Model Data Assimilation Research Articles

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R2 = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters.

Which filter for data assimilation in water quality models? Focus on oxygen reaeration and heterotrophic bacteria activity

With the development of sophisticated water quality models and the advances in computational power, data assimilation (DA) techniques, especially ensemble-based methods (the ensemble Kalman filter and particle filter), are attracting considerable attention in water quality modeling for improving the estimation of state variables and parameters in water quality models. The ensemble Kalman filter (EnKF) has become the most popular DA method while the particle filter (PF), which does not rely on Gaussian or quasi-linearity assumptions, is seldom applied in water quality modeling. Here, we present a comparison between the PF and EnKF for the update of model parameters related to river metabolism. The two filters are implemented in ProSe-PA, a hydro-biogeochemical software, and their performance is assessed on two synthetic case studies. The results indicate that PF and EnKF can estimate dissolved oxygen concentrations and the posterior probability distribution function of the associated parameters, either precisely for both filters in the case of a slightly nonlinear system (reaeration at the air–water interface) or more precisely for the PF in the case of a strongly nonlinear system (organic matter degradation) dominated by heterotrophic bacterial activities. Since the PF is more accurate, its usage is recommended for water quality modeling and guidelines are provided for its set-up.

A reduced order with data assimilation model: Theory and practice

Numerical simulations are extensively used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks and entire cities. Fast-running Non-Intrusive Reduced Order Model (NIROM) for predicting the turbulent air flows has been proved to be an efficient method to provide numerical forecasting results. However, due to the reduced space on which the model operates, the solution includes uncertainties. Additionally, any computational methodology contributes to uncertainty due to finite precision and the consequent accumulation and amplification of round-off errors. Taking into account these uncertainties is essential for the acceptance of any numerical simulation. In this paper we combine the NIROM method with Data Assimilation (DA), the main question is how to incorporate data (e.g. from physical measurements) in models in a suitable way, in order to improve model predictions and quantify prediction uncertainty. Here, the focus is on the prediction of nonlinear dynamical systems (the classical application example being weather forecasting). DA is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The Reduced Order Data Assimilation (RODA) model we propose in this paper achieves both efficiency and accuracy including Variational DA into NIROM. The model we present is applied to the pollutant dispersion within an urban environment.

Influence of the Caribbean Sea eddy field on Loop Current predictions

Previous studies have shown how the passage of eddies from the Caribbean Sea (CS) to the Gulf of Mexico (GoM) can impact the Loop Current (LC) system, in particular the detachments of LC Eddies (LCEs). Here we used numerical modeling to investigate the impact of the eddy field in the CS on LC predictions. We used a HYCOM ocean model configuration of the North Atlantic at 1/12° resolution to perform two data-assimilative experiments: one in which all available observations were assimilated (Ref), and one in which all available observations were assimilated except in the CS, where climatological altimetry values were assimilated instead of actual observations, leading to dampening the mesoscale activity there (NoCarib). These experiments took place in 2015, when the LC was very active with several LCE detachments, re-attachments, and separations. Each of these experiments was used to initialize 28 60-day forecast simulations every 10 days. In terms of model Sea Surface Height (SSH), the forecasts initialized with the Ref experiment had, on average, lower errors than the forecasts initialized with the NoCarib experiment in the southeastern part of the GoM, with a peak during the 31-40 day forecast period. More importantly, the errors in predicting the date of the next LCE detachment or separation were smaller in the forecasts initialized from the more realistic Ref experiment. Finally, the forecasts initialized by the NoCarib experiment showed a much higher level of false negatives predictions, meaning that no LCE detachment was predicted whereas a detachment actually happened. Overall, 68% of LCE detachments were predicted with an error smaller than 15 days in the forecasts initialized from the more realistic Ref experiment, but only 32% in the forecasts initialized from the NoCarib experiment, stressing the importance of the CS eddy field for predicting the LC evolution. These findings have implications on the GoM predictability, highlighting the need to either run data-assimilative models covering both the GoM and the CS, or pay particular attention to accurate boundary conditions for limited-area GoM models.

