Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

In this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport-diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport-diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented.     

The impact of assimilating MeghaTropiques SAPHIR radiances in the simulation of tropical cyclones over the Bay of Bengal using the WRF model

The present study aims to investigate the impact of assimilating SAPHIR (Sounder for Probing Vertical Profiles of Humidity) radiances in the simulation of tropical cyclones over the Indian region by the Weather Research and Forecasting (WRF) model. Three tropical cyclones which formed over the Bay of Bengal are chosen as the case studies. Since SAPHIR is a humidity microwave sensor, it is interesting to assess the impact of these observations in simulating cyclones which depend significantly on moist-convective processes. The study makes use of the three-dimensional variational (3DVar) assimilation technique of the WRF variational assimilation system. The results of the study indicate that the assimilation of SAPHIR radiances do have a positive impact on the simulation of tropical cyclones considered here. Two model simulations are performed – a control run (Ctrl) with only conventional and satellite wind observations assimilated, and a SAPH run (SAPH) where SAPHIR radiances are also assimilated in addition to conventional and satellite wind observations. Both these simulations are compared to each other and to observations from the India Meteorological Department (IMD), Joint Typhoon Warning Centre (JTWC), and Tropical Rainfall Measurement Mission (TRMM), as well as analysis fields from Global Forecast System (GFS) from the National Centres for Environmental Prediction (NCEP). Comparison of minimum sea level pressure and maximum wind speed simulated by the model with the IMD and JTWC observations shows that the SAPHIR assimilation has a moderate impact on the simulation of these features by the model. Track prediction of the model is also improved at initial forecast times, as evidenced by the reduced track errors in the model run with SAPHIR radiances assimilated. The warm core structure, as well as the relative vorticity structure of the cyclones, are also impacted in a moderate manner by the assimilation of SAPHIR radiances. The assimilation also positively impacted the rainfall simulation of the model. This is seen from the higher equitable threat score, lower false alarm ratio, and higher probability of detection estimated with respect to TRMM observations, in the SAPH run as compared to the Ctrl run.     

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.     

Enhanced biomass prediction by assimilating satellite data into a crop growth model

Complex crop growth models (CGM) require a large number of input parameters, which can cause large errors if they are uncertain. Furthermore, they often lack spatial information. The coupling of a CGM with a radiative transfer model offers the possibility to assimilate remote sensing data while taking into account uncertainties in input parameters. A particle filter was used to assimilate satellite data into a CGM coupled with a leaf-canopy radiative transfer model to update biomass simulations of maize. The synthetic experiment set up to test the reliability of the procedure, highlighted the importance of the acquisition time. The real case study with RapidEye observations confirmed these findings. Data assimilation increased the accuracy of biomass predictions in the majority of the six maize fields where biomass validation data was available, with improvements of up to 15%. The smallest and largest errors in biomass prediction after assimilation were 82 kg/ha and 2116 kg/ha, respectively. Furthermore, data assimilation enabled the production of biomass maps showing detailed spatial variability.     

AMSR-E亮温同化系统的数据分析方法研究

低频微波卫星观测信号由于其对土壤水分非常敏感,经常被同化到陆面模式来提高土壤水分和其它地表状态变量的模拟和预报。常用的同化算法主要利用统计学,优化理论等数学知识,对改进和理解模型的物理过程意义不大。通过研究发展一个数据分析方法,判断AMSR\|E亮温同化系统土壤水分的预报误差,为将来从物理角度定性分析提供基础。     

The impact of assimilation of satellite derived wind observations for the prediction of a monsoon depression over India using a mesoscale model

