This article presents the Centre for Weather Forecast and Climate Studies (CPTEC) developments for configuring a global sub‐seasonal prediction system and assessing its ability in producing retrospective predictions (hindcasts) for meteorological conditions of the following 4 weeks. Six Brazilian Global Atmospheric Model version 1.2 (BAM‐1.2) configurations were tested in terms of vertical resolution, deep convection and boundary‐layer parametrizations, as well as soil moisture initialization. The aim was to identify the configuration with best performance when predicting weekly accumulated precipitation, weekly mean 2 m temperature (T2M) and the Madden–Julian Oscillation (MJO) daily evolution. Hindcasts assessment was performed for 12 extended austral summers (November–March, 1999/2000– 2010/2011) with two start dates for each month for the six configurations and two ensemble approaches. The first approach, referred to as Multiple Configurations Ensemble (MCEN), was formed of one ensemble member from each of the six configurations. The second, referred to as Initial Condition Ensemble (ICEN), was composed of six ensemble members produced with the chosen configuration as the best using an empirical orthogonal function (EOF) perturbation methodology. The chosen configuration presented high correlation and low root‐mean‐squared error (RMSE) for precipitation and T2M anomaly predictions at the first week and these indices degraded as lead time increased, maintaining moderate performance up to week‐4 over the tropical Pacific and northern South America. For MJO predictions, this configuration crossed the 0.5 bivariate correlation threshold in 18 days. The ensemble approaches improved the correlation and RMSE of precipitation and T2M anomalies. ICEN improved precipitation and T2M predictions performance over eastern South America at week‐3 and over northern South America at week‐4. Improvements were also noticed for MJO predictions. The time to cross the above‐mentioned threshold increased to 21 days for MCEN and to 20 days for ICEN.

The Global Positioning System (GPS) Radio Occultation (RO) technique allows valuable information to be obtained about the state of the atmosphere through vertical profiles obtained at various processing levels. From the point of view of data assimilation, there is a consensus that less processed data are preferable because of their lowest addition of uncertainties in the process. In the GPSRO context, bending angle data are better to assimilate than refractivity or atmospheric profiles; however, these data have not been properly explored by data assimilation at the CPTEC (acronym in Portuguese for Center for Weather Forecast and Climate Studies). In this study, the benefits and possible deficiencies of the CPTEC modeling system for this data source are investigated. Three numerical experiments were conducted, assimilating bending angles and refractivity profiles in the Gridpoint Statistical Interpolation (GSI) system coupled with the Brazilian Global Atmospheric Model (BAM). The results highlighted the need for further studies to explore the representation of meteorological systems at the higher levels of the BAM model. Nevertheless, more benefits were achieved using bending angle data compared with the results obtained assimilating refractivity profiles. The highest gain was in the data usage exploring 73.4% of the potential of the RO technique when bending angles are assimilated. Additionally, gains of 3.5% and 2.5% were found in the root mean square error values in the zonal and meridional wind components and geopotencial height at 250 hPa, respectively.

The Center for Weather Forecast and Climate Studies [Centro de Previsão e Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] has recently operationally implemented a three-dimensional variational data assimilation (3DVAR) scheme based on the Gridpoint Statistical Interpolation analysis system (GSI). Implementation of the GSI system within the atmospheric global circulation model from CPTEC/INPE (AGCM-CPTEC/INPE) is hereafter referred to as the Global 3DVAR (G3DVAR) system. The results of an observing system experiment (OSE) measuring the impacts of radiosonde, satellite radiance, and GPS radio occultation (RO) data on the new G3DVAR system are presented here. The observational impact of each of these platforms was evaluated by measuring the degradation of the geopotential height anomaly correlation and the amplification of the RMSE of the wind. Losing the radiosonde, GPS RO, and satellite radiance data in the OSE resulted in negative impacts on the geopotential height anomaly correlations globally. Nevertheless, the strongest impacts were found over the Southern Hemisphere and South America when satellite radiance data were withheld from the data assimilation system.

Studies about the sensibility of numerical weather prediction (NWP) model to uncertainty in the initial conditions have revealed the actual contribution of the data assimilation on the description of future atmosphere state. Among other data sources, the atmospheric profiles obtained by radio occultation Global Navigation Satellite System (GNSS) have become an additional tool in the reduction of the meteorological data base deficiencies. In order to explore the benefits of that additional data source in the NWP generated by the CPTEC/INPE general circulation model, some experiments were carried out by assimilating geopotencial height and humidity atmospheric profiles obtained by Constellation Observing System for Meteorology Ionosphere & Climate (COSMIC) constellation data. The period of these experiments were the months of January and July of 2009. The results show that this data assimilation exhibits a significant positive impact during the summer on all the state variables. Results also show a positive improvement during the summer for all state variables, with expressive improvements in the valid forecasts time range (anomaly correlation above 60%), which were in some cases greater than 48 hours. This impact was still greater over South America with positive results even during the winter.

The Local Ensemble Transform Kalman Filter (LETKF) is a system for data assimilation in the testing phase at the Center for Weather Forecast and Climate Studies of the Brazilian Institute for Space Research (CPTEC/INPE). This system introduces significant advantages over the traditional variational methodology in the inclusion of new observation systems because determining the observation operator does not involve inverse problems. The refractivity profile from the Global Navigation Satellite System (GNSS) radio occultation (RO) technique is an example of an additional source of unconventional observation with promising results over regions with low data coverage. To assimilate RO profiles using the LETKF system at CPTEC/INPE, the Radio Occultation Processing Package (ROPP) from Radio Occultation Meteorology Satellite Applications Facilities/European Organization for the Exploration of Meteorological Satellites (ROM SAF/EUMETSAT) was used as an observation operator. The goal of this work was to investigate the impact on the Atmospheric General Circulation Model of the CPTEC using ROPP as an operator of the assimilation of RO data into the LETKF system. The results showed that positive impacts were obtained in several evaluated variables, including humidity, particularly over South America, where the model used by the CPTEC produces better results.

The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are responsible for the rain episodes, and at the west side the northeastern hinterland and dry air masses are observed. This work is a case study that aims to evaluate the impact of updating the model initial condition using the 3DEnVar (Three-Dimensional Ensemble Variational) system in heavy rain episodes associated with Mesoscale Convective Systems (MCS). The results were compared to 3DVar (Three-Dimensional Variational) and EnSRF (Ensemble Square Root Filter) systems and with no data assimilation. The study enclosed two MCS cases occurring on 14 and 24 January 2017. For that purpose, the RMS (Regional Modeling System) version 3.0.0, maintained by the Center for Weather Forecasting and Climate Studies (CPTEC), used two components: the Weather Research and Forecasting (WRF) mesoscale model and the GSI (Gridpoint Statistical Interpolation) data assimilation system. Currently, the RMS provides the WRF initial conditions by using 3DVar data assimilation methodology. The 3DVar uses a climatological covariance matrix to minimize model errors. In this work, the 3DEnVar updates the RMS climatological covariance matrix through the forecast members based on the errors of the day. This work evaluated the improvements in the detection and estimation of 24 h accumulated precipitation in MCS events. The statistic index RMSE (Root Mean Square Error) showed that the hybrid data assimilation system (3DEnVar) performed better in reproducing the precipitation in the MCS occurred on 14 January 2017. On 24 January 2017, the EnSRF was the best system for improving the WRF forecast. In general, the BIAS showed that the WRF initialized with different initial conditions overestimated the 24 h accumulated precipitation. Therefore, the viability of using a hybrid system may depend on the hybrid algorithm that can modify the weights attributed to the EnSRF and 3DVar matrix in the GSI over the assimilation cycles.