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest

Abstract. Today, the most popular approaches in agricultural forecasting leverage process-based crop models, crop monitoring data, and/or remote sensing imagery. Individually, each of these tools has its own unique advantages but is, nonetheless, limited in prediction accuracy, precision, or both. In this study we integrate in situ and remote sensing (RS) soil moisture observations with APSIM model through sequential data assimilation to evaluate the improvement in model predictions of downstream state variables across five experimental sites in the US Midwest. Four RS data products and in situ observations spanning 19 site years were used through two data assimilation approaches, namely ensemble Kalman filter (EnKF) and generalized ensemble filter (GEF), to constrain model states at observed time steps and estimate joint background and observation error matrices. Then, the assimilation's impact on estimates of soil moisture, yield, normalized difference vegetation index (NDVI), tile drainage, and nitrate leaching was assessed across all site years. When assimilating in situ observations, the accuracy of soil moisture forecasts in the assimilation layers was improved by reducing RMSE by an average of 17 % for 10 cm and ∼28 % for 20 cm depth soil layer across all site years. These changes also led to improved simulation of soil moisture in deeper soil layers by an average of 12 %. Although crop yield was improved by an average of 23 %, the greatest improvement in yield accuracy was demonstrated in site years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of including additional data constraint such as evapotranspiration. The assimilation of RS soil moisture showed a weaker constraint of downstream model state variables when compared to the assimilation of in situ soil moisture. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 5. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2 %. Crop yield prediction was improved when assimilating both in situ and remote sensing soil moisture observations, and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations.

Flow Regime-Dependent, Discharge Uncertainty Envelope for Uncertainty Analysis with Ensemble Methods

A discharge uncertainty envelope is presented that provides an observation error model for data assimilation (DA) using discharge observations derived from measurement of stage using a rating curve. It uniquely represents the rating curve representation error, which is due to scale and process incompatibility between the rating curve hydrodynamic model and “true” discharge, within the observation error model. Ensemble methods, specifically, the iterative ensemble smoother (IES) algorithms in PEST++, provide the DA framework for this observation error model. The purpose of the uncertainty envelope is to describe prior observation uncertainty for ensemble methods of DA. Envelope implementation goals are (1) limiting the spread of the envelope to avoid conditioning to extreme parameter values and producing posterior parameter distributions with increased variance, and (2) incorporating a representative degree of observation uncertainty to avoid overfitting, which will introduce bias into posterior parameter estimates and predicted model outcomes. The expected uncertainty envelope is flow regime dependent and is delineated using stochastic, statistical methods before undertaking history matching with IES. Analysis of the goodness-of-fit between stochastically estimated “true” discharge and observed discharge provides criteria for the selection of best-fit parameter ensembles from IES results.

Scripps Argo Trajectory-Based Velocity Product: Global Estimates of Absolute Velocity Derived from Core, Biogeochemical, and Deep Argo Float Trajectories at Parking Depth

Abstract Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800–1200 dbar layer at 1° horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems. Significance Statement The aim of this study is to provide observation-based estimates of the large-scale, subsurface ocean circulation. We exploit the drift of autonomous profiling floats to carefully isolate the inferred circulation at the parking depth, and combine observations from over 11 000 floats, sampling between 2001 and 2020, to deliver a new dataset with unprecedented accuracy. The new estimates of subsurface currents are suitable for assessing global models, reanalyses, and forecasts, and for constraining ocean circulation in data-assimilating models.

Fundamentals of Numerical Modeling of the Mid-latitude Ionosphere

The ionosphere is one of the key components of the near-Earth’s space environment and has a practical consequence to the human society as a nearest region of the space environment to the Earth. Therefore, it becomes essential to specify and forecast the state of the ionosphere using both the observations and numerical models. In particular, numerical modeling of the ionosphere is a prerequisite not only for better understanding of the physical processes occurring within the ionosphere but also for the specification and forecast of the space weather. There are several approaches for modeling the ionosphere, including data-based empirical modeling, physics-based theoretical modeling and data assimilation modeling. In this review, these three types of the ionospheric model are briefly introduced with recently available models. And among those approaches, fundamental aspects of the physics-based ionospheric model will be described using the basic equations governing the mid-latitude ionosphere. Then a numerical solution of the equations will be discussed with required boundary conditions.

