Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

Abstract

Due to the high spatial and temporal variability of atmospheric water vapor associated with the deficient methodologies used in its quantification and the imperfect physics parameterizations incorporated in the models, there are significant uncertainties in characterizing the moisture field. The process responsible for incorporating the information provided by observation into the numerical weather prediction is denominated data assimilation. The best result in atmospheric moisture depend on the correct choice of the moisture control variable. Normalized relative humidity and pseudo-relative humidity are the variables usually used by the main weather prediction centers. The objective of this study is to assess the sensibility of the Center for Weather Forecast and Climate Studies to choose moisture control variable in the data assimilation scheme. Experiments using these variables are carried out. The results show that the pseudo-relative humidity improves the variables that depend on temperature values but damage the moisture field. The opposite results show when the simulation used the normalized relative humidity. These experiments suggest that the pseudo-relative humidity should be used in the cyclical process of data assimilation and the normalized relative humidity should be used in non-cyclic process (e.g., nowcasting application in high resolution).

Assimilation of GPSRO Bending Angle Profiles into the Brazilian Global Atmospheric Model

“…With this variable, it is ensured that the relative humidity control variable can only change through changes in specific humidity [32]. The choice of this variable modifies the impact of the data, especially for humidity fields [33]. Since NBAM is implemented in GSI v3.3, the system offers the capability to use compressibility factors to calculate the geopotential heights of the model layers in both observation operators.…”

The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning

“…As a result, choosing the optimum values for this undertaking in order for the model to function at its best is fairly difficult. Estimating the model sensitivity of these factors is an important first step in overcoming this challenge [1][2][3][4][5][6][7][8]. In the present work, this trait is realized for the local version of ICON model (ICON-LAM), a state-of-the-art NWP model [9,10], for a considerably large number of 24 tunable parameters under the motivation to consider the most sensible ones in various model performance optimization techniques [4,7,[11][12][13][14].…”

ICON (ICOsahedral Non-hydrostatic) Model Sensitivity over the Central Mediterranean

“…A lot of them are interrelated, while their number increases as the model development progresses, especially towards the proper inclusion of complicated physical atmospheric processes. A rather crucial step towards tackling these features is to estimate the model sensitivity of these parameters [1][2][3][4][5]. In the present work, this estimation is performed for ICON, a state-of-the-art NWP model [6,7], for a large number of 24 parameters under the motivation to consider the most sensitive ones in various model performance optimization techniques [8,9].…”