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1.
One of the main drawbacks in modern sea wave data assimilation models is the limited temporal and spatial improvement obtained
in the final forecasting products. This is mainly due to deviations coming either from the relevant atmospheric input or from
the dynamics of the wave model, resulting to systematic errors of the forecasted fields of numerical wave models, when no
observation is available for assimilation. A potential solution is presented in this work, based on a combination of advanced
statistical techniques, data assimilation systems, and wave models. More precisely, Kalman filtering algorithms are implemented
into the wave model WAM and the results are assimilated by an Optimum Interpolation Scheme, in order to extend the beneficial
influence of the latter in time and space. The case studied concerns a 3-month period in an open sea area near the South-West
coast of the USA (Pacific Ocean). 相似文献
2.
Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational
data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation
Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts
of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance
has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance)
as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July
2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in
comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the
assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control
run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible
for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis
indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded
only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature
and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B
and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture,
due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model. 相似文献
3.
Ching-Yuang Huang Ying-Hwa Kuo Shu-Ya Chen Anisetty S. K. A. V. Prasad Rao Chien-Ju Wang 《Pure and Applied Geophysics》2007,164(8-9):1577-1591
In this study, the Weather Research and Forecasting (WRF-2.0.3.1) model with three-dimensional variational data assimilation
(3DVAR) was utilized to study a heavy rainfall event along the west coast of India with and without the assimilation of GPS
occultation refractivity soundings in the monsoon period of 2002. The WRF model is a next-generation mesoscale numerical weather
prediction system designed to serve both operational forecasting and atmospheric research communities. The Global Positioning
System (GPS) radio occultation (RO) refractivity data, processed by UCAR, were obtained from the CHAMP and SAC-C missions.
This study investigates the impact of thirteen GPS occultation refractivity soundings only, as assimilated into the WRF model
with 3DVAR, on the rainfall prediction over the western coastal mountain of India. The model simulation, with the finest resolution
of 10 km, was in good agreement with rainfall observations, up to 72-h forecast. There are some subtle but important differences
in predicted rainfalls between the control run CN (without the assimilation of refractivity soundings) and G13 (with the assimilation
of thirteen GPS RO soundings). In general, the assimilation run G13 gives a better prediction in terms of both rainfall locations
and amounts at later times. The moisture increments were analyzed at the initial and forecast times to assess the impact of
GPS RO data assimilation. The results indicate that remote soundings in the forcing region could have significant impacts
on distant downstream regions. It is anticipated, based on this study, that considerably occultation soundings available from
the six-satellite constellation of FORMOSAT-3/COSMIC would have even more significant impacts on weather prediction in this
region. 相似文献
4.
Remotely sensed observations of sea-level anomaly and sea-surface temperature have been assimilated into an implementation
of the Miami Isopycnic Coordinate Ocean Model (MICOM) for the Indian Ocean using the Ensemble Kalman Filter (EnKF). The system
has been applied in a hindcast validation experiment to examine the properties of the assimilation scheme when used with a
full ocean general circulation model and real observations. This work is considered as a first step towards an operational
ocean monitoring and forecasting system for the Indian Ocean. The assimilation of real data has demonstrated that the sequential
EnKF can efficiently control the model evolution in time. The use of data assimilation requires a significant amount of additional
processing and computational resources. However, we have tried to justify the cost of using a sophisticated assimilation scheme
by demonstrating strong regional and temporal dependencies of the covariance statistics, which include highly anisotropic
and flow-dependent correlation functions. In particular, we observed a marked difference between error statistics in the equatorial
region and at off-equatorial latitudes. We have also demonstrated how the assimilation of SLA and SST improves the model fields
with respect to real observations. Independent in situ temperature profiles have been used to examine the impact of assimilating
the remotely sensed observations. These intercomparisons have shown that the model temperature and salinity fields better
resemble in situ observations in the assimilation experiment than in a model free-run case. On the other hand, it is also
expected that assimilation of in situ profiles is needed to properly control the deep ocean circulation.
Received: 8 January 2002 / Accepted: 8 April 2002 相似文献
5.
For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data. 相似文献
6.
Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems. 相似文献
7.
Jenny A. U. Nilsson Srdjan Dobricic Nadia Pinardi Vincent Taillandier Pierre-Marie Poulain 《Ocean Dynamics》2011,61(10):1475-1490
The Mediterranean Forecasting System (MFS) has been operational for a decade, and is continuously providing forecasts and
analyses for the region. These forecasts comprise local- and basin-scale information of the environmental state of the sea
and can be useful for tracking oil spills and supporting search-and-rescue missions. Data assimilation is a widely used method
to improve the forecast skill of operational models and, in this study, the three-dimensional variational (OceanVar) scheme
has been extended to include Argo float trajectories, with the objective of constraining and ameliorating the numerical output
primarily in terms of the intermediate velocity fields at 350 m depth. When adding new datasets, it is furthermore crucial
to ensure that the extended OceanVar scheme does not decrease the performance of the assimilation of other observations, e.g.,
sea-level anomalies, temperature, and salinity. Numerical experiments were undertaken for a 3-year period (2005–2007), and
it was concluded that the Argo float trajectory assimilation improves the quality of the forecasted trajectories with ~15%,
thus, increasing the realism of the model. Furthermore, the MFS proved to maintain the forecast quality of the sea-surface
height and mass fields after the extended assimilation scheme had been introduced. A comparison between the modeled velocity
fields and independent surface drifter observations suggested that assimilating trajectories at intermediate depth could yield
improved forecasts of the upper ocean currents. 相似文献
8.
