Search Results

You are looking at 1 - 1 of 1 items for :

  • Author or Editor: Thibaut Montmerle x
  • Weather and Forecasting x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Thibaut Montmerle, Alain Caya, and Isztar Zawadzki

Abstract

A new method based on four-dimensional variational radar data assimilation into a cloud-resolving model has been developed for nowcasting purposes. This method allows for the retrieval of the model prognostic variables that compose the initial state of the simulation. The echo-free regions are filled by a 3D wind analysis from single-Doppler data based on linearity of the horizontal wind components in a moving reference frame, which provides a realistic mesoscale flow that is in better agreement with the air circulation retrieved from dual-Doppler observations within the precipitating regions. Furthermore, the near-ground refractivity index of air derived from ground targets is used to diagnose a high-resolution and two-dimensional distribution of relative humidity in the mixed layer. Two experiments are performed: one uses multiple-Doppler information coming from McGill University's bistatic radar network and the second considers only single-Doppler observations. This updated algorithm has been applied to a shallow hailstorm and shows very encouraging skill in predicting the short-term evolution of this convective system. The time evolution of the storm is captured well, and a significant improvement is noticed when compared with the nowcasting method based on Lagrangian persistence. When compared with the results obtained with the bistatic network, results when a single-Doppler radar is used show weaker capability to forecast the radial velocity than the precipitation pattern but still give a better forecast than the Lagrangian persistence method does.

Full access