Journal articles on the topic 'Assimilation'
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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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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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Cheng, Yueming, Tie Dai, Daisuke Goto, Nick A. J. Schutgens, Guangyu Shi, and Teruyuki Nakajima. "Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter." Atmospheric Chemistry and Physics 19, no. 21 (November 5, 2019): 13445–67. http://dx.doi.org/10.5194/acp-19-13445-2019.
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Abstract. Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation.
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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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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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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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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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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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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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Anderson, Jeffrey L., Bruce Wyman, Shaoqing Zhang, and Timothy Hoar. "Assimilation of Surface Pressure Observations Using an Ensemble Filter in an Idealized Global Atmospheric Prediction System." Journal of the Atmospheric Sciences 62, no. 8 (August 1, 2005): 2925–38. http://dx.doi.org/10.1175/jas3510.1.
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Abstract An ensemble filter data assimilation system is tested in a perfect model setting using a low resolution Held–Suarez configuration of an atmospheric GCM. The assimilation system is able to reconstruct details of the model’s state at all levels when only observations of surface pressure (PS) are available. The impacts of varying the spatial density and temporal frequency of PS observations are examined. The error of the ensemble mean assimilation prior estimate appears to saturate at some point as the number of PS observations available once every 24 h is increased. However, increasing the frequency with which PS observations are available from a fixed network of 1800 randomly located stations results in an apparently unbounded decrease in the assimilation’s prior error for both PS and all other model state variables. The error reduces smoothly as a function of observation frequency except for a band with observation periods around 4 h. Assimilated states are found to display enhanced amplitude high-frequency gravity wave oscillations when observations are taken once every few hours, and this adversely impacts the assimilation quality. Assimilations of only surface temperature and only surface wind components are also examined. The results indicate that, in a perfect model context, ensemble filters are able to extract surprising amounts of information from observations of only a small portion of a model’s spatial domain. This suggests that most of the remaining challenges for ensemble filter assimilation are confined to problems such as model error, observation representativeness error, and unknown instrument error characteristics that are outside the scope of perfect model experiments. While it is dangerous to extrapolate from these simple experiments to operational atmospheric assimilation, the results also suggest that exploring the frequency with which observations are used for assimilation may lead to significant enhancements to assimilated state estimates.
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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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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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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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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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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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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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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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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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Mizzi, Arthur P., David P. Edwards, and Jeffrey L. Anderson. "Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles." Geoscientific Model Development 11, no. 9 (September 17, 2018): 3727–45. http://dx.doi.org/10.5194/gmd-11-3727-2018.
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Abstract. Assimilation of atmospheric composition retrievals presents computational challenges due to their high data volume and often sparse information density. Assimilation of compact phase space retrievals (CPSRs) meets those challenges and offers a promising alternative to assimilation of raw retrievals at reduced computational cost (Mizzi et al., 2016). This paper compares analysis and forecast results from assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) CPSRs with independent observations. We use MetOp-A/Infrared Atmospheric Sounding Interferometer (IASI) CO retrievals and Measurement of OZone, water vapor, carbon monoxide, and nitrogen oxides by in-service AIrbus airCraft (MOZAIC) in situ CO profiles for our independent observation comparisons. Generally, the results confirm that assimilation of MOPITT CPSRs improves the Weather Research and Forecasting model with chemistry coupled to the ensemble Kalman filter data assimilation from the Data Assimilation Research Testbed (WRF-Chem/DART) analysis fit and forecast skill at a reduced computational cost compared to assimilation of raw retrievals. Comparison with the independent observations shows that assimilation of MOPITT CO generally improved the analysis fit and forecast skill in the lower troposphere but degraded it in the upper troposphere. We attribute that degradation to assimilation of MOPITT CO retrievals with a possible bias of ∼ 14 % above 300 hPa. To discard the biased retrievals, in this paper, we also extend CPSRs to assimilation of truncated retrieval profiles (as opposed to assimilation of full retrieval profiles). Those results show that not assimilating the biased retrievals (i) resolves the upper tropospheric analysis fit degradation issue and (ii) reduces the impact of assimilating the remaining unbiased retrievals because the total information content and vertical sensitivities are changed.
