Global Multisensor Automated satellite-based Snow and Ice Mapping System (GMASI) for cryosphere monitoring

Remote Sensing of Environment 196 (2017) 42–55

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Global Multisensor Automated satellite-based Snow and Ice Mapping System (GMASI) for cryosphere monitoring Peter Romanov NOAA-CREST, City University of New York, 160 Convent Ave, New York, NY, USA Office of Satellite Applications and Research, NOAA NESDIS, 5830 University Research Court, College Park, MD 20740, USA

a r t i c l e

i n f o

Article history: Received 13 October 2016 Received in revised form 27 March 2017 Accepted 23 April 2017 Available online xxxx Keywords: Satellite monitoring Snow and ice extent Visible Infrared Passive microwave

a b s t r a c t Synergy of satellite observations in the visible infrared and microwave spectral bands presents an attractive and powerful approach to improve monitoring of the Earth's snow and ice cover. This approach is implemented in the Global Multisensor Automated Snow and Ice Mapping System (GMASI) operated by NOAA NESDIS since 2006. Combined observations in the visible and infrared from the AVHRR sensor onboard METOP satellites and in the microwave spectral bands from SSMIS onboard DMSP satellites allows for providing spatially continuous characterization of the snow and ice distribution on a daily basis. The paper presents a basic description of techniques and algorithms implemented in the system and examines the system performance in the course of the last ten years. It is demonstrated that the GMASI product adequately reproduces spatial patterns of the snow and ice distribution and their seasonal variations. Validation of GMASI daily snow retrievals over the Northern Hemisphere has demonstrated their close correspondence to surface observations of the snow cover with the yearly mean rate of over 94%. Automated maps are found to agree even better to the daily snow and ice cover map produced interactively at NOAA. In this latter case the agreement rates on the snow cover and ice cover distribution amounted correspondingly to 96% and 98%. A larger part of mismatches was due to underestimated snow and ice extent in the automated maps as compared to in situ data and interactive analysis. © 2017 Elsevier Inc. All rights reserved.

1. Introduction Monitoring of the Earth's cryosphere and in particular of its snow and ice cover is one of the primary applications of weather satellites. Accurate characterization of the snow and ice cover distribution, its seasonal and long-term variations is important for numerical weather prediction, climate analysis and hydrological forecasts. This information is also needed in a large number of other applications including in particular transportation, hydropower generation, recreation and water management. Various techniques, both interactive and automated have been used to infer information on the Earth's snow and ice cover from satellite observations (Dietz et al., 2012; Frei et al., 2011; Ivanova et al., 2015). These techniques rely on specific spectral response of snow and ice in the visible, infrared and microwave spectral bands and on specific spatio-temporal signatures of snow and ice which can be identified in the satellite imagery. Since early 1970s NOAA has been conducting routine monitoring of snow and ice in the Northern Hemisphere through interactive visual analysis and interpretation of satellite imagery. Charts produced by

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http://dx.doi.org/10.1016/j.rse.2017.04.023 0034-4257/© 2017 Elsevier Inc. All rights reserved.

NOAA analysts include four categories, snow-free land, snow cover, ice-free water and ice. In the course of more than four decades the image analysis and snow mapping technique has undergone a number of upgrades including a computer-based Interactive Multisensor Snow and Ice Mapping System (IMS) introduced in 1998. Owing to these upgrades both the spatial and temporal resolution of the product has improved from the original weekly mapping on a 180 km size grid (Robinson et al., 1993) to 24 km and daily updates in 1999 (Ramsay, 1998) and, further to 4 km resolution in 2004 (Helfrich et al., 2006) and to 1 km resolution in 2014. Interactive snow and ice charts are currently generated at the National Ice Center (NIC) and present one of the primary inputs to weather prediction models operated by NOAA National Centers for Environmental Prediction (NCEP). Substantial human efforts required to produce IMS maps along with concerns of subjectivity in interpretation of satellite imagery and its possible effects on the consistency of the product time series stimulate a wide interest to automated satellite-based algorithms for mapping snow and ice. As compared to the interactive approach, automated techniques can better utilize advanced capabilities of satellite observations including their high spatial resolution, multispectral sampling, and frequent repeat observation cycle. Automated algorithms ensure objective classification of the imagery and allow for consistent reprocessing of historical satellite data into snow and ice climate data records.

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The vast majority of automated algorithms developed to infer information on snow and ice from satellite data utilize passive satellite observations either in the visible/infrared or in the microwave spectral range. Visible/infrared snow mapping algorithms have been developed and applied to the data of Moderate Resolution Spectroradiometer (MODIS) onboard NASA's Earth Observing Satellites (Hall et al., 2002), Visible Infrared Radiometer Suite (VIIRS) onboard Suomi-NPP (S-NPP) satellite (Key et al., 2013), Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA and Metop satellites (Latifovic et al., 2005; Zhao and Fernandes, 2009; Key et al., 2016), as well as to the data of several other sensors onboard polar orbiting and geostationary satellites (Metsamaki et al., 2005; deWildt et al., 2007; Romanov et al., 2003). Microwave-based algorithms to identify snow cover have been proposed by Grody and Basist (1996), Derksen et al. (2000), Tedesco et al. (2004) Pulliainen (2006), Royer et al. (2010), Kelly et al. (2003) and others. Besides the snow extent most of these later algorithms also provide estimates of the snow depth or the snow water equivalent (SWE). Similarly to the snow cover information on the ice cover can be inferred from satellite observations both in the microwave (e.g. Cavalieri and Comiso, 2000; Comiso, 1986; Kaleschke et al., 2001) and in the visible/ infrared spectral bands (e.g., Key et al., 2013; Temimi et al., 2011; Williams et al., 2002). Observations in the two spectral ranges, visible/infrared and microwave, have their own specific advantages and weaknesses with respect to the snow and ice identification and mapping. Owing to high, 1 km and better, spatial resolution of current visible/infrared sensors onboard operational weather satellites these observations provide spatially detailed characterization of the snow and ice cover but are inefficient at night and in overcast conditions. Gaps in the area coverage due to clouds or insufficient daylight conditions reduce the value of snow and ice products derived from these data and complicate their use in model applications. The spatial resolution of satellite microwave measurements is generally much coarser, on the order of 10–50 km, however they are practically unaffected by clouds, do not require daylight and hence are often referred to as weather-independent. These properties are extremely important for mapping and monitoring of the ice cover in high latitudes. Compared to satellite observations in the visible/infrared bands, observations in the microwave typically provide less accurate retrievals due to specific physical limitations of the technique (e.g., Romanov et al., 1999; Foster et al., 2011). With respect to the snow cover the latter includes poor sensitivity to shallow and to wet snow and confusion of snow with frozen rocks and soil (Grody and Basist, 1996). Ice cover identification in the microwave is hampered by slush and melt ponds that frequently develop on top of ice in spring and summer and radically modify the spectral response of the scene (e.g., Grenfell, 1992). Coarse spatial resolution of microwave sensors and confusion of mixed landwater scenes with water scenes having small concentration of ice (often referred to as “land-to-ocean spillover” effect) precludes monitoring of the ice cover over small inland water bodies and hampers accurate ice identification along the coastal line. As a result, microwavebased ice products usually provide ice retrievals only over open ocean areas and often mask out coastal areas. A number of algorithms have been proposed where satellite observations in the visible/infrared and in the microwave are combined in an effort to achieve spatially continuous and potentially more accurate characterization of the snow cover (e.g., Romanov et al., 1999; Armstrong et al., 2004; Liang et al., 2008; Gao et al., 2010; Foster et al., 2011; Solberg et al., 2014; Bergeron et al., 2014). Most of these multisensor algorithms complement snow retrievals made in the optical bands under clear sky conditions with microwave retrievals in overcast areas or where optical data were not available for any other reason. Although this “either – or” approach generally improves the area coverage by the derived product, errors inherent to microwave snow retrievals (e.g., misses of melting and shallow snow) can still propagate into and corrupt the combined blended map. Some of the algorithms

