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Submitted by meghavi_admin on
April 2026
Improving Operational Prediction of Urban Rainfall and Heat Extremes through Multi-Source LCLU Remote Sensing
Bridging LES, Remote Sensing, and WRF for Urban Climate Modeling
  • Coarse NWP models poorly represent urban processes
  • Missing sub-grid LCLU variability
  • Anthropogenic heat (AH) is poorly constrained

What LES Reveals

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Fig. 1: Urban morphology controls convection and cloud formation

 

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Fig. 2: Accumulated rainfall from IMERG, NOAA observations, and model simulations

 

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Why is this Important?

Urban land use and land cover strongly control surface heat, moisture, and momentum fluxes, so poor representation of these processes directly limits prediction of urban heat island and extreme precipitation. High-resolution remote sensing is important because it provides spatially detailed, physically relevant information, especially for anthropogenic heat and urban surface properties,that can be translated into effective parameters for WRF.

 

 

How satellite data are being used?

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 References

[1] Li, Q., Bou‐Zeid, E., Grimmond, S., Zilitinkevich, S., & Katul, G. (2020). Revisiting the relation between momentum and scalar roughness lengths of urban surfaces. Quarterly Journal of the Royal Meteorological Society, 146(732), 3144-3164.

[2] Cui, Y., Chu, M., He, Z., Albertson, J., Wang, Z., & Li, Q. (2025). Estimating anthropogenic heat flux by assimilating meteorological observations with a Kalman filter approach. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383(2308)

 

Project Investigator: John D. Albertson, Cornell University, NY, USA; Email: albertson@cornell.edu

The opinions expressed are solely the PI's and do not reflect NASA's or the US Government's views.