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Longtao Wu

Data Scientist

About

Education

  • PH.D. (09/04-12/09) Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, USA
  • M.S. (09/01-06/04) Atmospheric Sciences, Peking University, China
  • B.S. (09/97-07/01) Atmospheric Sciences, Peking University, China

Research Interests

  • Numerical modeling of air quality, weather and climate
  • Observing System Simulation Experiments
  • Machine Learning
  • Tropical cyclone and polar low
  • Satellite data analysis

Experience

Professional Experience

  • Data Scientist, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA (2014 – present)
  • Project Scientist, JIFRESSE, University of California, Los Angeles, CA (2024– present)
  • Assistant Researcher, JIFRESSE, University of California, Los Angeles, CA (2012 – 2019)
  • Postdoctoral Scholar, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA (2010 – 2011)

Research Community Service

  • Reviewer for scientific journals and proposals
  • Judge for the Outstanding Student Paper Awards at AGU 2012, 2016

Achievements

Awards & Recognitions

  • Professional Society and External Organization Awards | AMS Banner I. Miller Award (2024)
  • JPL Voyager Award (2024)
  • JPL Voyager Award (2023)
  • JPL Team Award (2022)
  • JPL Award (2021)
  • JPL Award (2020)
  • JPL Voyager Award (2019)
  • NASA Award | Early Career Public Achievement Medal (2017)
  • JPL Voyager Award (2017)
  • JPL Team Award (2016)
  • NASA Award | NASA Group Achievement Award to Hurricane and Severe Storm Sentinel (2015)

