Assessment of Satellite and Reanalysis Cold Season Snowfall Estimates Over Arctic Sea Ice. Issue 16 (24th August 2020)

• Record Type: Journal Article
• Authors: Song, Yang; Behrangi, Ali; Blanchard‐Wrigglesworth, E.
• Is Part Of: Geophysical research letters. Volume 47:Issue 16(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-24
• Subjects: satellite precipitation estimates -- reanalysis precipitation estimates -- reconstruction of snow depth -- Arctic sea ice -- operational ice bridge; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2020GL088970
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Plain Language Summary: Snow depth measurement on sea ice is sporadically distributed making it difficult to obtain basin‐scale values. Satellite observations and models known as reanalysis provide precipitation estimates that can be used to reconstruct snow depth over the entire sea ice. A systematic assessment of precipitation estimates from various satellite and reanalysis products over Arctic sea ice is conducted by reconstructing snow depth from precipitation products and comparing the reconstructions with airborne snow depth observations. Results show that the observed snow depth pattern is generally captured by most of the reconstructions. Snow depth reconstructed from one of the reanalysis products outperformed others by showing the highest correlation and lowest error with the observed snow depth. We also showed that using new sensors, existing satellite products can be improved. Key Points: Satellite and reanalysis precipitation were assessed over Arctic sea ice using snow depth reconstruction comparisons with observations CloudSat precipitation was found valuable to improve GPCP V1.3 estimates over sea ice Reanalysis products show large variations in skill for snowfall estimation over sea ice but can outperform current satellite estimates