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Snow falling around some pine trees

Snowfall Rate Product Captures First Nor’easter in 2022

The first nor’easter of 2022 swept through the Mid-Atlantic and the Northeast on January 2-4, 2022, resulting in a heavy snow accumulation of up to 14 inches in Virginia and southern Maryland and stranding hundreds of drivers on Interstate 95 in Virginia. The NOAA NESDIS Snowfall Rate (SFR) product captured the evolution of the snowstorm with retrievals from the Advanced Technology Microwave Sounder (ATMS) sensor aboard the S-NPP and NOAA-20 satellite missions, and the AMSU-A/MHS sensors aboard NOAA-19, Metop-B, and Metop-C.

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Figure. The 6 × 6 km2 Howland forest area in Maine: (A) the true color image obtained by the EO-1 Hyperion on March 5, 2014 (DOY 64) at a spatial resolution of 30 m; and maps of (B) snow cover fraction (SNOWCF); (C) surface water cover fraction (WaterBodyCF); (D)soil cover fraction (SOILCF); (E) vegetation cover fraction (VGCF); (F) fAPARcanopy; (G) fAPARchl; (H) fAPARnon-chl.

An Evergreen Forest Ecosystem from Satellites

ESSIC/CISESS Scientist Qingyuan Zhang has a new article to be published in International Journal of Applied Earth Observation and Geoinformation that characterizes the seasonally snow-covered Howland boreal forest ecosystem in Maine, USA with satellite images.

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Snow falls over a blurry landscape

Announcing the 2021-22 Annual ESSIC Snow Prediction Pool

With Halloween behind us, wintery temps are fast approaching College Park! This year, we are resurrecting the historic ESSIC Annual Snow Prediction Pool. This is your opportunity to show off your weather prediction skills to your colleagues and earn bragging rights for the entire 2021-22 snow season.

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Figure 1. Global and regional validation of the logistic regression (blue), deep neural network (orange), random forest(green) and XGboost (red) snowfall detection (SD) model for S-NPP.

Fan and Meng Develop New Machine Learning Snowfall Detection Algorithm

ESSIC/CISESS scientists Huan Meng and Yongzhen Fan have recently developed a new machine learning snowfall detection (SD) algorithm, based on eXtreme Gradient Boosting (XGB). The algorithm was developed for the Advanced Technology Microwave Sounder (ATMS) onboard NPP and NOAA-20 as well as the MHS/AMSU-A onboard Metop-A, Metop-B, Metop-C and NOAA-19.

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