque (oxygen and water vapor absorption) and window channels in combination. For colder and less opaque atmospheres, retrievals are considered too noisy due to surface effects, and thus are not performed. A major limitation of the above methods is that snowfall is not detected in colder weather conditions. Also, snowfall events that do not result in decreased brightness temperatures are not detected. A recent study by Liu and Seo [2013] found that on most occasions, snowfall is light to moderate and brightness temperatures are higher under snowfall than no snowfall conditions, likely due to emission by cloud liquid water. The authors suggest that this brightness temperature increase masks the scattering signal and complicates the retrieval of snowfall. A statistical snowfall detection algorithm was developed trained with CloudSat radar data that computes the probability of snowfall from three principal components of the five AMSU-B or MHS brightness temperatures. Given a multichannel microwave observation, the algorithm first transforms the brightness temperature vector into Empirical Orthogonal Function (EOF) space, and then retrieves a probability of snowfall by using the CloudSat radar-trained look-up-table. Evaluation results showed that the algorithm has clear skills in identifying snowfall areas under colder conditions. This paper presents a snowfall detection technique over land applied to ATMS high-frequency measurements that is also applicable in colder weather conditions. The algorithm computes the probability of snowfall using logistic regression and the principal components of the seven higher frequency ATMS channels at 89 GHz and above as independent variables. In addition, ATMS channel 6 is used as temperature proxy to define the snowfall retrieval domain. The algorithm is trained with surface meteorological data collected over Conterminous (CONUS) U.S. and Alaska during two winter seasons. This retrieval method differs from Liu and Seo s [2013] algorithm in two main aspects: first, the algorithm presented here is trained with ground station data, and second, it uses logistic regression to compute the probability of snowfall. The paper is organized as follows. In section 2, ATMS, ground truth data, and matching methodology are described. Section 3 presents statistical analysis of multichannel brightness temperatures and meteorological measurements for the snowfall and no-snowfall samples. The proposed algorithm is described in section 4, followed by application examples and evaluation in section 5. Finally, summary and conclusions are given in section Data 2.1. ATMS Instrument Table 1 shows ATMS channel characteristics, including center frequencies, noise equivalent temperature, horizontal spatial resolution, and corresponding AMSU channels. ATMS observes millimeter-wave spectra at 22 frequencies. Channels 1 15 observe 53 GHz oxygen and 23 GHz water vapor absorption bands and resemble AMSU-A channels. Channels observe near and below the 183 GHz water vapor resonance and resemble AMSU-B (MHS) channels. As shown, ATMS s horizontal spatial resolution at nadir is 75 km for channels 1 and 2, 33 km for channels 3 16, and 15 km for channels AMSU s spatial resolution at nadir is 50 km for channels below 60 GHz and 15 km otherwise. The improvements of ATMS over AMSU include (1) about 400 km wider swath reducing gaps between orbits; (2) the addition of observation at GHz providing more information about surface, stratiform precipitation, and tropospheric temperature profiles; KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1919

3 Table 1. ATMS Versus AMSU Channel Characteristics ATMS AMSU a Channel Frequencies(GHz) Predicted NEΔT (K) b Nadir (km) Channel Measured NEΔT (K) ± f f 0 ± f 0 ± ± f 0 ± ± f 0 ± ± f 0 ± ± c c ± ± ± ± ± a AMSU resolution at nadir is 50 km below 60 GHz ad 15 km otherwise. b Integration times for ATMS channels 1 16 are one ninth those of AMSU. c Center frequencies for channels 16 and 17 are 89 and 157 GHz, respectively. (3) the addition of observation at ± 1.8 and ±4.5 GHz providing more information about water vapor and precipitation; (4) better spatial resolution for 50 GHz channels, that is, from ~50 to ~33 km at nadir; and (5) Nyquist spatial sampling for channels below 90 GHz, which enables discretionary image sharpening. ATMS surface channels near 23.8, 31.4, and 88.2 GHz have lower spatial resolution than those of AMSU In Situ Data and Collocation Methodology In situ data were collected over CONUS U.S. and Alaska during the and winter seasons and collocated with passive microwave measurements collected from the ATMS instrument on board the Suomi National Polar-Orbiting Partnership (NPP) satellite. ATMS brightness temperature measurements were used in algorithm training and evaluation at their native resolution. In situ data were obtained from the Quality Controlled Local Climatology Data (QCLCD) product distributed by NOAA s National Climate Data Center (NCDC, It consists of hourly, daily, and monthly summaries for approximately 1600 U.S. locations. Data are available beginning 1 January 2005 and continue to the present. In this study the quality-controlled version 3 hourly data set was used, which