Sensitivity of Surface Currents to Bathymetry in a Partially-Mixed Estuary with Applications to Inverse Modeling

An inversion technique was tested for estimating bathymetry from observations of surface currents in a partially-mixed estuary, Mouth of the Columbia River (MCR). The methodology uses an iterative ensemble-based assimilation scheme which is found to have good skill for recovering bathymetry from observations distributed in space and time. However, the inversion skill is highly dependent on the tidal phase, location of the observations, and flow-dependent estuary dynamics. Inversion skill was found to degrade during periods of higher river discharge (up to ~ 12,000m3), or low tidal amplitude, while inversion of depth-averaged velocities instead of surface velocities caused increased skill throughout the domain. These results point to dynamical limits on inversion skill, caused by changes in estuary dynamics that affect the sensitivity of surface velocities to bathymetry. An adjoint sensitivity analysis is used to visualize these effects and is combined with data-denial experiments to explore the flow-dependent inversion skill.

WRF-model sensitivity test and assimilation studies of Cempaka tropical cyclone

The WRF model was used to forecast the most intensive stage of Cempaka Tropical Cyclone (TC) on 27 - 29 November 2017. This study evaluates the combination of cumulus and microphysics parameterization and the efficiency of assimilation method to predict pressure values at the center of the cyclone, maximum wind speed, and cyclone track. This study tested 18 combinations of cumulus and microphysics parameterization schemes to obtain the best combination of both parameterization schemes which later on called as control model (CTL). Afterward, assimilation schemes using 3DVAR cycles of 1, 3, 6 hours, and 4DVAR, namely RUC01, RUC03, RUC06, and 4DV, were evaluated for two domains with grid size of each 30 and 10 km. GFS data of 0.25-degree and the Yogyakarta Doppler Radar data were used as the initial data and assimilation data input, respectively. The result of the parameterization test shows that there is no combination of parameterization schemes that constantly outperform all variables. However, the combination of Kain-Fritsch and Thompson can produce the best prediction of tropical cyclone track compared to other combinations. While, the RUC03 assimilation scheme was noted as the most efficient method based on the accuracy of track prediction and duration of model time integration.

FYRE Climate: a high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012

Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).

The improvements to the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv1)

South China Sea Operational Oceanography Forecasting System (SCSOFS) had been constructed and operated in National Marine Environmental Forecasting Center of China to provide daily updated hydrodynamic forecasting in SCS for the future 5 days since 2013. This paper presents recent comprehensive updates of the configurations of the physical model and data assimilation scheme in order to improve SCSOFS forecasting skills. It highlights three of the most sensitive updates, including sea surface atmospheric forcing method, tracers advection discrete scheme, and modification of data assimilation scheme. Inter-comparison and accuracy assessment among five versions during the whole upgrading processes are performed by employing OceanPredict Inter-comparison and Validation Task Team Class4 metrics. The results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version known as SCSOFSv1. Domain averaged monthly mean root mean square errors decrease from 1.21 °C to 0.52 °C for sea surface temperature, from 21.6 cm to 8.5 cm for sea level anomaly, respectively.

Ocean Plastic Assimilator v0.2: assimilation of plastic concentration data into Lagrangian dispersion models

A numerical scheme to perform data assimilation of concentration measurements in Lagrangian models is presented, along with its first implementation called Ocean Plastic Assimilator, which aims to improve predictions of the distributions of plastics over the oceans. This scheme uses an ensemble method over a set of particle dispersion simulations. At each step, concentration observations are assimilated across the ensemble members by switching back and forth between Eulerian and Lagrangian representations. We design two experiments to assess the scheme efficacy and efficiency when assimilating simulated data in a simple double-gyre model. Analysis convergence is observed with higher accuracy when lowering observation variance or using a circulation model closer to the real circulation. Results show that the distribution of the mass of plastics in an area can effectively be improved with this simple assimilation scheme. Direct application to a real ocean dispersion model of the Great Pacific Garbage Patch is presented with simulated observations, which gives similarly encouraging results. Thus, this method is considered a suitable candidate for creating a tool to assimilate plastic concentration observations in real-world applications to estimate and forecast plastic distributions in the oceans. Finally, several improvements that could further enhance the method efficiency are identified.

Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System Part 1: Methods

The future Surface Water Ocean Topography (SWOT) mission due to be launched in 2022 will extend the capability of existing nadir altimeters to enable two-dimensional mapping at a much higher effective resolution. A significant challenge will be to assimilate this kind of data in high-resolution models. In this context, Observing System Simulation Experiments (OSSEs) have been performed to assess the impact of SWOT on the Mercator Ocean and Copernicus Marine Environment Monitoring Service (CMEMS) global, high-resolution analysis and forecasting system. This paper focusses on the design of these OSSEs, in terms of simulated observations and assimilation systems (ocean model and data assimilation schemes). The main results are discussed in a companion paper. Two main updates of the current Mercator Ocean data assimilation scheme have been made to improve the assimilation of information from SWOT data. The first one is related to a different parametrisation of the model error covariance, and the second to the use of a four-dimensional (4D) version of the data assimilation scheme. These improvements are described in detail and their contribution is quantified. The Nature Run (NR) used to represent the “truth ocean” is validated by comparing it with altimeter observations, and is then used to simulate pseudo-observations required for the OSSEs. Finally, the design of the OSSEs is evaluated by ensuring that the differences between the assimilation system and the NR are statistically consistent with the misfits between real ocean observations and real-time operational systems.

Improving the Estimation of Temperature and Salinity by Assimilation of Observed Sound Speed Profiles

Sound Speed Profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation (referenced as SSP-constraint 3DVAR (SSPC-3DVAR) ), which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. In order to validate the performance of the new assimilation scheme, ideal experiments are firstly carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% (11.1%) for the temperature (salinity), compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future.

Multidecadal assessment of the FYRE Climate daily high-resolution surface reanalysis over France (1871-2012)

<p>Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented by Devers <em>et al.</em> (2020) to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. This work thus proposes (1) to apply this scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset (Caillouet <em>et al.</em>, 2019) and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly time scale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8-km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Compared to reference homogenized series, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran surface reanalysis over France (Vidal <em>et al.</em>, 2010) available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is made available from http://doi.org/10.5281/zenodo.4005573 (precipitation) and http://doi.org/10.5281/zenodo.4006472 (temperature). Further details on FYRE Climate can be found in Devers <em>et al.</em> (2021).</p><p>Caillouet, L., Vidal, J.-P., Sauquet, E., Graff, B., Soubeyroux, J.-M. (2021) SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France. <em>Earth System Science Data</em>, 11, 241-260. https://doi.org/10.5194/essd-11-241-2019</p><p>Devers, A., Vidal, J.-P., Lauvernet, C., Graff, B., Vannier, O. (2020) A framework for high-resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions. <em>Quarterly Journal of the Royal Meteorological Society</em>, 2020, 146, 153-17. https://doi.org/10.1002/qj.3663</p><p>Devers, A., Vidal, J.-P., Lauvernet, C., Vannier, O. (2021) FYRE Climate: A high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012. C<em>limate of the Past Discussions</em>, in review, https://doi.org/10.5194/cp-2020-156</p><p>Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. <em>International Journal of Climatology</em>, 30, 1627-1644. https://doi.org/10.1002/joc.2003</p>

The Impact of Solar Activity on Forecasting the Upper Atmosphere via Assimilation of Electron Density Data

<p>We present a comprehensive comparison of the impact of solar activity on forecasting the ionosphere and thermosphere. Here we investigate the response of physics-based TIE-GCM (thermosphere-ionosphere-electrodynamics general circulation model) in a data assimilation scheme through assimilating radio occultation (RO)-derived electron density (Ne) using an ensemble Kalman filter (KF). Constellation observations of Ne profiles offer opportunities to assess the accuracy of the model forecasted state on a global scale. In this study, we emphasise the importance of understanding how the assimilation results vary with solar activity, which is one of the primary drivers of thermosphere-ionosphere dynamics.</p><p>We validate the assimilation results with independent RO-derived GRACE (Gravity Recovery and Climate Experiment mission) Ne data. The main result is that the forecast Ne agree best with data during the solar minimum compared to solar maximum. The results also show that the assimilation scheme significantly adjusts both the nowcast and forecast states during the two solar activity periods. The results show that TIE-GCM significantly underestimate Ne in low altitudes below 250 km and the assimilation of Ne is not as effective in these lower altitudes compared to higher altitudes. The results demonstrate that assimilation of Ne significantly impacts the neutral mass density estimates via the KF state vector. This impact is larger during solar maximum than solar minimum relative to a control run. The results also demonstrate that the impact of assimilation of Ne on neutral mass density state persists through to forecast state better during solar minimum compared to solar maximum. The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique.</p>