{'fraction_non_alphanumeric': 0.026543566070398154, 'fraction_numerical': 0.006347374495095211, 'mean_word_length': 5.398523985239852, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Application of microwave space-based environmental monitoring (SBEM) data for operational tropical cyclone intensity estimation at the Joint Typhoon Warning Center


The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture & Ocean Salinity (SMOS) radiometers and Synthetic Aperture Radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the post-storm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the post-storm BT process and provide examples of how these data influenced forecaster decision making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new data sets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.