Abstract
Abstract The impact of the data assimilation process of air temperature and relative humidity from surface meteorological stations and sounding at airports in the terminal area of Rio de Janeiro is evaluated using the Weather Research and Forecast Data Assimilation system. Synthetic data of temperature, relative humidity and wind are generated in the locations of airport sensors by applying a white-noise perturbation in the forecast data. Results show a positive overall impact of the assimilation process with the removal of part of the noise in the observation data but keeping the effect of local conditions in the later timesteps of the simulation. In addition, with the assimilation process there is a global reduction of the error between the analysis data and the observation data. In the future, a neural network will be trained to emulate the data assimilation process to speed-up the assimilation process in the WRF model.
Highlights
Numerical weather forecasting is considered an initial-value problem where the present state of the atmosphere is used as input to a numerical model for simulating or forecasting its evolution on space and time
The FNL data are available on 1-degree grids prepared operationally every 6 h. This product is from the Global Data Assimilation System (GDAS), which continuously collects observational data from the Global Telecommunications System (GTS), and other sources
The results of data assimilation process using synthetic data, show that the 3D-Var method in the WRF Data Assimilation (WRFDA) system is able to perform a good estimate of the control field, here representing the “true” state of the dynamic system
Summary
Numerical weather forecasting is considered an initial-value problem where the present state of the atmosphere is used as input to a numerical model for simulating or forecasting its evolution on space and time. The present article is part of a sequence of studies related to nowcasting that have been executed by the Applied Meteorological Laboratory at the Federal University of Rio de Janeiro, following Almeida (2009), Silva et al (2016), França, Almeida, and Rossete (2016), França et al (2018), Paulucci et al (2019), and Almeida et al (2020a, 2020b). All these studies encompass researches based on artificial intelligence and methods of limited-area numerical weather forecasts. This work relates to the latter, exploring the sensibility of the Weather Research and Forecasting (WRF) regional model for surface and upperair data assimilation in the metropolitan area of Rio de Janeiro
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