is generated from surface meteorological observations obtained from approximately 480 first-order weather stations over Conterminous U.S. and Alaska. These are Automatic Surface Observing System stations maintained by trained NOAA s National Weather Service (NWS) staff. The data undergo interactive and manual quality control at NCDC in addition to the automated quality control (Version 2). Version 3 data for these stations become available after the end of the data month, as the final database is built. Version 3 applies to January 2006 forward. Basic weather elements provided in this data set includes sky condition: cloud height and amount (clear, scattered, broken, or overcast) up to 3600 m above ground, visibility up to 16 km, present weather information (type and intensity for rain, snow, and freezing rain), obstruction to vision (fog and haze), sea level pressure, ambient air temperature, wet bulb temperature, wind speed and direction, relative humidity, and precipitation accumulation in liquid water equivalent. A major advantage of this data set is that it provides direct measurement of present weather including snowfall occurrence and other related variables at high temporal frequency, which can be a valuable independent ground truth reference for satellite snowfall identification studies. A major disadvantage is that these measurements are local scale and strictly valid at sensor locations. In addition, most of the first-order stations are located at airports which tend to occur more in low-elevation KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1920

4 Table 2. Correlation Statistics for Selected ATMS Channels and Station-Measured Variables of 2 m Surface Temperature in C (Temp), Relative Humidity in % (Hum), and Snowfall Rate in mm h 1 (SFR) for the Snowfall Sample and the Cosine of Satellite Zenith Angle Greater Than 0.9 Temp Hum SFR TB50 TB51 TB52 TB53 TB89 TB165 TB176 TB179 TB180 TB181 Hum 0.43 SFR TB TB TB TB TB TB TB TB TB TB TB plain terrain sites. Due to measurement system and station relocation changes, these data should not be used directly for climate change or regional climatologies [Changnon, 2006]. During the period of this study, all the stations were of first order, and thus this type of errors was not present. Another potential problem concerns snowfall accumulation measurements which are typically underreported and thus need to be corrected for gauge under-catch. For example, Kongoli and Bland [2000] found a mean snow correction factor of 1.3 which they applied to first-order station-measured liquid equivalents of snowfall for a number of stations in the U.S. Midwest. Two ATMS station data match-up samples were compiled for algorithm training and evaluation: one sample containing snowfall and the other one containing no-precipitation (rain or snowfall) data. This sampling approach implies that possible rain scenes need to be screened out (by other methods) before snowfall detection algorithm is applied consistent with training data. A simple way to accomplish this during algorithm application would be to use surface temperature information. This approach is applied for snowfall and rain retrievals from AMSU measurements in an operational system called Microwave Surface Precipitation Product System [Ferraro et al., 2005]. Here the snowfall algorithm of Kongoli et al. [2003] is applied when the surface temperature is below a fixed threshold or when land surface is snow covered. Rain retrieval is performed using a different algorithm that is only applicable over snow-free land. For the collocation between ATMS and station data, maximum time offset was set at 15 min with satellite time stamp following station time, and maximum separation distance was set at 25 km. Only the closest station within the 25 km distance from the ATMS footprint centroid was matched. Snowfall cases were identified when two criteria were met: the present weather flag of the matched station indicated snowing condition, and the accumulated station-measured precipitation amount was greater than 0 or reported as trace. The minimum liquid water equivalent rate measured and reported is 0.25 mm h 1, and thus, rates below this value are reported as trace. No-precipitation cases selected were only those that had no snow or rain reported at observation time and the liquid water equivalent rate reported was Brightness Temperature Comparisons for Snowfall and No-Precipitation Samples Figure 1. Scatterplot between ATMS TB53L (K) and collocated station-measured 2 m surface temperature ( C) for the snowfall sample. An analysis of the utility of ATMS channel 6 for use as temperature proxy is presented below. A statistical analysis is also presented on the variations of ATMS brightness temperature measurements in response to weather conditions. KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1921