The Penn State/NCAR mesoscale model (MM5) has been used in this study to ingest and assimilate the INSAT‐CMV (Indian National Satellite System‐Cloud Motion Vector) wind observations using analysis nudging (four‐dimensional data assimilation, FDDA) to improve the prediction of a monsoon depression which occurred over the Bay of Bengal, India during 28 July 2005 to 31 July 2005. To determine the impact of assimilation of INSAT‐CMV winds on the prediction of a monsoon depression, three sets of numerical experiments (NOFDDA, FDDA and FDDA CMV) were designed. While the FDDA CMV run assimilated satellite derived winds only, the FDDA run assimilated both satellite and conventional observations. The NOFDDA run used neither satellite nor conventional observations. The results of the study indicate that the simulated sea level pressure field from the FDDA run is more consistent with the sea level pressure field from NCEP‐FNL compared to the FDDA CMV and NOFDDA runs. The highest correlation and lowest rms error of the sea level pressure field are associated with the FDDA run, and this provides a quantitative verification of the improvement due to the assimilation of satellite derived winds and the conventional upper air observations for the prediction of monsoon depression. All the three model simulated winds are in good agreement with the analysis winds at 850 hPa, 500 hPa and 200 hPa levels. The simulated structure of the spatial precipitation pattern for the assimilation experiments (FDDA and FDDA CMV) are closer to the TRMM observations with more rainfall simulated over the east coast regions in the assimilation experiments. The rms errors of the wind speed for the FDDA run show lower values at 500 hPa for all the three model runs, with a reduction in all three levels of up to 0.8–1.4 m s^?1 for the FDDA run and 0.5–1.9 m s^?1 for the FDDA CMV run with respect to the NOFDDA run. The statistical significance of the sea level pressure and the precipitation differences between the FDDA and the NOFDDA as well as the differences between the FDDA CMV and the NOFDDA have been calculated using the two‐tailed Student's t‐test and were found to be statistically significant. The influence of varying the nudging coefficients in the FDDA experiment has been studied.     

Inversion of three-dimensional tidal currents in marginal seas by assimilating satellite altimetry

The four-dimensional variational data assimilation technology based on the theory of inverse problem is applied to simulate the three-dimensional tidal currents in the marginal seas by assimilating the satellite altimetry. The model is calibrated by the twin experiments where the prescribed open boundary conditions for a three-dimensional barotropic tidal model are successfully inverted. By assimilating the tidal harmonic constants derived from TOPEX/Poseidon altimeter data, the open boundary conditions are optimized and the M₂ tidal currents in the Bohai and Yellow Seas (BYS) are simulated in the practical experiment. During the assimilation, the cost function and the gradients of cost function with respect to the open boundary conditions have been decreased significantly. Although the current observations are not assimilated into the model, the cost function composed of the data misfit between model-produced and observed currents is still decreased from 1.00 to 0.09, which demonstrates the reasonability and feasibility of inverting tidal currents from satellite altimetry or other elevation measurements. The co-tidal charts and the near-surface M₂ tidal current ellipses obtained in the practical experiment are in good agreement with the observed tides and tidal currents in BYS.     

Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study

Predicted latent and sensible heat fluxes from Land Surface Models (LSMs) are important lower boundary conditions for numerical weather prediction. While assimilation of remotely sensed surface soil moisture is a proven approach for improving root zone soil moisture, and presumably latent (LE) and sensible (H) heat flux predictions from LSMs, limitations in model physics and over-parameterisation mean that physically realistic soil moisture in LSMs will not necessarily achieve optimal heat flux predictions. Moreover, the potential for improved LE and H predictions from the assimilation of LE and H observations has received little attention by the scientific community, and is tested here with synthetic twin experiments. A one-dimensional single column LSM was used in 3-month long experiments, with observations of LE, H, surface soil moisture and skin temperature (from which LE and H are typically derived) sampled from truth model run outputs generated with realistic data inputs. Typical measurement errors were prescribed and observation data sets separately assimilated into a degraded model run using an Ensemble Kalman Filter (EnKF) algorithm, over temporal scales representative of available remotely sensed data. Root Mean Squared Error (RMSE) between assimilation and truth model outputs across the experiment period were examined to evaluate LE, H, and root zone soil moisture and temperature retrieval. Compared to surface soil moisture assimilation as will be available from SMOS (every 3 days), assimilation of LE and/or H using a best case MODIS scenario (twice daily) achieved overall better predictions for LE and comparable H predictions, while achieving poorer soil moisture predictions. Twice daily skin temperature assimilation achieved comparable heat flux predictions to LE and/or H assimilation. Fortnightly (Landsat) assimilations of LE, H and skin temperature performed worse than 3-day moisture assimilation. While the different spatial resolutions of these remote sensing data have been ignored, the potential for LE and H assimilation to improve model predicted LE and H is clearly demonstrated.     

Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations

This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). The LDAS consists of a land surface scheme as the model operator, a radiative transfer model as the observation operator, and the simulated annealing method for minimizing the difference between the observed and simulated microwave brightness temperature. The atmospheric model produces forcing data for the LDAS, and the LDAS produces better initial surface conditions for the modelling system. This coupled system can take into account land surface heterogeneities through assimilating satellite data for a better precipitation prediction. To assess the effectiveness of the new system, 3-dimensional numerical experiments were carried out in a mesoscale area of the Tibetan Plateau during the wet monsoon season. The results show significant improvement compared with a no assimilation regional atmospheric model simply nested from the global model. The surface soil moisture content and its distribution from the assimilation system were more consistent to in situ observations. These better surface conditions affect the land-atmosphere interactions through convection systems and lead to better atmospheric predictability as confirmed by satellite-based cloud observations and in situ sounding observations. Through the use of satellite brightness temperature, the developed coupled land-atmosphere assimilation system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.     

Detection of blue-absorbing aerosols using near infrared and visible (ocean color) remote sensing observations

An algorithm is presented, which is designed to identify blue-absorbing aerosols from near infrared and visible remote-sensing observations, as they are in particular collected by satellite ocean color sensors. The technique basically consists in determining an error budget at one wavelength around 510 nm, based on a first-guess estimation of the atmospheric path reflectance as if the atmosphere was of a maritime type, and on a reasonable hypothesis about the marine signal at this wavelength. The budget also includes the typical calibration uncertainty and the natural variability in the ocean optical properties. Identification of blue-absorbing aerosols is then achieved when the error budget demonstrates a significant over-correction of the atmospheric signal when using non-absorbing maritime aerosols. Implementation of the algorithm is presented, and its application to real observations by the MERIS and SeaWiFS ocean color sensors is discussed. The results demonstrate the skill of the algorithm in various regions of the ocean where absorbing aerosols are present, and for two different sensors. A validation of the results is also performed against in situ data from the AERONET, and further illustrates the skill of the algorithm and its general applicability.     

A study of sediment transport in a shallow estuary using MODIS imagery and particle tracking simulation

The study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) red-channel reflectance with a spatial resolution of 250 m to estimate suspended sediment concentration (SSC) in the Mobile Bay estuary, Alabama. Based on monthly in situ sampling, a new algorithm was developed using an exponential regression model. The concentration of inorganic suspended sediments (ISS) in Mobile Bay and Mississippi Sound was concerned and mapped by applying the new algorithm. The ISS maps during a cold front passage have revealed how the resuspension and transport of sediments respond to the variable wind forcing in this micro-tidal system. Particle tracking based on a three-dimensional hydrodynamic model was utilized to explain what was observed from the satellite imagery. It has been found that the rapid disappearance of the surface ISS after a cold front passage was mainly caused by settling of sediments rather than flushing out of the estuary. The study demonstrates that a combination of ISS mapped from the MODIS band-1 reflectance and three-dimensional numerical modelling is an effective tool to analyse sediment dynamics in the Mobile Bay estuary and other similar estuaries.     

观测误差协方差估计下的集合鲁棒滤波数据同化方法

在数据同化方法中,观测误差协方差矩阵是相关的,且与时间和状态有一定的依赖性。针对这种相关特性,将鲁棒滤波方法与观测误差协方差估计方法相结合,得到随状态时间变化的观测误差协方差,提出一种带有观测误差估计的鲁棒数据同化新方法,更新观测误差协方差,改善估计效果。从分析误差协方差,转移矩阵特征值放大等角度优化同化方法。利用非线性Lorenz-96混沌系统,对三种不同优化角度下带有观测误差估计的鲁棒滤波和原鲁棒滤波方法的鲁棒性和同化精度进行评估,并比较分析了两种方法在模型误差、观测数目和性能水平系数变化时的性能。结果表明：观测误差估计技术能够提高状态估计的精确性,带有观测误差估计的鲁棒滤波对系统参数变化具有较好的鲁棒性。     

Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter

Ensemble Kalman filter is a new sequential data assimilation algorithm which was originally developed for atmospheric and oceanographic data assimilation. It can be applied to calculate error covariance matrix through Monte-Carlo simulation. This approach is able to resolve the nonlinearity and discontinuity existed within model operator and observation operator. When observation data are assimilated at each time step, error covariances are estimated from the phase-space distribution of an ensemble of model states. The error statistics is then used to calculate Kalman gain matrix and analysis increments. In this study, we develop a one-dimensional soil moisture data assimilation system based on ensemble Kalman filter, the Simple Biosphere Model (SiB2) and microwave radiation transfer model (AIEM, advanced integration equation model). We conduct numerical experiments to assimilate in situ soil surface moisture measurements and low-frequency passive microwave remote sensing data into a land surface model, respectively. The results indicate that data assimilation can significantly improve the soil surface moisture estimation. The improvement in root zone is related to the model bias errors at surface layer and root zone. The soil moisture does not vary significantly in deep layer. Additionally, the ensemble Kalman filter is predominant in dealing with the nonlinearity of model operator and observation operator. It is practical and effective for assimilating observations in situ and remotely sensed data into land surface models.     

Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone

This study aims to investigate the impact of the Three-Dimensional Variational (3DVAR) assimilation of Doppler Weather Radar (DWR) wind data together with the India Meteorological Department (IMD) upper air and surface data for the prediction of a tropical cyclone, which formed over the Bay of Bengal. The National Centers for Environmental Prediction Final Analyses (NCEP FNL) data are used to produce initial conditions. Three numerical experiments were designed to study the effect of 3DVAR assimilation. For the first experiment, the model integrations were performed without any assimilation of observations. IMD upper air and surface observations were assimilated using 3DVAR for the second experiment and the third experiment assimilated DWR wind data along with IMD observations. The model results are compared with one another and also with the observations. The results of the study indicate that the assimilation of DWR wind data and IMD data have resulted in improvements in the simulation of strong vertical velocity, higher warm core temperature and strong gradients in the horizontal wind speed as well as improved spatial distribution of the precipitation.     

Scaling of coastal phytoplankton features by optical remote sensors: comparison with a regional ecosystem model

Different scales of hydrological and biological patterns of the Bay of Biscay are assessed using space‐borne and airborne optical remote sensing data, field measurements and a 3‐dimensional biophysical model. If field measurements provide accurate values on the vertical dimension, ocean colour data offer frequent observations of surface biological patterns at various scales of major importance for the validation of ecosystem modelling. Although the hydro‐biological model of the continental margin reproduces the main seasonal variability of surface biomass, the optical remote sensing data have helped to identify low grid resolution, input inaccuracies and neglect of swell‐induced erosion mechanism as model limitations in shallow waters. Airborne remote sensing is used to show that satellite data and field measurements are unsuitable for comparison in the extreme case of phytoplankton blooms in patches of a few hundred metres. Vertically, the satellite observation is consistent with near surface in situ measurements as the sub‐surface chlorophyll maximum usually encountered in summer is not detected by optical remote sensing. A mean error (δC/C) of 50.5% of the chlorophyll‐a estimate in turbid waters using the SeaWiFS‐OC5 algorithm allows the quantitative use of ocean colour data by the coastal oceanographic community.     