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referred above (e.g. Liang et al., 2008; Gao et al., 2010; Bergeron et al., 2014) have been tested only over certain geographical regions and/or over a relatively short period of time which raises concerns about their possible application for all year round global snow cover monitoring. Much less interest has attracted so far a possible use of combined satellite observations in the visible/infrared and in the microwave for the ice mapping. Potential benefits of utilizing synergy of observations of two types to improve monitoring of sea ice have been discussed in particular by Maslanik et al. (1989) however no attempts to practically implement this approach have been reported so far. This paper presents the Global Automated Snow and Ice Mapping System (GMASI) operated by NOAA NESDIS. The principal intent for the development of this system was to generate daily satellite-based maps of the global extent of both snow and ice cover for use in NOAA operational applications. Maps of snow and ice are produced with a completely automated algorithm using synergy of observations from visible/infrared and microwave sensors onboard multiple operational meteorological satellites. The GMASI system was developed and implemented into operations at NOAA in 2006. A small number of modifications to the algorithm and to the code have been made in the course of the last ten years to enhance its performance and to incorporate data from replacement and newer satellites and sensors. At present the output product of the system is used in several operational environmental applications including interactive snow and ice charting at the National Ice Center (since 2006), environmental product generation with Visible Infrared Imaging Radiometer Suite (VIIRS) data onboard Suomi National Polar-Orbiting Partnership (Suomi NPP) spacecraft and in a number of other satellite-based land surface and atmospheric products produced at NOAA National Environmental Satellite, Data, and Information Service (NESDIS). Since 2016 the GMASI product has been incorporated in the data processing and retrieval system of NOAA-NASA Global Precipitation Measurement (GPM) mission. In the paper we give a basic description of algorithms and techniques currently used in GMASI and provide examples illustrating the system performance. Automated snow and ice maps generated by GMASI are subject to routine rigorous validation. The paper presents the technique used to assess the quality of the derived maps and the results of the product accuracy evaluation. 2. Snow and ice mapping with GMASI The multisensor approach presents the core of the Global Automated Snow and Ice Mapping System (GMASI). The primary objective of GMASI consists in generating of a spatially continuous distribution of the global snow and ice cover on a daily basis. Continuity in the spatial coverage is achieved by combining snow and ice cover retrievals from satellite observations in the visible/infrared and in the microwave spectral bands. The algorithm goes beyond a simple “either-or” approach inherent to most other visible/infrared and microwave blending techniques by identifying and incorporating only the most reliable (not all) retrievals of the two types (visible/infrared and microwave) in the blended product and by utilizing climatological datasets along with a series of consistency tests to eliminate and disregard potentially erroneous retrievals. 2.1. GMASI system overview The current version of the system uses observations of the Advanced Very High Resolution Radiometer (AVHRR) onboard MetOP satellites and observations from Special Sensor Microwave Imager/Sounder (SSMIS) sensors on all active Defense Meteorological Satellite Program (DMSP) satellites (currently, F-15, -16, -17, and -18). Prior to 2011 AVHRR observations from NOAA polar orbiting satellites were used rather than MetOP. Earlier version of the system also incorporated observations in the visible and infrared from geostationary satellites

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(GOES-East, GOES-West, Meteosat and MTSAT), however benefits of having these data in the system were found insufficient to justify substantial additional efforts needed to routinely calibrate and cross calibrate visible sensors onboard all polar orbiting and geostationary platforms to obtain comparable snow and ice retrievals. The selection of particular satellites and sensors for the system was driven by two major considerations: guaranteed availability of data with low latency and global daily coverage and sufficiently long uninterrupted period of observations with the same or similar sensor. The first aspect is critical for fitting the needs of numerical weather prediction, hydrological forecasting and potentially other operational applications. The second is essential for generation of consistent in time series of the product and its potential application in climate modeling and climate change studies. Global observations from AVHRR onboard NOAA and METOP satellites and from SSMI/SSMIS onboard DMSP satellites span back to the beginning and the middle of 1980s, respectively and are available near-real time. Data from sensors onboard research satellites, even though they could be superior to AVHRR and SSMIS with respect to the spectral range, spatial resolution, navigation accuracy (e.g., MODIS onboard Terra and Aqua, Advanced Microwave Scanning Radiometer for Earth Observing Satellites, AMSR-E, onboard Aqua) do not fully satisfy criteria specified above either due to the lack of operational availability or due to relatively short time length of the dataset. The GMASI algorithm first performs snow and ice retrievals individually from satellite observations in the visible/infrared bands and from observations in the microwave (see the algorithm flow chart in Fig. 1). Retrievals are made by examining spectral features of the satellite-observed outgoing radiation and analyzing it spatial variations. At the next step daily snow and ice maps derived from visible/infrared and from microwave sensors data are combined. Unlike other techniques a more sophisticated approach is used at this stage: the algorithm cautiously treats satellite retrievals which are error-prone and utilizes auxiliary climatic datasets to eliminate or at least reduce spurious snow and ice classifications. Finally to ensure full spatial continuity of the maps a recurrent gap-filling is applied. This latter algorithm identifies grid cells in the current day snow and ice map that remained undetermined for any reason and fills them in with the most recent (usually the previous day) valid retrieval results. The primary output of the system is a global continuous daily map of snow and ice cover distribution generated on a latitude-longitude grid with 0.040 grid cell size or approximately at 4 km spatial resolution. Every land grid cell of the daily map is labeled as “snow-free land” or “snow cover” whereas every map grid cell over the water surface labeled as “clear water” or “ice cover”. No snow sub-pixel snow or ice cover fraction is estimated at this time. The system is scheduled to continuously collect and preprocess available satellite observations during the day. Satellite data acquired within a 24-hour period (0000– 2359UTC) are processed in the beginning of the next day and the daily snow and ice map is produced. The output daily snow and ice map typically becomes available at 1100–1200 UTC of the next day. A full detailed description of the retrieval technique and of the product format is given in the GMASI Algorithm Theoretical Basis Document, ATBD. (Romanov, 2014). Below we provide a basic description of the major elements of the system.

2.2. Snow cover mapping technique in GMASI Snow cover in the current GMASI system is derived from the AVHRR sensor data onboard MetOP satellites (currently, MetOP-B) and from observations of SSMIS onboard all operational DMSP satellites (currently, F-16, -17 and -18) as well as of the older SSMI sensor onboard DMSP F-15. Level 1B data files are used in the system where AVHRR reflective bands are calibrated to reflectance whereas thermal AVHRR bands and SSMIS bands are calibrated to brightness temperature. The developed AVHRR-based snow identification algorithm implements a threshold-based decision-tree image classification technique and incorporates tests found in various snow detection algorithms proposed in particular for the Moderate Resolution Spectroradiometer (MODIS) onboard Terra and Aqua satellites, AVHRR, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) onboard Meteosat Second Generation (MSG) satellites, and Imager instrument onboard Geostationary Operational Environmental Satellite (GOES) (Hall et al., 2002; Baum and Trepte, 1999; deWildt et al., 2007; Romanov et al., 2003; Dietz et al., 2014). The AVHRR algorithm relies on the observed reflectances in bands 1 (centered at 0.6 μm, R1), 2 (0.8 μm, R2) and 3a (1.6 μm, R3), the observed brightness temperature in band 4 (10.8 μm, T4) along with the calculated value of the Normalized Difference Snow Index (NDSI = (R1 − R3)/(R1 + R3)), the Normalized Difference Vegetation Index (NDVI = (R2 − R1)/(R2 + R1)) and two Snow Indices expressed as a ratio of reflectances in AVHRR bands 1 and 3a (SI1) and bands 2 and 3a (SI2). Large NDSI, SI1, SI2 and R1 values combined with relatively low, below 290 K, infrared brightness temperature in band 4, small R3 and small NDVI are used as the primary indicator of the presence of snow within the instrument field of view. Pixels that have not been attributed to the “snow” category are classified into “snow-free land”, “clouds” and “undetermined”. The latter category usually includes a small number of pixels corresponding to conflicting satellite observations. The image spectral classification algorithm implements a threshold-based decision-tree technique applied on a pixel-by-pixel basis. A detailed description of particular threshold tests implemented in the algorithm and corresponding threshold values is provided in the GMASI system ATBD (Romanov, 2014). Threshold values in the AVHRR snow spectral identification tests are set to minimize possible “losses” of the snow cover. Therefore, the results of the image classification typically yield more spurious snow than snow misses. False snow identifications occur primarily due to the confusion of snow with clouds, with mixed land-cloud scenes and land scenes involving smoke plumes which can also produce a spectral response similar to snow. To identify and eliminate “spurious snow” we included additional tests examining the consistency of classification results with the available snow cover climatology and the land surface temperature climatology. Pixel identified as “snow” by the spectral classification algorithm satisfies the consistency tests if, first, in the past snow has ever been observed at this location on the current week or any of the adjacent weeks of the year and, second, if the pixel infrared brightness temperature was within 200 K of the climatic surface temperature. The latter test involving the land surface temperature climatology was found particularly efficient in eliminating clouds with ice

Fig. 1. High level flow chart of the Global Multisensor Snow and Ice Mapping System (GMASI).

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tops which may be misinterpreted as “snow” by the spectral-based image classification algorithm. Monthly land surface temperature climatology has been acquired from International Satellite Cloud Climatology Project (ISCCP) dataset (http://isccp.giss.nasa.gov/pub/data/ surface/), whereas the snow cover climatology was produced by compiling coarse resolution NOAA weekly interactive snow cover charts for the 26-years time period from 1972 to 1997 (http://www.cpc. ncep.noaa.gov/data/snow/). Another test included in the snow mapping algorithm identifies snow pixels adjacent to clouds as well as isolated “snow” pixels and small compact clusters of “snow” pixels enclosed by clouds. In all these cases the probability of spurious snow identification is high therefore these retrievals are considered questionable and are flagged as

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“undetermined”. “Snow” pixels that pass all consistency tests are labeled “confirmed snow”. AVHRR-based daily snow map is produced on a latitude-longitude grid with 0.04° by 0.04° or about 4 km grid cell size. An example of the daily snow and ice cover map generated with MetOP AVHRR data is shown in Fig. 2a. Snow cover in the microwave imagery of SSMI and SSMIS is identified following Grody and Basist (1996) with minor modifications. The algorithm uses brightness temperature values at 22, 37 and 85 GHz frequency at vertical polarization and at 19 GHz at both vertical and horizontal polarization. It is applied to the microwave data brought to a global latitude-longitude grid with the grid cell size of 1/3 of a degree or approximately 30 km. Image classification is performed with a series of threshold-based tests which incorporate brightness temperature

Fig. 2. (a): Daily snow and ice map of Eurasia generated with MetOP AVHRR data. (b): Map of the daily number of positive snow identifications (“snow hits”) generated from observations of SSMIS instruments onboard three DMSP satellites, F-16, F-17 and F-18. Both maps contribute to the daily combined multisensor snow and ice map generated by the GMASI system.