Publications

  1. Yanovsky, I., D. J. Posselt, L. Wu, and S. Hristova-Veleva, 2024: Quantifying Uncertainty in Atmospheric Winds Retrieved from Optical Flow: Dependence on Weather Regime. J. Appl. Meteor. Climatol., 63, 1113–1135, https://doi.org/10.1175/JAMC-D-23-0169.1.
  2. Stephens, G.L., Shiro, K.A., Hakuba, M.Z. et al. Tropical Deep Convection, Cloud Feedbacks and Climate Sensitivity. Surv Geophys (2024). https://doi.org/10.1007/s10712-024-09831-1
  3. Nguyen, H., Posselt, D., Yanovsky, I., Wu, L., and Hristova-Veleva, S.: Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs, Atmos. Meas. Tech., 17, 3103–3119, https://doi.org/10.5194/amt-17-3103-2024, 2024.
  4. Zeng, X., and Coauthors (2024). Vientos - A new satellite mission concept for 3D wind measurements by combining passive water vapor sounders with Doppler wind lidar. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-22-0283.1
  5. Wu, L., H. Su, X. Zeng, D. J. Posselt, S. Wong, S. Chen and A. Stoffelen (2024). Uncertainty of Atmospheric Winds in Three Widely Used Global Reanalysis Datasets, J Appl Meteorol Climatol, 63, 165-180, https://doi.org/10.1175/JAMC-D-22-0198.1.
  6. Li, J.-L. F., Xu, K.-M., Tsai, Y.-C., Lee, W.-L., Jiang, J. H., Yu, J.-Y., E. Fetzer, L. Wu and G. Stephens (2023). Evaluation of radiatively active frozen hydrometeors mass in CMIP6 global climate models using CloudSat-CALIPSO observations. Journal of Geophysical Research: Atmospheres, 128, e2023JD039200. https://doi.org/10.1029/2023JD039200
  7. Li, J.-L. F., Xu, K.-M., Lee, W.-L., Jiang, J. H., Tsai, Y.-C., Yu, J.-Y., E. Fetzer, L. Wu and G. Stephens (2023). Warm clouds biases in CMIP6 models linked to indirect effects of falling ice-radiation interactions over the tropical and subtropical Pacific. Geophysical Research Letters, 50, e2023GL104990. https://doi.org/10.1029/2023GL104990
  8. Jiang, X., Su, H., Jiang, J.H., J.D. Neelin, L. Wu, Y. Tsushima and G. Elsaesser (2023). Muted extratropical low cloud seasonal cycle is closely linked to underestimated climate sensitivity in models. Nat Commun 14, 5586. https://doi.org/10.1038/s41467-023-41360-0
  9. Branch, A.; Marchetti, Y.; Mason, J.; Montgomery, J.; Johnson, M. C.; Chien, S.; Wu, L.; Smith, B.; Mandrake, L.; and Tavallali, P. Federating Planning of Observations for Earth Science. In Proc. of International Workshop on Planning and Scheduling for Space, July 2023. https://ai.jpl.nasa.gov/public/documents/papers/Branch-IWPSS2021-paper-12.pdf
  10. Morabito, D. D., L. Wu, J. Teixeira (2022). An Assessment of Weather Analysis Data for the DSN Sites. The Interplanetary Network Progress Report, Volume 42-231, pp. 1-19, November 15, 2022. https://ipnpr.jpl.nasa.gov/progress_report/42-231/42-231A.pdf
  11. Wu, L., Morabito, D. D., Teixeira, J. P., Huang, L., Nguyen, H. M., Su, H., et al. (2022). Prediction of Atmospheric Noise Temperature at the Deep Space Network with Machine Learning. Radio Science, 57, e2022RS007483. https://doi.org/10.1029/2022RS007483
  12. Morabito, D. D., Wu, L., & Heckman, D. (2022). Radio refractive index of wet atmosphere estimated from Site Test Interferometer data. Radio Science, 57, e2021RS007408. https://doi.org/10.1029/2021RS007408
  13. Millan, L., H. Pumphrey, A. Lambert, F. Werner, M.J. Schwartz, Y. Wang, H. Su, L. Wu, W.G. Read, M. Santee, N. Livesey, G.L Manney and L. Froidevaux (2022), The Hunga Tonga-Hunga Ha’apai Hydration of the Stratosphere, Geophys. Res. Lett., https://doi.org/10.1029/2022GL099381
  14. Morabito, D. D., D. Kahan, M. Paik, L. Wu, E. Barbinis, D. Buccino, and M. Parisi (2022), A Study of Twenty Years of Advanced Water Vapor Radiometer Data at Goldstone, California, IPN PR 42-228, pp. 1-12, Feb 15, 2022. https://ipnpr.jpl.nasa.gov/progress_report/42-228/42-228A.pdf
  15. Natraj, V., Luo, M., Blavier, J.-F., Payne, V. H., Posselt, D. J., Sander, S. P., Zeng, Z.-C., Neu, J. L., Tremblay, D., Wu, L., Roman, J. A., Wu, Y.-H., and Dorsky, L. I. (2022), Simulated Multispectral Temperature and Atmospheric Composition Retrievals for the JPL GEO-IR Sounder, Atmos. Meas. Tech., https://doi.org/10.5194/amt-2021-290
