Four-dimensional Variational Data Assimilation System Research Articles

Ocean data assimilation focusing on integral quantities characterizing observation profiles

An observation operator in data assimilation was formalized based on the signatures extracted from the integral quantities contained within observed vertical profiles in the ocean. A four-dimensional variational global ocean data assimilation system, founded on this observation operator, was developed and utilized to conduct preliminary data assimilation experiments over a ten-year assimilation window, comparing the proposed method, namely profile-by-profile matching, with the traditional method, namely point-by-point matching. The proposed method not only demonstrated a point-by-point skill comparable to the traditional method but also provided superior analysis fields in terms of profile shapes on the temperature-salinity plane. This is an indication of a well-balanced analysis field, in contrast to the traditional method, which can produce extremely poor relative errors for certain metrics. Additionally, signatures were shown to successfully represent properties of the water column, such as steric height, and serve as an effective new diagnostic tool. The top-down, or macro–micro, viewpoint in this method is fundamental to the extent that it can offer an alternative view of how we comprehend ocean observations, holding significant implications for the advancement of data assimilation.

Influence of Assimilation of NEXRAD-Derived 2D Inner-Core Structure Data from Single Radar on Numerical Simulations of Hurricane Charley (2004) near Its Landfall

This study presents the first research that assimilates the ground-based NEXRAD observations-derived two-dimensional (2D), azimuthally averaged radar radial velocity and reflectivity within 60 km of radius from the hurricane center to examine their influence on the analysis and prediction of a hurricane near and after its landfall. The mesoscale community Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-VAR) data assimilation system are utilized to conduct data assimilation experiments for Hurricane Charley (2004). Results show that assimilation of the radar inner-core data leads to better forecasts of hurricane tracks, intensity, and precipitation. The improved forecast outcomes imply that the changes in dynamical, thermal, and moisture structures from data assimilations made more reasonable conditions for the hurricane development near and after its landfall. Overall results indicate that the assimilation of the radar-derived 2D inner-core structure could be a feasible way to utilize the radar data for improved hurricane prediction.

Enhancing the Assimilation of SWOT Simulated Observations Using a Multi-Scale 4DVAR Method in Regional Ocean Modeling System

This paper presents an innovative approach to enhance the assimilation of high-resolution simulated observations, specifically targeting Surface Water Ocean Topography (SWOT) Ka-band Radar Interferometer Sea Surface Height (SSH) products, within the Regional Ocean Modeling System (ROMS). Responding to the demand for improved assimilation techniques, we developed a multi-scale Four-Dimensional Variational Data Assimilation (4DVAR) system, building upon validated fine-scale correction capabilities from prior studies. The multi-scale strategy was extended to the ROMS-4DVAR system, providing a comprehensive solution for assimilating high-resolution observations. Leveraging the Observing System Simulation Experiment (OSSE) framework, we conducted a twin experiment comprising a nature run and a free run case. Subsequently, synthetic SWOT SSH measurements were decomposed, considering the model configuration resolution. These components, derived from dense SSH observations, were integrated into a two-step 4DVAR assimilation scheme. The first cycle targets large-scale features for model field correction, and the updated analysis serves as the background for the second assimilation step, addressing fine-scale observation components. Comparisons with the primitive ROMS-4DVAR using a single-scale scheme highlight the superiority of the multi-scale strategy in reducing gaps between the model and the SSH observations. The Root Mean Squared Error (RMSE) is halved, and the Mean Absolute Percentage Error (MAPE) decreases from 2.237% to 0.93%. The two-step assimilation procedure ensures comprehensive multi-scale updates in the SSH field simulation, enhancing fine-scale features in the analysis fields. The quantification of three-dimensional-model dynamic fields further validates the efficiency and superiority of the multi-scale 4DVAR approach, offering a robust methodology for assimilating high-resolution observations within the ROMS.

Comparison of Cold Pool Characteristics of Two Distinct Gust Fronts over Bohai Sea Bay in China