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values in individual spectral bands, along with their spectral and polarization differences. Snow is identified primarily by the “scattering signal” defined as the positive brightness temperature spectral gradient SG19V37V = T19V–T37V or SG22V85V = T22V–T85V where T19V, T22V, T37V and T85V are the observed brightness temperatures at 19, 22, 73 and 85 GHz respectively at vertical polarization. A series of additional spectral tests in the Grody and Basist (1996) algorithm identify glacialized snow inherent mostly to Greenland and Antarctica and discriminate snow cover from other natural scenes which may produce a spectral response in the microwave similar to snow, e.g., precipitating clouds, cold deserts and frozen soil. The original algorithm of Grody and Basist (1996) has been developed and tested with observations of SSMI. Since spectral bands of SSMIS do not exactly match the corresponding bands of SSMI we adjusted SSMIS brightness temperatures to fit the SSMI data following Yan and Weng (2008). Since the end of the 1990s SSMI and, later, SSMIS observations have been available from three or more DMSP satellites operating concurrently. These observations typically yielded four to six daily “looks” over each location in the mid- and high latitude region. Repeated observations allow for assessing the temporal consistency of the spectral response of the scene and thus help achieving more reliable snow and ice retrievals. In the GMASI system the microwave snow mapping algorithm aggregates all daily SSMI and SSMIS observations from all operational satellites and calculates the number of positive snow identifications in every map grid cell. Grid cell with snow cover identified three or more times during the day or in more than half of all daily retrievals is labeled “confirmed snow”. Pixels getting a smaller number of positive “snow hits” are labeled as “undetermined”. Retaining only snow which was repeatedly identified in the course of the day reduces the amount of spurious snow caused by the instrument noise and atmospheric effects. An example of a daily map of the number of positive snow identifications (or “snow hits”) by SSMIS sensors onboard three DMSP satellites is given in Fig. 2b. The comparison of this map with the snow map derived from METOP AVHRR in Fig. 2a shows that on many occasions snow cover identified by the microwave sensors only once during the day (shown in white in Fig. 2b) is not confirmed by the AVHRR-based snow retrievals and therefore is very likely spurious. At the next step daily snow maps generated with observations in the visible/infrared and in the microwave bands are combined into a blended snow map. To merge snow and ice retrievals from different sensors microwave snow and ice maps are resampled to the 4 km latitude-longitude grids used by the visible and infrared snow and ice product. The approach to combining snow observations in the visible/infrared and in the microwave has three important features. First, it relies on a wellestablished fact that snow retrievals in the visible and infrared are more accurate and reliable than retrievals with microwave data (e.g. Romanov et al., 1999; Foster et al., 2011). Therefore when valid satellite retrievals in the visible and infrared are available they are first used to fill in corresponding grid cells in the combined daily snow map. Microwave snow retrievals are incorporated next. Second, because of known physical limitations of the snow remote sensing in the microwave, a cautious approach is adopted with respect to the use of microwave retrievals. In particular, because of frequent misses of snow in the microwave products, all microwave “no snow” retrievals are considered unreliable and are not incorporated in the combined snow map. Furthermore, due to inability to properly discriminate snow cover from cold rocky surfaces in the microwave spectral range, spurious snow cover frequently occurs in microwave snow products in mountainous regions. To prevent these errors from propagating into the combined map, microwave snow retrievals in elevated areas of over 1 km altitude are disregarded. Similar approach limiting application of microwave observations for snow retrievals to low altitude areas is implemented in the GlobSnow product (Luojus et al., 2013). Third, the algorithm incorporates and accounts for the snow cover climatology. In particular, in the regions that have been always snow covered on the given day of the year, snow identified with either technique, visible/infrared or

microwave, is considered valid. On the opposite, if the snow cover previously has never been observed in the region and thus is climatologically unrealistic, we utilize only satellite retrievals in the visible and infrared and retain only the snow cover identified at the elevation of over 1 km. Although adding microwave data to snow retrievals in the visible and infrared substantially improves the area coverage, the combined daily snow map may still have some grid cells left undetermined due to the lack of valid or reliable data. Therefore a recurrent gap-filling technique is applied where these grid cells are filled in with the most recent earlier snow retrieval results. This last step ensures a fully continuous coverage of the area by the GMASI snow map. 2.3. Ice cover mapping technique in GMASI Similarly to the snow detection, the algorithm to identify ice with AVHRR data employs a threshold-based decision-tree approach. All threshold values in the ice identification algorithm were established empirically through a trial and error approach. Ice is distinguished from the ice-free water and from clouds primarily by its larger reflectance in both visible and near infrared spectral bands, small reflectance in the shortwave infrared and relatively low infrared brightness temperature. The pixel is labeled as ice-covered if the following conditions are met: SI1 N 6, T4 b 274 K, R1 N 0.15, R3 b 0.012 and −0.3 b NDVI b 0. Pixels having the infrared brightness temperature of above 285 K are labeled as “water”. “Cold” pixels with brightness temperature below 260 K that were not identified as “ice” by the ice detection algorithm are labeled as “cloudy”. Clouds in the remaining pixels with the infrared temperature within 260 K to 285 K are identified with two tests, R1 N 0.15, or R3 N 0.10. All other pixels are labeled as open water. Mixed water-cloud pixels present one of the primary sources of the ice misclassification, therefore ice identified in a pixel adjacent to the “cloudy” pixel is considered an unreliable retrieval and is labeled as “undetermined”. The same algorithm is applied both over the open ocean and over inland water bodies. The developed microwave-based ice identification and mapping algorithm for SSMIS involves data compositing, image spectral classification, temporal consistency testing, spatial filtering and blending stages. To ensure a complete daily coverage of the globe we combine SSMIS observations acquired on both nodes from all operational DMSP platforms. If multiple SSMIS observations are available over the same grid cell, the one with the largest temperature gradient at 19GHz and 37GHz spectral bands at vertical polarization is retained in the composited image. Icecovered scenes typically exhibit a larger T19V–T37V difference than the ice-free scenes, therefore this compositing approach minimizes the probability of the ice “loss”. The microwave ice identification algorithm in GMASI generally follows the NASA Team algorithm (Cavalieri, 1994) by using two indices, the polarization ratio at 19 GHz, PR19V = (T19V − T19H) / (T19V + T19H) and the 19V to 37V normalized spectral gradient: NG37V19V = (T37V − T19V)/(T37V + T19V), where T37V, T19V and T19H are, correspondingly the brightness temperature in the 37 GHz band at vertical polarization, and the 19 GHz band brightness temperature at vertical and horizontal polarization. Ice is identified with the following threshold criteria: NG37V19V b 0.07 and PR19V b 0.15. With these threshold values positive ice identifications occur when the ice fraction exceeds approximately 30%. No ice fraction or concentration is estimated. An additional threshold test involving brightness temperatures at 22 and 19 GHz at vertical polarization, T22V–T19V b 20 K, is applied to reduce and possibly to eliminate false ice identifications resulting from atmospheric effects, e.g., wind-roughening of the ocean surface and precipitating clouds. These conditions can cause a pixel radiometric signature to resemble ice. A similar “weather filter” incorporating the normalized difference of the two brightness temperatures rather than their absolute difference is used in the NASA Team algorithm.

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Application of the spectral criteria specified above provides generally robust identification of the sea ice with only a small amount of occasional spurious ice. Besides the atmospheric effects part of these misclassifications is due to the land-to-ocean spillover effects. To further reduce the amount of false ice identifications due to weather effects, all grid cells classified as “ice” by the spectral threshold test (or “potential ice” further on) are subjected to a temporal consistency test. With this test we assess the temporal stability of the spectral response of the “potential ice” grid cell. The test examines all daily satellite observations within the grid cell and calculates the number of SSMIS observations that were spectrally similar to the observation retained in the daily composited image. Observations are assumed spectrally similar if the difference of the brightness temperature values in all respective SSMIS bands remains within 5 K. The test is considered passed at least one such observation is found and if all other observations within the grid cell are classified as “ice” by the ice spectral identification algorithm. “Potential ice” pixels which pass the temporal consistency test are then subjected to a spatial filter similar to the one of Markus and Cavalieri (2009) which eliminates spurious ice along the coastal line. At this stage ice identified in the grid cell next to the shore line is rejected if ice was not identified in any of adjacent grid cells located further out into the open sea. “Potential ice” pixels which fail any of the consistency tests are labeled as “undetermined” in the microwavebased daily ice map. Lastly a static map defining a “no ice” region is applied (Fig. 3). Ice identified within this region is rejected. Unlike the snow cover blending algorithm, when combining ice cover estimates from satellite observations in the visible/infrared and in the microwave the priority is given to the microwave product due to its weather independence and, hence, better effective area coverage. If available, ice retrievals from observations in the visible and infrared spectral bands are used to complement microwave retrievals in the vicinity of the shore line and in grid cells adjacent to the ice edge as identified in the microwave product. AVHRR-based ice retrievals are also utilized (1) over small inland water bodies where the spatial resolution of the microwave data is insufficient to accurately identify ice, (2) over midlatitude inland water bodies where frequent mid-winter melt-

