jgacituag/L96_TEnKF: Tempered EnKF for sequential data assimilation (L96 experiments

This release contains the Python implementation and experimental scripts for the paper "Tempered EnKF for sequential data assimilation" Key Features included in this version: Implementation of the Tempered Local Ensemble Transform Kalman Filter (LETKF-T) . Scripts for the Lorenz-96 model experiments. Nonlinear observation operator h(x) designed to mimic radar reflectivity thresholding and logarithmic scaling . Configuration files for sensitivity analysis regarding tempering schedules (αs), ensemble sizes (Nens), and observational network density (Den) . Plotting scripts to reproduce the RMSE, spread-RMSE, and bias figures presented in the manuscript.