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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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Fu, Xiao Lei, Bao Ming Jin, Xiao Lei Jiang, and Cheng Chen. "One-dimensional soil temperature assimilation experiment based on unscented particle filter and Common Land Model." E3S Web of Conferences 38 (2018): 03009. http://dx.doi.org/10.1051/e3sconf/20183803009.
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Data assimilation is an efficient way to improve the simulation/prediction accuracy in many fields of geosciences especially in meteorological and hydrological applications. This study takes unscented particle filter (UPF) as an example to test its performance at different two probability distribution, Gaussian and Uniform distributions with two different assimilation frequencies experiments (1) assimilating hourly in situ soil surface temperature, (2) assimilating the original Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) once per day. The numerical experiment results show that the filter performs better when increasing the assimilation frequency. In addition, UPF is efficient for improving the soil variables (e.g., soil temperature) simulation/prediction accuracy, though it is not sensitive to the probability distribution for observation error in soil temperature assimilation.
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Wang, Jingnan, Lifeng Zhang, Jiping Guan, and Mingyang Zhang. "Comparison of Assimilating All-Sky and Clear-Sky Satellite Radiance for Typhoon Chan-Hom and Nangka Forecasts." Atmosphere 11, no. 6 (June 7, 2020): 599. http://dx.doi.org/10.3390/atmos11060599.
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The impacts of assimilating all-sky satellite radiance from the Advanced Microwave Scanning Radiometer 2 (AMSR2) on typhoon Chan-hom and Nangka are evaluated over traditional clear-sky radiance assimilation. Results show that more AMSR2 radiance data around typhoon core area are assimilated in all-sky experiment than clear-sky, which improves the utilization of satellite radiance data. Community Radiative Transfer Model (CRTM) brightness temperature simulation under all-sky conditions is in better agreement with observations than in the case of clear-sky conditions. In a cycle assimilation experiment, all-sky assimilation reduces typhoon track forecast errors by 14.84%, and intensity errors by approximately 16.89%. Wind, temperature and humidity analysis are clearly improved in all-sky assimilation, as evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data. All-sky assimilation better captures the structures of typhoons, with a stronger warm core and tighter circulation around the typhoon eye. This study explores the contributions to the improvements in all-sky assimilation. These improvements are attributed to the enhancements in initial geopotential height, temperature and moisture in the typhoon core areas. Moreover, assimilating cloud- and precipitation-affected radiance data improves hydrometer simulations, which leads to higher hydrometeor concentrations than clear-sky radiance and conventional data assimilation. The results demonstrate that assimilation of all-sky AMSR2 data improves the analysis and forecast of multiple typhoons.
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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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Rahman, Azbina, Viviana Maggioni, Xinxuan Zhang, Paul Houser, Timothy Sauer, and David M. Mocko. "The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model." Remote Sensing 14, no. 3 (January 18, 2022): 437. http://dx.doi.org/10.3390/rs14030437.
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This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.
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Tang, Youhua, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius Lee, Barry Baker, Rajesh Kumar, Luca Delle Monache, Daniel Tong, and Hyun-Cheol Kim. "A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods." Geoscientific Model Development 10, no. 12 (December 22, 2017): 4743–58. http://dx.doi.org/10.5194/gmd-10-4743-2017.
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Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.
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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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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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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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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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Xu, Dongmei, Aiqing Shu, and Feifei Shen. "Effects of Clear-Sky Assimilation of GPM Microwave Imager on the Analysis and Forecast of Typhoon “Chan-Hom”." Sensors 20, no. 9 (May 8, 2020): 2674. http://dx.doi.org/10.3390/s20092674.
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The module of assimilating a new GMI (GPM Microwave Imager) satellite detector was built in the framework of the Weather Research and Forecasting Model (WRF) and its three-dimensional variational (3DVar) data assimilation system (WRFDA). Typhoon “Chan-Hom” in the 2015 Pacific typhoon season was selected to verify the effectivity of the GMI clear-sky assimilation. The results show that, after assimilating the GMI radiance data, the background information in the model is modified positively when compared with the experiment without any assimilation and the one with assimilation of the conventional data. The obvious warm core structure of the typhoon, the modified geopotential height field, and the intensified circulation of the typhoon are favorable for the northwest twist of the typhoon, thus contributing to a better track forecast with a maximum error below 160 km in the 48-h deterministic forecast.
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Bakczynian, Arcwi. "Ազգային հիշողությունը՝ ուծացած հայերի սերունդների մեջ." Lehahayer 10 (December 19, 2023): 205–18. http://dx.doi.org/10.12797/lh.10.2023.10.09.
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NATIONAL MEMORY, ASSIMILATED IN GENERATIONS OF ARMENIANS
Assimilation and national acculturation are inevitable phenomena to which every ethnic community separated from its native environment is vulnerable. The ways and degrees of its manifestation vary, but the direction is the same: moving away from the native national profile and assimilating characteristics typical of the nation and culture of the country of residence. Cases of assimilation also occur to a lesser extent when a nation becomes a minority in its own homeland or when its native environment is absorbed by an empire. The article analyzes such situations among Armenians, specifically in Armenian diasporas. It concludes that the pace of assimilative changes depends on various factors, including maintaining formal points of contact with Armenia (citizenship, organizational ties, owning a house in Armenia, etc.), and a measured and consistent policy of the Armenian state towards the diaspora.
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Gwyther, David E., Shane R. Keating, Colette Kerry, and Moninya Roughan. "How does 4DVar data assimilation affect the vertical representation of mesoscale eddies? A case study with observing system simulation experiments (OSSEs) using ROMS v3.9." Geoscientific Model Development 16, no. 1 (January 4, 2023): 157–78. http://dx.doi.org/10.5194/gmd-16-157-2023.
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Abstract. Accurate estimates and forecasts of ocean eddies in key regions such as western boundary currents are important for weather and climate, biology, navigation, and search and rescue. The dynamic nature of mesoscale eddies requires data assimilation to produce accurate eddy timings and locations in ocean model simulations. However, data assimilating models are rarely assessed below the surface due to a paucity of observations; hence it is not clear how data assimilation impacts the subsurface eddy structure. Here, we use a suite of observing system simulation experiments to show how the subsurface representation of eddies is changed within data assimilating simulations even when assimilating nearby observations. We examine in detail two possible manifestations of how the data assimilation process impacts three-dimensional eddy structure, namely, by producing overly active baroclinic instability and through inaccurate vertical mode structure. Therefore, in DA simulations, subsurface temperature structures can be too deep and too warm, particularly in dynamic eddy features. Our analyses demonstrate the need for further basic research in ocean data assimilation methodologies to improve the representation of the subsurface ocean structure.
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Pagowski, M., Z. Liu, G. A. Grell, M. Hu, H. C. Lin, and C. S. Schwartz. "Implementation of aerosol assimilation in Gridpoint Statistical Interpolation v. 3.2 and WRF-Chem v. 4.3.1." Geoscientific Model Development Discussions 7, no. 2 (April 24, 2014): 2483–500. http://dx.doi.org/10.5194/gmdd-7-2483-2014.
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Abstract. Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is used at the National Centers for Environmental Prediction in operational weather forecasting. In this article we describe implementation of an extension to the GSI for assimilating surface measurements of PM2.5, PM10, and MODIS Aerosol Optical Depth at 550 nm with WRF-Chem. We also present illustrations of the results. In the past the aerosol assimilation system has been employed to issue daily PM2.5 forecasts at NOAA/ESRL and, in our belief, is well tested and mature enough to make available for wider use. We provide a package that, in addition to augmented GSI, consists of software for calculating background error covariance statistics and for converting in-situ and satellite data to BUFR format, plus sample input files for an assimilation exercise. Thanks to flexibility in the GSI and coupled meteorology-chemistry of WRF-Chem, assimilating aerosol observations can be carried out simultaneously with meteorological data assimilation. Both GSI and WRF-Chem are well documented with user guides available on-line. This article is primarily intended as a technical note on the implementation of the aerosol assimilation. Its purpose is also to provide guidance for prospective users of the computer code. Limited space is devoted to scientific aspects of aerosol assimilation.
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Wang, Hongli, Juanzhen Sun, Shuiyong Fan, and Xiang-Yu Huang. "Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events." Journal of Applied Meteorology and Climatology 52, no. 4 (April 2013): 889–902. http://dx.doi.org/10.1175/jamc-d-12-0120.1.
