Intercomparison and Validation of MIRS, MSPPS, and IMS Snow Cover Products

Jessica Chiu,Tarendra Lakhankar,Stephany Paredes-Mesa,Peter Romanov,Ralph Ferraro,Reza Khanbilvardi,Nir Krakauer

doi:10.1155/2020/4532478

Abstract

We evaluate the agreement between automated snow products generated from satellite observations in the microwave bands within NESDIS Microwave Integrated Retrieval System (MIRS) and Microwave Surface and Precipitation Products System (MSPPS), on the one hand, and snow cover maps produced with manual input by the NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS), on the other. MIRS uses physically based retrievals of atmospheric and surface state parameters to provide daily global maps of snow cover and snow water equivalent at 50 km resolution. The older MSPPS delivers daily global maps at the spatial resolution of 45 km and utilizes mostly simple empirical algorithms to retrieve information. IMS daily maps of snow and sea ice cover for the Northern Hemisphere are produced interactively through the analysis of satellite imagery in the visible, infrared, and microwave spectral bands. We compare the performances of these products across the Northern Hemisphere for 2014–2017, using IMS as the standard. In this intercomparison, the daily overall agreement of the automated snow products with IMS ranges between 88% and 99% for MIRS and 87% and 99% for MSPPS. However, daily snow sensitivity is lower, ranging between 36% and 90% for MIRS and 26% and 91% for MSPPS. We analyze this disagreement rate as a function of terrain and land cover type, finding that, relative to IMS, MIRS shows fewer false positives but more false negatives than MSPPS over high elevation and grassland areas.

Highlights

Snow cover plays an important role in Earth’s climate, water resources, and weather
Results and Discussion e comparison of daily NESDIS Microwave Integrated Retrieval System (MIRS) and Microwave Surface and Precipitation Products System (MSPPS) microwave snow cover products with Ice Mapping System (IMS) was performed throughout the years 2014 to 2017. e results of the comparison were reported in terms of the corresponding areas of the True/False Positive/Negative classifications
It is observed that both microwave products make very similar mistakes as compared to IMS; false negatives dominate in winter, spring, and fall while false snow pixels are minimal in the summer

Summary

Introduction

Snow cover plays an important role in Earth’s climate, water resources, and weather. Us, snow properties are important for applications in climate, hydrology, water management, agriculture, transportation, and recreation [2,3,4]. Observations from weather satellites provide continuous wide area coverage and offer a tool for mapping and monitoring of the snow cover on a global and continental scale. Ere is a considerable number of satellite-based snow cover products developed both for operational and climate applications. Since 2000, an automated image classification algorithm has been used to produce global daily snow cover maps from observations in the visible and infrared spectral bands of Advances in Meteorology Ere is a considerable number of satellite-based snow cover products developed both for operational and climate applications. e retrieval techniques for these products employ both interactive and automated data processing approaches and utilize satellite observations in the visible/ infrared spectral bands [5,6,7,8], passive microwave observations [9,10,11,12,13], or a synergy of observations in the visible/ infrared and in the microwave [14,15,16,17].

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Conclusion