Journal articles on the topic 'Data assimilation'
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Nie, Suping, Xiaolong Jia, Weitao Deng, Yixiong Lu, Dongyan He, Liang Zhao, Weihua Cao, and Xueliang Deng. "The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model." Remote Sensing 15, no. 1 (December 22, 2022): 59. http://dx.doi.org/10.3390/rs15010059.
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Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.
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Liu, Huaran, Feiyu Lu, Zhengyu Liu, Yun Liu, and Shaoqing Zhang. "Assimilating atmosphere reanalysis in coupled data assimilation." Journal of Meteorological Research 30, no. 4 (June 2016): 572–83. http://dx.doi.org/10.1007/s13351-016-6014-1.
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Wang, Zhaoyi, Andrea Storto, Nadia Pinardi, Guimei Liu, and Hui Wang. "Data assimilation of Argo profiles in a northwestern Pacific model." Natural Hazards and Earth System Sciences 17, no. 1 (January 5, 2017): 17–30. http://dx.doi.org/10.5194/nhess-17-17-2017.
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Abstract. Based on a novel estimation of background-error covariances for assimilating Argo profiles, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme was developed for the northwestern Pacific Ocean model (NwPM) for potential use in operational predictions and maritime safety applications. Temperature and salinity data extracted from Argo profiles from January to December 2010 were assimilated into the NwPM. The results show that the average daily temperature (salinity) root mean square error (RMSE) decreased from 0.99 °C (0.10 psu) to 0.62 °C (0.07 psu) in assimilation experiments throughout the northwestern Pacific, which represents a 37.2 % (27.6 %) reduction in the error. The temperature (salinity) RMSE decreased by ∼ 0.60 °C ( ∼ 0.05 psu) for the upper 900 m (1000 m). Sea level, temperature and salinity were in better agreement with in situ and satellite datasets after data assimilation than before. In addition, a 1-month experiment with daily analysis cycles and 5-day forecasts explored the performance of the system in an operational configuration. The results highlighted the positive impact of the 3DVAR initialization at all forecast ranges compared to the non-assimilative experiment. Therefore, the 3DVAR scheme proposed here, coupled to ROMS, shows a good predictive performance and can be used as an assimilation scheme for operational forecasting.
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Sun, Juanzhen, Ying Zhang, Junmei Ban, Jing-Shan Hong, and Chung-Yi Lin. "Impact of Combined Assimilation of Radar and Rainfall Data on Short-Term Heavy Rainfall Prediction: A Case Study." Monthly Weather Review 148, no. 5 (May 1, 2020): 2211–32. http://dx.doi.org/10.1175/mwr-d-19-0337.1.
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Abstract Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.
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Tang, Wenfu, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden. "Advantages of assimilating multispectral satellite retrievals of atmospheric composition: a demonstration using MOPITT carbon monoxide products." Atmospheric Measurement Techniques 17, no. 7 (April 5, 2024): 1941–63. http://dx.doi.org/10.5194/amt-17-1941-2024.
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Abstract. The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem+DART) to assimilate different MOPITT carbon monoxide (CO) products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral; near-IR and thermal IR) column product leads to better model–observation agreement at and near the surface than assimilating the MOPITT thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can outperform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral and joint products results in similar or slightly better agreement with observations compared to assimilating the single spectral products separately.
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Li, Jiajing, Yu Zhang, Siqi Chen, Duanzhou Shao, Jiazheng Hu, Junjie Feng, Qichang Tan, Deping Wu, and Jiaqi Kang. "Comparing Quality Control Procedures Based on Minimum Covariance Determinant and One-Class Support Vector Machine Methods of Aircraft Meteorological Data Relay Data Assimilation in a Binary Typhoon Forecasting Case." Atmosphere 14, no. 9 (August 25, 2023): 1341. http://dx.doi.org/10.3390/atmos14091341.
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This study investigates the impact of assimilating Aircraft Meteorological Data Relay (AMDAR) observations on the prediction of two typhoons, Nesat and Haitang (2017), using the Gridpoint Statistical Interpolation (GSI) assimilation system and the Weather Research and Forecasting (WRF) model. Two quality control (QC) methods, Minimum Covariance Determinant (MCD) and one-class Support Vector Machine (OCSVM), were employed to perform QC on the AMDAR observations before data assimilation. The QC results indicated that both methods significantly reduced kurtosis, skewness, and discrepancies between the AMDAR data and the reanalysis data. The data distribution after applying the MCD-QC method exhibited a closer resemblance to a Gaussian distribution. Four numerical experiments were conducted to assess the impact of different AMDAR data qualities on typhoon forecasting, including a control experiment without data assimilation (EXP-CNTL), assimilating all AMDAR observations (EXP-RAW), assimilating observations after applying MCD-QC (EXP-MCD), and assimilating observations after applying OCSVM-QC (EXP-SVM). The results demonstrated that using AMDAR data in assimilation improved the track and intensity prediction of the typhoons. Furthermore, utilizing QC before assimilation enhanced the performance of track forecasting prediction, with EXP-MCD showing the best performance. As for intensity prediction, the three assimilation experiments exhibited varying strengths and weaknesses at different times, with EXP-MCD showing smaller intensity forecast errors on average.
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Santana, Rafael, Helen Macdonald, Joanne O'Callaghan, Brian Powell, Sarah Wakes, and Sutara H. Suanda. "Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var." Geoscientific Model Development 16, no. 13 (July 6, 2023): 3675–98. http://dx.doi.org/10.5194/gmd-16-3675-2023.