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freeze events may hamper ice identification in the microwave and (3) when there were no reliable microwave ice retrievals on the current day or when for any reason microwave observations over a given location were not available at all. Similarly to the snow mapping procedure, the final stage of the ice mapping consists in the recurrent gap-filling of the ice map. Undetermined grid cells in the current day blended ice map are filled in with the most recent in time valid ice retrieval results and thus yield a spatially continuous, gap-free characterization of the global ice cover distribution. 2.4. Regional specifics of the snow and ice mapping algorithm Specifics of the global snow and ice cover climatology and peculiarities of individual satellite-based snow and ice monitoring techniques motivated a number of regional modifications of the original snow and ice mapping algorithm. The multisensor algorithm as it is presented above is applied for mapping snow and ice cover only in the middle and high latitudes (above 25°N) of the Northern Hemisphere. In the Southern Hemisphere except of Antarctica snow cover is mostly confined to mountainous areas. Seasonal snow outside of the mountains is typically patchy, shallow and/or melting. Since proper identification of both melting snow and snow in the mountains present a considerable problem for the microwave-based techniques, snow cover in the Southern Hemisphere is mapped only using observations in the visible and infrared spectral bands of AVHRR. In the current version of the algorithm Antarctica is assumed always snow covered. In the tropical and equatorial Africa, Asia and Australia within the 25°S to 25°N latitude belt as well as in South America east of 60°W there are no areas with a substantial perennial snow cover or areas which receive a sizable amount of seasonal snow. At this time these regions are assumed snow free at any time of the year. In contrast to the Northern Hemisphere where the extent of ice over inland water bodies may be substantial in the Southern Hemisphere ice is confined only to the Southern Ocean. Observations in the optical and infrared spectral bands add little to the ice retrievals in the microwave over

Fig. 3. Static boundaries delineating “ice possible” regions in the Northern and Southern Hemisphere. Ice is identified north and south of the two boundaries. Ice is also mapped on lakes in high-elevated areas in the Tibet region.

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wide open ocean areas, therefore in Antarctica the ice cover is mapped solely with the microwave data. As a result, despite the same, 4 km, grid size of the map, the effective spatial resolution of information on the ice cover extent in the Southern Hemisphere comprises about 30 km. Within the system blended gap-filled daily snow and ice maps over the Northern and Southern Hemisphere are generated separately and are then combined to achieve the full global coverage. The primary output product of the system is the global daily snow and ice map generated on a latitude-longitude grid at about 4 km spatial resolution (see an example in Fig. 4). Daily snow and ice map images are posted at http:// www.star.nesdis.noaa.gov/smcd/emb/snow/HTML/multisensor_ global_snow_ice.html. Snow and ice daily maps in the binary format can be downloaded from ftp://www.star.nesdis.noaa.gov/pub/smcd/ emb/snow/binary/multisensor/global. 3. Performance and accuracy assessment of snow and ice maps Both qualitative and quantitative examinations of the GMASI automated maps have shown that the product realistically characterizes the global distribution of the snow and ice cover and adequately reproduces their seasonal variations. A good quality of retrievals is evident in

particular from two examples in Fig. 5 covering the Tibet/Tian Shan region and the western part of North America. Similar reflectance of snow and clouds in the visible part of spectrum makes difficult to visually discriminate between these features in parts of true color images in Fig. 5a affected by clouds, however in the clear sky portion of both images there is an obvious close agreement between the snow and ice cover patterns seen in the image and mapped in the automated product. Over the Tibet/Tian Shan region the snow map is mostly based on the high-accuracy snow retrievals from AVHRR, which explains its spatially detailed reproduction of the snow cover pattern in the mountains. In the second example (Fig. 5c, d) the automated product accurately reproduces the position of the snow cover boundary stretching north-west from south of Hudson Bay as well as a distinct pattern of snow cover in the Rocky mountains. It also correctly identifies partial ice cover on Lake Winnipeg in the center of the image and gaps in the ice cover in the coastal region in the western Hudson Bay. A “blocky” pattern of the ice cover boundary in the Hudson Bay indicates that coarse spatial resolution SSMIS data were used to map ice cover in that area. A more thorough quantitative assessment of the GMASI product performance is based on its comparison with independent snow products. The latter includes snow reports from ground-based stations and interactive snow and ice cover maps generated the NOAA IMS system.

Fig. 4. Top (a): GMASI daily global snow and ice map valid on March 10, 2016. Bottom (b): Interactive NOAA IMS snow/ice chart for the Northern Hemisphere regridded to a latitudelongitude projection for the same date, March 10, 2016. Ice cover is shown in yellow, snow is shown in white. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5. (a) GMASI daily snow and ice map over Tibet and Tian Shan area on April 21, 2016 and (b) corresponding true color image from MODIS Terra; (c) GMASI daily snow and ice map over north-west US and south-west Canada on May 4, 2016 and (d) corresponding true color image from MODIS Terra. In the GMASI snow and ice map snow is shown in white and ice is shown in yellow. MODIS Terra true color images were acquired from NASA Worldview at https://worldview.earthdata.nasa.gov. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Comparison of GMASI with both datasets is fully automated and is conducted on a daily basis. Inasmuch only a few stations in the Southern Hemisphere provide reports on the snow depth and since the IMS domain is limited to the area north of the equator, a detailed quantitative evaluation of snow and ice maps is performed only over the Northern Hemisphere. 3.1. GMASI product validation with in situ snow data To compare GMASI snow retrievals with in situ observations we use reports from weather stations operating under the auspices of the World Meteorological Organization (WMO) and from US Cooperative Network stations. It is important that WMO stations do not explicitly report “zero” snow depth when there is no snow on the ground but rather report missing data. Therefore these reports can only be utilized to evaluate correct snow identifications and snow misses in the automated snow maps (or “true positive” accuracy), but not false snow identifications. Stations within the US Cooperative Network do report “zero” snow depth when there is no snow on the ground and thus can be used for calculating of all components of the satellite-based snow product error matrix, i.e., true and false positives as well as true and false negatives. Owing to a considerable number of WMO and US Cooperative stations routinely reporting information on the snow cover over the Continental US (CONUS) area, this region is used as the primary site for validation of GMASI snow retrievals with ground-based data. In the peak of the winter season snow depth reports from over 2000 stations may be available. A variety of land cover types (grasslands, croplands, forests) and landscapes (plains, mountains) in the region along with

