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.

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional and convective scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. Results show that a baseline RRFS run without data assimilation is able to represent the observed convection, but with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

Abstract. An ensemble of three-dimensional ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality, and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) Ensemble Adjustment Kalman Filter (EAKF). The experiment using En-3DEnVar produces similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting; although, there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the hybrid three-dimensional ensemble–variational data assimilation (3DEnVar) algorithm in the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. A domain of 3 km horizontal grid spacing is configured, and hourly update cycles are performed using initial and lateral boundary conditions from the 3 km grid High-Resolution Rapid Refresh (HRRR). Results show that a baseline RRFS run is able to represent the observed convection, although with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and 25 % of the static BEC with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius from 3 layers to 1 layer in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection is greatly improved when using planetary boundary layer pseudo-observations, especially at 4 h forecast, and the bias of the 2 h forecast of temperature is reduced below 800 hPa. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into the current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

Recebido em 23 de Março de 2017 -Aceito em 22 de Agosto de 2017 ResumoAtualmente tem crescido o número de satélites de órbita baixa dedicados à rádio ocultação dos sinais do Sistema de Posicionamento Global (GPS). O MetOp-B é um desses novos satélites, mas ainda não foi explorado na assimilação de dados realizada no Brasil. Com o intuito de incluir essa fonte de observação na base de dados utilizada na assimilação do CPTEC/INPE e avaliar o impacto da mesma na melhoria do desempenho do Modelo de Circulação Geral Atmosférica, foi realizado um experimento para os meses de janeiro e agosto de 2014. Os resultados foram comparados com um experimento controle, onde tais dados não foram assimilados. Os resultados mostraram que a inclusão dos perfis de refratividade do MetOp-B impactou beneficamente na assimilação de uma maior quantidade de dados dos demais satélites em operação, COSMIC, TerraSAR-X e MetOp-A. No mês de agosto foram observados resultados mais proeminentes, pois o ganho em valores da raiz do erro quadrático médio foi maior que 40% nas variáveis de estado nas primeiras 24 h de previsão no Hemisfério Sul, variáveis essas diretamente relacionadas com os valores de refratividade. Além disso, os valores do coeficiente de correlação de anomalia sobre a América do Sul indicaram que a inclusão dos dados do MetOp-B impactou de forma indireta as componentes zonal e meridional do vento em 250 hPa, o que evidencia a importância de assimilar tais dados. Palavras-chave: rádio ocultação GPS, assimilação de dados, MetOp-B. Impact of the Assimilation of Metop-B Satellite Refractivity Profiles in the Forecasts of the CPTEC/INPE During the Months of January and August 2014Abstract Currently, there is an increase in the number of low orbit satellites dedicated to radio occultation of GPS signals. The MetOp-B is one of these new satellites, but it has not been explored in the data assimilation performed in Brazil. In order to include this source of observation in the database used in the assimilation of the CPTEC/INPE and evaluate its impact in the performance of the Atmospheric Global Circulation Model used operationally at the center, an experiment with data from January and August 2014 was executed. The results were compared with another experiment without assimilating those data, which showed that the inclusion of the MetOp-B refractivity profiles, has impacted beneficially the assimilation of more data from the other satellites in use, such as COSMIC, TerraSAR-X and MetOp-A. The most prominent results were observed in August, with root mean squared gain values greater than 40% in the state variables for the 24-h forecast over the Southern Hemisphere, variables that are directly related to the refractivity values. Beyond that, the anomaly correlation coefficient values over South America indicated that, adding the MetOp-B data, the zonal and meridional wind components at 250 hPa were indirectly impacted, showing the importance of assimilate such data.

Abstract. This paper describes the three-dimensional variational (3DVar) data assimilation (DA) system for the Model for Prediction Across Scales-Atmosphere with the Joint Effort for data Assimilation Integration (JEDI-MPAS). Its core element is a multivariate background error covariance implemented through multiple linear variable changes, including a wind variable change from stream function and velocity potential to zonal and meridional wind components, a vertical linear regression representing wind-mass balance, and multiplication by a diagonal matrix of error standard deviations. The univariate spatial correlations for the ``unbalanced'' variables utilize the Background error on an Unstructured Mesh Package (BUMP), which is one of generic components in the JEDI framework. The variable changes and univariate correlations are modeled directly on the native MPAS unstructured mesh. BUMP provides utilities to diagnose parameters of the covariance model, such as correlation lengths, from an ensemble of forecast differences, though some manual adjustment of the parameters is necessary because of mismatches between the univariate correlation function assumed by BUMP and the correlation structure in the sample of forecast differences. The resulting multivariate covariances, as revealed by single-observation tests, are qualitatively similar to those found in previous global 3DVar systems. Month-long cycling DA experiments using a global quasi-uniform 60 km mesh demonstrate that 3DVar, as expected, performs somewhat worse than a pure ensemble-based covariance, while a hybrid covariance that combines that used in 3DVar with the ensemble covariance, significantly outperforms both 3DVar and the pure ensemble covariance. Due to its simple workflow and minimal computational requirements, the JEDI-MPAS 3DVar can be useful for the research community.

An analysis of the coastal flood behavior onCuban shore area, the influence of the thermohaline structure and its trends is presented, using data archive information from the Cuban Institute of Meteorology, the Institute of Physical Planning and other sources. Weather events that have generated these floods (hurricanes, cold front systems, southern winds and extratropical system combinations) are described, taking into account the influence of ENSO event and thermohaline structure changes at the end of the XX Century. The coastal flooding behavior shows an increase in frequency and intensity in the last 40 years, as a consequence of severe event intensity and frequency growth, in coincidence with higher sea surface temperature, mixed layer depth and salinity on the Cuban surrounding waters. Most of the maximum values of thermohaline parameters were located around the Cuban Western Region, in coincidence with the most favorable area for tropical cyclone development. ENSO acts as an important modulator of the coastal flood occurrence over the Cuban territory. When it is active, its behavior influences on the frequency and intensity increase of winter floods, but inhibits the hurricane activity over the Cuban coastal zone. Hence, in this case, the coastal flood occurrence by hurricanes decreases and the other way around.

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