Sharanya J. Majumdar

TL;DR: The Ensemble Transform Kalman Filter (ET KF) as discussed by the authors is a suboptimal Kalman filter that uses ensemble transformation and a normalization to obtain the prediction error covariance matrix associated with a particular deployment of observational resources.

TL;DR: The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones and tropical convection as discussed by the authors.

TL;DR: The North Pacific Experiment (NORPEX) as mentioned in this paper was an inter-government field program to address the issue of observational sparsity over the North Pacific basin, which is a major contributing factor in short-range (less than 4 days) forecast failures for landfalling Pacific winter-season storms that affect the United States, Canada, and Mexico.

TL;DR: In this article, the effects of targeted dropsonde observations on operational global numerical weather analyses and forecasts made at the National Centers for Environmental Prediction (NCEP) are evaluated, and it is found that large analysis errors appear in areas of intense baroclinic energy conversion over the northeast Pacific and are strongly associated with errors in the first-guess field.

TL;DR: The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described.