5 Figure 2. Scatterplot between ATMS TB53L (K) and collocated station-measured 2 m surface temperature ( C) for the no-snowfall sample. Table 2 presents correlations among ATMS brightness temperatures at the window channels 1, 2,3, 15, and 16 (hereafter referred to as TB23, TB31, TB50, TB89, and TB165), oxygen absorption channels 4 through 6 (TB51, TB52, and TB53), water vapor absorption channels 17 through 22 (TB176, TB179, TB180, TB181, and TB182), and 2 m station-measured temperature, relative humidity, and hourly surface snowfall accumulation for the snowfall sample and at low zenith angles (cosine greater than 0.9). Most notably, channels 3 to 6 (TB50, TB51, TB52, and TB53) have moderately high correlations with 2 m station-measured air temperature, with channel 6 (TB53) having the highest correlation (0.77) among the ATMS channels selected. Note that the absorption peak of channel 6 is at 4 km altitude (~700 mba) for a standard tropical atmosphere at nadir, but it can be much lower for a colder and drier winter atmosphere. Figures 1 and 2 show scatter plots between the limb-corrected ATMS brightness temperature at channel 6 (hereafter referred to as TB53L ) and2m station-measured air temperature for the snowfall and the no-precipitation samples, respectively. Limb correction for channel 6 was done following the methodology of Goldberg et al. [2001]. Channel 6 variations to surface emissivity and atmospheric moisture and hydrometeors during the winter are smaller than those from temperature and scan angle variations, so scan angle effects need to be removed for this channel to be used as temperature proxy. Correlation is high, at 0.87 for the no-precipitation and 0.80 for the snowfall sample. The no-precipitation sample has a wide temperature range corresponding to 2 m station-measured air temperature between 40 C and 20 C. The standard error is smaller for the snowfall (2.9 K) than for the no-precipitation (4.7 K) sample. The smaller absolute error for the snowfall sample can be explained by a smaller temperature range. Figures 3 and 4 show histograms of station-measured relative humidity and ATMS TB53L for the snowfall sample. Most of snowfall sampled had humidity at 60% and above and TB53L between 240 K and 251 K. The low fraction of snowfall cases sampled with TB53L above 251 K is explained by the predominance of above-freezing temperatures that cause precipitation to be mostly in liquid form. On the other hand, the low fraction of snowfall cases with TB53L below 240 K in the data set is explained by the fact that very cold snowfall conditions were not sufficiently sampled by the existing station data for the period of this study. Snowfall colder than 240 K would occur more frequently over Alaska and high-elevation areas, which were not adequately sampled due to the scarcity of first-order stations over these regions. To gain insight into the variations of ATMS higher frequency measurements in response to weather conditions, the data sample was partitioned into two subsamples and intercompared: one subsample corresponding to TB53L between 244 and 251 K, and the other one corresponding to TB53L below 244 K. Tables 3 and 4 give summary statistics for selected ATMS channel brightness temperatures and station-measured variables of air temperature, relative humidity, and Snowfall Rate (SFR) for each subsample. For trace snowfall cases, a default SFR value was set at 0.1 mm h 1.For the warmer temperature subsample (Table 3), mean station-measured air temperature and TB53L, and the ATMS brightness temperatures at 165 GHz and above are lower for the snowfall than for the no-precipitation cases, and the two-sample t test showed that the differences are all significant (P value < 0.001). For the colder temperature subsample, mean station-measured air temperature and TB53L, and TB89 through 180 GHz are higher for snowfall cases, whereas TB181 and TB182 are Figure 3. Histogram of station-measured relative humidity for the snowfall sample. lower. Note that TB181 and TB182 peak at high altitudes (7 km and above) for a standard KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1922

6 tropical atmosphere. However, for winter high-latitude atmospheres at low temperatures, their absorption peaks at lower altitudes. Table 5 gives correlation values between station-measured SFR and selected ATMS channel brightness temperatures for snowfall cases of the colder temperature subsample (TB53L < 244 K) and warmer temperature subsample (TB53L between 244 and 251 K). For both colder and Figure 4. Histogram of ATMS TB53L for the snowfall sample. warmer temperature subsamples, the 2 m station-measured air temperature and its TB53L proxy are positively correlated with SFR. However, the response of high-frequency brightness temperatures to SFR is quite different. For the snowfall cases in the warmer temperature subsample, TB89 through TB182 have statistically significant (P value < 0.001) negative correlations with measured SFR, whereas for the snowfall cases in the colder temperature subsample, correlation values are all positive except for TB182, although only TB89, TB165, and TB176 were statistically significant. Note that for the snowfall cases in the colder temperature subsample the 2 m station-measured air temperature and TB53L have the highest correlation with SFR. The brightness temperature increase during snowfall, and the positive correlations with SFR in colder weather would suggest that emission dominates the brightness temperature signal. In contrast, the brightness temperature decrease during snowfall and the negative correlations with SFR in relatively warmer weather would suggest that the scattering effect dominates the brightness temperatures. 