A‐CHAIM: Near‐Real‐Time Data Assimilation of the High Latitude Ionosphere With a Particle Filter

AbstractThe Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is an operational ionospheric data assimilation model that provides a 3D representation of the high latitude ionosphere in Near‐Real‐Time (NRT). A‐CHAIM uses low‐latency observations of slant Total Electron Content (sTEC) from ground‐based Global Navigation Satellite System (GNSS) receivers, ionosondes, and vertical TEC from the JASON‐3 altimeter satellite to produce an updated electron density model above 45° geomagnetic latitude. A‐CHAIM is the first operational use of a particle filter data assimilation for space environment modeling, to account for the nonlinear nature of sTEC observations. The large number (>104) of simultaneous observations creates significant problems with particle weight degeneracy, which is addressed by combining measurements to form new composite observables. The performance of A‐CHAIM is assessed by comparing the model outputs to unassimilated ionosonde observations, as well as to in‐situ electron density observations from the SWARM and DMSP satellites. During moderately disturbed conditions from 21 September 2021 through 29 September 2021, A‐CHAIM demonstrates a 40%–50% reduction in error relative to the background model in the F2‐layer critical frequency (foF2) at midlatitude and auroral reference stations, and little change at higher latitudes. The height of the F2‐layer (hmF2) shows a small 5%–15% improvement at all latitudes. In the topside, A‐CHAIM demonstrates a 15%–20% reduction in error for the Swarm satellites, and a 23%–28% reduction in error for the DMSP satellites. The reduction in error is distributed evenly over the assimilation region, including in data‐sparse regions.

Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter

Water level simulation for complex water river networks is complex, and existing forecasting models are mainly used for single-channel rivers. In this paper, we present a new data assimilation model based on the ensemble Kalman filter (EnKF) for accurate water level simulation in complex river networks. The EnKF-based data model was tested on simulated water level data from a river network hydrodynamic model and optimized through parameter analysis. It was then applied to a real mountainous single-channel river and plain river network and compared with a data assimilation model based on the extended Kalman filter (EKF). The results showed that the EnKF-based model, with a medium ensemble sample size of 100–150, normal observation noise of 0.0001–0.01 m, and a high standard deviation of 0.01–0.1 m, outperformed the EKF-based model, with a 49% reduction in simulation errors, a 45% reduction in calculation cost, and a 43% reduction in filtering time. Furthermore, the EnKF-based data assimilation model predicted the water level in the plain river network better than the mountainous single-channel river. Around 5 to 8 h were required for data assimilation; afterwards, the model could make accurate predictions covering 20 to 30 h. The EnKF-based data assimilation model offers a potential solution for water level predictions in river networks.

Enable high-resolution, real-time ensemble simulation and data assimilation of flood inundation using distributed GPU parallelization

Numerical modeling of the intensity and evolution of flood events are affected by multiple sources of uncertainty such as precipitation and land surface conditions. To quantify and curb these uncertainties, an ensemble-based simulation and data assimilation model for pluvial flood inundation is constructed. The shallow water equation is decoupled in the x and y directions, and the inertial form of the Saint-Venant equation is chosen to realize fast computation. The probability distribution of the input and output factors is described using Monte Carlo samples. Subsequently, a particle filter is incorporated to enable the assimilation of hydrological observations and improve prediction accuracy. To achieve high-resolution, real-time ensemble simulation, heterogeneous computing technologies based on CUDA (compute unified device architecture) and a distributed storage multi-GPU (graphics processing unit) system are used. Multiple optimization skills are employed to ensure the parallel efficiency and scalability of the simulation program. Taking an urban area of Fuzhou, China as an example, a model with a 3-m spatial resolution and 4.0 million units is constructed, and 8 Tesla P100 GPUs are used for the parallel calculation of 96 model instances. Under these settings, the ensemble simulation of a 1-hour hydraulic process takes 2.0 min, which achieves a 2680 × estimated speedup compared with a single-thread run on CPU. The calculation results indicate that the particle filter method effectively constrains simulation uncertainty while providing the confidence intervals of key hydrological elements such as streamflow, submerged area, and submerged water depth. The presented approaches show promising capabilities in handling the uncertainties in flood modeling as well as enhancing prediction efficiency.

A Review on the Development of Two-Way Coupled Atmospheric-Hydrological Models

The past two decades have seen an intensive development in two-way coupled atmospheric and hydrological models, providing new opportunities to thoroughly understand hydrology–atmosphere coupling and improve hydrometeorological forecasting, which has not been possible before. This paper summarizes recent developments in hydrological presentation in land surface models (LSMs) and climate models, and the two-way coupling of atmospheric and hydrological models. The fully coupled models have been widely applied in identifying the impact of lateral surface and subsurface water transport in a land–atmosphere coupled system, and hydrometeorological simulations using techniques such as parameter calibration, data assimilation, and hydrology model structure revision have been used to improve the model accuracy. However, their applications still face major challenges, e.g., the complexity of hydrological parameter calibration, the lack of understanding of the physical mechanisms at high resolution, the parameterization of anthropogenic activities, and the limitations in simulation domain and period. Despite these difficulties, fully coupled atmospheric and hydrological models will gradually evolve into powerful tools to reproduce regional water cycles, offering significant potential for scientifically investigating water resources security issues affected by both climate change and human activities.