relatively long, 100 days and over, seasonal snow cover duration in some areas (Leathers and Luff, 1997) allows for assessing the algorithm performance in different physio-geographical conditions. An example of a daily multisensor snow map over the CONUS area with the station data overlaid in Fig. 6 gives an idea of a typical amount of daily station snow depth reports available in the middle of the winter season, of their distribution across the region and of the agreement between the satellite-based and in situ dataset. Stations which reported any snow depth rather than “zero” are labeled with red color in Fig. 6. As it is seen from the map, there is a small number of “snow misses” in the automated map in the southern part of the CONUS area which may be associated with shallow snow cover that was not properly identified with satellite data. Quantitative estimates of the correspondence between the satellite snow product and the station data were obtained via direct spatial matching of the two datasets. Their rate of agreement was then calculated as the ratio of the number of matchups where the two products agree on the land surface cover type (snow covered or snow-free) to the total number of comparisons. The scene was assumed snow-covered if any “non-zero” snow depth was reported by the in situ observer. The statistics of comparison of automated snow maps and surface observations in the CONUS region presented in Fig. 7 demonstrates a strong seasonal change. The two datasets agree almost perfectly in late spring, summer, and early fall when the CONUS area is mostly snow free whereas in the middle of the winter season when snow cover extends over approximately half of the CONUS area, the agreement rate drops to 75–85%. The yearly mean agreement between in situ data and satellite retrievals amounts to about 94%.

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Fig. 6. Example of GMASI snow cover over Conterminous US (CONUS) with surface observations data overlaid. Locations with non-zero snow on the ground are shown in red, stations which reported no snow on the ground are shown in yellow. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

A close agreement between datasets during the predominantly snowfree period of the year is indicative of a negligible number of false snow identifications in the automated snow maps. As it follows from Fig. 7, “snow misses” in the automated product occur about 2–4 times more frequently than snow commission errors. Larger disagreement rates in winter months are quite expectable since over the CONUS area this time of the year corresponds to the largest spatio-temporal variations of the snow extent and of the position of the snow cover boundary. Limited accuracy of snow identification and mapping with individual techniques, visible/infrared and microwave presents an important, but not the only factor that determines the accuracy of the combined snow map and its correspondence to in situ observations. Disagreement may be also caused by the time difference between satellite and in situ observations and coarse, about 30 km spatial resolution of microwave observations utilized by the system. Some inaccuracies in the combined satellite product may occur due to its certain inertia: Since none of the two techniques, visible/infrared or microwave provides an accurate retrieval of snow cover properties beneath precipitating clouds, fresh-fallen snow is typically identified and mapped on the second, or sometimes on the third day after the event. The inertia inherent to snow maps may be larger in mountainous areas of the Northern Hemisphere and in the Southern Hemisphere

where the system relies only on observations in the visible and infrared. Here the time lag in reproducing changes in the snow cover distribution by the product is determined by the frequency of occurrence of cloudy scenes. Table 1 gives an idea of the yearly mean cloud cover persistence in several mountain regions of the world as estimated from AVHRRbased snow maps. It presents estimates of the yearly mean cloud occurrence in several mountain regions. As it is seen from Table 1, the mean duration of persistent cloudiness ranges from 1.61 to 2.11 days meaning that on the average updates with AVHRR observations are available every second or, more probably, every third day. The probability of not having updates over a particular grid cell for more than three and more than seven consecutive days varies depending on the region within 0.04 to 0.089 and within 0.006 and 0.027 respectively. Larger mean duration of cloudy weather along with larger probability multiple consecutive cloudy days was found for sites in mid-latitudes of the Southern Hemisphere. Lastly, besides inaccuracies in satellite-based snow maps the rate of agreement between satellite and ground-based measurements may be reduced due to possible errors in ground-based snow depth reports. A typical problem inherent to the snow depth reports coming from US Cooperative Network stations consists in reporting zero snow depth in the presence of the snow cover. Our qualitative analysis of the data has

Fig. 7. Time series of daily estimates of the percent of correct snow identifications (“Total Match”) and errors in the GMASI product as compared to reports from ground-based WMO stations (SYNOP) and US Cooperative network stations (Coop) in the winter season of 2015–2016.

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Table 1 Statistics of cloud occurrence in several mountainous regions where snow cover is mapped solely with satellite observations in the visible and infrared. Mean values were calculated by averaging cloud occurrence statistics over all 0.04° by 0.04° land grid cells within corresponding regions over a one-year time period from July 2015 to June 2016. Region

Location (latitude, longitude range)

Mean number of consecutive cloudy days

Mean probability of over 3 consecutive cloudy days

Mean probability of over 7 consecutive cloudy days

Tibet-Himalaya Alps Rocky Mountains Patagonia New Zealand

30–35°N; 75–100°E 45–47°N; 5–15°E 37–47°N; 105–115°W 40–55°S; 62–75°W 34–47°S; 165–179°E

1.61 1.85 1.65 2.10 2.11

0.040 0.069 0.049 0.083 0.089

0.006 0.016 0.012 0.027 0.015

shown that at least about 1–2% of all snow depth reports may be affected. Eliminating these erroneous reports should bring the automated snow maps accuracy up by approximately the same number.

3.2. Comparison with IMS interactive snow charts Interactive charts of snow and ice generated by the IMS system present one of the most reliable sources of information on the Northern Hemisphere snow and ice cover and are extensively used in operational numerical weather prediction models of NOAA National Weather Service (NWS). Similarly to GMASI, IMS charts are heavily based on the satellite imagery and therefore can hardly be considered a fully independent source of validation data. Still, a different approach to the image analysis and snow mapping utilized by IMS as well as an implicit quality assurance of the maps by human analysts justifies their use as “truth” in the comparison and accuracy assessment of GMASI products. Spatial continuity of IMS charts allows for the GMASI product validation over the whole Northern Hemisphere on a daily basis. For this study IMS daily snow and ice cover charts produced since 1998 were obtained from the National Snow and Ice Data Center (http://nsidc.org/data/ G02156), whereas earlier coarse resolution weekly snow and ice charts for the time period from 1972 to 1997 were acquired from NOAA Climate Prediction Center at http://www.cpc.ncep.noaa.gov/data/snow/. To assess the accuracy of automated snow maps we have used IMS snow maps generated at 4 km spatial resolution. The comparison was performed by matching grid cells in the GMASI daily product with the closest grid cells of IMS. There are minor differences in the land-water mask in the two products therefore a small, b 0.5%, number of grid cells where the two products disagree on the type of the surface (land or water) were excluded from the statistics. Similarly to the comparison with the station data, the rate of agreement between GMASI and IMS was calculated as the ratio of the number of grid cells where the two products agree on the surface cover type (snow-free or snow-covered) to the total number of matched grid cells. Mismatches of the two

