Virtual California: Fault Model, Frictional Parameters, Applications

Virtual California is a topologically realistic simulation of the interacting earthquake faults in California. Inputs to the model arise from field data, and typically include realistic fault system topologies, realistic long-term slip rates, and realistic frictional parameters. Outputs from the simulations include synthetic earthquake sequences and space-time patterns together with associated surface deformation and strain patterns that are similar to those seen in nature. Here we describe details of the data assimilation procedure we use to construct the fault model and to assign frictional properties. In addition, by analyzing the statistical physics of the simulations, we can show that that the frictional failure physics, which includes a simple representation of a dynamic stress intensity factor, leads to self-organization of the statistical dynamics, and produces empirical statistical distributions (probability density functions: PDFs) that characterize the activity. One type of distribution that can be constructed from empirical measurements of simulation data are PDFs for recurrence intervals on selected faults. Inputs to simulation dynamics are based on the use of time-averaged event-frequency data, and outputs include PDFs representing measurements of dynamical variability arising from fault interactions and space-time correlations. As a first step for productively using model-based methods for earthquake forecasting, we propose that simulations be used to generate the PDFs for recurrence intervals instead of the usual practice of basing the PDFs on standard forms (Gaussian, Log-Normal, Pareto, Brownian Passage Time, and so forth). Subsequent development of simulation-based methods should include model enhancement, data assimilation and data mining methods, and analysis techniques based on statistical physics.     

Formation and Suppression of Strike–Slip Fault Systems

Strike–slip faults are a defining feature of plate tectonics, yet many aspects of their development and evolution remain unresolved. For intact materials and/or regions, a standard sequence of shear development is predicted from physical models and field studies, commencing with the formation of Riedel shears and culminating with the development of a throughgoing fault. However, for materials and/or regions that contain crustal heterogeneities (normal and/or thrust faults, joints, etc.) that predate shear deformation, kinematic evolution of strike–slip faulting is poorly constrained. We present a new plane-stress finite-strain physical analog model developed to investigate primary deformation zone evolution in simple shear, pure strike–slip fault systems in which faults or joints are present before shear initiation. Experimental results suggest that preexisting mechanical discontinuities (faults and/or joints) have a marked effect on the geometry of such systems, causing deflection, lateral distribution, and suppression of shears. A lower limit is placed on shear offset necessary to produce a throughgoing fault in systems containing preexisting structures. Fault zone development observed in these experiments provides new insight for kinematic interpretation of structural data from strike–slip fault zones on Earth, Venus, and other terrestrial bodies.     

On Integrating Groundwater Transport Models with Wireless Sensor Networks

The emerging technology of wireless sensor networks (WSNs) is an integrated, distributed, wireless network of sensing devices. It has the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by providing environmental information at greater spatial and temporal resolutions. Furthermore, due to continuing high-performance computing development, these data may be introduced into increasingly robust and complex numerical models; for instance, the parameters of subsurface transport simulators may be automatically updated. Early field deployments and laboratory experiments conducted using in situ sensor technology and WSNs indicated significant fundamental issues concerning sensor and network hardware reliability—suggesting that investigations should first be conducted in controlled environments before field deployment. A first step in this validation process involves evaluating the predictive capability of a computational advection-dispersion transport model when incorporating concentration data from a WSN simulation. Data quality is a major concern, especially when sensor readings are automatically fed into data assimilation procedures. The appropriate employment of an independent WSN fault detection service can ensure that erroneous data (e.g., missing or anomalous values) do not mislead the model. Parameter estimation regularization techniques may then deal with remaining data noise. The primary purpose of this study is to determine the suitability of WSNs (and other in situ data delivery technologies) for use in contaminant transport modeling applications by conducting research in a realistic simulative environment.     

Improving flood forecasting using an input correction method in urban models in poorly gauged areas

ABSTRACT

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.     

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km² mixed-forest basin in the Great Lakes region of central Ontario, Canada. Sub-basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi-objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi-objective calibration or data assimilation, as demonstrated in this study.     

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic conductivity field calibration.     