A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers

In recent years, machine learning methods represented by deep neural networks (DNNs) have been a new paradigm of turbulence modeling. However, in the scenario of high Reynolds numbers, there are still some bottlenecks, including the lack of high-fidelity data and the stability problem in the coupling process of turbulence models and the Reynolds-averaged Navier–Stokes (RANS) solvers. In this paper, we propose an improved ensemble Kalman inversion method as a unified approach of data assimilation and turbulence modeling for separated flows at high Reynolds numbers. A novel ensemble design method based on transfer learning and a regularizing strategy are proposed to improve the method. The trainable parameters of DNN are optimized according to the given experimental surface pressure coefficients in the framework of mutual coupling between the RANS solvers and DNN eddy viscosity models. In this way, data assimilation and model training are integrated into one step to get the high-fidelity turbulence models agree well with experiments directly. The effectiveness of the method is verified by cases of flows around S809 airfoil at high Reynolds numbers. Through assimilation of few experimental states, we can get turbulence models generalizing well to both attached and separated flows at different angles of attack, which also perform well in stability and robustness. The errors of lift coefficients at high angles of attack are significantly reduced by more than three times compared with the traditional Spalart–Allmaras model.

Reducing uncertainty on land subsidence modeling prediction by a sequential data-integration approach. Application to the Arlua off-shore reservoir in Italy

In recent years, the awareness about the critical importance of correctly dealing with uncertainty in numerical models is spreading over an increasing number of application fields, including geomechanics for energy resources. Sources of uncertainty are related for instance to the mathematical constitutive law that describes the deep rock behavior, the geomechanical parameters and the geological nature of the investigated field. Data assimilation techniques take advantage of the increasing availability of in-situ measurements in order to account for and reduce uncertainties in modeling outcomes. Recently, a comprehensive workflow for a stochastic analysis of land subsidence has been proposed. It combines a geomechanical model with successive steps of model diagnostic and data assimilation techniques, like χ2-test, Red Flag and Ensemble Smoother. Successive steps require increasing computational effort, but provide more accurate outcomes. The application of the workflow is repeated in time when new measurements become available so that the model is dynamically updated and the uncertainties are reduced. The objective of this study is to apply and validate the workflow on the Arlua reservoir. The outcome is the development and experimentation of a comprehensive geomechanical model that automatically integrates real measurements and progressively reduces the prediction uncertainties by a continuous training in time. The application confirms the effectiveness of the proposed integrated approach and proves its robustness and quality in a complex off-shore reservoir in Italy.

Feasibility study of data assimilation for Thermoregulation models

Feasibility study of data assimilation for Thermoregulation models

Improving the particle filter for data assimilation in hydraulic modeling by using a Cauchy likelihood function

Over the recent decades, particle filter (PF)-based data assimilation has been adopted to improve hydrodynamic simulation due to its wide applicability and effectiveness for nonlinear and non-Gaussian models. The particle weighting is the core procedure to combine the observations and simulations within the PF framework, in which the Gaussian likelihood function (GLF) is commonly used to update weight values. However, there are some widely acknowledged challenges within the GLF-based PF. Among them, the excessive dispersion problem occurs when the standard deviation of the Gaussian likelihood is small, which may even cause filtering failures. Unfortunately, this issue has not been sufficiently addressed in the hydrology community. More attention needs to be paid to the likelihood function since the errors of the observed water stage (represented by the standard deviation) from hydrometric stations are usually small. This study focuses on the form of the likelihood function and parameter tuning within the particle weighting procedure of the PF for data assimilation. The limitations of using the common GLF were clarified and a novel Cauchy likelihood function (CLF) was proposed and tested in both a synthetic and a real-world case study using a PF-based hydraulic model. Comparisons showed that the CLF-based PF can effectively relieve the problem of excessive dispersion within the GLF-based PF framework, resulting in more stable and more accurate results. These findings not only provide another option for the selection of likelihood function within the PF framework, but also offer reliable evidence for improving hydraulic modeling by incorporating high-precision observations using the data assimilation technique.

Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method

Suspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of modeling the suspended sediment concentrations (SSCs) with newly developed techniques. Based on a three-dimensional suspended cohesive sediment transport model, the transport of suspended sediment and SSCs are simulated by assimilating SSCs retrieved from the Geostationary Ocean Color Imager (GOCI) with the adjoint data assimilation in the Hangzhou Bay, a typical strong tidal estuary along the coast of the East China Sea. To improve the effect of the data assimilation, the penalty function method, in which the reasonable constraints of the estimated model parameters are added to the cost function as penalty terms, will be introduced for the first time into the adjoint data assimilation in the SSCs modeling. In twin experiments, the prescribed spatially varying settling velocity is estimated by assimilating the synthetic SSC observations, and the results show that the penalty function method can further improve the effect of data assimilation and parameter estimation, regardless of synthetic SSC observations being contaminated by random artificial errors. In practical experiments, the spatially varying settling velocity is firstly estimated by assimilating the actual GOCI-retrieved SSCs. The results demonstrate that the simulated results can be improved by the adjoint data assimilation, and the penalty function method can additionally reduce the mean absolute error (MAE) between the independent check observations and the corresponding simulated SSCs from 1.44 × 10−1 kg/m3 to 1.30 × 10−1 kg/m3. To pursue greater simulation accuracy, the spatially varying settling velocity, resuspension rate, critical shear stress and initial conditions are simultaneously estimated by assimilating the actual GOCI-retrieved SSCs to simulate the SSCs in the Hangzhou Bay. When the adjoint data assimilation and the penalty function method are simultaneously used, the MAE between the independent check observations and the corresponding simulated SSCs is just 9.90 × 10−2 kg/m3, which is substantially less than that when only the settling velocity is estimated. The MAE is also considerably less than that when the four model parameters are estimated to be without using the penalty function method. This study indicates that the adjoint data assimilation can effectively improve the SSC simulation accuracy, and the penalty function method can limit the variation range of the estimated model parameters to further improve the effect of data assimilation and parameter estimation.

The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.

Combined assimilation of radar and lightning data for the short-term forecast of severe convection system

In this study, a lightning data assimilation (LDA) method adjusting dynamical fields was examined in the rapid cycle with Weather Research and Forecasting (WRF) model and WRF Data Assimilation (WRFDA). This method retrieves pseudo vertical velocity profiles from total lightning observations and promotes wind convergence over lightning regions. This newly developed LDA scheme is compared with radar data assimilation (RDA) that has been routinely applied in severe storm nowcasting models. Depending on whether the radar or lightning data is assimilated, four experiments were designed to evaluate the positive impacts of dynamical adjustment from LDA. Using a typical severe mesoscale convection system occurred in Beijing, we found that the effect of RDA and LDA are different. The assimilation of radar radial velocity mainly improves the forecast in a longer time, while assimilation of pseudo-vertical-velocity from lightning data mainly corrects intensity and location of the forecasted precipitation. When both radar and lightning data are assimilated, the small-scale wind convergence are promoted and contributes to the intensified updrafts. Thermal and water vapor fields are also adjusted indirectly. Consequently, the convective precipitation is significantly improved and the positive impacts from the combined assimilation scheme persisted over a longer period (at least 3 h). It can be concluded that a combined assimilation of convective data from multiple sources such as radar and lightning data enhance prominently the accuracy of short-term convection forecasts.

Assimilation of Clear-sky FY-4A AGRI radiances within the WRFDA system for the prediction of a landfalling Typhoon Hagupit (2020)

The capability of assimilating Advanced Geostationary Radiance Imager (AGRI) radiances of FengYun-4A (FY-4A) satellite is realized in the framework of the Weather Research and Forecasting model's Data Assimilation (WRFDA) system. Two experiments are carried out based on a landfalling Typhoon Hagupit to discuss the added values of assimilating the clear-sky radiances on typhoon's analysis and prediction. One experiment assimilates both the AGRI radiances and the Global Telecommunications System (GTS) conventional data, while another experiment only applies GTS conventional data. The results show that the errors of the analysis fields are reduced after the assimilation of AGRI radiances, which can be reflected by the vertical root mean square error (RMSE) profile of both dynamic and thermodynamic variables against the radiosonde observations. Besides, the final improvement of the track and rainfall prediction can possibly be accredited to the analyzed 500 hPa geopotential height increment and the vertically averaged horizontal water vapor flux to some degree. The area is enlarged with relative humidity exceeding 95% by the assimilation of AGRI radiances in the typhoon core, leading to the refinement of both the rainfall distribution and intensity in the prediction. With the improved dynamic and thermodynamic fields, the track of the AGRI experiment is more accurate with a mean error 37 km in the whole 36 h.