Previous studies have demonstrated that cold pools play a pivotal role in the initiation and organization of convection, yet their influence on the evolution of gust fronts (GFs) remains inadequately understood. A destructive wind event associated with a rearward gust front (RGF; 8 grade gale after passing GF) and a prior gust front (PGF; 10 grade gale before passing GF) over the north coast of China on 10 June 2016 was analyzed. Using multiple forms of observation data, as well as the four-dimensional Variational Doppler Radar Data Assimilation System (VDRAS), we found that the depth and intensity of the cold pool in RGF are relatively shallower and weaker, leading to a correspondingly reduced strength in both outflow and convergence. In contrast, the enhanced vertical shear and boundary northeaster inflow of PGF generate intensified and more organized downdrafts, resulting in a deeper cold pool, robust outflow, and convergence. Two schematic models were proposed to explain the discrepancy between GFs and associated cold pools. We further show that there is an internal correlation between meso-γ-scale vortices (MVs) and cold pools, the collision of MVs strengthened low-level convergence and updraft between these two GFs. Moreover, the consolidation of the two cold pools exacerbates low-layer instability and rotation, generating an intense horizontal vorticity that leads to rapid convective storm intensification. These findings offer novel insights into the diversity of GFs and associated cold pools.

Mesoscale Factors Contributing to the Extreme Rainstorm on 20 July 2021 in Zhengzhou, China, as Revealed by Rapid Update 4DVar Analysis

Abstract The purpose of this study is to diagnose mesoscale factors responsible for the formation and development of an extreme rainstorm that occurred on 20 July 2021 in Zhengzhou, China. The rainstorm produced 201.9 mm of rainfall in 1 h, breaking the record of mainland China for 1-h rainfall accumulation in the past 73 years. Using 2-km continuously cycled analyses with 6-min updates that were produced by assimilating observations from radar and dense surface networks with a four-dimensional variational (4DVar) data assimilation system, we illustrate that the modification of environmental easterlies by three mesoscale disturbances played a critical role in the development of the rainstorm. Among the three systems, a mesobeta-scale low pressure system (mesolow) that developed from an inverted trough southwest of Zhengzhou was key to the formation and intensification of the rainstorm. We show that the rainstorm formed via sequential merging of three convective cells, which initiated along the convergence bands in the mesolow. Further, we present evidence to suggest that the mesolow and two terrain-influenced flows near the Taihang Mountains north of Zhengzhou, including a barrier jet and a downslope flow, contributed to the local intensification of the rainstorm and the intense 1-h rainfall. The three mesoscale features coexisted near Zhengzhou in the several hours before the extreme 1-h rainfall and enhanced local wind convergence and moisture transport synergistically. Our analysis also indicated that the strong midlevel south/southwesterly winds from the mesolow along with the gravity-current-modified low-level northeasterly barrier jet enhanced the vertical wind shear, which provided favorable local environment supporting the severe rainstorm.

Assimilating AMSU-A Radiance Observations with an Ensemble Four-Dimensional Variational (En4DVar) Hybrid Data Assimilation System

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localization in implementation, but more difficult to directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations of radiance observations are undetermined and the transmission of observational information is thereby obstructed. To determine the vertical coordinates of radiance observations, a weighted average hypsometry is proposed. Using this hypsometry, AMSU-A radiance observations are directly assimilated with an ensemble four-dimensional variational (En4DVar) DA system. It consists of a four-dimensional ensemble-variational (4DEnVar) system providing ensemble covariance and a 4DVar system. Observing system simulation experiments show that the hypsometry alleviates the degradations in the late period of medium-range forecast in the Northern Extratropics that occur in the traditional peak-based hypsometry. It obviously improves the analysis qualities and forecast skills of the En4DVar system and its two components, especially in the Southern Extratropics, when incorporating AMSU-A radiance observations. The improvement in the En4DVar-initialized forecast is comparable to that in the 4DVar-initialized forecast in the Southern Extratropics and Tropics. It indicates that a proper hypsometry enables efficient extraction of useful information from AMSU-A radiance observations by 4DEnVar with observation space localization. Therefore, the 4DEnVar provides high-quality ensemble covariances for En4DVar.

A global ocean state estimation using tidally induced vertical-mixing schemes

We updated the Estimated STate of the global Ocean for Climate research (ESTOC), a long-term ocean state estimation dataset that includes physical and biogeochemical fields. The physical field is optimized through a data synthesis experiment using a quasi-global four-dimensional variational data assimilation system, and under the optimized physical field, the biogeochemical field is optimized by using a Green’s function. To improve the reliability of the dynamics reproduced in the model, we implemented tidally induced turbulent mixing schemes and geothermal heat flux. This paper provides an overview of the assimilation system with an emphasis on updated elements and including details of the mixing schemes and presents preliminary results focusing on the warming in the abyssal ocean (below 3000 m) using this updated ESTOC published as Version 04a. The reproduction of both the climatological state and temporal change in abyssal-water properties is, on the whole, improved in this updated ESTOC in comparison with a previous version. This improved reproduction of the abyssal ocean state with the refined physical processes that play major roles in determining abyssal-water properties makes our new synthesized dataset valuable, at least for understanding the abyssal ocean. In this updated ESTOC, the abyssal-water warming results from a near-steady imbalance between the heating effect due to vertical diffusion and geothermal flux, and the cooling effect by advection and horizontal diffusion in the North Pacific, suggesting that the warming occurs over a long timescale. We also show that the downward heat flux related to bottom-intensified vertical mixing near the generation site of the internal tide plays an important role in determining the improved distribution of abyssal-water warming in the Pacific Ocean. These results suggest the importance of using knowledge of vertical mixing as a priori information in ocean state estimation.

