Implications of Self-Contained Radiance Bias Correction for Data Assimilation within the Hurricane Analysis and Forecasting System (HAFS)

Abstract

The Hurricane Analysis and Forecasting System (HAFS) is the next-generation, FV3-based tropical cyclone (TC) forecasting system. Unlike operational implementations of NOAA’s Hurricane Weather Research and Forecast (HWRF) modeling system, current data assimilation (DA) capabilities in HAFS permit the uninterrupted basin-wide assimilation of measurements. This feature of HAFS opens a variety of new research directions for TC prediction, including new strategies for DA algorithm development and self-contained probabilistic forecasting. The present research focuses more narrowly on new opportunities HAFS brings for optimizing the use of satellite measurements for TC prediction. While satellite radiometers provide a wealth of information for characterizing temperature, moisture, and wind in TC environments, the provided measurements are often biased and contain unknown cross-channel error correlations. For mature global modeling systems, these statistics are estimated from information gathered during DA, namely innovations collected over large spatial and temporal training periods. The estimated statistics, however, are imperfect owing to unknown error sources such as model process error, which are difficult to separate from observation error. As such, bias and uncertainty specifications that rely on information from external models are suboptimal—as is the current strategy for HWRF. In this paper, it will be demonstrated that bias estimation for satellite radiance observations is particularly sensitive to common design choices, such as using a bias model trained from the Global Data Assimilation System instead of within the native modeling system. Implications of this finding for TC prediction are examined over a 6-week period from 2020, which included the development and intensification of 9 tropical cyclones.