Search the RAMMB website
Citation: Trabing, B., K. Musgrave, M. DeMaria, and E. Blake, 2022: A Simple Bias and Uncertainty Scheme for Tropical Cyclone Intensity Change Forecasts, Wea. and Forecasting, 37(11), 1957-1972. https://doi.org/10.1175/WAF-D-22-0074.1
Summary: It is critical to recognize that forecast models have inherent limitations when it comes to forecasting tropical cyclone intensity change. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. In this work we present the Intensity Bias and Uncertainty Scheme (IBUS) which is a simple scheme to estimate the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and tropical cyclone basin relative to the original forecasts. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts. (POC: Ben Trabing btrabing@colostate.edu. Funding: HFIP)
Citation: Noh, Y. J., J. M. Haynes, S. D. Miller, C. J. Seaman, A. K. Heidinger, J. Weinrich, M. S. Kulie, M. Niznik, and B. J. Daub, 2022: A Framework for Satellite-based 3D Cloud Data: An Overview of VIIRS Cloud Base Height Retrieval and User Engagement for Aviation Applications. Remote Sensing, 14(21), 5524, Special Issue “VIIRS 2011–2021: Ten Years of Success in Earth Observations”, https://doi.org/10.3390/rs14215524
Summary: Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the JPSS VIIRS Cloud Calibration/Validation research, we developed a statistical Cloud Base Height algorithm using the NASA A-Train satellite data. This retrieval, which is currently part of the NOAA Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study was to provide users with high-quality three-dimensional cloud structure information which can maximize the benefits and performance of JPSS cloud products. All products were evaluated using multiple satellite data sources and surface measurements. The paper presents the CIRA Cloud Team accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era. (POC: Yoo-Jeong Noh, CIRA, Yoo-Jeong.Noh@colostate.edu Funding: JPSS)