An Impact Study of GNSS RO Data on the Prediction of Typhoon Nepartak (2016) Using a Multi-resolution Global Model with 3D-Hybrid Data Assimilation

Conventional soundings are rather limited over the western North Pacific and can be largely compensated by GNSS radio occultation (RO) data. We utilize the GSI hybrid assimilation system to assimilate RO data and the multi-resolution global model (MPAS) to investigate the RO data impact on prediction of Typhoon Nepartak that passed over southern Taiwan in 2016. In this study, the performances of assimilation with local RO refractivity and bending angle operators are compared for the assimilation analysis and typhoon forecast.Assimilations with both RO data have shown similar and comparable temperature and moisture increments after cycling assimilation and largely reduce the RMSEs of the forecast without RO data assimilation at later times. The forecast results at 60-15-km resolution show that RO data assimilation largely improves the typhoon track prediction compared to that without RO data assimilation, and assimilation with bending angle has better performances than assimilation with refractivity, in particular for wind forecast. The improvement in the forecasted track is mainly due to the improved simulation for the translation of the typhoon. Diagnostics of wavenumber-one potential vorticity (PV) tendency budget indicates that the northwestward typhoon translation dominated by PV horizontal advection is slowed down by the southward tendency induced by the stronger differential diabatic heating south of the typhoon center for bending-angle assimilation. Simulations with the enhanced resolution of 3 km in the region of the storm track show further improvements in both typhoon track and intensity prediction with RO data assimilation. Positive RO impacts on track prediction are also illustrated for other two typhoons using the MPAS-GSI system.

Machine learning-based uncertainty quantification for data assimilation: a simple model experiment

<p>Quantifying forecast uncertainty is a key aspect of state-of-the-art data assimilation systems which has a large impact on the quality of the analysis and then the following forecast. In recent years, most operational data assimilation systems incorporate state-dependent uncertainty quantification approaches based on 4-dimensional variational approaches, ensemble-based approaches, or their combination. However, these quantifications of state-dependent uncertainties have a large computational cost. Machine learning techniques consist of trainable statistical models that can represent complex functional dependencies among different groups of variables. In this work, we use a fully connected two hidden layer neural network for the state-dependent quantification of forecast uncertainty in the context of data assimilation. The input to the network is a set of three consecutive forecasted states centered at the desired lead time and the network’s output is a corrected forecasted state and an estimation of its uncertainty. We train the network using a loss function based on the observation likelihood and a large database of forecasts and their corresponding analysis. We perform observing system simulation experiments using the Lorenz 96 model as a proof-of-concept and for an evaluation of the technique in comparison with classic ensemble-based approaches.</p><p> Results show that our approach can produce state-dependent estimations of the forecast uncertainty without the need for an ensemble of states (at a much lower computational cost),  particularly in the presence of model errors. This opens opportunities for the development of a new type of hybrid data assimilation system combining the capabilities of machine learning and ensembles.</p>

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

Evaluation of the Four-dimensional Ensemble-Variational Hybrid Data Assimilation with Self-consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the GSI-4DEnVar data assimilation system for the HWRF model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-3DEnVar data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the NCEP GFS ensemble Kalman filtering data assimilation system. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, MSLP, and MSW forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

An epidemiological modelling approach for COVID-19 via data assimilation

The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.

Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble

The viability of a parameterless hybrid data assimilation algorithm is investigated. As an alternative to the traditional hybrid covariance scheme, hybrid gain data assimilation (HGDA) was proposed to blend the gain matrix derived from the variational method and the ensemble-based Kalman filter (EnKF). A previously proposed HGDA algorithm uses a two-step process applying the EnKF with a variational update. The algorithm is modified here to limit the variational correction to the subspace orthogonal to the ensemble perturbation subspace without the use of a hybrid weighting parameter, as the optimization of such a parameter is nontrivial. The modified HGDA algorithm is investigated with a quasigeostrophic (QG) model. Results indicate that when the climatological background error covariance matrix B and the observation error covariance R are well estimated, state estimates from the parameterless HGDA are more accurate than the parameter-dependent HGDA. The parameterless HGDA not only has potential advantages over the standard HGDA as an online data assimilation algorithm but can also serve as a valuable diagnostic tool for tuning the B and R matrices. It is also found that in this QG model, the empirically best static B matrix for the stand-alone 3DVAR has high variance at larger spatial scales, which degrades the accuracy of the HGDA systems and may not be the best choice for hybrid methods in general. A comparison of defining the orthogonal subspace globally or locally demonstrates that global orthogonality is more advantageous for stabilizing the hybrid system and maintains large-scale balances.