products with respect to the state of the land surface, “snow-free” or “snow-covered”, were split into snow omission and commission errors. Time series of the results of comparison presented in Fig. 8 indicate that the agreement between the two daily products never falls below 91%. It improves to over 98% during summer months when most of Eurasia and North America (except Greenland) were snow-free, decreases to 91–93% in October and in the middle of April and remains within 94–97% during winter months. Larger differences between the products in the fall and in the spring are explained by fast and spatially extensive changes of the snow extent during the transition seasons. Adequate and timely reproduction of these changes presents a challenge both for the automated algorithm (due to its inherent inertia explained above) and for human analysts working on interactive snow maps. Except of the spring season commission and omission errors contribute equally to the total disagreement between the maps. In the spring the difference is mostly due to the omission of snow by the automated product. The yearly mean rate of agreement between daily automated and interactive maps on the snow cover distribution in the Northern Hemisphere in the last four years was 96.2% with somewhat better agreement over Eurasia (96.5%) than over North America (95.6%). Besides the fact that both GMASI and IMS snow maps are based on satellite remote sensing data the high rate of their agreement is partially due to the fact that the comparison is performed over the whole land mass of the Northern Hemisphere. Excluding from the comparison areas which have been always snow covered or always snow-free at a given time of the year, i.e., accounting for the seasonal snow cover climatology may present a more adequate way to assess and report the product accuracy. In the same time reducing the comparison domain to the area of climatologically variable snow cover naturally results in a larger estimated relative error rate. Our calculations have shown that if rather than the whole land area in the Northern Hemisphere, evaluation of the GMASI snow product is performed only within the area of climatologically variable snow cover, the estimated daily agreement rate with IMS drops from 94–97% to 87–91% during winter

Fig. 8. Time series of the daily rate of agreement (“Total match”) and disagreement (“Total mismatch”) on the snow cover mapped in the automated (GMASI) and interactive (IMS) snow/ ice maps over Northern Hemisphere for the time period from May 2012 to May 2016.

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months. Further narrowing the area of comparison to within 100 km of the position of the snow cover boundary on the day of comparison results in a further decrease of the estimated agreement between GMASI and IMS to 81–87%. These values are close to the mean agreement of the automated snow product and in situ snow observations over the CONUS area in the middle of the winter season. 3.3. Comparison of snow area extent estimates Spatial continuity of GMASI maps allows for estimating and monitoring of the large scale, continental and/or hemispherical, snow area extent. This is an important parameter frequently used to characterize the overall state of the Earth's cryosphere and assess the climate variability and trends (e.g., Estilow et al., 2015). Snow area extent estimation with GMASI daily snow and ice maps involves summing up the area of all land grid cells labeled as “snow-covered”. Time series of the daily snow extent over Northern Hemisphere, North America and Eurasia in the last decade estimated from the automated snow maps are presented in Fig. 9. The results demonstrate small variability and, hence, good consistency or the product with respect to the estimated yearly minimum snow extent in both continents. In the same time the yearly maximum snow extent values experienced substantial year to year changes sometimes exceeding 10% of the magnitude. In particular, in the 2006–2007 winter season the maximum snow extent in the Northern Hemisphere hardly reached 43.7 · 106 km2, whereas in the next year it exceeded 48 · 106 km2. Approximately the same, about 5 million km2, range of year to year variations of the maximum snow extent is inherent to Eurasia (26.6 · 106 to 31.6 · 106 km2). Smaller land area of North America explains correspondingly smaller absolute variations of the estimated maximum snow extent ranging within 16.2 · 106 to 18.1 · 106 km2. Year-to-year variations in the continental and hemispherical snow area extent estimated from GMASI automated snow maps correlate well with similar snow extent estimates of SnowLab of Rutgers University made from NOAA interactive snow maps. This is evident from Fig. 9 where daily time series of GMASI snow extent are plotted along with monthly mean snow extent values for the month of January provided by the SnowLab. Both datasets show the smallest winter time snow extent over the Northern Hemisphere in 2006–2007 and the largest snow extent in the following year. Although the two datasets agree on the year-to-year relative variations of the snow extent, the absolute values

estimated by Rutgers University are consistently larger than corresponding values derived from the automated maps. The difference reaching in some years 2.5 · 106 km2 for the Northern Hemisphere may be attributed at least partially to a specific technique applied at Rutgers University to calculate the snow extent. Rather than the original 4 km-spatial resolution IMS daily snow cover maps, the snow extent is calculated from snow maps downscaled to ~190 km spatial resolution (Estilow et al., 2015). This is done in an effort to make the current interactive snow maps compatible to historical snow charts produced from 1966 to 1997 and hence to ensure the overall consistency of the snow extent estimates spanning over the last 50 years. A better correspondence between GMASI and IMS on the continental-scale snow extent is achieved when the IMS snow extent is calculated directly from the original daily high spatial resolution snow and ice cover maps. Fig. 10 presents daily anomalies of the snow extent estimated from the two products. The average or baseline snow extent for these estimates was established from NOAA weekly snow charts for the time period from 1972 to 1997 by interpolating weekly mean values onto a daily time step. As it is seen from Fig. 10, the two datasets agree well both on the absolute values and day-to-day variation. In the last two years (May 2014 to May 2016) the mean absolute difference between the two daily products over Northern Hemisphere amounted to 0.629 · 106 km2 or 2.8% of the yearly mean snow extent. Corresponding mean daily differences of the estimated snow extent over individual continents were equal to 0.295 · 106 km2 or 3.2% of the yearly mean snow extent in North America and 0.444 · 106 km2 (3.4%) in Eurasia. The magnitude of these differences was comparable to the mean dayto-day change of the snow area extent of 0.490 · 106 (or 2.1% of the yearly mean value), 0.213 · 106 (1.9%) and 0.345 · 106 km2 (4.2%) correspondingly over the Northern Hemisphere, North America and Eurasia. Averaging of the derived snow extent over longer time periods brings the IMS and GMASI-based estimates even closer. The difference in the monthly mean snow extent over individual continents and over the Northern Hemisphere decreases to 2.1–2.4% whereas the difference in the estimated yearly mean values drops further down to 1.3–1.4%. These latter values are noticeably smaller than the natural variation of the yearly mean snow extent of 1.7%, 2.7% and 3.0% over Northern Hemisphere, Eurasia and North America, respectively. The latter estimates were derived from the last ten years (2006–2016) of monthly mean snow extent data provided by Rutgers University.

Fig. 9. Time series of GMASI-estimated daily snow extent over Northern Hemisphere, North America and Eurasia. Rhombs show the monthly mean snow extent for the month of January estimated at the Global Snow Lab of Rutgers University, RU from NOAA IMS snow and ice charts (http://climate.rutgers.edu/snowcover/).

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Fig. 10. Time series of daily anomalies of snow extent over the Northern Hemisphere derived from GMASI automated snow maps and IMS interactive snow maps.

