Articles | Volume 28, issue 16
https://doi.org/10.5194/hess-28-3855-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3855-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Aug 2024
Research article |
| 23 Aug 2024
| 23 Aug 2024
Detecting snowfall events over the Arctic using optical and microwave satellite measurements
Emmihenna Jääskeläinen
CORRESPONDING AUTHOR
Finnish Meteorological Institute, Erik Palménin aukio 1, Helsinki, Finland
Kerttu Kouki
Finnish Meteorological Institute, Erik Palménin aukio 1, Helsinki, Finland
Aku Riihelä
Finnish Meteorological Institute, Erik Palménin aukio 1, Helsinki, Finland
Related authors
Emmihenna Jääskeläinen, Miska Luoto, Pauli Putkiranta, Mika Aurela, and Tarmo Virtanen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-390, https://doi.org/10.5194/hess-2024-390, 2025
Revised manuscript under review for HESS
Short summary
Short summary
The challenge with current satellite-based soil moisture products is their coarse resolution. Therefore, we used machine-learning model to improve spatial resolution of well-known SMAP soil moisture data, by using in situ soil moisture observations and additional soil and vegetation properties. Comparisons against independent data set show that the model estimated soil moisture values have better agreement with in situ observations compared to other SMAP-related soil moisture data.
Aku Riihelä, Emmihenna Jääskeläinen, and Viivi Kallio-Myers
Earth Syst. Sci. Data, 16, 1007–1028, https://doi.org/10.5194/essd-16-1007-2024, https://doi.org/10.5194/essd-16-1007-2024, 2024
Short summary
Short summary
We describe a new climate data record describing the surface albedo, or reflectivitity, of Earth's surface (called CLARA-A3 SAL). The climate data record spans over 4 decades of satellite observations, beginning in 1979. We conduct a quality assessment of the generated data, comparing them against other satellite data and albedo observations made on the ground. We find that the new data record in general matches surface observations well and is stable through time.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
Short summary
Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Leena Leppänen, Antero Kukko, Aleksi Rimali, Aku Riihelä, and Priit Tisler
EGUsphere, https://doi.org/10.5194/egusphere-2025-2059, https://doi.org/10.5194/egusphere-2025-2059, 2025
Short summary
Short summary
We present field measurements collected during the FINNARP 2022 expedition at the Aboa station, located in Dronning Maud Land, Antarctica. Field observations were carried out weekly at the automatic weather station site as well as at selected overpass locations of two satellites. The measurements included continuous meteorological observations from the weather station, detailed snow pit profiles, ground-based and drone-based radiation measurements, and snow surface roughness with laser scanners.
Kerttu Kouki and Andreas Colliander
EGUsphere, https://doi.org/10.5194/egusphere-2025-245, https://doi.org/10.5194/egusphere-2025-245, 2025
Short summary
Short summary
Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.
Emmihenna Jääskeläinen, Miska Luoto, Pauli Putkiranta, Mika Aurela, and Tarmo Virtanen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-390, https://doi.org/10.5194/hess-2024-390, 2025
Revised manuscript under review for HESS
Short summary
Short summary
The challenge with current satellite-based soil moisture products is their coarse resolution. Therefore, we used machine-learning model to improve spatial resolution of well-known SMAP soil moisture data, by using in situ soil moisture observations and additional soil and vegetation properties. Comparisons against independent data set show that the model estimated soil moisture values have better agreement with in situ observations compared to other SMAP-related soil moisture data.
Adriano Lemos and Aku Riihelä
EGUsphere, https://doi.org/10.5194/egusphere-2024-869, https://doi.org/10.5194/egusphere-2024-869, 2024
Short summary
Short summary
Here we used satellite imagery to measure snow depth in northern Finland and compared to on-site weather stations from 2019–2022. We correlated snow depths and vegetation coverage, and found thicker snow over non-vegetated areas and frozen water bodies due to the satellite's sensitivity. Our estimates showed underestimated results of snow depth and need further investigation, but they highlight the potential in monitoring seasonal snow changes, particularly where direct measurements are lacking.