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AbstractAn indirect radar reflectivity assimilation scheme has been developed within the Weather Research and Forecasting model three-dimensional data assimilation system (WRF 3D-Var). This scheme, instead of assimilating radar reflectivity directly, assimilates retrieved rainwater and estimated in-cloud water vapor. An analysis is provided to show that the assimilation of the retrieved rainwater avoids the linearization error of the Z–qr (reflectivity–rainwater) equation. A new observation operator is introduced to assimilate the estimated in-cloud water vapor. The performance of the scheme is demonstrated by assimilating reflectivity observations into the Rapid Update Cycle data assimilation and forecast system operating at Beijing Meteorology Bureau. Four heavy-rain-producing convective cases that occurred during summer 2009 in Beijing, China, are studied using the newly developed system. Results show that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. A diagnosis of the analysis fields of one case shows that the assimilation of reflectivity increases humidity, rainwater, and convective available potential energy in the convective region. As a result, the analysis successfully promotes the developments of the convective system and thus improves the subsequent prediction of the location and intensity of precipitation for this case.
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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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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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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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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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Yuliana, Atika, Imran Hadi, and Esteria Romauli Panjaitan. "Phonemes assimilation in Ed Sheeran’s Songs." ARDU: Journal of Arts and Education 1, no. 1 (November 30, 2020): 25–33. http://dx.doi.org/10.56724/ardu.v1i1.14.
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Background: Within phonemes as English spoken by the native speakers, it sometimes undergoes simplication to ease the native speakers in expressing their feelings. That is why, it is common for them to speak English in high speed along with their emotion. As the result they make a ‘shortcut’ to get ease of their pronunciation.
Purpose: this research is meant to analyze phoneme assimilation in the three songs of Ed Sheeran.
Design and methods: This research uses qualitative method with the content of analyzing the phonemes assimilation in the songs.
Results: The findings revealed 35 kinds phonemes assimilation in the three songs of Ed Sheeran in Divide album. There are 29 regressive assimilations and 6 progressive assimilation
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Zhang, S., X. Zheng, Z. Chen, B. Dan, J. M. Chen, X. Yi, L. Wang, and G. Wu. "A Global Carbon Assimilation System using a modified EnKF assimilation method." Geoscientific Model Development Discussions 7, no. 5 (October 1, 2014): 6519–47. http://dx.doi.org/10.5194/gmdd-7-6519-2014.
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Abstract. A Global Carbon Assimilation System based on Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 abundance data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is based on the ensemble Kalman filter (EnKF), but with several new developments, including using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is tested in observing system simulation experiments and then used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.
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Cao, Yujie, Bingying Shi, Xinyu Zhao, Ting Yang, and Jinzhong Min. "Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall." Remote Sensing 15, no. 17 (September 1, 2023): 4314. http://dx.doi.org/10.3390/rs15174314.
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Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.
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Ngodock, Hans, and Matthew Carrier. "A 4DVAR System for the Navy Coastal Ocean Model. Part I: System Description and Assimilation of Synthetic Observations in Monterey Bay*." Monthly Weather Review 142, no. 6 (May 28, 2014): 2085–107. http://dx.doi.org/10.1175/mwr-d-13-00221.1.
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Abstract A 4D variational data assimilation system was developed for assimilating ocean observations with the Navy Coastal Ocean Model. It is described in this paper, along with initial assimilation experiments in Monterey Bay using synthetic observations. The assimilation system is tested in a series of twin data experiments to assess its ability to fit assimilated and independent observations by controlling the initial conditions and/or the external forcing while assimilating surface and/or subsurface observations. In all strong and weak constraint experiments, the minimization of the cost function is done with both the gradient descent method (in the control space) and the representer method (observation space). The accuracy of the forecasts following the analysis and the relevance of the retrieved forcing correction in the case of weak constraints are evaluated. It is shown that the assimilation system generally fits the assimilated and nonassimilated observations well in all experiments, yielding lower forecast errors.
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Tietsche, S., D. Notz, J. H. Jungclaus, and J. Marotzke. "Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects." Ocean Science 9, no. 1 (January 15, 2013): 19–36. http://dx.doi.org/10.5194/os-9-19-2013.
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Abstract. We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions.