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Abstract. This study analyses data assimilative numerical simulations in an eddy-dominated western boundary current: the East Auckland Current (EAuC). The goal is to assess the impact of assimilating surface and subsurface data into a model of the EAuC via running observing system experiments (OSEs). We used the Regional Ocean Modeling System (ROMS) in conjunction with the 4-dimensional variational (4D-Var) data assimilation scheme to incorporate sea surface height (SSH) and temperature (SST), as well as subsurface temperature, salinity and velocity from three moorings located at the upper, mid and lower continental slope using a 7 d assimilation window. Assimilation of surface fields (SSH and SST) reduced SSH root mean square deviation (RMSD) by 25 % in relation to the non-assimilative (NoDA) run. The inclusion of velocity subsurface data further reduced SSH RMSD up- and downstream the moorings by 18 %–25 %. By improving the representation of the mesoscale eddy field, data assimilation increased complex correlation between modelled and observed velocity in all experiments by at least three times. However, the inclusion of temperature and salinity slightly decreased the velocity complex correlation. The assimilative experiments reduced the SST RMSD by 36 % in comparison to the NoDA run. The lack of subsurface temperature for assimilation led to larger RMSD (>1 ∘C) around 100 m in relation to the NoDA run. Comparisons to independent Argo data also showed larger errors at 100 m in experiments that did not assimilate subsurface temperature data. Withholding subsurface temperature forces near-surface average negative temperature increments to the initial conditions that are corrected by increased net heat flux at the surface, but this had limited or no effect on water temperature at 100 m depth. Assimilation of mooring temperature generates mean positive increments to the initial conditions that reduces 100 m water temperature RMSD. In addition, negative heat flux and positive wind stress curl were generated near the moorings in experiments that assimilated subsurface temperature data. Positive wind stress curl generates convergence and downwelling that can correct interior temperature but might also be responsible for decreased velocity correlations. The few moored CTDs (eight) had little impact in correcting salinity in comparison to independent Argo data. However, using doubled decorrelation length scales of tracers and a 2 d assimilation window improved model salinity and temperature in comparison to Argo profiles throughout the domain. This assimilation configuration, however, led to large errors when subsurface temperature data were not assimilated due to incorrect increments to the subsurface. As all reanalyses show improved model-observation skill relative to HYCOM–NCODA (the model boundary conditions), these results highlight the benefit of numerical downscaling to a regional model of the EAuC.
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Gwirtz, K., M. Morzfeld, W. Kuang, and A. Tangborn. "A testbed for geomagnetic data assimilation." Geophysical Journal International 227, no. 3 (August 14, 2021): 2180–203. http://dx.doi.org/10.1093/gji/ggab327.
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SUMMARY Geomagnetic data assimilation merges past and present-day observations of the Earth’s magnetic field with numerical geodynamo models and the results are used to initialize forecasts. We present a new ‘proxy model’ that can be used to test, or rapidly prototype, numerical techniques for geomagnetic data assimilation. The basic idea for constructing a proxy is to capture the conceptual difficulties one encounters when assimilating observations into high-resolution, 3-D geodynamo simulations, but at a much lower computational cost. The framework of using proxy models as ‘gate-keepers’ for numerical methods that could/should be considered for more extensive testing on operational models has proven useful in numerical weather prediction, where advances in data assimilation and, hence, improved forecast skill, are at least in part enabled by the common use of a wide range of proxy models. We also present a large set of systematic data assimilation experiments with the proxy to reveal the importance of localization and inflation in geomagnetic data assimilation.
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Valler, Veronika, Yuri Brugnara, Jörg Franke, and Stefan Brönnimann. "Assimilating monthly precipitation data in a paleoclimate data assimilation framework." Climate of the Past 16, no. 4 (July 24, 2020): 1309–23. http://dx.doi.org/10.5194/cp-16-1309-2020.
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Abstract. Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information, from proxy records and documentary data to instrumental measurements, were assimilated in previous studies to reconstruct past climate fields. However, precipitation reconstructions are often based on indirect sources (e.g., proxy records). Assimilating precipitation measurements is a challenging task because they have high uncertainties, often represent only a small region, and generally do not follow a Gaussian distribution. In this paper, experiments are conducted to test the possibility of using information about precipitation in climate reconstruction with monthly resolution by assimilating monthly instrumental precipitation amounts or the number of wet days per month, solely or in addition to other climate variables such as temperature and sea-level pressure, into an ensemble of climate model simulations. The skill of all variables (temperature, precipitation, sea-level pressure) improved over the pure model simulations when only monthly precipitation amounts were assimilated. Assimilating the number of wet days resulted in similar or better skill compared to assimilating the precipitation amount. The experiments with different types of instrumental observations being assimilated indicate that precipitation data can be useful, particularly if no other variable is available from a given region. Overall the experiments show promising results because with the assimilation of precipitation information a new data source can be exploited for climate reconstructions. The wet day records can become an especially important data source in future climate reconstructions because many existing records date several centuries back in time and are not limited by the availability of meteorological instruments.
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Fabry, Frédéric, and Véronique Meunier. "Why Are Radar Data so Difficult to Assimilate Skillfully?" Monthly Weather Review 148, no. 7 (June 24, 2020): 2819–36. http://dx.doi.org/10.1175/mwr-d-19-0374.1.
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Abstract Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.
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Hernandez-Lasheras, Jaime, Baptiste Mourre, Alejandro Orfila, Alex Santana, Emma Reyes, and Joaquín Tintoré. "Evaluating high-frequency radar data assimilation impact in coastal ocean operational modelling." Ocean Science 17, no. 4 (August 27, 2021): 1157–75. http://dx.doi.org/10.5194/os-17-1157-2021.
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Abstract. The impact of the assimilation of HFR (high-frequency radar) observations in a high-resolution regional model is evaluated, focusing on the improvement of the mesoscale dynamics. The study area is the Ibiza Channel, located in the western Mediterranean Sea. The resulting fields are tested against trajectories from 13 drifters. Six different assimilation experiments are compared to a control run (no assimilation). The experiments consist of assimilating (i) sea surface temperature, sea level anomaly, and Argo profiles (generic observation dataset); the generic observation dataset plus (ii) HFR total velocities and (iii) HFR radial velocities. Moreover, for each dataset, two different initialization methods are assessed: (a) restarting directly from the analysis after the assimilation or (b) using an intermediate initialization step applying a strong nudging towards the analysis fields. The experiments assimilating generic observations plus HFR total velocities with the direct restart provide the best results, reducing by 53 % the average separation distance between drifters and virtual particles after the first 48 h of simulation in comparison to the control run. When using the nudging initialization step, the best results are found when assimilating HFR radial velocities with a reduction of the mean separation distance by around 48 %. Results show that the integration of HFR observations in the data assimilation system enhances the prediction of surface currents inside the area covered by both antennas, while not degrading the correction achieved thanks to the assimilation of generic data sources beyond it. The assimilation of radial observations benefits from the smoothing effect associated with the application of the intermediate nudging step.
