University of Oulu

Siljamo, N., Hyvärinen, O., Riihelä, A., & Suomalainen, M. (2020). MetOp/AVHRR Snow Detection Method for Meteorological Applications. Journal of Applied Meteorology and Climatology, 59(12), 2001–2019. https://doi.org/10.1175/jamc-d-20-0032.1

MetOp/AVHRR snow detection method for meteorological applications

Saved in:
Author: Siljamo, Niilo1; Hyvärinen, Otto1; Riihelä, Aku1;
Organizations: 1Finnish Meteorological Institute, Helsinki, Finland
2Center of Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202101283008
Language: English
Published: American Meteorological Society, 2020
Publish Date: 2021-01-28
Description:

Abstract

Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.

see all

Series: Journal of applied meteorology and climatology
ISSN: 1558-8424
ISSN-E: 1558-8432
ISSN-L: 1558-8424
Volume: 59
Issue: 12
Pages: 2001 - 2019
DOI: 10.1175/JAMC-D-20-0032.1
OADOI: https://oadoi.org/10.1175/JAMC-D-20-0032.1
Type of Publication: A1 Journal article – refereed
Field of Science: 1172 Environmental sciences
Subjects:
Funding: This work was financially supported by the H SAF project, co-funded by EUMETSAT. We are grateful to Drs. Terhikki Manninen, Elena Saltikoff, and Kati Anttila for their valuable comments during the writing of this paper. We also thank Drs. Carl Fortelius, Laura Rontu, and Kalle Eerola from the FMI and Dr. Samantha Pullen from the Met Office for the discussions we had about the needs of numerical weather prediction during the development of our snow extent products and Ari Aaltonen for his help in the data retrieval from the FMI data archives.We acknowledge the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov), part of the NASA Earth Observing System Data and Information System (EOSDIS).
Copyright information: © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). CC-BY 4.0.
  https://creativecommons.org/licenses/by/4.0/