4. Snowfall Detection Algorithm 4.1. Methodology The proposed algorithm is a scheme that combines principal component analysis (PCA) of seven ATMS high-frequency brightness temperature measurements with the logistic regression technique to compute the probability of snowfall. Logistic regression is used to estimate the probability of a binary outcome Y as an exponential continuous function of a set of predictor variables: P ¼ exp ð β 0 þ β 1 X 1 þ β 2 X 2 þ :::: β n X n Þ (1) 1 þ expðβ 0 þ β 1 X 1 þ β 2 X 2 þ :::: β n X n ÞÞ where P is the probability of success of the binary variable Y, which in our case is the probability of snowfall; X is the vector of independent variables, which in our case are brightness temperature measurements; and β is the vector of regression coefficients. The logarithm of the odds of Y called the logit can be expressed as linear combination of independent variables as in multiple regression: P Log itðpþ ¼ Ln ¼ β 1 P 0 þ β 1 X 1 þ β 2 X 2 þ :::: β n X n (2) Table 3. Summary Statistics for the Snowfall and No-Snowfall Samples for TB53L > 244 K and TB53L 251 K No-Snowfall Snowfall Sample/Variable Mean SD Mean SD Temp Hum SFR TB TB TB TB TB TB TB TB53L KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1923

7 Table 4. Summary Statistics for the Snowfall and No-Snowfall Samples for TB53L 244 K No-Snowfall Snowfall Sample/Variable Mean SD Mean SD Temp Hum SFR TB TB TB TB TB TB TB TB53L The inverse of the logit function is called the logistic function: P ¼ expðbþ 1 þ expðbþ (3) where B is the logit function or the multiple linear regression term in equation (2). The fitting procedure consists in iteratively finding the set of regression coefficients using maximum likelihood estimation of the joint distribution of the response Y: n gy ð 1 ; y 2 ; y n Þ ¼ y p i ið1 pi Þ 1 y i (4) i¼1 where y i is an individual measured value of Y, e.g., arbitrarily assigned 1 for snowfall and 0 for no-precipitation, and p i is the probability that y i takes on the value of 1, e.g., for snowfall. Note that p i is computed using equation (1). This differs from ordinary least squares regression where a unique analytic solution can be found in closed form. One potential problem in applying logistic regression is the stability of regression coefficients when predictor variables are correlated. To address the problem of multicollinearity in predictor variables while retaining most of the information content, the seven-dimensional ATMS input data set is reduced to two or three uncorrelated principal components that retain most of the variance of the original data. In addition, the simple multivariate form in equations (1) and (2) would be preferable to more complex expressions, e.g., the power of predictor variables or other nonlinear terms, to achieve a solution more easily [Crosby et al., 1995]. Another predictor variable considered (in addition to principal Table 5. Correlation Values Between Station-Measured SFR in mm h 1 components) is the satellite local and the 2 m Surface Temperature in C (Temp), Surface Relative Humidity zenith angle. One way to account in % (Hum), and Selected ATMS Measurements a for scan angle effects on the results SFR without using it as a predictor variable Variable Warmer Colder would be to partition the data set into bins of scan angle ranges and for each Temp Hum bin to compute principal components TB and logistic regression coefficients. TB A major problem with this approach TB is that it requires a sufficiently large TB and representative subset for each bin, TB TB which was not possible to accomplish TB in this study. It is important to note, TB53L however, that the input variable a 1 Trace snowfall rate was given a default value of 0.1 mm h. Warmer selection in the final algorithm and Colder and warmer refer to the subsets of the snowfall sample for including the local zenith angle is TB53L > 244 and 251 K, and for TB53L 244 K, respectively. made based on statistical significance KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1924

8 Figure 5. Eigevalues of individual principal components computed from the seven-channnel ATMS training data set. Regime 1 and Regime 2 refer to the warmer and colder temperature subsets. results. Based on these considerations, the final algorithm considered for training has the following form: LnðP= ð1 PÞÞ ¼ a0 þ a1*pc1 þ a2*pc2 þ a3*pc3 þ a4* cos LZA (5) Where cos LZA is the cosine of the zenith angle, PC1, PC2, and PC3 are the first three principal components computed from the seven-channel ATMS training data set, and a0, a1, a2, a3, and a4 are the logistic regression coefficients computed from the training data set using maximum likelihood estimation. To optimize accuracy, the combined PCA- Logistic Regression Scheme was applied to the colder and warmer temperature samples (composed of both snowfall and no-precipitation cases) separately. This choice was driven by the statistical analysis results that showed a different microwave response to snowfall in colder and warmer temperature