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Wang, Bingli, Wei Cheng, Yansong Bao, Shudong Wang, George P. Petropoulos, Shuiyong Fan, Jiajia Mao, Ziqi Jin, and Zihui Yang. "Effects of Assimilating Ground-Based Microwave Radiometer and FY-3D MWTS-2/MWHS-2 Data in Precipitation Forecasting." Remote Sensing 16, no. 14 (July 22, 2024): 2682. http://dx.doi.org/10.3390/rs16142682.
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This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment’s TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL.
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Xie, J., F. Counillon, J. Zhu, and L. Bertino. "An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI." Ocean Science 7, no. 5 (October 6, 2011): 609–27. http://dx.doi.org/10.5194/os-7-609-2011.
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Abstract. The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation.
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Arcucci, Rossella, Jiangcheng Zhu, Shuang Hu, and Yi-Ke Guo. "Deep Data Assimilation: Integrating Deep Learning with Data Assimilation." Applied Sciences 11, no. 3 (January 26, 2021): 1114. http://dx.doi.org/10.3390/app11031114.
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In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables.
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Tian, Yingze, Tongren Xu, Fei Chen, Xinlei He, and Shi Li. "Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?" Remote Sensing 14, no. 20 (October 16, 2022): 5172. http://dx.doi.org/10.3390/rs14205172.
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Data assimilation methods have been used to improve the performances of land surface models by integrating remote sensing and in situ measurements. However, the impact of data assimilation on improving the forecast of land surface variables has not been well studied, which is essential for weather and hydrology forecasting. In this study, a multi-pass land data assimilation scheme (MLDAS) based on the Noah-MP model was used to predict short-term land surface variables (e.g., sensible heat fluxes (H), latent heat fluxes (LE), and surface soil moisture (SM)) by jointly assimilating soil moisture, leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF). The test was conducted at the Mead site during the growing season (1 May to 30 September) in 2003, 2004, and 2005. Four assimilation-prediction scenarios (assimilating for 15 days, 45 days, 75 days, and 105 days from 1 May, then predicting one future month) are adapted to evaluate the influence of assimilation on subsequent prediction against Noah-MP open-loop simulation (OL). On average, MLDAS produces 28.65%, 27.79%, and 19.15% lower root square deviations (RMSD) for daily H, LE, and SM prediction compared to open-loop run, respectively. The influence of assimilation on prediction can reach around 60 days and 100 days for H (LE) and SM, respectively. Our findings indicate that data assimilation can improve the accuracy of land surface variables in a short-term prediction period.
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Zhang, Feimin, Yi Yang, and Chenghai Wang. "The Effects of Assimilating Conventional and ATOVS Data on Forecasted Near-Surface Wind with WRF-3DVAR." Monthly Weather Review 143, no. 1 (January 1, 2015): 153–64. http://dx.doi.org/10.1175/mwr-d-14-00038.1.
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Abstract In this paper, the Weather Research and Forecasting (WRF) Model with the three-dimensional variational data assimilation (WRF-3DVAR) system is used to investigate the impact on the near-surface wind forecast of assimilating both conventional data and Advanced Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) radiances compared with assimilating conventional data only. The results show that the quality of the initial field and the forecast performance of wind in the lower atmosphere are improved in both assimilation cases. Assimilation results capture the spatial distribution of the wind speed, and the observation data assimilation has a positive effect on near-surface wind forecasts. Although the impacts of assimilating ATOVS radiances on near-surface wind forecasts are limited, the fine structure of local weather systems illustrated by the WRF-3DVAR system suggests that assimilating ATOVS radiances has a positive effect on the near-surface wind forecast under conditions that ATOVS radiances in the initial condition are properly amplified. Assimilating conventional data is an effective approach for improving the forecast of the near-surface wind.
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Thodsan, Thippawan, Falin Wu, Kritanai Torsri, Thakolpat Khampuenson, and Gongliu Yang. "Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand." Atmosphere 12, no. 11 (November 12, 2021): 1497. http://dx.doi.org/10.3390/atmos12111497.
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Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.
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GU, FENG, and XIAOLIN HU. "ANALYSIS AND QUANTIFICATION OF DATA ASSIMILATION BASED ON SEQUENTIAL MONTE CARLO METHODS FOR WILDFIRE SPREAD SIMULATION." International Journal of Modeling, Simulation, and Scientific Computing 01, no. 04 (December 2010): 445–68. http://dx.doi.org/10.1142/s1793962310000298.
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Data assimilation is an important technique to improve simulation results by assimilating real time sensor data into a simulation model. A data assimilation framework based on Sequential Monte Carlo (SMC) methods for wildfire spread simulation has been developed in previous work. This paper provides systematic analysis and measurement to quantify the effectiveness and robustness of the developed data assimilation method. Measurement metrics are used to evaluate the robustness of SMC methods in data assimilation for wildfire spread simulation. Sensitivity analysis is carried out to examine the influences of important parameters to the data assimilation results. This work of analysis and quantification provides information to assess the effectiveness of the data assimilation method and suggests guidelines to further improve the data assimilation method for wildfire spread simulation.
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Pan, Zongmei, Shuwen Zhang, and Weidong Zhang. "Impact of Radar and Surface Data Assimilation on the Forecast of a Nocturnal Squall Line in the Yangtze–Huaihe River." Atmosphere 13, no. 9 (September 17, 2022): 1522. http://dx.doi.org/10.3390/atmos13091522.
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The impact of radar and surface data assimilation on the forecast of a nocturnal squall line initiated above the stable boundary layer in the Yangtze–Huaihe River is investigated by the Weather Research and Forecasting (WRF) model and its three-dimensional variational assimilation system (WRFDA 3DVar). Results show that the assimilation of radar and surface data can improve the prediction of the convection initiation time, height and vertical ascending motion during the early stage of the squall line formation by adjusting the thermodynamic structure, circulation patterns, water vapor conditions and hydrometeor mixing ratios. Although the radar and surface data assimilation can improve the forecast of the location of the squall line to a certain extent, the squall line is stronger in the radar data assimilation than that in the surface data assimilation, leading to stronger radar reflectivity and heavier precipitation. The assimilation of both radar and surface data has a more positive impact on the forecast skill than the assimilation of either type of data. Moreover, during the mature stage of the squall line, radar and surface data assimilation can enhance the intensity of the surface cold pool. Specifically, radar data assimilation or assimilating the two data simultaneously can produce a stronger cold pool than only assimilating surface data, which is more conducive to the maintenance and development of the squall line.