Lidar Observations and Data Assimilation of Low-Level Moist Inflows Causing Severe Local Rainfall Associated with a Mesoscale Convective System

Abstract We conducted an observational survey using a ground-based water vapor Raman lidar (RL) during the warm season in Japan to investigate the water vapor structure of low-level inflows that contribute to the formation of a mesoscale convective system (MCS). After the passage of a warm front, low-level moisture convergence contributed to the initiation and development of numerous convective clouds that composed the MCS. The RL observations showed that the vertical profiles of the water vapor mixing ratio (WVMR) associated with low-level inflows into the MCS exceeded 20 g kg−1 below 500 m above sea level, which is comparable to WVMRs in previous reports associated with MCSs in Japan and the United States. We conducted two assimilation experiments using a four-dimensional variational data assimilation system: one is to assimilate operational observational data (CNTL), and the other is to assimilate WVMR vertical profiles and operational observational data (TEST). A comparison between TEST and CNTL showed that data assimilation of the WVMR vertical profiles not only modified the moisture field but also the wind field. It appears that the modifications observed in horizontal wind are related to the modification of the WVMR in the analysis fields. These WVMR and wind modifications improved the reproduction of the frontal surface and forecasting of 6-h precipitation amount slightly. Data assimilation of vertical profiles of the WVMR has positive and negative impacts on the WVMR and horizontal wind, respectively, implying that the vertical profiles of both the horizontal wind and the WVMR might better estimate initial conditions and forecasts. Significance Statement Low-level moisture inflows are one of the key parameters involved in the formation of mesoscale convective systems (MCSs). Therefore, data assimilation of low-level moisture profiles is one of the prospective methods for better forecasting heavy precipitation associated with MCSs. However, few direct observations of the low-level moisture structure associated with MCSs and data assimilation experiments have been undertaken to date. We observed the vertical profiles of moisture associated with an MCS in Japan using a ground-based water vapor Raman lidar and show the existence of a relatively moist low-level inflow into the MCS. The data assimilation of low-level moisture has positive and negative impacts on moisture and horizontal wind, respectively, and improves slightly 6-h precipitation forecasts.

Validation of a Prototype Global 4D-Var Data Assimilation System for the MPAS-Atmosphere Model

AbstractA four-dimensional variational (4D-Var) data assimilation (DA) system is developed for the global nonhydrostatic atmospheric dynamical core of the Model for Prediction Across Scales (MPAS). The nonlinear forward and adjoint models of the MPAS-Atmosphere dynamic core are included in a Python-driven structure to formulate a continuous 4D-Var DA system, shown to effectively minimize the cost function that measures the distances between the nonlinear model simulations and observations. In this study, three idealized experiments with a six-hour assimilation window are conducted to validate and demonstrate the numerical feasibilities of the 4D-Var DA system for both uniform- and variable-resolution meshes. In the first experiment, only a single point observation is assimilated. The resulting solution shows that the analysis increments have highly flow-dependent features. The observations in the second experiment are all model prognostic variables that span the entire global domain, the purpose of which is to check how well the initial conditions six hours prior to the observations can be reversely inferred. The differences between the analysis and the referenced "truth" are significantly smaller than those calculated with the first guess. The third experiment assimilates the mass field only, i.e., potential temperatures in the case of MPAS-Atmosphere, and examines the impacts on the wind field and the mass field under initial conditions. Both the wind vectors and potential temperatures in the analysis agree more with the referenced "truth" than the first guess because the adjustments made to the initial conditions are dynamically consistent in the 4D-Var system.