conditions. In colder weather, brightness temperatures were higher on average during snowfall than noprecipitation, whereas the opposite was true in warmer weather. The training data set was therefore split into two subsets: the warmer temperature and the colder temperature subset (hereafter referred to as warmer and colder temperature regimes). The training data set was also filtered with respect to surface relative humidity: All cases with relative humidity below 60% were removed as snowfall is unlikely but also to minimize false detection that can be operationally unacceptable especially in colder weather conditions. In addition, cases with TB53L below 240 K were not considered since these very cold weather conditions were underrepresented (see Figure 4). Note, however, that TB53L values of 240 K and below correspond to 2 m air temperatures colder than about 14 C (see equations in Figures 1 and 2) Algorithm Training Results PCA and logistic regression were performed using the SPSS statistical software. First, principal component (PC) scores were computed from the training data set. Next, each ATMS seven-channel measurement was converted into a three-pc measurement, followed by application of logistic regression to the PCs and the cosine of the zenith angle to compute the regression coefficients. Figure 5 presents the eigenvalues of the seven principal components for the warmer (denoted as regime 1 ) and the colder (denoted as regime 2 ) temperature samples, respectively. Each eigenvalue indicates the amount of variability of the original Table 6. First Three Principal Component (PC) Scores Computed From the brightness temperature data on a Training Data Set scale of 0 to the number of ATMS PC1 PC2 PC3 channels considered (7). The first Regime1 TB component has the largest eigenvalue, 5.1 for the warmer temperature regime TB and 3.5 for the colder temperature TB TB regime, accounting for 72% and 49% TB of the total variance, respectively. The TB181 TB first three components of the warmer temperature regime explain 98.5% of the total variance, and the first three components of the colder temperature regime sample explain 97.4% of the total variance. Regime2 TB TB TB TB TB TB TB Table 6 gives the first three PC component scores, and Table 7 gives the values of the logistic regression KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1925

9 Table 7. Precomputed Logistic Regression Coefficients a Coefficient Regime 1 Regime 2 a a a a a a Regime 1 and Regime 2 refer to the warmer and colder temperature subsets of the training data set undergoing additional filters (relative humidity above 60% and TB53L > 240 K). coefficients for the warmer and colder temperature regimes, respectively. For the warmer temperature regime, the first three principal components and the cosine of the zenith angle were statistically significant (P < 0.001). For the colder temperature regime, only the first two components were statistically significant (P < 0.001), whereas the third component and the cosine of zenith angle were not statistically significant (P > 0.05) and thus were removed as independent variables. Given a multichannel ATMS observation, the steps to compute the probability of snowfall from ATMS measurements are as follows: Step 1: Filter the data a. For surface humidity less than 60%, probability of snowfall is 0. b. For 2 m air temperature at 2 C and above, probability of snowfall is 0. c. For TB53L less than 240 K, snowfall is undetermined. d. For TB53L > 251 K, probability of snowfall is 0. Step 2: Compute the PCs from TB89 through TB182 as the sum product between each channel TB and the PC scores given in Table 6. a. Regime 1 (warmer temperature regime): TB53L > 244 K and TB53L 251 K. b. Regime 2 (colder temperature regime): TB53L > 240 K and TB53L 244 K. Step 3: Compute the probability of snowfall from the logistic function of equation (5) using the logistic regression coefficientsgivenintable7. For a probability of snowfall threshold of 0.5 (above which a case is considered snowing ), snowfall detection rate is 76% for both regimes, with false detection rate (overestimation of snowfall) at 19% for the warmer weather regime sample and 45% for the colder weather regime sample. Note that these statistics refer to the training data with surface humidity above 60% and with a large fraction of trace snowfall cases. Raising the probability of snowfall threshold to 0.6 lowers the snowfall detection rate down to 60% and 55% for the warmer and colder weather regime samples, respectively, but improves false snowfall detection statistics: 12% for the warmer weather regime sample and 25% for the colder weather regime sample. 