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Tian, Jiyang, Ronghua Liu, Liuqian Ding, Liang Guo, and Bingyu Zhang. "Typhoon rainstorm simulations with radar data assimilation on the southeast coast of China." Natural Hazards and Earth System Sciences 21, no. 2 (February 23, 2021): 723–42. http://dx.doi.org/10.5194/nhess-21-723-2021.
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Abstract. As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the numerical weather prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events on the southeast coast of China. Radar data from a Doppler radar station in Changle, China, are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data and at three assimilation time intervals. The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices, including relative error (RE), critical success index (CSI), and root mean square error (RMSE). Assimilating radial velocity at a time interval of 1 h can significantly improve the rainfall simulations, and it outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulations as the time interval shortens. The rainfall simulations can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts at catchment scale and have great significance on typhoon rainstorm warning.
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Rosenthal, W. Steven, Shankar Venkataramani, Arthur J. Mariano, and Juan M. Restrepo. "Displacement data assimilation." Journal of Computational Physics 330 (February 2017): 594–614. http://dx.doi.org/10.1016/j.jcp.2016.10.025.
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Yu, Y., J. Koller, V. K. Jordanova, S. G. Zaharia, and H. C. Godinez. "Radiation belt data assimilation of a moderate storm event using a magnetic field configuration from the physics-based RAM-SCB model." Annales Geophysicae 32, no. 5 (May 6, 2014): 473–83. http://dx.doi.org/10.5194/angeo-32-473-2014.
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Abstract. Data assimilation using Kalman filters provides an effective way of understanding both spatial and temporal variations in the outer electron radiation belt. Data assimilation is the combination of in situ observations and physical models, using appropriate error statistics to approximate the uncertainties in both the data and the model. The global magnetic field configuration is one essential element in determining the adiabatic invariants for the phase space density (PSD) data used for the radiation belt data assimilation. The lack of a suitable global magnetic field model with high accuracy is still a long-lasting problem. This paper employs a physics-based magnetic field configuration for the first time in a radiation belt data assimilation study for a moderate storm event on 19 December 2002. The magnetic field used in our study is the magnetically self-consistent inner magnetosphere model RAM-SCB, developed at Los Alamos National Laboratory (LANL). Furthermore, we apply a cubic spline interpolation method in converting the differential flux measurements within the energy spectrum, to obtain a more accurate PSD input for the data assimilation than the commonly used linear interpolation approach. Finally, the assimilation is done using an ensemble Kalman filter (EnKF), with a localized adaptive inflation (LAI) technique to appropriately account for model errors in the assimilation and improve the performance of the Kalman filter. The assimilative results are compared with results from another assimilation experiment using the Tsyganenko 2001S (T01S) magnetic field model, to examine the dependence on a magnetic field model. Results indicate that the data assimilations using different magnetic field models capture similar features in the radiation belt dynamics, including the temporal evolution of the electron PSD during a storm and the location of the PSD peak. The assimilated solution predicts the energy differential flux to a relatively good degree when compared with independent LANL-GEO in situ observations. A closer examination suggests that for the chosen storm event, the assimilation using the RAM-SCB predicts a better flux at most energy levels during storm recovery phase but is slightly worse in the storm main phase than the assimilation using the T01S model.
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Li, Ying, Chunhui Zou, Liping Feng, Xiaochuang Yao, Tongxiao Li, Xianguan Chen, and Jin Zhao. "Estimation of winter wheat yield in Hebi city based on crop models and remote sensing data assimilation." Journal of Physics: Conference Series 2791, no. 1 (July 1, 2024): 012079. http://dx.doi.org/10.1088/1742-6596/2791/1/012079.
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Abstract Assimilating remote sensing data with crop models is an effective approach to improve the accuracy of crop model applications at the regional scale. Sobol global sensitivity analysis and Markov Chain Monte Carlo methods were used to calibrate parameters in the WOFOST and WheatSM models within a gridded model framework in the study area. Time series data reconstruction techniques were employed to correct MODIS LAI and ET data. Four assimilation scenarios were tested, including assimilation of only leaf area index (LAI), assimilation of only evapotranspiration (ET), simultaneous assimilation of LAI and ET and a control scenario without assimilation. These scenarios were applied to model winter wheat yields in Hebi City, China, from 2013 to 2018. Statistical validation demonstrated that assimilating ET or LAI individually significantly improved model accuracy compared to the control scenario with similar levels of improvement. The highest model accuracy was achieved when assimilating both ET and LAI simultaneously, showing the highest correlation coefficients and the lowest root mean square errors among the four scenarios. This research provides a basis for selecting assimilation variables when applying crop models at the regional scale. The coupling of crop growth models with remote sensing data empowers governments and agricultural producers to devise more effective agricultural strategies, allocate resources efficiently, and implement disaster response measures. This enhances scientific management within the agricultural sector, promotes increased food production and elevates farmers’ incomes.
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Liu, Jia, Jiyang Tian, Denghua Yan, Chuanzhe Li, Fuliang Yu, and Feifei Shen. "Evaluation of Doppler radar and GTS data assimilation for NWP rainfall prediction of an extreme summer storm in northern China: from the hydrological perspective." Hydrology and Earth System Sciences 22, no. 8 (August 16, 2018): 4329–48. http://dx.doi.org/10.5194/hess-22-4329-2018.
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Abstract. Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations, i.e., the Doppler weather radar and the Global Telecommunication System (GTS) data, in improving the mesoscale NWP rainfall products. A 24 h summer storm occurring over the Beijing–Tianjin–Hebei region of northern China on 21 July 2012 is selected as a case study. The Weather Research and Forecasting (WRF) Model is used to obtain 3 km rainfall forecasts, and the observations are assimilated using the three-dimensional variational (3DVar) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Both the rainfall accumulative amount and its distribution in space and time are examined for the forecasting results with and without data assimilation. The results show that data assimilation can effectively help improve the WRF rainfall forecasts, which is of great importance for hydrologic applications through the rainfall–runoff transformation process. Both the radar reflectivity and the GTS data are good choices for assimilation in improving the rainfall products, whereas special attention should be paid to assimilating radial velocity where unsatisfactory results are always found. The assimilation of the GTS data in the coarser domain has positive effects on the radar data assimilation in the finer domain, which can make the rainfall forecasts more accurate than assimilating the radar data alone. It is also found that the assimilation of more observations cannot guarantee further improvement of the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are further made.