Using coarse remote sensing radar observations to control the trajectory of a simple Sahelian land surface model

In the Sahel, land surface processes are significantly interacting with climate dynamics. In this paper, we present an original method to control a simple Sahelian land surface model coupled to a radiative transfer model (RTM) on the basis of ERS wind scatterometer (WSC) observations. In a first step, a sensitivity study is implemented to identify those parameters of the land surface model that can be estimated through the assimilation of WSC data. The assimilation scheme relies on evolution strategies (ES) algorithm that aims at solving the parameter evaluation problem. These algorithms are particularly well suited for complex (nonlinear) inverse problems. The assimilation scheme is applied to several study sites located in the Sahelian mesoscale site of the African Monsoon Multidisciplinary Analysis Project (Gourma region, Mali). The results are compared with ground observations of herbaceous mass. After the WSC data assimilation, the simulated herbaceous mass curves compare well with observations [187 kilogram of dry matter per hectare (kg DM/ha) of average error]. The simulated water fluxes exhibit a behaviour in agreement with ground measurements performed over similar ecosystems during the Hapex Sahel experiment. The accuracy of estimated herbaceous mass and water fluxes resulting from uncertainties on climatic forcing variable is evaluated using a stochastic approach. The average error on the herbaceous mass values mainly depends on the rainfall estimate accuracy and ranges from 139 to 268 kg DM/ha that compares well with a previous study based on the sole inversion of the radiative transfer model. Finally, this study underlines the need for a multispectral assimilation approach to get a better constraint on water fluxes estimation.     

基于时间相关性的无线传感器网络数据压缩与优化算法

针对无线传感器网络(WSN)数据采集存在数据冗余度大、累积误差大和数据精度低等问题,根据采集数据之间的时间相关性,提出一种无线传感器网络数据压缩与优化算法。该算法通过分析时间序列中采集数据的线性关系,建立分段一元线性回归模型;根据采集数据与回归模型预测值之间的误差,自适应地调整下一个采集时间,并动态地优化回归模型。仿真结果表明该算法在不同的数据变化情况下,均能降低数据冗余度和网络通信量,提高采集数据的重构精度。最后在真实的无线传感器网络应用环境中验证了算法的可行性。     

The impact of background error parameters on heavy rainfall prediction

A heavy rainfall event over the northwest of India is selected to investigate the impact of Atmospheric Infrared Sounder (AIRS)-retrieved temperature and moisture profile assimilation on regional model prediction. The Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system (WRFDA) is used to assimilate AIRS profiles with tuning of two major background error parameters – viz. length and variance scales. Assimilation of AIRS profiles improves the WRF model analyses, which are closer to the Moderate Resolution Imaging Spectrometer (MODIS) profiles compared to those without assimilation experiment. Results show that within a wide parameter range of length and variance scales, the assimilation of AIRS-retrieved profiles has a positive influence on heavy rainfall prediction. Approximately 9–30, 5–42, and 0.5–3.0% domain average values of improvement are observed after AIRS profile assimilation for different values of length and variance scales in temperature, water vapour mixing ratio, and rainfall prediction, respectively. This study shows that the impact of observations on the WRF model forecast is dependent on the length and variance scale parameters of background error, and lower values of length scale in WRFDA result in degradation of the forecast.     

Ensemble member generation for sequential data assimilation

Using an ensemble of model forecasts to describe forecast error covariance extends linear sequential data assimilation schemes to nonlinear applications. This approach forms the basis of the Ensemble Kalman Filter and derivative filters such as the Ensemble Square Root Filter. While ensemble data assimilation approaches are commonly reported in the scientific literature, clear guidelines for effective ensemble member generation remain scarce. As the efficiency of the filter is reliant on the accurate determination of forecast error covariance from the ensemble, this paper describes an approach for the systematic determination of random error. Forecast error results from three factors: errors in initial condition, forcing data and model equations. The method outlined in this paper explicitly acknowledges each of these sources in the generation of an ensemble. The initial condition perturbation approach presented optimally spans the dynamic range of the model states and allows an appropriate ensemble size to be determined. The forcing data perturbation approach treats forcing observations differently according to their nature. While error from model physics is not dealt with in detail, discussion of some commonly used approaches and their limitations is provided. The paper concludes with an example application for a synthetic coastal hydrodynamic experiment assimilating sea surface temperature (SST) data, which shows better prediction capability when contrasted with standard approaches in the literature.     

Quantifying the uncertainty in passive microwave snow water equivalent observations

Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models.This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990-1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements.