Plots showing a visual comparison of spatial pattern of snowfall rate obtained from four different sources
Visual comparison of spatial pattern of snowfall rate obtained from microwave humidity sounder using machine learning approach (panel d), ground-based radar observation (panel a), reanalysis product (panel b), and the current NASA official precipitation product from microwave humidity sounder (panel c). The precipitation event occurred on 20 December 2010 over the United States. Credit: Adhkiari et al. [2020], Figure 7
Source: Earth and Space Science

Global snowfall measurement is important for hydrologic applications and to understand global energy and water cycles. Satellite-based observation from CloudSat radar has provided global snowfall estimates with relatively high quality. However, CloudSat has limited lifespan and a relatively poor temporal sampling, restricting its application for long-term snowfall monitoring.

Using coincident observations between microwave humidity sounder (MHS) and CloudSat snowfall, Adhikari et al. [2020] utilize a machine learning approach to estimate global snowfall occurrence and rate from MHS, offering much higher temporal sampling and longer record than CloudSat. The estimated snowfall compared well with independent CloudSat data and outperformed that from the Atmospheric Infrared Sounder (AIRS) used in the Global Precipitation Climatology Project (GPCP) product and the current precipitation estimate from MHS using the Goddard Profiling Algorithm (GPROF) approach.

Citation: Adhikari, A., Ehsani, M. R., Song, Y., & Behrangi, A. [2020]. Comparative assessment of snowfall retrieval from Microwave Humidity Sounders using machine learning methods. Earth and Space Science, 7, e2020EA001357.

—Jonathan H. Jiang, Editor, Earth and Space Science

Text © 2020. The authors. CC BY-NC-ND 3.0
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