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Chen, I.-Han, Jing-Shan Hong, Ya-Ting Tsai, and Chin-Tzu Fong. "Improving Afternoon Thunderstorm Prediction over Taiwan through 3DVar-Based Radar and Surface Data Assimilation." Weather and Forecasting 35, no. 6 (December 2020): 2603–20. http://dx.doi.org/10.1175/waf-d-20-0037.1.
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AbstractRecently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.
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Yang, Chun, Lijian Zhu, and Jinzhong Min. "Impact Study of FY-3B MWRI Data Assimilation in WRFDA." Atmosphere 12, no. 4 (April 15, 2021): 497. http://dx.doi.org/10.3390/atmos12040497.
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In the first attempt to configure the Fengyun-3B satellite’s Microwave Radiation Imager (MWRI) radiance data in the Weather Research Forecast (WRF) model’s Data Assimilation system (WRFDA), the impact of MWRI data assimilation on the analysis and forecast of Typhoon Son-Tinh in 2012 was evaluated with WRFDA’s three-dimensional variational (3DVAR) data-assimilation scheme. Compared to a benchmark experiment with no MWRI data, assimilating MWRI radiances improved the analyses of typhoon central sea level pressure (CSLP), warm core structure, and wind speed. Moreover, verified with European Center for Medium-Range Weather Forecasts (ECMWF) analysis data, significant improvements in model variable forecast, such as geopotential height and specific humidity, were produced. Substantial error reductions in track, CSLP, and maximum-wind-speed forecasts with MWRI assimilation was also obtained from analysis time to 48 h forecast.
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Liu, Fuhong, Jeremy R. Krieger, and Jing Zhang. "Toward Producing the Chukchi–Beaufort High-Resolution Atmospheric Reanalysis (CBHAR) via the WRFDA Data Assimilation System." Monthly Weather Review 142, no. 2 (January 24, 2014): 788–805. http://dx.doi.org/10.1175/mwr-d-13-00063.1.
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Abstract The Weather Research and Forecasting Model (WRF) and its variational data assimilation system (WRFDA) are applied to the Chukchi–Beaufort Seas and adjacent Arctic Slope region for high-resolution regional atmospheric reanalysis study. To optimize WRFDA performance over the study area, a set of sensitivity experiments are carried out to analyze the model sensitivity to model background errors (BEs) and the assimilation of various observational datasets. Observational data are assimilated every 6 h and the results are verified against unassimilated observations. In the BE sensitivity analyses, the results of assimilating in situ surface observations with a customized, domain-dependent BE are compared to those using the WRF-provided global BE. It is found that the customized BE is necessary in order to achieve positive impacts from WRFDA assimilation for the study area. When seasonal variability is incorporated into the customized BE, the impacts are minor. Sensitivity analyses examining the assimilation of different datasets via WRFDA demonstrate that 1) positive impacts are always seen through the assimilation of in situ surface and radiosonde measurements, 2) assimilating Quick Scatterometer (QuikSCAT) winds improves the simulation of the 10-m wind field over ocean and coastal areas, and 3) selectively assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved profiles under clear-sky and snow-free conditions is essential to avoid degradation of assimilation performance, while assimilation of Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) retrievals has little impact, most likely due to limited data availability. Based on the sensitivity results, a 1-yr (2009) experimental reanalysis is conducted and consistent improvements are achieved, particularly in capturing mesoscale processes such as mountain barrier and sea-breeze effects.
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Mallick, Swapan. "Impact of Adaptively Thinned GOES-16 Cloud Water Path in an Ensemble Data Assimilation System." Meteorology 1, no. 4 (December 5, 2022): 513–30. http://dx.doi.org/10.3390/meteorology1040032.
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Assimilation of cloud properties in the convective scale ensemble data assimilation system is one of the prime topics of research in recent years. Satellites can retrieve cloud properties that are important sources of information of the cloud and atmospheric state. The Advance Baseline Imager (ABI) aboard the GOES-16 geostationary satellite brings an opportunity for retrieving high spatiotemporal resolution cloud properties, including cloud water path over continental United States. This study investigates the potential impacts of assimilating adaptively thinned GOES-16 cloud water path (CWP) observations that are assimilated by the ensemble-based Warn-on-Forecast System and the impact on subsequent weather forecasts. In this study, for CWP assimilation, multiple algorithms have been developed and tested using the adaptive-based thinning method. Three severe weather events are considered that occurred on 19 July 2019, 7 May and 21 June 2020. The superobbing procedure used for CWP data smoothed from 5 to 15 km or more depending on thinning algorithm. The overall performance of adaptively thinned CWP assimilation in the Warn-on-Forecast system is assessed using an object-based verification method. On average, more than 60% of the data was reduced and therefore not used in the assimilation system. Results suggest that assimilating less than 40% of CWP superobbing data into the Warn-on-Forecast system is of similar forecast quality to those obtained from assimilating all available CWP observations. The results of this study can be used on the benefits of cloud assimilation to improve numerical simulation.
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Crow, Wade T., and Emiel Van Loon. "Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture." Journal of Hydrometeorology 7, no. 3 (June 1, 2006): 421–32. http://dx.doi.org/10.1175/jhm499.1.
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Abstract Data assimilation approaches require some type of state forecast error covariance information in order to optimally merge model predictions with observations. The ensemble Kalman filter (EnKF) dynamically derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or type of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of modeling error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrades the performance of a land surface model (relative to open-loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances and adaptively correcting the culpable model error assumptions using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture observations appears to offer a more robust assimilation framework where incorrect model error assumptions are more readily diagnosed via filter innovations.
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Xu, Dongmei, Aiqing Shu, Feifei Shen, Jinzhong Min, Hong Li, and Xiaoli Xia. "Impacts of Multiple Radiance Data Assimilation on the Simulation of Typhoon Chan-Hom." Atmosphere 11, no. 9 (September 8, 2020): 957. http://dx.doi.org/10.3390/atmos11090957.
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With the module of assimilating AMSU-A (Advanced Microwave Sounding Unit-A) and AIRS (Atmospheric Infrared Sounder) data in the WRFDA (Weather Research and Forecasting Model Data Assimilation) system, the impacts of joint assimilation of the radiance observations from two satellites on the simulation of typhoon Chan-hom (2015) are addressed. For comparison, experiments with the assimilation of solely GTS (Global Telecommunications System) data, AMSU-A data, or AIRS data are also performed. The results show that, compared to other experiments, the analysis field after assimilating multiple radiance data is closer to the observation. The simulated steering flow in its forecast field is conductive to the northeast twist of the typhoon. In addition, the simulated rainband and the FSS (fraction skill score) calculated from the experiment with assimilating multiple radiance data are better. In the deterministic forecast, better performance is obtained from the simulation with multiple radiance data in the forecast of track, MSLP (minimum sea level pressure), and MSW (maximum surface wind).
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Sun, Tao, Yaodeng Chen, Juanzhen Sun, Hongli Wang, Haiqin Chen, Yuanbing Wang, and Deming Meng. "A Multi-Time-Scale Four-Dimensional Variational Data Assimilation Scheme and Its Application to Simulated Radial Velocity and Reflectivity Data." Monthly Weather Review 148, no. 5 (April 27, 2020): 2063–85. http://dx.doi.org/10.1175/mwr-d-19-0203.1.
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Abstract In this study, a multi-time-scale four-dimensional variational data assimilation (MTS-4DVar) scheme is developed and applied to the assimilation of radar observations. The MTS-4DVar employs multitime windows with various time lengths in the framework of incremental 4DVar in the Weather Research and Forecasting Data Assimilation (WRFDA). The objective of MTS-4DVar is to enable the 4DVar data assimilation system to extract multiscale information from radar observations, and the algorithm of MTS-4DVar is first discussed in detail. Using a heavy rainfall case, it is shown that the nonlinearity growth of reflectivity is faster than that of radial velocity, suggesting that the time window for assimilating reflectivity in the incremental 4DVar should be shorter than that of radial velocity. A series of single observation tests and observing system simulation experiments (OSSEs) are then presented to examine the physical characteristics and performance of MTS-4DVar. These experiments demonstrate that the MTS-4DVar is capable of combining the larger-scale information from a longer time window and the local-scale features from a shorter time window. With the OSSEs it is shown that the value of the cost function is reduced properly in the minimization of the MTS-4DVar with a combination of longer and shorter time windows. By assimilating the radar radial velocity alone, we found that the MTS-4DVar reduces the analysis and forecast errors and improves the precipitation forecasts in comparison with the normal incremental 4DVar. Additional assimilation of reflectivity further improved the precipitation forecasts, and the results show that the radar reflectivity can also be well assimilated by using MTS-4DVar.
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Hastuti, Miranti Indri, Ki-Hong Min, and Ji-Won Lee. "Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data." Remote Sensing 15, no. 11 (May 25, 2023): 2760. http://dx.doi.org/10.3390/rs15112760.
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Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.
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Watkinson, Laura R., Amos S. Lawless, Nancy K. Nichols, and Ian Roulstone. "Weak constraints in four-dimensional variational data assimilation." Meteorologische Zeitschrift 16, no. 6 (December 17, 2007): 767–76. http://dx.doi.org/10.1127/0941-2948/2007/0249.
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Zhang, Donghua, Henrik Madsen, Marc E. Ridler, Jacob Kidmose, Karsten H. Jensen, and Jens C. Refsgaard. "Multivariate hydrological data assimilation of soil moisture and groundwater head." Hydrology and Earth System Sciences 20, no. 10 (October 26, 2016): 4341–57. http://dx.doi.org/10.5194/hess-20-4341-2016.
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Abstract. Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation.
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Salman, H., L. Kuznetsov, C. K. R. T. Jones, and K. Ide. "A Method for Assimilating Lagrangian Data into a Shallow-Water-Equation Ocean Model." Monthly Weather Review 134, no. 4 (April 1, 2006): 1081–101. http://dx.doi.org/10.1175/mwr3104.1.
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Abstract Lagrangian measurements provide a significant portion of the data collected in the ocean. Difficulties arise in their assimilation, however, since Lagrangian data are described in a moving frame of reference that does not correspond to the fixed grid locations used to forecast the prognostic flow variables. A new method is presented for assimilating Lagrangian data into models of the ocean that removes the need for any commonly used approximations. This is accomplished by augmenting the state vector of the prognostic variables with the Lagrangian drifter coordinates at assimilation. It is shown that this method is best formulated using the ensemble Kalman filter, resulting in an algorithm that is essentially transparent for assimilating Lagrangian data. The method is tested using a set of twin experiments on the shallow-water system of equations for an unsteady double-gyre flow configuration. Numerical simulations show that this method is capable of correcting the flow even if the assimilation time interval is of the order of the Lagrangian autocorrelation time scale (TL) of the flow. These results clearly demonstrate the benefits of this method over other techniques that require assimilation times of 20%–50% of TL, a direct consequence of the approximations introduced in assimilating their Lagrangian data. Detailed parametric studies show that this method is particularly effective if the classical ideas of localization developed for the ensemble Kalman filter are extended to the Lagrangian formulation used here. The method that has been developed, therefore, provides an approach that allows one to fully realize the potential of Lagrangian data for assimilation in more realistic ocean models.
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Sun, Leqiang, Ousmane Seidou, and Ioan Nistor. "Data Assimilation for Streamflow Forecasting: State–Parameter Assimilation versus Output Assimilation." Journal of Hydrologic Engineering 22, no. 3 (March 2017): 04016060. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001475.
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Yang, Shu-Chih, Zih-Mao Huang, Ching-Yuang Huang, Chih-Chien Tsai, and Ta-Kang Yeh. "A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction." Monthly Weather Review 148, no. 3 (February 25, 2020): 1075–98. http://dx.doi.org/10.1175/mwr-d-18-0418.1.
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Abstract The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.
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Zang, Z., X. Pan, W. You, and Y. Liang. "A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2215–18. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2215-2018.
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A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.
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Fontana, C., P. Brasseur, and J. M. Brankart. "Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998–2006 based on the assimilation of SeaWiFS chlorophyll data." Ocean Science 9, no. 1 (January 16, 2013): 37–56. http://dx.doi.org/10.5194/os-9-37-2013.
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Abstract. Today, the routine assimilation of satellite data into operational models of ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical–biogeochemical model of the North Atlantic. The aim is on the one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical–biogeochemical modeling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9-year period. In this frame, two experiments are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, nitrate of the World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface analysis and forecast chlorophyll concentrations, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related literature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentrations deeper than 50 meters. The assessment of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalyzing the ocean biogeochemistry based on ocean color data.