5. Application Examples and Evaluation The snowfall detection algorithm has been applied at NOAA since February 2014 for routine monitoring and evaluation and as a snowfall mask for an ATMS-based SFR algorithm. SFR retrieval is based on an inversion method and a two-stream radiative transfer forward model [Yan et al., 2008]. A method developed by Heymsfield and Westbrook [2010] is adopted to calculate snow particle terminal velocity, which in combination with the retrieved cloud properties of particle size and ice water path [Yan et al., 2008] are used to estimate SFR. Snowfall detection algorithm being presented here has no physical connection with the SFR algorithm other than the fact that the former is being applied as a snowfall mask for the latter. Consistent with training data, surface relative humidity and temperature information are obtained from the NOAA s Global Forecast System (GFS) and used to initially screen out possible rain and dry weather scenes, respectively. The probability of snowfall threshold for considering an ATMS pixel as snowing or no-snowing is set at 0.50 for the warmer temperature regime and 0.60 for the colder temperature regime. Presented here are two snowfall events over CONUS U.S.: 5 and 6 February and February The 5 and 6 February event was sampled around the in situ station locations for training the algorithm, whereas the February event was sampled only for quantitative evaluation against in situ data. Qualitative comparisons of the algorithm s ability for mapping significant snowfall events on a regional scale were made against the NOAA s KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1926

10 Figure 6. ATMS-retrieved snowfall (top left), temperature regime flag (top right), ATMS footprint matched Stage IV (bottom left), and the 4 km Stage IV (bottom right) hourly precipitation rate on 5 February The nonoverlapping ATMS and Stage IV retrieved areas (Figure 6, bottom left) are denoted as light blue. Retrieved snowfall shown is the snowfall rate (SFR) in mm h 1 using an experimental algorithm applied over the snow-detected areas. The temperature regime flag denotes colder snowfall conditions (regime 2) as red color, and warmer snowfall conditions (regime 1) as light olive color. The light blue color denotes weather conditions corresponding to TB53L above 251 K where rain is more likely. National Centers for Environmental Prediction (NCEP) Stage IV hourly and daily precipitation analysis. It consists of hourly, 6-hourly, and daily precipitation accumulations on 4.7 km polar stereographic grid across the CONUS U.S. beginning in 2001 [Lin and Mitchell, 2005]. The production process utilizes a combination of the National Weather Surveillance Radar-1988 Doppler (WSR-88D) network of ground radars and surface gauges. The NCEP Stage IV accumulations are computed as a national mosaic of the multisensor precipitation estimator, which is a fusion of the digital precipitation arrays from the National Weather Service (NWS) Precipitation Processing System (originally at polar 1 km resolution) with available surface gauges at each of the 12 CONUS U.S. River Forecast Centers [Lawrence et al., 2003; Lin and Mitchell, 2005]. The product is frequently employed as the benchmark, or truth, when evaluating other remotely sensed precipitation products [Wu et al., 2012; Gourley et al., 2010; Lin and Hou, 2012]. However, theproductis stillsusceptible tounavoidable uncertainties from beam blockage, beam overshoot, reduced sensitivity at long ranges, and scarcity of surface gauges [Smalley et al., 2014]. Figure 6 shows a snowfall retrieval example using one swath of ATMS measurements over eastern U.S. on 5 February 2014 along with the ATMS TB53L-derived temperature regime flag and the closest-in-time Stage IV multisensor hourly precipitation. Shown is the ATMS-retrieved SFR applied over snowfall detected areas. Most of the snowfall detected by ATMS occurred in colder weather conditions (red-coded area), i.e., TB53L between 240 K and 244 K. Visual inspection of the Stage IV hourly precipitation product and comparison with ATMS maps shows precipitation over much of the ATMS snowfall detected areas that included light and heavier accumulations. However, the precipitation coverage retrieved from the Stage IV analysis is larger in extent, due in part to the fact that a portion of the retrieved Stage IV precipitation is in the form of rain. Another possible explanation is that some legitimate cold snowfall is not being detected by ATMS. Further inspection of the ATMS snowfall and Stage IV precipitation maps shows an area east of Lake Michigan, with geographical coordinates between latitude north and longitude east, where only the ATMS detects snowfall. Inspection of in situ data showed that two stations in the area reported trace hourly snowfall accumulations that were flagged as snowing within 15 min after the satellite overpass. Figure 7 shows an example of ATMS-retrieved snowfall over western U.S. along with closest in time 4 km Stage IV hourly KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1927

11 Figure 7. (left) NPP ATMS SFR and (right) 4 km Stage IV hourly precipitation rate in mm h 1 on 5 February Experimental ATMS SFR was applied over snowfall detected pixels. precipitation on 6 and 7 February Shown again is the snowfall rate over ATMS-detected snowfall areas. Note the persistent ATMS-detected snowfall over Oregon on 6 and 7 February 2014, whereas the Stage IV analysis shows mostly no precipitation. As noted earlier, the 5 and 6 February event was sampled for algorithm development. However, inspection of the snowfall training sample revealed that these specific snowfall occurrences over Oregon were not sampled. Examination of daily snowfall totals from the NOAA s Cooperative Observation Stations network over Oregon revealed snowfall over most of Oregon with substantial amounts in some areas. Figure 8 shows the modeled 24 h snow depth change on 7 February at Figure 8. Modeled 24 h snow depth change over CONUS U.S. by NOAA s SNODAS on 7 February 2014 at 6:00 Zulu time. KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1928