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Kawabata, Takuya, Tohru Kuroda, Hiromu Seko, and Kazuo Saito. "A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area." Monthly Weather Review 139, no. 6 (June 1, 2011): 1911–31. http://dx.doi.org/10.1175/2011mwr3428.1.
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Abstract A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity. The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity. An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals. The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.
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Ling, Xiao-Lu, Cong-Bin Fu, Zong-Liang Yang, and Wei-Dong Guo. "Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)." Geoscientific Model Development 12, no. 7 (July 22, 2019): 3119–33. http://dx.doi.org/10.5194/gmd-12-3119-2019.
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Abstract. The leaf area index (LAI) is a crucial parameter for understanding the exchanges of mass and energy between terrestrial ecosystems and the atmosphere. In this study, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model with explicit carbon and nitrogen components (CLM4CN) by assimilating Global Land Surface Satellite (GLASS) LAI data. Within this framework, four sequential assimilation algorithms, including the kernel filter (KF), the ensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), and the particle filter (PF), are thoroughly analyzed and compared. The results show that assimilating GLASS LAI into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the EnKF and EAKF algorithms are better than those of the KF and PF algorithm. From the perspective of the average and RMSD, the PF algorithm performs worse than the EAKF and EnKF algorithms because of the gradually reduced acceptance of observations with assimilation steps. In other words, the contribution of the observations to the posterior probability during the assimilation process is reduced. The EAKF algorithm is the best method because the matrix is adjusted at each time step during the assimilation procedure. If all the observations are accepted, the analyzed LAI seem to be better than that when some observations are rejected, especially in low-latitude regions.
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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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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 Discussions 8, no. 2 (April 21, 2011): 873–916. http://dx.doi.org/10.5194/osd-8-873-2011.
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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 are important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscales 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). The 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 cm to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme improves the results at intermediate depth, but a slight degradation of the results at the surface is noted. The comparison to surface drifters shows an improvement of surface current by approximately −8.3%, with largest improvements in the Northern SCS and east of Vietnam.
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Emili, Emanuele, Brice Barret, Eric Le Flochmoën, and Daniel Cariolle. "Comparison between the assimilation of IASI Level 2 ozone retrievals and Level 1 radiances in a chemical transport model." Atmospheric Measurement Techniques 12, no. 7 (July 19, 2019): 3963–84. http://dx.doi.org/10.5194/amt-12-3963-2019.
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Abstract. The prior information used for Level 2 (L2) retrievals in the thermal infrared can influence the quality of the retrievals themselves and, therefore, their further assimilation in atmospheric composition models. In this study we evaluate the differences between assimilating L2 ozone profiles and Level 1 (L1) radiances from the Infrared Atmospheric Sounding Interferometer (IASI). We minimized potential differences between the two approaches by employing the same radiative transfer code (Radiative Transfer for TOVS, RTTOV) and a very similar setup for both the L2 retrievals (1D-Var) and the L1 assimilation (3D-Var). We computed hourly 3D-Var analyses assimilating L1 and L2 data in the chemical transport model MOCAGE and compared the resulting O3 fields among each other and against ozonesondes. We also evaluated the joint assimilation of limb measurements from the Microwave Limb Sounder (MLS) in combination with IASI to assess the impact of stratospheric O3 on tropospheric analyses. Results indicate that significant differences can arise between L2 and L1 assimilation, especially in regions where the L2 prior information is strongly biased (at low latitudes in this study). In these regions the L1 assimilation provides a better variability of the free-troposphere ozone column. L1 and L2 assimilation instead give very similar results at high latitudes, especially when MLS measurements are used to constrain the stratospheric O3 column. A critical analysis of the potential benefits and drawbacks of L1 assimilation is given in the conclusions. We also list remaining issues that are common to both the L1 and L2 approaches and that deserve further research.
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Shen, Feifei, Xiaolin Yuan, Hong Li, Dongmei Xu, Jingyao Luo, Aiqing Shu, and Lizhen Huang. "Improving Typhoon Muifa (2022) Forecasts with FY-3D and FY-3E MWHS-2 Satellite Data Assimilation under Clear Sky Conditions." Remote Sensing 16, no. 14 (July 17, 2024): 2614. http://dx.doi.org/10.3390/rs16142614.
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This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon’s structure and providing a prediction of the typhoon’s intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system.