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.

An analysis of the coastal flood behavior on Cuban 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.

The Global Navigation Satellite System (GNSS) airborne radio occultation (ARO) technique is used to retrieve profiles of the atmosphere during reconnaissance missions for atmospheric rivers (ARs) on the west coast of the United States. The measurements of refractive bending angle integrate the effects of variations in refractive index over long near‐horizontal ray‐paths from a spaceborne transmitter to a receiver onboard an aircraft. A forward operator is required to assimilate ARO observations, which are sensitive to pressure, temperature, and humidity, into numerical weather prediction models to support forecasting of ARs. A two‐dimensional (2D) bending angle operator is proposed to enable capturing key atmospheric features associated with strong ARs. Comparison to a one‐dimensional (1D) forward model supports the evidence of large bending angle departures within 3–7 km impact heights for observations collected in a region characterized by the integrated water vapor transport (IVT) magnitude above 500 kg . The assessment of the 2D forward model for ARO retrievals is based on a sequence of six flights leading up to a significant AR precipitation event in January 2021. Since the observations often sample regions outside the AR where moisture is low, the significance of horizontal variations is obscured in the average bending angle statistics. Examples from individual flights sampling the cross‐section of an AR support the need for the 2D forward model. Additional simulation experiments are performed to quantify forward modeling errors due to tangent point drift and horizontal gradients suggesting contributions on the order of 5% and 20%, respectively.

A global Four‐Dimensional Ensemble Variational (4DEnVar) data assimilation system for the Atmospheric component of the Model for Prediction Across Scales (MPAS‐A) is presented that uses the Joint Effort for Data assimilation Integration (JEDI). Dual‐resolution cycling experiments with a 30 km analysis but an ensemble run at a coarser (60 km) resolution are shown to perform well, thereby reducing the computational cost. Month long global cycling data assimilation experiments show that 4DEnVar updates have lower mean errors in both observation and model space than comparable 3DEnVar experiments. Additional improvements over 4DEnVar are then demonstrated when using Hybrid‐4DEnVar, which leverages the benefits of both flow‐dependent ensemble covariance and a static climatological covariance, and when assimilating all‐sky Advanced Microwave Sounding Unit‐A (AMSU‐A) radiance observations. Lastly, extended forecasts initialized from the four‐dimensional analyses are compared with forecasts initialized from three‐dimensional analyses. A particular focus is on the prediction of clouds and precipitation in forecasts initialized from Hybrid‐4DEnVar versus Hybrid‐3DEnVar analyses. Results from extended forecasts show that both forecasts of traditional meteorological fields and precipitation are improved through use of Hybrid‐4DEnVar. However, improvements in precipitation forecasts from 4D methods are shown to be most significant in the southern hemisphere, consistent with where the largest improvements in other meteorological fields are found. Significant improvements in precipitation forecasts in the tropics are found in both 3D and 4D experiments assimilating all‐sky AMSU‐A radiance observations. In summary, 4DEnVar and Hybrid‐4DEnVar capabilities are available through MPAS‐JEDI—an open‐source community developed tool—and perform well during continuous global cycling experiments across traditional verification metrics.

Atmospheric River Reconnaissance (AR Recon) collects targeted observations during major winter weather events associated with heavy precipitation and flooding on the US West Coast. Ten WC‐130J weather reconnaissance aircraft equipped with Global Navigation Satellite System airborne radio occultation (ARO) technology provide additional data on the broader AR environment, specifically as the AR approaches the coastline. This study evaluates the impact of assimilating ARO bending angle observations using the Model for Prediction Across Scales—Atmosphere—Joint Effort for Data assimilation Integration (MPAS‐JEDI). Experiments use the Local Ensemble Transform Kalman Filter and Three‐Dimensional Ensemble‐Variational algorithms. The control experiment assimilates only global conventional data, while the other experiments incorporate additional ARO observations. The results show that ARO assimilation corrects moisture, temperature, and wind fields, and reduces the error in the integrated vapor transport forecast at landfall. The changes mitigate overestimated precipitation in mountainous regions of Washington and Idaho.

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.