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Fontana, C., P. Brasseur, and J. M. Brankart. "Toward a multivariate reanalysis of the North Atlantic ocean biogeochemistry during 1998–2006 based on the assimilation of SeaWiFS chlorophyll data." Ocean Science Discussions 9, no. 2 (April 27, 2012): 1887–931. http://dx.doi.org/10.5194/osd-9-1887-2012.
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Abstract. Today, the routine assimilation of satellite data into operational models of the ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical-biogeochemical model of the North-Atlantic. The aim is on one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical-biogeochemical modelling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9 year-long period. In this frame, two experiences are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, the nitrate World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface chlorophyll concentrations analysis and forecast, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related litterature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentration deeper than 50 m. The assessement of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalysing the ocean biogeochemistry based on ocean color data.
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Chen, Wenrui, Daosheng Wang, Xiujuan Liu, Jun Cheng, and Jicai Zhang. "Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method." Remote Sensing 15, no. 1 (December 27, 2022): 148. http://dx.doi.org/10.3390/rs15010148.
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Suspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of modeling the suspended sediment concentrations (SSCs) with newly developed techniques. Based on a three-dimensional suspended cohesive sediment transport model, the transport of suspended sediment and SSCs are simulated by assimilating SSCs retrieved from the Geostationary Ocean Color Imager (GOCI) with the adjoint data assimilation in the Hangzhou Bay, a typical strong tidal estuary along the coast of the East China Sea. To improve the effect of the data assimilation, the penalty function method, in which the reasonable constraints of the estimated model parameters are added to the cost function as penalty terms, will be introduced for the first time into the adjoint data assimilation in the SSCs modeling. In twin experiments, the prescribed spatially varying settling velocity is estimated by assimilating the synthetic SSC observations, and the results show that the penalty function method can further improve the effect of data assimilation and parameter estimation, regardless of synthetic SSC observations being contaminated by random artificial errors. In practical experiments, the spatially varying settling velocity is firstly estimated by assimilating the actual GOCI-retrieved SSCs. The results demonstrate that the simulated results can be improved by the adjoint data assimilation, and the penalty function method can additionally reduce the mean absolute error (MAE) between the independent check observations and the corresponding simulated SSCs from 1.44 × 10−1 kg/m3 to 1.30 × 10−1 kg/m3. To pursue greater simulation accuracy, the spatially varying settling velocity, resuspension rate, critical shear stress and initial conditions are simultaneously estimated by assimilating the actual GOCI-retrieved SSCs to simulate the SSCs in the Hangzhou Bay. When the adjoint data assimilation and the penalty function method are simultaneously used, the MAE between the independent check observations and the corresponding simulated SSCs is just 9.90 × 10−2 kg/m3, which is substantially less than that when only the settling velocity is estimated. The MAE is also considerably less than that when the four model parameters are estimated to be without using the penalty function method. This study indicates that the adjoint data assimilation can effectively improve the SSC simulation accuracy, and the penalty function method can limit the variation range of the estimated model parameters to further improve the effect of data assimilation and parameter estimation.
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Tian, X., Z. Xie, Y. Liu, Z. Cai, Y. Fu, H. Zhang, and L. Feng. "A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO<sub>2</sub> fluxes and 3-D atmospheric CO<sub>2</sub> concentrations from observations." Atmospheric Chemistry and Physics 14, no. 23 (December 12, 2014): 13281–93. http://dx.doi.org/10.5194/acp-14-13281-2014.
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Abstract. We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.
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Kivi, Marissa, Noemi Vergopolan, and Hamze Dokoohaki. "A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest." Hydrology and Earth System Sciences 27, no. 5 (March 16, 2023): 1173–99. http://dx.doi.org/10.5194/hess-27-1173-2023.
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Abstract. Today, the most popular approaches in agricultural forecasting leverage process-based crop models, crop monitoring data, and/or remote sensing imagery. Individually, each of these tools has its own unique advantages but is, nonetheless, limited in prediction accuracy, precision, or both. In this study we integrate in situ and remote sensing (RS) soil moisture observations with APSIM model through sequential data assimilation to evaluate the improvement in model predictions of downstream state variables across five experimental sites in the US Midwest. Four RS data products and in situ observations spanning 19 site years were used through two data assimilation approaches, namely ensemble Kalman filter (EnKF) and generalized ensemble filter (GEF), to constrain model states at observed time steps and estimate joint background and observation error matrices. Then, the assimilation's impact on estimates of soil moisture, yield, normalized difference vegetation index (NDVI), tile drainage, and nitrate leaching was assessed across all site years. When assimilating in situ observations, the accuracy of soil moisture forecasts in the assimilation layers was improved by reducing RMSE by an average of 17 % for 10 cm and ∼28 % for 20 cm depth soil layer across all site years. These changes also led to improved simulation of soil moisture in deeper soil layers by an average of 12 %. Although crop yield was improved by an average of 23 %, the greatest improvement in yield accuracy was demonstrated in site years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of including additional data constraint such as evapotranspiration. The assimilation of RS soil moisture showed a weaker constraint of downstream model state variables when compared to the assimilation of in situ soil moisture. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 5. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2 %. Crop yield prediction was improved when assimilating both in situ and remote sensing soil moisture observations, and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations.
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Wu, Chun-Chieh, Kun-Hsuan Chou, Yuqing Wang, and Ying-Hwa Kuo. "Tropical Cyclone Initialization and Prediction Based on Four-Dimensional Variational Data Assimilation." Journal of the Atmospheric Sciences 63, no. 9 (September 1, 2006): 2383–95. http://dx.doi.org/10.1175/jas3743.1.
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Abstract Issues on the initialization and simulation of tropical cyclones (TCs) have been studied here based on four-dimensional variational data assimilation. In particular, experiments have been carried out to assess 1) what the most critical parameters for the so-called bogus data assimilation are and 2) how the current procedures for the bogus data assimilation can be further improved. It is shown that the assimilation of wind fields is more successful than that of pressure fields in improving the initial structure and prediction of TCs. It is emphasized that the geostrophic adjustment favors the pressure field to adjust to the wind field because the TC vortex is much smaller than the radius of Rossby deformation. The results suggest that a better initial condition in the wind field is critical to the simulation of TCs. Experiments from this study also show that inclusion of the initial TC movement in the data assimilation window can help improve the track prediction, particularly during the early integration period. This method is able to shed light on the improvement of TC simulation based on the bogus data assimilation. In all, the results add a theoretical interpretation of the importance of the wind field to the sea level pressure field in terms of geostrophic adjustment, as well as a time dimension of the bogus data assimilation, by assimilating a movable vortex in the four-dimensional variational data assimilation.
