ASPB and CIMSS Weekly Report
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ASPB AND CIMSS WEEKLY HIGHLIGHTS FOR THE WEEK ENDING SEPTEMBER 13, 2014

IN THE PRESS:

ITEMS FOR THE ADMINISTRATOR:

ITEMS FOR THE ASSISTANT ADMINISTRATOR:

ITEMS FOR THE OFFICE DIRECTOR, STAR:

Cooperative Research Program (CoRP) Symposium: The NESDIS Cooperative Research Program (CoRP) convened at City College New York September 9-10, 2014 (http://crest.ccny.cuny.edu/corp2014/). Four graduate students from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) attended and presented their research. All CIMSS students gave outstanding presentations and Alexa Ross received the third place prize for the best oral presentation. Meanwhile Steve Ackerman, Wayne Feltz, and Margaret Mooney from CIMSS provided feedback to graduate students from all CoRP institutions presenting oral and poster sessions. (M. Mooney, CIMSS, 608-265-2123, W. Feltz, CIMSS, 608-263-3647, S. Ackerman, CIMSS, 608-263-3647) 

  (Click image to enlarge)

Figure caption: A collage of pictures from 2014 CORP Symposium.

ITEMS FOR THE DIVISION CHIEF, CoRP:

Manuscript Submitted on Probabilistic Prediction of Tropical Cyclone Rapid Intensification Methods: A manuscript entitled "Improvements in the probabilistic prediction of tropical cyclone rapid intensification with passive microwave observations" has been submitted to the journal Weather and Forecasting. This paper examines the probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins using a series of logistic regression models trained on environmental and infrared satellite-derived features. These models are compared against equivalent models enhanced with additional TC predictors created from satellite passive microwave imagery (MI). Independent testing on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models. The models are tested in a simulated real-time environment as well, using archived real-time data in the period 2004-2013. Here, again, the MI-based predictors are shown to significantly improve the performance of the models. (C. Rozoff, CIMSS, 608-512-5099; C. Velden, CIMSS; J. Kaplan, NOAA/HRD; J. Kossin, NOAA/NCDC; A. Wimmers, CIMSS) 

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