  16. Posselt, D.J., L. Wu, M. Schreier, J. Roman, M. Minamide, and B.H. Lambrigtsen (2022), Assessing the forecast impact of a geostationary microwave sounder using regional and global OSSEs, Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-21-0192.1
  17. Branch, A.; Chien, S.; Marchetti, Y.; Su, H.; Wu, L.; Montgomery, J.; Johnson, M.; Smith, B.; Mandrake, L.; and Tavallali, P. Federated Scheduling of Model-Driven Observations for Earth Science. In International Workshop on Planning & Scheduling for Space (IWPSS), July 2021. https://ai.jpl.nasa.gov/public/documents/papers/Branch-IWPSS2021-paper-12.pdf
  18. Lambrigtsen, B. H., P. Kangaslhti, O. Montes, D. J. Posselt, J. Roman, M. M. Schreier, A. Tanner, L. Wu, I. Yanovski, and N. Niamsuwan (2021), A Geostationary Microwave Sounder: Design, Implementation and Performance, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2021.3132238.
  19. Ouyed, A., X. Zeng, L. Wu, D. J. Posselt, and H. Su (2021), Two stage artificial intelligence algorithm for calculating moisture-tracking atmospheric motion vectors, J Appl Meteorol Climatol, https://doi.org/10.1175/JAMC-D-21-0070.1
  20. Jiang, J. H., H. Su, L. Wu, C. Zhai, and K. A. Schiro (2021), Improvements in cloud and water vapor simulations over the tropical oceans in CMIP6 compared to CMIP5, Earth Sp. Sci., 8, Article e2020EA001520, 10.1029/2020EA001520
  21. Teixeira, J. V., Nguyen, H., Posselt, D. J., Su, H., and Wu, L.: Using machine learning to model uncertainty for water vapor atmospheric motion vectors, Atmos. Meas. Tech., 14, 1941–1957, https://doi.org/10.5194/amt-14-1941-2021, 2021.
  22. Tavallali, P., Chien, S., Mandrake, L., Marchetti, Y., Su, H., Wu, L., Smith, B., Branch, A., Mason, J., and Swope, J.: Adaptive Model-driven Observation for Earth Science. In Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation for Space, of i-SAIRAS'2020, Noordwijk, NL, 2020. European Space Agency. https://www.hou.usra.edu/meetings/isairas2020fullpapers/pdf/5003.pdf
  23. Su, H., L. Wu, J. H. Jiang, R. Pai, A. Liu, A. J. Zhai, P. Tavallali and M. DeMaria (2020), Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast with Machine Learning, Geophys. Res. Lett., https://doi.org/10.1029/2020GL089102
  24. Su, H., L. Wu, C. Zhai, J. H. Jiang, J. D. Neelin and Y. L. Yung (2020), Observed Tightening of Tropical Ascent in Recent Decades and Linkage to Regional Precipitation Changes, Geophys. Res. Lett., https://doi.org/10.1029/2019GL085809.
  25. Posselt, D. J., L. Wu, K. Mueller, L. Huang, F. W. Irion, S. Brown, H. Su, D. Santek and C.S. Velden (2019), Quantitative Assessment of State-Dependent Atmospheric Motion Vector Uncertainties, J Appl Meteorol Climatol, https://doi.org/10.1175/JAMC-D-19-0166.1
  26. Jiang, J. H., Q. Yue, H. Su, P. P. Kangaslahti, M. Lebsock, S. C. Reising, M. Schoeberl, L. Wu, and R. L. Herman (2019), Simulation of remote sensing of clouds and humidity from space using a combined platform of radar and multi-frequency microwave radiometers, Earth Sp. Sci., 6, 7, 1234-1243, https://doi.org/10.1029/2019EA000580.
  27. Su, H., C. Zhai, J. H. Jiang, L. Wu, J. D. Neelin and Y. L. Yung (2019) A dichotomy between model responses of tropical ascent and descent to surface warming, npj Clim. Atmos. Sci., 2, 8, 2397-3722, https://doi.org/10.1038/s41612-019-0066-8.
  28. Wu, L., Gu, Y., Jiang, J. H., Su, H., Yu, N., Zhao, C., Qian, Y., Zhao, B., Liou, K.-N., and Choi, Y.-S.: Impacts of aerosols on seasonal precipitation and snowpack in California based on convection-permitting WRF-Chem simulations, Atmos. Chem. Phys., 18, 5529-5547, https://doi.org/10.5194/acp-18-5529-2018, 2018.
  29. Wong, S., C. M. Naud, B. H. Kahn, L. Wu and E. J. Fetzer (2018) Coupling of Precipitation and Cloud Structures in Oceanic Extratropical Cyclones to Large-Scale Moisture Transport, Journal of Climate, 9565–9584, https://doi.org/10.1175/JCLI-D-18-0115.1.
  30. Kabir, F., N. Yu, W. Yao, L. Wu, J.H. Jiang, Y. Gu and H. Su (2018), Impact of Aerosols on Reservoir Inflow: A Case Study for Big Creek Hydroelectric System in California, Hydrological Processes, https://doi.org/10.1002/hyp.13265