12 Figure 9. NPP ATMS SFR over snowfall detected areas during the February 2014 storm over CONUS U.S. 6:00 Zulu time over U.S. estimated from the NOAA s Snow Data Assimilation System (SNODAS) [Carroll et al., 2001], with largest accumulations over northwest Oregon and southwest Washington. This ancillary information would suggest that the ATMS-detected snowfall is probably correct and that the Stage IV product missed these specific events. A recent in-depth assessment of the stage IV precipitation product by Smalley et al. [2014] highlights the limitations of this analysis scheme in detecting solid precipitation accumulations especially in Western U.S. Figure 9 gives a sequence of ATMS-retrieved snowfall images over CONUS U.S. during the significant high-impact storm event that brought heavy rain, ice, and snow over eastern U.S. during February Figure 10 gives the 24 h Stage IV precipitation analysis (left) and the daily snow cover maps over U.S. (right) from the NOAA s Interactive Multisensor Snow and Ice Mapping System (IMS) [Helfrich et al., 2007]. This storm brought new snow on the ground in the south on 12 and 13 February and additional snow cover in the north on 13 and 14 February. The south-to-north snowfall movement as shown in the images is very well captured by the ATMS algorithm. Table 8 gives summary statistics of the snowfall sample collected for evaluation from in situ data matched to ATMS during February and algorithm performance statistics following the matching procedure as described in the data section. Summary and performance statistics are given for the warmer and colder temperature regimes. In situ measured average relative humidity of snowing cases was above 83%, with minimum humidity at 51% and 71% for the warmer and colder temperature regimes, respectively. Only two snowing cases had relative humidity at 51% and were flagged as trace; all the other snowing cases had humidity above 60%. Average measured SFR for the warmer temperature regime was 1.1 mm h 1, much higher than that for the colder temperature regime (0.21 mm h 1 ). Most of the colder regime snowfall cases matched had trace accumulations. False detection rate for the warmer temperature regime is very low, at 4.0%, and correct detection rate is 73%. For the colder temperature regime, however, false detection rate is higher at 14% and correct detection rate lower at 55%. Correct detection statistics here are close to those of the training data (75%/55%), whereas false detection statistics are much lower, due in part to the weather filters applied. Analysis of the match-up samples indicated that most of the missed snowfall cases had trace or minimum (0.25 mm h 1 ) measured snowfall accumulations. These results demonstrate clear skill of the algorithm in detection of variable rate snowfall, as well the remaining challenges in lighter and colder snowfall retrievals. KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1929

13 Figure 10. (left) The 24 h Stage IV precipitation accumulation in mm and (right) NOAA s IMS daily snow cover images over the U.S. during the February 2014 winter storm. Table 8. Summary Statistics of Some Measured Station Parameters of the Snowfall Sample Obtained for Evaluation From in situ Data Matched to ATMS Measurements, and Algorithm Performance Statistics for the February 2014 Snowfall Event Measured/Computed Variable Regime 1 Regime 2 Minimum/average snowfall surface temperature ( C) 8.3/ / 7.9 Minimum/average snowfall surface humidity (%) 71/88 51/83 Minimum/average SFR (mm h 1 ) 0.1/ /0.21 Correct snowfall detection rate (%) False snowfall detection rate (%) KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1930

14 6. Summary and Conclusions Snowfall detection and measurement from space represent highly difficult problems in modern hydrometeorology. This paper presents a snowfall detection algorithm over land utilizing high-frequency passive microwave measurements that can detect light snowfall and in colder weather conditions. The algorithm was applied to ATMS measurements and trained with in situ meteorological data collected over CONUS U.S. and Alaska during two winter seasons in and The in situ data were obtained from the Quality Controlled Local Climatology Data (QCLCD) hourly product distributed by NOAA s National Climate Data Center. Light snowfall cases flagged as trace were a significant fraction of the training data set. Statistical analysis of the in situ data matched with ATMS measurements showed very good agreement and highest correlation between ATMS oxygen absorption channel 6 at 53.6 GHz and 2 m station-measured air temperature in both snowing and no-snowing conditions. Using this channel as temperature proxy, the training data set was partitioned into two subsets representative of warmer and colder weather conditions. Statistical significance tests showed that for the warmer temperature sample, snowfall was associated with lower high-frequency brightness temperatures at 165 GHz and above than the no-precipitation sample, and the brightness temperatures were negatively correlated with measured snowfall rate. For the colder temperature sample, on the other hand, snowfall occurred at higher microwave brightness temperatures than no-precipitation, and the brightness temperatures were positively correlated with snowfall rate. Based on these results, a two-step algorithm was developed that optimized the detection of snowfall over these two distinct temperature regimes. Given a seven-dimensional ATMS brightness temperature observation at 89 GHz and above, the algorithm computes the principal components using the precomputed component scores, followed by the calculation of the probability of snowfall using a logistic regression equation with precomputed coefficients from the training data set. To minimize false snowfall detection especially in colder weather, relative humidity information was used as weather filter and applied to the training and evaluation data set to filter out scenes with relative humidity below 60%. The algorithm was applied to ATMS for two recent snowfall events over the U.S. Surface relative humidity information was obtained from the NOAA s Global Forecast System (GFS) and used as weather filter. Surface temperature was also obtained from GFS to filter out possible rain scenes. Evaluation of the algorithm with ground data shows clear skill in capturing variable rate snowfall, as well as the remaining challenges in the retrieval of lighter and colder snowfall. Acknowledgments This research was partially supported by NOAA grant NA09NES awarded to the Cooperative Institute for Climate and Satellites (CICS) at the Earth System Science Interdisciplinary Center (ESSIC) of the University of Maryland, College Park. The data for producing this paper are distributed by NOAA s National Climate Data Center (NCDC, for the Quality Controlled Local Climatology Data (QCLCD) and by NOAA s Comprehensive Large Array-Data Stewardship System (CLASS, class.ncdc.noaa.gov) for S-NPP ATMS Temperature Data Record (TDR). References Carroll, T., D. Cline, G. Fall, A. Nilsson, L. Li, and A. Rost (2001), NOHRSC operations and the simulation of snow cover properties for the Conterminous U.S., Proceedings of the 69th Annual Meeting of the Western Snow Conference, pp Changnon, S. A. (2006), Problems with heavy snow data at first-order stations in the United States, J. Atmos. Oceanic Technol., 23, , doi: /jtech Chen, F. W., and D. H. Staelin (2003), AIRS/AMSU/HSB precipitation estimates, IEEE Trans. Geosci. Remote Sens., 41(2), Crosby, D. S., R. R. Ferraro, and H. Wu (1995), Estimating the probability of rain in an SSM/I FOV using logistic regression, J. Appl. Meteorol., 34, ESA (2004), EGPM European Contribution to Global Precipitation Measurement, ESA SP-1279(5), ESA, Noordwijk, Netherlands. Ferraro, R. R., F. Weng, N. Grody, L. Zhao, H. Meng, C. Kongoli, P. Pellegrino, S. Qiu, and C. Dean (2005), NOAA operational hydrological products derived from the AMSU, IEEE Trans. Geosci. Remote Sens., 43, Goldberg, M. D., D. S. Crosby, and L. Zhou (2001), The limb adjustment of AMSU-A observations: Methodology and validation, J. Appl. Meteorol., 40, Gourley, J. J., Y. Hong, Z. L. Flamig, L. Li, and J. Wang (2010), Intercomparison of rainfall estimates from radar, satellite, gauge, and combinations for a season of record rainfall, J. Appl. Meteorol. Climatol., 49, , doi: /2009jamc Helfrich, S. R., D. McNamara, B. H. Ramsay, T. Baldwin, and T. Kasheta (2007), Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS), Hydrol. Processes, 21, Heymsfield, A. J., and C. D. Westbrook (2010), Advancements in the estimation of ice particle fall speeds using laboratory and field measurements, J. Atmos. Sci., 67, , doi: /2010jas Kongoli, C., and W. L. Bland (2000), Long-term simulations of snow depth using a modified atmosphere-land exchange model, Agric. For. Meteorol., 104, Kongoli, C., P. Pellegrino, R. R. Ferraro, N. C. Grody, and H. Meng (2003), A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU), Geophys. Res. Lett., 30(4), 1756, doi: /2003gl Lawrence, B. A., M. I. Shebsovich, M. J. Glaudemans, and P. S. Tilles (2003), Enhancing precipitation estimation capabilities at National Weather Service field offices using multi-sensor precipitation data mosaics, 19th International Conference on Interactive Information Processing Systems for Meteorology, Oceanography and Hydrology, Am. Meteorol. Soc., Long Beach, Calif. Levizzani, V., S. Laviola, and E. Cattani (2011), Detection and measurement of snowfall from space, Remote Sens., 3, , doi: / rs Lin, X., and A. Y. Hou (2012), Estimation of rain intensity spectra over the continental United States using ground radar gauge measurements, J. Clim., 25, , doi: /jcli-d Lin, Y., and K. E. Mitchell (2005), The NCEP Stage II/IV hourly precipitation analyses: Development and applications, 19th Conf. on Hydrology, Am. Meteorol. Soc., San Diego, Calif., 9 13 Jan. KONGOLI ET AL American Geophysical Union. All Rights Reserved. 1931