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Assessment of Satellite and Reanalysis Cold Season Snowfall Estimates Over Arctic Sea Ice
Author(s) -
Song Yang,
Behrangi Ali,
BlanchardWrigglesworth E.
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl088970
Subject(s) - snow , precipitation , environmental science , climatology , satellite , mean squared error , arctic , atmospheric sciences , meteorology , geology , mathematics , statistics , geography , aerospace engineering , engineering , oceanography
This work presents a systematic assessment of precipitation estimates from satellite and reanalysis products over Arctic sea ice by reconstructing snow depths from precipitation products and comparing them with snow depth observations from National Aeronautics and Space Administration (NASA)'s Operation IceBridge (OIB). Results show that the observed snow depth pattern is generally captured through reconstruction of snow depth using various precipitation products, but the use of passive microwave precipitation estimates results in significant underestimation of the snow depth. By using CloudSat monthly precipitation rate, to adjust the Global Precipitation Climatology Product (GPCP V1.3), the modified product (GPCP V1.3‐mod) shows improved statistics over GPCP V1.3 as compared with OIB snow depth observations. Snow depth reconstructed from ERA‐Int precipitation rate outperformed other products by showing the highest correlation coefficient and lowest root‐mean‐square error ( RMSE ). ERA5 shows larger RMSE than ERA‐Int, while MERRA‐2 results in large overestimation of snow depth and larger RMSE compared to GPCP and other reanalysis products.