A nonlinear least squares four-dimensional variational data assimilation system for PM2.5 forecasts (NASM): Description and preliminary evaluation

Air quality is a vital concern globally, especially in China. To improve fine particulate matter (PM2.5) forecasts, a nonlinear least squares four-dimensional variational (NLS-4DVar) data assimilation system was established and applied into the Weather Research and Forecasting model coupled with the “offline” Community Multiscale Air Quality (WRF-CMAQ) model. By assimilating hourly surface PM2.5 observations, the optimal initial conditions (ICs) of the state variable were solved iteratively with the NLS-4DVar method, which uses a Gauss–Newton iterative scheme to handle nonlinearity without a tangent linear or adjoint model, thereby rendering the aerosol assimilation process fast and simple. Observing system simulation experiments (OSSEs) were designed from 10 to 16 November 2018 to evaluate the effectiveness of the NLS-4DVar data assimilation system for PM2.5 forecasts (NASM) assimilation system. The results derived from the OSSEs indicated that the NASM system could effectively assimilate multi-time PM2.5 observations, reduce uncertainty in surface initial PM2.5 concentrations, and thus improve the accuracy of predictions.

Development of four-dimensional variational assimilation system based on the GRAPES–CUACE adjoint model (GRAPES–CUACE-4D-Var V1.0) and its application in emission inversion

Abstract. In this study, a four-dimensional variational (4D-Var) data assimilation system was developed based on the GRAPES–CUACE (Global/Regional Assimilation and PrEdiction System – CMA Unified Atmospheric Chemistry Environmental Forecasting System) atmospheric chemistry model, GRAPES–CUACE adjoint model and L-BFGS-B (extended limited-memory Broyden–Fletcher–Goldfarb–Shanno) algorithm (GRAPES–CUACE-4D-Var) and was applied to optimize black carbon (BC) daily emissions in northern China on 4 July 2016, when a pollution event occurred in Beijing. The results show that the newly constructed GRAPES–CUACE-4D-Var assimilation system is feasible and can be applied to perform BC emission inversion in northern China. The BC concentrations simulated with optimized emissions show improved agreement with the observations over northern China with lower root-mean-square errors and higher correlation coefficients. The model biases are reduced by 20 %–46 %. The validation with observations that were not utilized in the assimilation shows that assimilation makes notable improvements, with values of the model biases reduced by 1 %–36 %. Compared with the prior BC emissions, which are based on statistical data of anthropogenic emissions for 2007, the optimized emissions are considerably reduced. Especially for Beijing, Tianjin, Hebei, Shandong, Shanxi and Henan, the ratios of the optimized emissions to prior emissions are 0.4–0.8, indicating that the BC emissions in these highly industrialized regions have greatly reduced from 2007 to 2016. In the future, further studies on improving the performance of the GRAPES–CUACE-4D-Var assimilation system are still needed and are important for air pollution research in China.

Ensemble 4DVAR (En4DVar) data assimilation in a coastal ocean circulation model. Part II: Implementation offshore Oregon–Washington, USA

The ensemble four-dimensional variational (En4DVar) data assimilation (DA) system introduced in Part I (Pasmans and Kurapov, 2019) is tested in the coastal waters offshore Oregon and Washington, U.S. West coast, during the spring and summer of 2011. The background error covariance B is derived from the forecast ensemble. Satellite sea-surface temperature (SST), sea-surface height (SSH), and daily-averaged radial surface currents from high-frequency radars (HFRs) are assimilated. The performance of the En4DVar system is compared with a “traditional” 4DVAR system using a static B. It is found that the presence of the Columbia River plume has a profound impact on the ensemble B. Near the plume front the SST–SSS covariance can be up to a factor 20 larger in magnitude than in the static B. This introduces large spatial and temporal variability in the ensemble B. The En4DVar system is more successful than the 4DVAR with the static B preserving the temperature–salinity properties when compared to glider data. The En4DVar system also produces more accurate forecasts and analyses for temperature in the subsurface below 30 m at a buoy location on the continental shelf. In comparisons with other surface and subsurface observations En4DVar shows consistent, albeit not significant, improvement over traditional 4DVAR. Large surface temperature–salinity covariances in combination with the episodic occurrence of large-scale errors in the SST observations lead to erroneous freshening in the centre of the model domain. Adding constraints on the surface salinity corrections based on the prior model reduces this effect.