耦合敏感参数实时识别的新型数据同化算法研究——以湖泊藻类模拟为例

数据同化是提升复杂机理过程模型精度的关键技术之一,而湖泊藻类模型的敏感参数具有随时间动态变化的特征,导致数据同化过程中无法精准更新某一时段的敏感参数,影响数据同化的模型精度提升效果.针对上述问题,本研究耦合了参数敏感性分析与集合卡尔曼滤波,研发了一种能够实时识别模型敏感参数的新型数据同化算法;为验证研发算法的效率,依托巢湖的高频水质自动监测数据,测试算法对藻类动态模型的精度提升效果.测试结果表明：研发算法能够精准跟踪模型敏感参数的动态变化,并根据监测数据实时更新模型敏感参数,实现了水质高频自动监测数据与藻类动态模型的深度融合,藻类生物量模拟精度提升了55%,即纳什系数(NSE)从0.49提升到0.76,模拟精度提升效果也显著优于传统数据同化算法(NSE=0.63).研发算法可应用于其它水生态环境模型的数据同化,为水生态环境相关要素的精准模拟预测提供关键技术支撑.     

Application of altimetry data assimilation on mesoscale eddies simulation

Mesoscale eddy plays an important role in the ocean circulation. In order to improve the simulation accuracy of the mesoscale eddies, a three-dimensional variation (3DVAR) data assimilation system called Ocean Variational Analysis System (OVALS) is coupled with a POM model to simulate the mesoscale eddies in the Northwest Pacific Ocean. In this system, the sea surface height anomaly (SSHA) data by satellite altimeters are assimilated and translated into pseudo temperature and salinity (T-S) profile data. Then, these profile data are taken as observation data to be assimilated again and produce the three-dimensional analysis T-S field. According to the characteristics of mesoscale eddy, the most appropriate assimilation parameters are set up and testified in this system. A ten years mesoscale eddies simulation and comparison experiment is made, which includes two schemes: assimilation and non-assimilation. The results of comparison between two schemes and the observation show that the simulation accuracy of the assimilation scheme is much better than that of non-assimilation, which verified that the altimetry data assimilation method can improve the simulation accuracy of the mesoscale dramatically and indicates that it is possible to use this system on the forecast of mesoscale eddies in the future.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.     

GRACE terrestrial water storage data assimilation based on the ensemble four-dimensional variational method PODEn4DVar:Method and validation

Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity changes by the Gravity Recovery and Climate Experiment(GRACE) satellites,are mainly caused by precipitation,evapotranspiration,river transportation and downward infiltration processes.In this study,a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage(TWS) data into the Community Land Model(CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4 DVar,disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time,and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations.The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables,indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.     

岩石介质中多裂纹扩展相互作用及其贯通机制的数值模拟

运用岩石破裂过程分析RFPA^2D系统，通过对岩石试样中预置的一组右行右阶雁列式裂纹扩展过程的数值模拟，研究了非均匀岩石介中多裂纹扩展的相互作用模式及其贯通机制。数植模拟再现的受压混合型裂纹扩展过程中逐步演变的全场变形过程，以及与细观非均匀性有关的声发射和断续扩展模式的“岩桥”现象，清晰地揭示出多裂纹扩展的相互作用及其贯通机制。模拟结果说明，对于岩石这种地质介质而言，忽略其非均匀性影响，可能会掩盖岩石变形与破裂过程中的许多与非均匀性有关的特殊现象，包括声发射或微震模式、岩桥或雁行断裂等。     

A new methodology for using buoy measurements in sea wave data assimilation

One of the main drawbacks in modern sea wave data assimilation models is the limited temporal and spatial improvement obtained in the final forecasting products. This is mainly due to deviations coming either from the relevant atmospheric input or from the dynamics of the wave model, resulting to systematic errors of the forecasted fields of numerical wave models, when no observation is available for assimilation. A potential solution is presented in this work, based on a combination of advanced statistical techniques, data assimilation systems, and wave models. More precisely, Kalman filtering algorithms are implemented into the wave model WAM and the results are assimilated by an Optimum Interpolation Scheme, in order to extend the beneficial influence of the latter in time and space. The case studied concerns a 3-month period in an open sea area near the South-West coast of the USA (Pacific Ocean).     