  31. Wu, L., S. Wong, T. Wang and G.J. Huffman (2018), Moist convection: a key to tropical wave-moisture interaction in Indian monsoon intraseasonal oscillation, Climate Dynamics, https://doi.org/10.1007/s00382-018-4103-9.
  32. Jiang, J. H., Q. Yue, H. Su, S. C. Reising, P. P. Kangaslahti, W. R. Deal, E. T. Schlecht, L. Wu, and K. Franklin Evans (2017), A Simulation of Ice Cloud Particle Size, Humidity and Temperature Measurements from the TWICE CubeSat, Earth and Space Science, 4, doi:10.1002/2017EA000296.
  33. Wu, L., Su, H., Kalashnikova, O. V., Jiang, J. H., Zhao, C., Garay, M. J., Campbell, J. R., and Yu, N.: WRF-Chem simulation of aerosol seasonal variability in the San Joaquin Valley, Atmos. Chem. Phys., 17, 7291-7309, https://doi.org/10.5194/acp-17-7291-2017, 2017.
  34. Morabito, D., L. Wu, and S. Slobin (2017), A Comparison of Atmospheric Quantities Determined from Advanced WVR and Weather Analysis Data, IPN PR 42-209, pp. 1-12, May 15, 2017.
  35. Morabito, D., L. Wu, and S. Slobin (2016), Weather Forecasting for Ka-band Operations: Initial Study Results, IPN PR 42-206, pp. 1-24, August 15, 2016.
  36. Wu, L., J.-L. F. Li, C.-J. Pi, J.-Y. Yu, and J.-P. Chen (2015), An observationally based evaluation of WRF seasonal simulations over the Central and Eastern Pacific, J. Geophys. Res. Atmos., 120, doi:10.1002/2015JD023561.
  37. Wu, L., Su, H., Fovell, R. G., Dunkerton, T. J., Wang, Z., and Kahn, B. H.: Impact of environmental moisture on tropical cyclone intensification, Atmos. Chem. Phys., 15, 14041-14053, doi:10.5194/acp-15-14041-2015, 2015.
  38. Jiang, J. H., H. Su, C. Zhai, L. Wu, et al. (2015), An assessment of upper-troposphere and lower-stratosphere water vapor in MERRA, MERRA2 and ECMWF reanalyses using Aura MLS observations, J. Geophys. Res. Atmos., doi: 10.1002/2015JD023752.
  39. Wu, L., H. Su, and J. H. Jiang (2013), Regional simulations of aerosol impacts on precipitation during the East Asian summer monsoon. J. Geophys. Res. Atmos., 118, doi: 10.1002/jgrd.50527.
  40. Wu, L., H. Su, R. G. Fovell, B. Wang, J. T. Shen, B. H. Kahn, S. M. Hristova-Veleva, B. H. Lambrigtsen, E. J. Fetzer, and J. H. Jiang (2012), Relationship of environmental relative humidity with North Atlantic tropical cyclone intensity and intensification rate, Geophys. Res. Lett., 39, L20809, doi:10.1029/2012GL053546.
  41. Wu, L., Su, H., Jiang, J. H., and Read, W. G. (2012), Hydration or dehydration: competing effects of upper tropospheric cloud radiation on the TTL water vapor, Atmos. Chem. Phys., 12, 7727-7735, doi:10.5194/acp-12-7727-2012, 2012.
  42. Hu, X.-M., F. Zhang, G. Yu, J. D. Fuentes, and L. Wu (2011), Contribution of mixed-phase boundary layer clouds to the termination of ozone depletion events in the Arctic. Geophys. Res. Lett., 38, L21801, doi:10.1029/2011GL049229.
  43. Wu, L., H. Su, and J. H. Jiang (2011), Regional simulations of deep convection and biomass burning over South America: 1. Model evaluations using multiple satellite data sets, J. Geophys. Res., 116, D17208, doi:10.1029/2011JD016105.
  44. Wu, L., H. Su, and J. H. Jiang (2011), Regional simulations of deep convection and biomass burning over South America: 2. Biomass burning aerosol effects on clouds and precipitation, J. Geophys. Res., 116, D17209, doi:10.1029/2011JD016106.
  45. Wu, L., J. E. Martin, and G. W. Petty (2011), Piecewise potential vorticity diagnosis of the development of a polar low over the Sea of Japan. Tellus A, 63:198-211. doi: 10.1111/j.1600- 0870.2011.00511.x
  46. Wu, L., and G. W. Petty (2010), Intercomparison of Bulk Microphysics Schemes in Simulations of Polar lows. Mon. Wea. Rev., 138, 2211-2228. doi: 10.1175/2010MWR3122.1.
  47. Wu L., H. Wu, L. Sun, et al. (2006), Retrieval of Sea Ice in the Bohai Sea from MODIS Data, Periodical of Ocean University of China, Vol. 36, No. 2, 173~179.
  48. Wu L., H. Wu, W. Li, et al. (2005), Sea Ice Drifts in Response to Winds and Tide in the Bohai Sea, Acta Oceanologica Sinica, 5, 15~21.
  49. Li W., L. Wu, C. Zhang, and J.-Y. Yu (2003), Retrieval of Atmospheric Vertical Temperature Profile above Sea Level with Satellite Remote Sensing, Acta Scientiarum Naturalium Universitatis Pekinensis, 39(5), 656~665.