A Markov Model Approach for Statistical Tropical Cyclone Wind Radii Baseline Forecasts

A skill baseline for 5-day, 34-, 50-, and 64-kt (1 kt = 0.514 m s −1 ) tropical cyclone (TC) wind radii forecasts is described. The Markov Model Cliper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial track and intensity forecast as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the north Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–22 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic and eastern North Pacific basins and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use. Significance Statement Skill baselines for forecasts play a key role in assessing the difficulties of individual forecasts and the forecast skill of more complex algorithms. Baselines serve as guidance when better-performing algorithms are unavailable or inconsistent with the operational forecasts. Here, we present a simple and always available algorithm for generating tropical cyclone (TC) wind radii that retain size and shape throughout the forecast better than the prior developed method. The algorithm also better captures the nearly ubiquitous wind field expansion through a tropical cyclone lifetime. This algorithm, developed to use the same initial data as its predecessor, provides more realistic forecast wind radii and has performance that is slightly superior to its predecessor.