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Hamill, Thomas M., and Jeffrey S. Whitaker. "Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches." Monthly Weather Review 133, no. 11 (November 1, 2005): 3132–47. http://dx.doi.org/10.1175/mwr3020.1.
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Abstract Insufficient model resolution is one source of model error in numerical weather predictions. Methods for parameterizing this error in ensemble data assimilations are explored here. Experiments were conducted with a two-layer primitive equation model, where the assumed true state was a T127 forecast simulation. Ensemble data assimilations were performed with the same model at T31 resolution, assimilating imperfect observations drawn from the T127 forecast. By design, the magnitude of errors due to model truncation was much larger than the error growth due to initial condition uncertainty, making this a stringent test of the ability of an ensemble-based data assimilation to deal with model error. Two general methods, “covariance inflation” and “additive error,” were considered for parameterizing the model error at the resolved scales (T31 and larger) due to interaction with the unresolved scales (T32 to T127). Covariance inflation expanded the background forecast members’ deviations about the ensemble mean, while additive error added specially structured noise to each ensemble member forecast before the update step. The method of parameterizing this model error had a substantial effect on the accuracy of the ensemble data assimilation. Covariance inflation produced ensembles with analysis errors that were no lower than the analysis errors from three-dimensional variational (3D-Var) assimilation, and for the method to avoid filter divergence, the assimilations had to be periodically reseeded. Covariance inflation uniformly expanded the model spread; however, the actual growth of model errors depended on the dynamics, growing proportionally more in the midlatitudes. The inappropriately uniform inflation progressively degradated the capacity of the ensemble to span the actual forecast error. The most accurate model-error parameterization was an additive model-error parameterization, which reduced the error difference between 3D-Var and a near-perfect assimilation system by ∼40%. In the lowest-error simulations, additive errors were parameterized using samples of model error from a time series of differences between T63 and T31 forecasts. Scaled samples of differences between model forecast states separated by 24 h were also tested as additive error parameterizations, as well as scaled samples of the T31 model state’s anomaly from the T31 model climatology. The latter two methods produced analyses that were progressively less accurate. The decrease in accuracy was likely due to their inappropriately long spatial correlation length scales.
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Wei, Shih-Wei, Cheng-Hsuan (Sarah) Lu, Quanhua Liu, Andrew Collard, Tong Zhu, Dustin Grogan, Xu Li, Jun Wang, Robert Grumbine, and Partha S. Bhattacharjee. "The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System." Atmosphere 12, no. 4 (March 28, 2021): 432. http://dx.doi.org/10.3390/atmos12040432.
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Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13 μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39 μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.
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MacBean, Natasha, Philippe Peylin, Frédéric Chevallier, Marko Scholze, and Gregor Schürmann. "Consistent assimilation of multiple data streams in a carbon cycle data assimilation system." Geoscientific Model Development 9, no. 10 (October 4, 2016): 3569–88. http://dx.doi.org/10.5194/gmd-9-3569-2016.
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Abstract. Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. The increase in the number of available datasets in recent years allows us to address different aspects of the model at a variety of spatial and temporal scales. However, combining data streams in a DA system is not a trivial task. In this study we highlight some of the challenges surrounding multiple data stream assimilation for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion algorithm that are typically used when optimising global LSMs – namely, Gaussian error distributions and linearity in the model dynamics. We explore the effect of biases and inconsistencies between the observations and the model (resulting in non-Gaussian error distributions), and we examine the difference between a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data stream is assimilated sequentially) in the presence of non-linear model dynamics. In addition, we perform a preliminary investigation into the impact of correlated errors between two data streams for two cases, both when the correlated observation errors are included in the prior observation error covariance matrix, and when the correlated errors are ignored. We demonstrate these challenges by assimilating synthetic observations into two simple models: the first a simplified version of the carbon cycle processes represented in many LSMs and the second a non-linear toy model. Finally, we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their model parameters.
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Kumar, Sujay V., Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez. "Role of forcing uncertainty and background model error characterization in snow data assimilation." Hydrology and Earth System Sciences 21, no. 6 (June 2, 2017): 2637–47. http://dx.doi.org/10.5194/hess-21-2637-2017.
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Abstract. Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.
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Zhang, Xubin, and Meiling Chen. "Assimilation of Data Derived from Optimal-member Products of TREPS for Convection-Permitting TC Forecasting over Southern China." Atmosphere 10, no. 2 (February 18, 2019): 84. http://dx.doi.org/10.3390/atmos10020084.
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To improve the landfalling tropical cyclone (TC) forecasting, the pseudo inner-core observations derived from the optimal-member forecast (OPT) and its probability-matched mean (OPTPM) of a mesoscale ensemble prediction system, namely TREPS, were assimilated in a partial-cycle data assimilation (DA) system based on the three-dimensional variational method. The impact of assimilating the derived data on the 12-h TC forecasting was evaluated over 17 TCs making landfall on Southern China during 2014–2016, based on the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model with the horizontal resolution of 0.03°. The positive impacts of assimilating the OPT-derived data were found in predicting some variables, such as the TC intensity, lighter rainfall, and stronger surface wind, with statistically significant impacts at partial lead times. Compared with assimilation of the OPT-derived data, assimilation of the OPTPM-derived data generally brought improvements in the forecasts of TC track, intensity, lighter rainfall, and weaker surface wind. When the data with higher accuracy was assimilated, the positive impacts of assimilating the OPTPM-derived data on the forecasts of heavier rainfall and stronger surface wind were more evident. The improved representation of initial TC circulation due to assimilating the derived data improved the TC forecasting, which was intuitively illustrated in the case study of Mujigae.
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Liang, Xi, Qinghua Yang, Lars Nerger, Svetlana N. Losa, Biao Zhao, Fei Zheng, Lin Zhang, and Lixin Wu. "Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter." Journal of Atmospheric and Oceanic Technology 34, no. 9 (September 2017): 1985–99. http://dx.doi.org/10.1175/jtech-d-16-0166.1.
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AbstractSea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.