Aku Riihelä, Emmihenna Jääskeläinen, and Viivi Kallio-Myers
Earth Syst. Sci. Data, 16, 1007–1028, https://doi.org/10.5194/essd-16-1007-2024, https://doi.org/10.5194/essd-16-1007-2024, 2024
Short summary
Short summary
We describe a new climate data record describing the surface albedo, or reflectivitity, of Earth's surface (called CLARA-A3 SAL). The climate data record spans over 4 decades of satellite observations, beginning in 1979. We conduct a quality assessment of the generated data, comparing them against other satellite data and albedo observations made on the ground. We find that the new data record in general matches surface observations well and is stable through time.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary
Short summary
We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
Short summary
Short summary
This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
Short summary
Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
Short summary
Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Cited articles
Barrett, A. P., Stroeve, J. C., and Serreze, M. C.: Arctic Ocean precipitation from atmospheric reanalyses and comparisons with North Pole drifting station records, J. Geophys. Res.-Oceans, 125, e2019JC015415, https://doi.org/10.1029/2019JC015415, 2020. a
Betts, A. K. and Ball, J. H.: Albedo over the boreal forest, J. Geophys. Res.-Atmos., 102, 28901–28909, 1997. a
Bintanja, R. and Andry, O.: Towards a rain-dominated Arctic, Nat. Clim. Change, 7, 263–267, https://doi.org/10.1038/nclimate3240, 2017. a
Bintanja, R. and Selten, F.: Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat, Nature, 509, 479–482, https://doi.org/10.1038/nature13259, 2014. a
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and Cullather, R. I.: Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses, J. Climate, 31, 8441–8462, 2018. a
Clerc, S. and MPC Team: Level 2A Data Quality Report, https://sentinel.esa.int/documents/247904/685211/Sentinel-2-L2A-data-Quality-Report (last access: 22 August 2024), 2022. a
D'Eon, R. G.: Snow depth as a function of canopy cover and other site attributes in a forested ungulate winter range in southeast British Columbia, BC Journal of Ecosystems and Management, 3, 136–144, 2004. a
Domine, F., Taillandier, A.-S., Cabanes, A., Douglas, T. A., and Sturm, M.: Three examples where the specific surface area of snow increased over time, The Cryosphere, 3, 31–39, https://doi.org/10.5194/tc-3-31-2009, 2009. a
ECMWF: IFS Documentation CY41R2 – Part IV: Physical Processes, ECMWF, https://doi.org/10.21957/tr5rv27xu, 2016. a
Edel, L., Claud, C., Genthon, C., Palerme, C., Wood, N., L'ecuyer, T., and Bromwich, D.: Arctic snowfall from CloudSat observations and reanalyses, J. Climate, 33, 2093–2109, 2020. a
ESA: MSI Level-2A BOA Reflectance Product. Collection 1, Copernicus Sentinel-2 (processed by ESA), https://doi.org/10.5270/S2_-znk9xsj, 2021. a, b
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung, D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The International Classification for Seasonal Snow on the Ground, IHP-VII Technical Documents in Hydrology No. 83, IACS Contribution No. 1, UNESCO-IHP, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 22 August 2024), 2009. a
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo evolution, J. Geophys. Res.-Atmos., 111, D12208, https://doi.org/10.1029/2005JD006834, 2006. a
FMI: MS Windows NT Kernel Description, https://en.ilmatieteenlaitos.fi/fmi-radar-network (last access: 22 August 2024), 2023. a
Gallet, J.-C., Domine, F., Zender, C. S., and Picard, G.: Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm, The Cryosphere, 3, 167–182, https://doi.org/10.5194/tc-3-167-2009, 2009. a
Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., Gaudel-Vacaresse, A., Languille, F., Alhammoud, B., Viallefont, F., Pflug, B., Bieniarz, J., Clerc, S., Pessiot, L., Trémas, T., Cadau, E., De Bonis, R., Isola, C., Martimort, P., and Fernandez, V.: Copernicus Sentinel-2A Calibration and Products Validation Status, Remote Sens.-Basel, 9, 584, https://doi.org/10.3390/rs9060584, 2017. a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Ishizaka, M., Motoyoshi, H., Yamaguchi, S., Nakai, S., Shiina, T., and Muramoto, K.-I.: Relationships between snowfall density and solid hydrometeors, based on measured size and fall speed, for snowpack modeling applications, The Cryosphere, 10, 2831–2845, https://doi.org/10.5194/tc-10-2831-2016, 2016. a, b
Jääskeläinen, E., Kouki, K., and Riihelä, A.: Data for “Detecting Snowfall Events over the Arctic Using Optical and Microwave Satellite Measurements” by Jääskeläinen et al., Finnish Meteorological Institute [data set], https://doi.org/10.57707/FMI-B2SHARE.B8E9F541ACC14C5692F3D55D18F53C43, 2024. a
Kokhanovsky, A., Lamare, M., Danne, O., Brockmann, C., Dumont, M., Picard, G., Arnaud, L., Favier, V., Jourdain, B., Le Meur, E., Di Mauro, B., Aoki, T., Niwano, M., Rozanov, V., Korkin, S., Kipfstuhl, S., Freitag, J., Hoerhold, M., Zuhr, A., Vladimirova, D., Faber, A.-K., Steen-Larsen, H. C., Wahl, S., Andersen, J. K., Vandecrux, B., van As, D., Mankoff, K. D., Kern, M., Zege, E., and Box, J. E.: Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument, Remote Sens.-Basel, 11, 2280, https://doi.org/10.3390/rs11192280, 2019. a, b
Kokhanovsky, A., Gascoin, S., Arnaud, L., and Picard, G.: Retrieval of Snow Albedo and Total Ozone Column from Single-View MSI/S-2 Spectral Reflectance Measurements over Antarctica, Remote Sens.-Basel, 13, 4404, https://doi.org/10.3390/rs13214404, 2021. a, b
Kokhanovsky, A., Vandecrux, B., Wehrlé, A., Danne, O., Brockmann, C., and Box, J. E.: An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation, Remote Sens.-Basel, 15, 77, https://doi.org/10.3390/rs15010077, 2023. a
Kouki, K., Räisänen, P., Luojus, K., Luomaranta, A., and Riihelä, A.: Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014, The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, 2022. a
Krasting, J. P., Broccoli, A. J., Dixon, K. W., and Lanzante, J. R.: Future Changes in Northern Hemisphere Snowfall, J. Climate, 26, 7813–7828, https://doi.org/10.1175/JCLI-D-12-00832.1, 2013. a
Kumjian, M.: Principles and Applications of Dual-Polarization Weather Radar. Part I: Description of the Polarimetric Radar Variables, Journal of Operational Meteorology, 1, 226–242, https://doi.org/10.15191/nwajom.2013.0119, 2013. a, b
Legagneux, L., Cabanes, A., and Dominé, F.: Measurement of the specific surface area of 176 snow samples using methane adsorption at 77 K, J. Geophys. Res.-Atmos., 107, ACH 5-1–ACH 5-15, https://doi.org/10.1029/2001JD001016, 2002. a
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013. a
Libois, Q., Picard, G., Arnaud, L., Dumont, M., Lafaysse, M., Morin, S., and Lefebvre, E.: Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau, The Cryosphere, 9, 2383–2398, https://doi.org/10.5194/tc-9-2383-2015, 2015. a
Luojus, K., Moisander, M., Pulliainen, J., Takala, M., Lemmetyinen, J., Derksen, C., Mortimer, C., Schwaizer, G., Nagler, T., and Venäläinen, P.: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979–2020), version 2.0, NERC EDS Centre for Environmental Data Analysis, https://doi.org/10.5285/4647cc9ad3c044439d6c643208d3c494, 2022. a
Marshall, J. S. and Palmer, W. M. K.: The distribution of raindrops with size, J. Atmos. Sci., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948. a
McCrystall, M. R., Stroeve, J., Serreze, M., Forbes, B. C., and Screen, J. A.: New climate models reveal faster and larger increases in Arctic precipitation than previously projected, Nat. Commun., 12, 6765, https://doi.org/10.1038/s41467-021-27031-y, 2021. a
McNay, R. S., Peterson, L. D., and Nyberg, J. B.: The influence of forest stand characteristics on snow interception in the coastal forests of British Columbia, Can. J. Forest Res., 18, 566–573, 1988. a
Merkouriadi, I., Gallet, J.-C., Graham, R. M., Liston, G. E., Polashenski, C., Rösel, A., and Gerland, S.: Winter snow conditions on Arctic sea ice north of Svalbard during the Norwegian young sea ICE (N-ICE2015) expedition, J. Geophys. Res.-Atmos., 122, 10837–10854, 2017. a
Miller, D. H.: Interception processes during snowstorms, vol. 18, Pacific Southwest Forest and Range Experiment Station, Forest Service, USA, https://www.fs.usda.gov/psw/publications/documents/psw_rp018/psw_rp018.pdf (last access: 22 August 2024), 1964. a
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020. a, b
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
National Centers for Environmental Information: Estimating the Water Equivalent of Snow, https://www.ncei.noaa.gov/sites/default/files/2021-09/Estimating_the_Water_Equivalent_of_Snow.pdf (last access: 22 August 2024), 2021. a
Pirazzini, R.: Surface albedo measurements over Antarctic sites in summer, J. Geophys. Res.-Atmos., 109, D20118, https://doi.org/10.1029/2004JD004617, 2004. a
Pirazzini, R., Vihma, T., Granskog, M. A., and Cheng, B.: Surface albedo measurements over sea ice in the Baltic Sea during the spring snowmelt period, Ann. Glaciol., 44, 7–14, 2006. a
Pulliainen, J.: Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations, Remote Sens. Environ., 101, 257–269, 2006. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Sato, K. and Inoue, J.: Comparison of Arctic sea ice thickness and snow depth estimates from CFSR with in situ observations, Clim. Dynam., 50, 289–301, 2018. a
Screen, J. A. and Simmonds, I.: Exploring links between Arctic amplification and mid-latitude weather, Geophys. Res. Lett., 40, 959–964, 2013. a
Skofronick-Jackson, G., Kulie, M., Milani, L., Munchak, S. J., Wood, N. B., and Levizzani, V.: Satellite estimation of falling snow: A global precipitation measurement (GPM) core observatory perspective, J. Appl. Meteorol. Clim., 58, 1429–1448, 2019. a
Taillandier, A.-S., Domine, F., Simpson, W. R., Sturm, M., and Douglas, T. A.: Rate of decrease of the specific surface area of dry snow: Isothermal and temperature gradient conditions, J. Geophys. Res.-Earth, 112, F03003, https://doi.org/10.1029/2006JF000514, 2007. a
Tomppo, E., Katila, M., Mäkisara, K., and Peräsaari, J.: The Multi-source National Forest Inventory of Finland – methods and results 2009, Natural Resources Institute Finland (Luke), http://urn.fi/URN:ISBN:978-952-380-538-5 (last access: 22 August 2024), 2013. a
Vázquez-Martín, S., Kuhn, T., and Eliasson, S.: Shape dependence of snow crystal fall speed, Atmos. Chem. Phys., 21, 7545–7565, https://doi.org/10.5194/acp-21-7545-2021, 2021. a, b
Vihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova, V., Deser, C., Holland, M., and Prowse, T.: The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts, J. Geophys. Res.-Biogeo., 121, 586–620, https://doi.org/10.1002/2015JG003132, 2016. a
Webster, M., Gerland, S., Holland, M., Hunke, E., Kwok, R., Lecomte, O., Massom, R., Perovich, D., and Sturm, M.: Snow in the changing sea-ice systems, Nat. Clim. Change, 8, 946–953, 2018. a
Zupanc, A.: Improving cloud detection with machine learning, https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13 (last access: 10 October 2019), 2017. a
Short summary
Snow cover is an important variable when studying the effect of climate change in the Arctic. Therefore, the correct detection of snowfall is important. In this study, we present methods to detect snowfall accurately using satellite observations. The snowfall event detection results of our limited area are encouraging. We find that further development could enable application over the whole Arctic, providing necessary information on precipitation occurrence over remote areas.
Snow cover is an important variable when studying the effect of climate change in the Arctic....
