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Vijay Tallapragada

Publications and source records attributed to Vijay Tallapragada.

2 recordsLinked to original sources

The Design and Performance of Meteorological Sensors for WindBorne Global Sounding Balloons

WindBorne Systems has developed a constellation of long-duration atmospheric balloons to collect meteorological data across the globe, filling gaps in current in-situ data collection methods. Each Global Sounding Balloon (GSB) is capable of flying for weeks or months and performing dozens of soundings while measuring pressure, temperature, humidity, and GNSS-derived position, altitude, and wind velocity. This data is transmitted to ground via satellite, processed, and made available within minutes of being collected. The current meteorological sensor package has remained largely unchanged since mid-2024 and has flown on thousands of GSBs totaling over one million hours of flight time. Here we present the design and performance of this sensor package. The custom readout architecture and housing allow for data collection across nearly all in-flight conditions while minimizing sources of bias and noise. Uncertainty is characterized via sounding reproducibility studies and in-house calibration of pressure, humidity, and temperature sensors. The calibration and data processing procedures have been optimized and validated by comparison with external datasets. We present external validation in the form of 1) side-by-side radiosonde launches performed in collaboration with the Center for Western Weather and Water Extremes at the Scripps Institution of Oceanography, which show agreement within expected uncertainty limits, and 2) intercomparison studies with European Centre for Medium-Range Weather Forecasts Reanalysis v5, which show an aggregate root mean square difference of: Geopotential height -- 14 m; Pressure -- 0.36 hPa; Temperature -- 0.91 K; Wind speed u -- 2.45 m/s; Wind speed v -- 2.50 m/s; Relative humidity -- 13%.

physics.ao-ph

Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback

Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.

physics.ao-ph