Graphs in Fig. 10 demonstrate slightly dampened day-to-day variations of the GMASI snow area extent as compared to IMS. This smaller variability may be partially due to inertia inherent to the automated snow maps. In particular short-term snow-fall/snow-melt event lasting a day or two may not be captured by the automated product at all. On the other hand, larger temporal variations of the snow extent in the IMS maps may also be caused by different skills and subjectivity in the assessment of satellite imagery by individual analysts working on interactive snow maps at the National Ice Center. 3.4. Validation of GMASI ice retrievals As compared to the snow cover, validation of ice retrievals made within the GMASI system is complicated by the lack of proper in situ observations. Comparison of GMASI ice cover with other automated ice remote sensing products has limited value due to considerable uncertainty in the accuracy of these latter products. Furthermore most of these products are focused solely on sea ice and do not cover inland water bodies. Therefore IMS daily charts delivering high spatial resolution information on the ice cover over both open seas and inland waters present the most adequate source of information to estimate the accuracy of GMASI ice cover. The procedure to compare the ice cover distribution in the IMS and GMASI products implemented in this study was similar to the one used when validating GMASI snow retrievals. Corresponding grid cells in the two maps were matched and the comparison statistics was calculated on a daily basis. As it is seen from Fig. 11, the rate of agreement between the two daily products ranges from N99% during most of the year to about 98% in three summer months, June, July and August and

averaged to about 99%. The corresponding mean difference in the estimated daily ice area extent in the Northern Hemisphere amounted to 0.49 million km2. Most disagreement is due to a larger ice extent mapped by interactive analysts. This is evident in particular from Fig. 12 presenting an example of a daily map of GMASI ice cover with IMS data overlaid. The overlay demonstrates a good general agreement between the two products on the ice cover spatial distribution but shows occasional underestimates of the ice extent in the automated product particularly along the ice edge and over small inland water bodies. Generally larger extent of ice mapped in the IMS product results from a better capability of interactive analysts to identify thin, broken ice of small concentrations along the ice cover boundary and from an “aggressive” ice mapping approach practiced by IMS analysts. Reliance of GMASI primarily on microwave-based ice retrievals which may not be efficient over melting ice is another factor contributing to the difference between the ice extent estimates. Larger fraction of thin, broken and melting ice in the Arctic in summer explains larger disagreement between the two products during these months. Similar underestimate of the seasonal minimum ice extent is also inherent to NASA microwave products based on SSMIS and AMSR-E (Meier et al., 2015). 4. Summary and conclusions Synergy of satellite observations in the visible/infrared and in the microwave presents a powerful approach to improve daily characterization and monitoring of the global snow and ice cover from satellites. This approach has been implemented within the NOAA NESDIS Global Automated Multisensor Snow and Ice Mapping System, GMASI. The

Fig. 11. Time series of the daily rate of agreement (“Total match”) and disagreement (“Total mismatch”) between ice cover mapped in the automated (GMASI) and interactive (IMS) snow/ ice maps over Northern Hemisphere for the time period from May 2012 to May 2016. Mean agreement: 0.99.

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Fig. 12. GMASI daily ice cover map with overlaid IMS ice cover over Northern Hemisphere. January 31, 2015.

system has been run operationally for the last 10 years. It has demonstrated robust performance throughout all seasons providing daily global spatially-continuous maps of snow and ice cover at the spatial resolution of about 4 km. Quality assessment of GMASI snow and ice cover maps has shown their consistency and good correspondence to in situ and other independent remote sensing datasets. Over Continental US the agreement of automated daily snow maps to ground-based snow reports ranges within 75 to 85% in the middle of the winter season and averages to about 94% over the year. The agreement of GMASI daily snow maps to IMS interactive maps calculated over the whole land area of the Northern Hemisphere ranges from 91 to 98% in the course of the year and averages to about 96% over the year. Because of unavailability of IMS data in the Southern Hemisphere, validation studies were limited to Eurasia and North America. However there is no reason to assume large differences in the product accuracy in the two hemispheres. Daily estimates of the total hemispherical snow extent derived from GMASI snow maps agree to corresponding estimates with the IMS product to within 0.629 · 106 km2 or 2.8% of the yearly mean snow extent. Similar agreement of about 3% is inherent to daily snow extent estimates over individual continents, Eurasia and North America. Better agreement of Northern Hemisphere snow extent estimates of about 2% and within 1.5% is achieved correspondingly for monthly and yearly averaged snow extent. The ice cover distribution derived within GMASI closely corresponds to the one produced interactively by NOAA analysts. Differences of 1–2% in the mapped ice distribution occur mostly due to underestimation of the ice extent by the automated algorithm. The latter may be caused by lower efficiency of identification of thin, low concentration and melting ice in the microwave bands. As compared to AVHRR and SSMIS, new imaging sensors launched onboard operational weather satellites in the last several years provide much better observing capabilities including broader spectral range of measurements, higher spatial resolution and better navigation accuracy. This concerns in particular the VIIRS radiometer onboard S-NPP satellite with 22 spectral bands in the visible to infrared spectral range and up to 375 m spatial resolution and Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard GCOM-W1 satellite delivering measurements in all major microwave atmospheric “window” spectral bands within 6.9 to 89 GHz frequency range at the spatial resolution of up to 5 km. The two sensors will be included in the GMAS system as part of its next major upgrade. Both sensors have been shown to provide highly accurate retrievals of snow and ice parameters (e.g., Key et al., 2013; Liu et al., 2016; Lee et al., 2015). With these sensors in the GMASI system we expect to improve the effective spatial resolution of the product to 0.5– 1 km and to 5–10 km under clear sky and cloudy conditions correspondingly. Although the primary application of GMASI products is in operational applications, snow and ice maps generated by the system are consistent and sufficiently accurate to contribute to climate studies. The need

to improve the existing climatological characterization is particularly pressing with respect to the snow cover and snow extent since the existing snow cover climatology is defined on a coarse spatial grid of around 100 km, limited to the Northern Hemisphere only and resolved at a weekly time step (Robinson and Mote, 2014). Availability of both AVHRR and SSMI/SSMIS observations since the middle of 1980s allows for extending the time series backwards and generating over-30years-long time series of global daily snow cover maps. Challenges in reprocessing of historical satellite data include the need for proper intercalibration of sensors of the same type onboard different satellites to ensure consistency in the derived time series as well as improved geolocation and georeferencing of the data. Due to the lack of the shortwave infrared spectral band in older AVHRR/2 instruments these observations have to be replaced in the retrieval algorithm with observations in the middle infrared. Reprocessing of AVHRR data is further complicated by the visible and near infrared sensor degradation with time and by the satellite orbital drift. Availability of the new long-term remote sensing-based snow cover dataset will open the way to establishing an upgraded fully consistent snow cover climatology at much better spatial and temporal resolution.

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