Direct and Inverse Problems in a Variational Concept of Environmental Modeling

A concept of environmental forecasting based on a variational approach is discussed. The basic idea is to augment the existing technology of modeling by a combination of direct and inverse methods. By this means, the scope of environmental studies can be substantially enlarged. In the concept, mathematical models of processes and observation data subject to some uncertainties are considered. The modeling system is derived from a specially formulated weak-constraint variational principle. A set of algorithms for implementing the concept is presented. These are: algorithms for the solution of direct, adjoint, and inverse problems; adjoint sensitivity algorithms; data assimilation procedures; etc. Methods of quantitative estimations of uncertainty are of particular interest since uncertainty functions play a fundamental role for data assimilation, assessment of model quality, and inverse problem solving. A scenario approach is an essential part of the concept. Some methods of orthogonal decomposition of multi-dimensional phase spaces are used to reconstruct the hydrodynamic background fields from available data and to include climatic data into long-term prognostic scenarios. Subspaces with informative bases are constructed to use in deterministic or stochastic-deterministic scenarios for forecasting air quality and risk assessment. The results of implementing example scenarios for the Siberian regions are presented.     

Impact of Doppler Radar Wind in Simulating the Intensity and Propagation of Rainbands Associated with Mesoscale Convective Complexes Using MM5-3DVAR System

Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.     

Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems.     

Using an eddy-tracking algorithm to understand the impact of assimilating altimetry data on the eddy characteristics of the Agulhas system

A complex and highly dynamical ocean region, the Agulhas Current System plays an important role in the transfer of energy, nutrients and organic material from the Indian to the Atlantic Ocean. Its dynamics are not only important locally, but affect the global ocean-atmosphere system. In working towards improved ocean reanalysis and forecasting capabilities, it is important that numerical models simulate mesoscale variability accurately—especially given the scarcity of coherent observational platforms in the region. Data assimilation makes use of scarce observations, a dynamical model and their respective error statistics to estimate a new, improved model state that minimises the distance to the observations whilst preserving dynamical consistency. Qualitatively, it is unclear whether this minimisation directly translates to an improved representation of mesoscale dynamics. In this study, the impact of assimilating along-track sea-level anomaly (SLA) data into a regional Hybrid Coordinate Ocean Model (HYCOM) is investigated with regard to the simulation of mesoscale eddy characteristics. We use an eddy-tracking algorithm and compare the derived eddy characteristics of an assimilated (ASSIM) and an unassimilated (FREE) simulation experiment in HYCOM with gridded satellite altimetry-derived SLA data. Using an eddy tracking algorithm, we are able to quantitatively evaluate whether assimilation updates the model state estimate such that simulated mesoscale eddy characteristics are improved. Additionally, the analysis revealed limitations in the dynamical model and the data assimilation scheme, as well as artefacts introduced from the eddy tracking scheme. With some exceptions, ASSIM yields improvements over FREE in eddy density distribution and dynamics. Notably, it was found that FREE significantly underestimates the number of eddies south of Madagascar compared to gridded altimetry, with only slight improvements introduced through assimilation, highlighting the models’ limitation in sustaining mesoscale activity in this region. Interestingly, it was found that the threshold for the maximum eddy propagation velocity in the eddy detection scheme is often exceeded when data assimilation relocates an eddy, causing the algorithm to interpret the discontinuity as eddy genesis, which directly influences the eddy count, lifetime and propagation velocity, and indirectly influences other metrics such as non-linearity. Finally, the analysis allowed us to separate eddy kinetic energy into contributions from detected mesoscale eddies and meandering currents, revealing that the assimilation of SLA has a greater impact on mesoscale eddies than on meandering currents.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean

The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10?% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85?% and 80?%, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.     

Multivariate Data Assimilation in the Tropics by Using Equatorial Waves

This paper reports recent advances in understanding of dynamical aspects of the tropical data assimilation. In contrast with the mid-latitudes, there is no a well-defined approach for the tropical data assimilation in numerical weather prediction (NWP) community which has traditionally been concentrated on the mid-latitude analysis problem. In particular, the impact of the equatorial Rossby, inertio-gravity, and mixed Rossby-gravity waves on the tropical forecast-error covariances is difficult to quantify. Various tropical waves are characterized by different couplings between the mass field and the wind field. The average mixture of these waves, built into the background-error covariance matrix for data assimilation provides analysis increments which appear nearly univariate even though they result from the advanced multivariate assimilation methodology. This applies to both dry and moist idealized tropical systems as well as to a 4D-Var NWP assimilation system.