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.

Hybrid systems have become the state of the art among data assimilation methods. These systems combine the benefits of two other systems that are traditionally used in operational weather forecasting: an ensemble-based system and a variational system. One of the most recently proposed hybrid approaches is called hybrid gain (HG). It obtains the final analysis as a linear combination of two analyses, assuming that the innovations (i.e. the forecast and the set of observations used) between the two data assimilation methods are identical. A perfect model experiment was performed using the HG in the SPEEDY model to show a new methodology to assign different weights to the two analyses, LETKF and 3D-Var in the generation of the final analysis. Our new approach uses, in the assignment of the weights, the ensemble spread, considered to be a measure of uncertainty in the LETKF. Thus, it is possible to use the estimation of the uncertainty of the analysis that the LETKF provides, to determine where the system should give more weight to the LETKF or the 3D-Var analysis. For this purpose, we define a geographically varying weighting factor alpha, which multiplies the 3D-Var analysis, as the normalised spread for each variable at each level. Then, (1-alpha), which decreases with increasing spread, becomes the factor that multiplies the LETKF analysis. The underlying mechanism of the spread–error relationship is explained using a toy model experiment. The results are very encouraging: the original HG and the new weighted HG analyses have similar high quality and are better than both 3D-Var and LETKF. However, the dynamically weighted HG analyses are significantly more balanced than the original HG analyses are, which has probably contributed to the consistently improved performance observed in the weighted HG, which increases with time throughout the 5-day forecasts.

This study analyses the unprecedented decline in Antarctic sea ice extent during 2022 and 2023. The results reveal that the minimum records in Antarctic sea ice, observed in both summer (February) and winter (September), display inhomogeneous spatial patterns and may be linked to intricate air‐sea interactions, including internal climate variability and changes in wind patterns. Events such as the positive southern annular mode, intensification of the Amundsen Sea Low and La Niña played a critical role during September 2022, driving atmospheric and oceanic conditions that enhanced reduction and limited ice formation, particularly in the Weddell and Amundsen Seas. Conversely, in September 2023, characterised by a negative southern annular mode, a weakened Amundsen Sea Low and El Niño conditions, the Ross Sea exhibits pronounced negative sea ice concentration anomalies. The results suggest that internal climate variability can contribute to changes in Antarctic sea ice, primarily through wind pattern variations associated with changes in the Amundsen Sea Low. However, the recent decline of the Antarctic sea ice remains a subject of debate and lacks conclusive answers.

At the end of April and beginning of May 2024, the state of Rio Grande do Sul, Brazil, experienced an unprecedented climatic disaster. A combination of meteorological factors,including extreme accumulated precipitation,and the region’s topography led to a rapid and severe rise in river levels. Therefore, numerous cities were inundated, leaving both people and animals homeless, and resulting in several fatalities. Given the magnitude of these events, this study aims to understand their underlying causes and explore strategies for identifying them both retrospectively and in near-real-time (nowcasting). To this end, we analyzed direct and indirect atmospheric measurements over the most affected areas in Rio Grande do Sul, particularly the cities of Porto Alegre and Santa Maria. Data were obtained from multiple sources, encompassing observations at the surface and throughout the atmospheric column. We mapped precipitable water vapor (PWV), precipitation, and flood extents. The recorded disasters were associated with accumulated rainfall levels that were over three times higher in Porto Alegre (457 mm) and five times higher in Santa Maria (719 mm) compared to previous El Niño events (2015 and 2016). Our findings underscore GNSS-derived PWV as a near-real-time predictor of extreme rainfall with up to 60 min of lead time .