The potential of Orbiting Carbon Observatory-2 data to reduce the uncertainties in CO<sub>2</sub> surface fluxes over Australia using a variational assimilation scheme

Abstract. This paper addresses the question of how much uncertainties in CO2 fluxes over Australia can be reduced by assimilation of total-column carbon dioxide retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite instrument. We apply a four-dimensional variational data assimilation system, based around the Community Multiscale Air Quality (CMAQ) transport-dispersion model. We ran a series of observing system simulation experiments to estimate posterior error statistics of optimized monthly-mean CO2 fluxes in Australia. Our assimilations were run with a horizontal grid resolution of 81 km using OCO-2 data for 2015. Based on four representative months, we find that the integrated flux uncertainty for Australia is reduced from 0.52 to 0.13 Pg C yr−1. Uncertainty reductions of up to 90 % were found at grid-point resolution over productive ecosystems. Our sensitivity experiments show that the choice of the correlation structure in the prior error covariance plays a large role in distributing information from the observations. We also found that biases in the observations would significantly impact the inverted fluxes and could contaminate the final results of the inversion. Biases in prior fluxes are generally removed by the inversion system. Biases in the boundary conditions have a significant impact on retrieved fluxes, but this can be mitigated by including boundary conditions in our retrieved parameters. In general, results from our idealized experiments suggest that flux inversions at this unusually fine scale will yield useful information on the carbon cycle at continental and finer scales.

Development of the tangent linear and adjoint models of the MPAS-Atmosphere dynamic core and applications in adjoint relative sensitivity studies

This study develops and tests a version of the Python-driven, non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A) dynamic model, as well as its tangent linear and adjoint models. The non-linear, non-hydrostatic dynamic core of the MPAS-A is restructured to have a Python driver for the convenience of parsing namelists, manipulating matrices, controlling simulation time flows, reading model inputs, and writing outputs, while the heavy-duty mediation and model layers are retained in Fortran for computational efficiency. Under the same Python-driving structure, developed are the tangent linear and adjoint models for the dynamic core of the MPAS-A model with verified correctness. The case of Jablonowski and Williamson’s baroclinic wave is used for demonstrating the approximation accuracy of the MPAS-A tangent linear model and the applicability of the MPAS-A adjoint model to relative sensitivity studies. Numerical experimental results show that the tangent linear model can well approximate the temporal evolutions of non-linear model perturbations for all model variables over a four-day forecast period. Employing the MPAS-A adjoint model, it is shown that the most sensitive regions of the 24-h forecast of surface pressure are weather dependent. An interesting westward vertical tilting is also found in the relative sensitivity results of a 24-h forecast of surface pressure at a point located within a trough to model initial conditions. This functionality of the MPAS-A adjoint model is highly essential in understanding dynamics and variational data assimilation. Plain Language Summary The MPAS-A is an advanced global numerical weather prediction model with a hexagonal mesh that can be compressed for higher resolutions in some targeted regions of interest and smoothly transitioned to coarse resolutions in others. In this study, a Python-driven MPAS-A model is first developed, combining a flexible Python driver and Fortran’s fast computation, making the MPAS-A model exceedingly user- and platform-friendly. The tangent linear and adjoint models of the MPAS-A dynamical core are then developed, both of which are required for various sensitivity studies. They are also indispensable components of a future MPAS-based global four-dimensional variational (4D-Var) data assimilation system. Finally, the relative sensitivity of a baroclinic instability wave development is obtained and shown using the MPAS-A adjoint model.

Forecast Sensitivity Observation Impact in the 4DVAR and Hybrid-4DVAR Data Assimilation Systems

AbstractIn this study, the observation impacts on 24-h forecast error reduction (FER), based on the adjoint method in the four-dimensional variational (4DVAR) data assimilation (DA) and hybrid-4DVAR DA systems coupled with the Unified Model, were evaluated from 0000 UTC 5 August to 1800 UTC 26 August 2014. The nonlinear FER in hybrid-4DVAR was 12.2% greater than that in 4DVAR due to the use of flow-dependent background error covariance (BEC), which was a weighted combination of the static BEC and the ensemble BEC based on ensemble forecasts. In hybrid-4DVAR, the observation impacts (i.e., the approximated nonlinear FER) for most observation types increase compared to those in 4DVAR. The increased observation impact from using hybrid-4DVAR instead of 4DVAR changes depending on the analysis time and regions. To calculate the ensemble BEC in hybrid-4DVAR, analyses at 0600 and 1800 UTC (0000 and 1200 UTC) used 3-h (9-h) ensemble forecasts. Greater observation impact was obtained when 3-h ensemble forecasts were used for the ensemble BEC at 0600 and 1800 UTC, than with 9-h ensemble forecasts at 0000 and 1200 UTC. Different from other observations, the atmospheric motion vectors (AMVs) deduced from geostationary satellite are more frequently observed in the same area. When the ensemble forecasts with longer integration times were used for the ensemble BEC in hybrid-4DVAR, the observation impact of the AMVs decreased the most in East Asia. This implies that the observation impact of AMVs in East Asia shows the highest sensitivity to the integration time of the ensemble members used for deducing the flow-dependent BEC in hybrid-4DVAR.

