1518 The JPL Tropical Cyclone Information System: A Wealth of Data for Quickly Advancing the Physical Understanding and Forecasting of Hurricanes

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Svetla Hristova-Veleva, JPL/California Institute of Technology, Pasadena, CA; and P. P. Li, B. Knosp, Q. A. Vu, F. J. Turk, W. L. Poulsen, Z. S. Haddad, B. H. Lambrigtsen, B. W. Stiles, T. P. J. Shen, N. Niamsuwan, S. Tanelli, O. O. Sy, H. Su, D. G. Vane, Y. Chao, P. S. Callahan, R. S. Dunbar, M. T. Montgomery, M. A. Boothe, V. Tallapragada, S. Trahan, A. Wimmers, R. Holz, J. S. Reid, F. D. Marks, T. Vukicevic, S. Bhalachandran, H. Leighton, S. Gopalakrishnan, A. Navarro, and F. J. Tapiador

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multi-scale processes and non-linear interactions. Even today there are still many unanswered questions about the physical processes that determine hurricane genesis, and evolution. Furthermore, there is a well-recognized need to improve the forecast accuracy of the hurricane models.

During the past 20 years there has been significant improvement in the track forecast error. However, the intensity forecasts have not improved significantly posing the question whether the models properly reflect the physical processes and their interactions. In looking for the sources of the intensity errors we need to investigate: i) Is the representation of the precipitation structure correct? ii) Is the storm scale and asymmetry reflected properly? iii) Is the environment captured correctly? iv) Is the interaction between the storm and its environment represented accurately?

Recognizing the high societal value of accurate hurricane forecasts, the NOAA-led, multi-agency Hurricane Forecast Improvement Project (HFIP) was established in 2007. The three critical pathways to hurricane forecast improvement are: i) increased understanding of the physical processes; ii) validation and improvement of hurricane models through the use of satellite data; iii) development and implementation of new techniques for assimilation of satellite observations inside the hurricane precipitating core.

The wealth of satellite and airborne observations collected over the past two decades can be brought to bear on addressing the outstanding scientific questions and improving our forecast models. However, despite the significant amount of satellite data today, they are still underutilized in hurricane research and operations, due to their volume and complexity.

To address this shortcoming, we developed the technology to provide fusion of observations and operational models to help improve the understanding and forecasting of the hurricane processes. In particular, we developed the Tropical Cyclone Information System (TCIS), a hurricane-specific Data Analytic Center Framework which integrates model forecasts with multi-parameter satellite and airborne observations from a variety of instruments and platforms and provides an interactive system for visualization and on-line analysis tools that work with both observations and models, allowing quick investigation of the storm structure and evolution.

TCIS includes the North Atlantic Hurricane Watch (NAHW - https://nahw.jpl.nasa.gov) site and the interactive data portals being developed to support field campaigns: NASA’s Convective Processes Experiment (CPEX) site (https://cpex.jpl.nasa.gov) and the 2019 CAMP2Ex site (https://camp2ex.jpl.nasa.gov) for the study of Tropical convection. The last two portals serve as a very rich information source during the planning and particularly during the analysis stages of field campaigns.

In addition to the three interactive portals, TCIS also includes a 12-year-long (1999–2011) global data archive of satellite observations of tropical cyclones. It provides a one-stop place to obtain an extensive set of multi-parameter data from multiple observing systems. The TC Data Archive (TCDA - https://tcis.jpl.nasa.gov/data/TC_Data_Archive/ ) offers both digital data and imagery that are subset to the domain and time of interest, thus greatly reducing the volume of unwanted data. This makes the TCDA a valuable source to quickly build statistics in support of research, forecast improvement and algorithm development (e.g. Hristova-Veleva et al., 2014, Wu et al, 2012).

In this presentation we will describe the TCIS and all its components. Furthermore, we will summarize pilot studies and analyses carried out recently, in order to encourage more users to take full advantage of the new capabilities.

A particular emphasis will be placed on a couple of recent studies that investigate the role precipitation intensity and organization play in hurricane rapid intensity changes (RIC). Recent composite study (Rogers et al. 2013) and case study (Reasor et al. 2009) of airborne Doppler observations have indicated that a very important aspect of the hurricane RIC process might involve the location of the convective activity with respect to the Radius of Maximum Wind (RMW). TCIS allows to examine the joint behavior of the structure of the 2D precipitation and that of the near-surface wind, for TCs that undergo rapid intensification and rapid decay, using satellite observations of Atlantic hurricanes. Using the on-line Wave Number Analysis (WNA – e.g. Vukicevic et al., 2014) tool available in the TCIS system, we studied the joint wind-rain distributions for a number of cases in the past 7 years. The careful analyses revealed potential capabilities for predicting hurricane RIC (Hristova-Veleva et al, 2016). In the process, we developed a new method for identifying the storm center, which is a critical component in the wave number-based analysis of the storm structure. These case studies uncovered the importance of monitoring the amount of precipitation inside the radius of maximum wind, a variable that is well represented within TCIS.

To understand the statistical importance of these results, next we used the TC Data Archive and applied the WNA to the 12-year collection of TC satellite observations over the Atlantic, focusing on semi-coincident wind and rain measurements. We will present our current findings (Tapiador et al, 2019) detailing the statistical relationship between precipitation and wind, regarding RIC.

Last, but not least, we will provide an overview of a current study that uses satellite observations of precipitation structure to sub-select the most realistic members of a high-resolution ensemble forecast. Our approach is based on objective comparison of observed and simulated precipitation structure, using again the WNA approach. While the use of high-resolution ensembles has enabled a great improvement in the forecast of TCs, there are still significant shortcomings due to the uncertainty in the initial conditions and in the physical representation of the processes. Our current approach aims at reducing this uncertainty by providing “guidance on guidance”, based on selecting the most realist forecasts.

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