Bio
Lin Lin is an Associate Research Scientist at the University of Maryland, specializing in atmospheric science and meteorology. With over 114 publications and 1,467 citations, Lin's work has advanced the understanding of hurricane intensity estimation using machine learning and diurnal variations in cloud liquid water path. Holding an h-index of 21 and an i10-index of 36, Lin is an active researcher and reviewer, contributing to journals such as GJSFR. Lin's professional profiles are available on ResearchGate, Google Scholar, and LinkedIn.
Educational Journey
University of Maryland
Associate Research Scientist
Experience
Editors Role
journal_reviewer
GJSFR
2024 - Presentjournal_editor
0 -Research
Estimation of Hurricane Intensity from ATMS-Derived Temperature Anomaly using Machine Learning
The warm-core structure is one of the basic characteristics that vary during the different stages of tropical cyclones (TCs). The warm core structure of the TCs during 2016-2019 over the Atlantic Ocean was derived based on the observations of the ATMS onboard S-NPP. From linear regression, the mean prediction error (MPE) is 39.04 mph for Vmax and 14.47 hPa for Pmin.The root-mean-square error (RMSE) is 42.70 mph for the maximum sustained wind (Vmax) and 77.69 hPa for the minimum sea-level pressure (Pmin). Several machine learning (ML) techniques are used to develop the Atlantic TC intensity (Vmax and Pmin) estimation models. The support vector machine (SVM) model has the best performance with the MPE of 14.62 mph for Vmax an 7.66 hPa for Pmin, and the RMSE of 19.91 mph for Vmax and 10.58 hPa for Pmin. Adding latitude and day of year can further improve the estimation of Vmax by decreasing MPE to 13.01 mph and RME to 17.33 mph using SVM. Best estimation of Pmin occurs when adding the day of year (DOY) to the training process, as the MPE is 7.23 hPa and RMS is 9.88 hPa. Other TC information, such as longitude and local time, does not help to improve the performance of the hurricane intensity estimation models significantly.