A Multiscale Approach to High-Resolution Ocean Profile Observations within a 4DVAR Analysis System

Abstract Most ocean data assimilation systems are tuned to process and assimilate observations to constrain features on the order of the mesoscale and larger. Typically this involves removal of observations or computing averaged observations. This procedure, while necessary, eliminates many observations from the analysis step and can reduce the overall effectiveness of a particular observing platform. Simply including these observations is not an option as doing so can produce an overdetermined, ill-conditioned problem that is more difficult to solve. An approach, presented here, aims to avoid such issues while at the same time increasing the number of observations within the assimilation. A two-step assimilation procedure with the four-dimensional variational data assimilation (4DVAR) system is adopted. The first step attempts to constrain the large-scale features by assimilating a set of super observations with appropriate background error correlation scales and error variances. The second step then attempts to correct smaller-scale features by assimilating the full observation set with shorter background error correlation scales and appropriate error variances; here the background state is taken as the analysis from the first step. Results using a real high-density observation set from underwater gliders in the region southeast of Iceland, collected during the 2017 Nordic Recognized Environmental Picture (NREP) experiment, will be shown using the Navy Coastal Ocean Model 4DVAR (NCOM-4DVAR).

Conjugate Gradient Algorithm in the Four-Dimensional Variational Data Assimilation System in GRAPES

Minimization algorithms are singular components in four-dimensional variational data assimilation (4DVar). In this paper, the convergence and application of the conjugate gradient algorithm (CGA), which is based on the Lanczos iterative algorithm and the Hessian matrix derived from tangent linear and adjoint models using a non-hydrostatic framework, are investigated in the 4DVar minimization. First, the influence of the Gram-Schmidt orthogonalization of the Lanczos vector on the convergence of the Lanczos algorithm is studied. The results show that the Lanczos algorithm without orthogonalization fails to converge after the ninth iteration in the 4DVar minimization, while the orthogonalized Lanczos algorithm converges stably. Second, the convergence and computational efficiency of the CGA and quasi-Newton method in batch cycling assimilation experiments are compared on the 4DVar platform of the Global/Regional Assimilation and Prediction System (GRAPES). The CGA is 40% more computationally efficient than the quasi-Newton method, although the equivalent analysis results can be obtained by using either the CGA or the quasi-Newton method. Thus, the CGA based on Lanczos iterations is better for solving the optimization problems in the GRAPES 4DVar system.

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

Regionally Enhanced Global (REG) 4D-Var

Abstract A regionally enhanced global (REG) data assimilation (DA) method is proposed. The technique blends high-resolution model information from a single or multiple limited-area model domains with global model and observational information to create a regionally enhanced analysis of the global atmospheric state. This single analysis provides initial conditions for both the global and limited-area model forecasts. The potential benefits of the approach for operational data assimilation are (i) reduced development cost, (ii) reduced overall computational cost, (iii) improved limited-area forecast performance from the use of global information about the atmospheric flow, and (iv) improved global forecast performance from the use of more accurate model information in the limited-area domains. The method is tested by an implementation on the U.S. Navy’s four-dimensional variational global data assimilation system and global and limited-area numerical weather prediction models. The results of the monthlong forecast experiments suggest that the REG DA approach has the potential to deliver the desired benefits.

GPS Radio Occultation Data Assimilation in the AREM Regional Numerical Weather Prediction Model for Flood Forecasts

Based on the Backward Four-Dimensional Variational Data Assimilation (Backward-4DVar) system with the Advanced Regional Eta-coordinate Model (AREM), which is capable of assimilating radio occultation data, a heavy rainfall case study is performed using GPS radio occultation (GPS RO) data and routine GTS data on July 5, 2007. The case study results indicate that the use of radio occultation data after quality control can improve the quality of the analysis to be similar to that of the observations and, thus, have a positive effect when improving 24-hour rainfall forecasts. Batch tests for 119 days from May to August during the flood season in 2009 show that only the use of GPS RO data can make positive improvements in both 24-hour and 48-hour regional rainfall forecasts and obtain a better B score for 24-hour forecasts and better TS score for 48-hour forecasts. When using radio occultation refractivity data and conventional radiosonde data, the results indicate that radio occultation refractivity data can achieve a better performance for 48-hour forecasts of light rain and heavy rain.