An effective method based on dynamic sampling for data assimilation in a global wave model 总被引：3，自引：2，他引：1
The ensemble Kalman filter (EnKF) performs well because that the covariance of background error is varying along time. It provides a dynamic estimate of background error and represents the reasonable statistic characters of background error. However, high computational cost due to model ensemble in EnKF is employed. In this study, two methods referred as static and dynamic sampling methods are proposed to obtain a good performance and reduce the computation cost. Ensemble adjustment Kalman filter (EAKF) method is used in a global surface wave model to examine the performance of EnKF. The 24-h interval difference of simulated significant wave height (SWH) within 1 year is used to compose the static samples for ensemble errors, and these errors are used to construct the ensemble states at each time the observations are available. And then, the same method of updating the model states in the EAKF is applied for the ensemble states constructed by a static sampling method. The dynamic sampling method employs a similar method to construct the ensemble states, but the period of the simulated SWH is changing with time. Here, 7 days before and after the observation time is used as this period. To examine the performance of three schemes, EAKF, static, or dynamic sampling method, observations from satellite Jason-2 in 2014 are assimilated into a global wave model, and observations from satellite Saral are used for validation. The results indicate that the EAKF performs best, while the static sampling method is relatively worse. The dynamic sampling method improves an assimilation effect dramatically compared to the static sampling method, and its overall performance is closed to the EAKF. In low latitudes, the dynamic sampling method has a slight advantage over the EAKF. In the dynamic or static sampling methods, only one wave model is required to run and their computational cost is reduced sharply. According to the performance of these three methods, the dynamic sampling method can treated as an effective alternative of EnKF, which could reduce the computational cost and provide a good performance of data assimilation. 相似文献
9.
Sea surface temperature (SST) from a near real-time data set produced from satellites data has been assimilated into a coupled
ice–ocean forecasting model (Canadian East Coast Ocean Model) using an efficient data assimilation method. The method is based
on an optimal interpolation scheme by which SST is melded into the model through the adjustment of surface heat flux. The
magnitude and space–time variation of the adjustment depend on the depth of heat diffusion into the water column in response
to changes in surface flux, the correlation time scale of the data, and model and data errors. The diffusion depth is scaled
by the eddy diffusivity for temperature. The ratio of the model and data errors is treated as an adjustable parameter. To
evaluate the quality of the assimilation, the results from the model with and without assimilation are compared to independent
ship data from the Atlantic Zone Monitoring Program and the World Ocean Circulation Experiment. It is shown that the assimilation
has a significant impact on the modeled SST, reducing the root mean square difference (RMSD) between the model SST and the
ship SST by 0.63°C or 37%. The RMSD of the assimilated SST is smaller than that of the satellite SST by 0.23°C. This suggests
that model simulations or predictions with data assimilation can provide the best estimate of the true SST. A sensitivity
study is performed to examine the change of the model RMSD with the adjustable parameter in the assimilation equation. The
results show that there is an optimal value of the parameter and the model SST is not very sensitive to the parameter. 相似文献
10.
Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4°) and regional (resolution 1/10°) domains with forecast ranges of +?7 and +?3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing +?10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events. 相似文献
11.
We present a method for real-time forecasting of water table depth and soil moisture profiles. The method combines a simple form of data-assimilation with a moving window calibration of a deterministic model describing flow in the unsaturated zone and local as well as regional drainage. The local drainage level is calibrated on-line using a moving window calibration. Assigning more weight to the last available measurements then yields a form of model adaptation that is in between on-line calibration and data-assimilation (i.e. a simplified form of Newtonian nudging). Five-day hydrological forecasts are performed based on 5-day weather forecasts, while on-line observations of phreatic level and soil moisture content are assimilated on a daily basis. Advantages of the proposed method are that it improves the real-time forecasts compared to off-line calibration and ordinary moving window calibration and that it yields physically consistent soil moisture profiles. 相似文献
12.
Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field‐scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
13.
Assimilating along-track SLA data using the EnOI in an eddy resolving model of the Agulhas system 总被引：1，自引：1，他引：0
Björn C. Backeberg François Counillon Johnny A. Johannessen Marie–Isabelle Pujol 《Ocean Dynamics》2014,64(8):1121-1136
The greater Agulhas Current is one of the most energetic current systems in the global ocean. It plays a fundamental role in determining the mean state and variability of the regional marine environment, affecting its resources and ecosystem, the regional weather and the global climate on a broad range of temporal and spatial scales. In the absence of a coherent in-situ and satellite-based observing system in the region, modelling and data assimilation techniques play a crucial role in both furthering the quantitative understanding and providing better forecasts of this complicated western boundary current system. In this study, we use a regional implementation of the Hybrid Coordinate Ocean Model and assimilate along-track satellite sea level anomaly (SLA) data using the Ensemble Optimal Interpolation (EnOI) data assimilation scheme. This study lays the foundation towards the development of a regional prediction system for the greater Agulhas Current system. Comparisons to independent in-situ drifter observations show that data assimilation reduces the error compared to a free model run over a 2-year period. Mesoscale features are placed in more consistent agreement with the drifter trajectories and surface velocity errors are reduced. While the model-based forecasts of surface velocities are not as accurate as persistence forecasts derived from satellite altimeter observations, the error calculated from the drifter measurements for eddy kinetic energy is significantly lower in the assimilation system compared to the persistence forecast. While the assimilation of along-track SLA data introduces a small bias in sea surface temperatures, the representation of water mass properties and deep current velocities in the Agulhas system is improved. 相似文献
14.
Annette Eicker Maike Schumacher Jürgen Kusche Petra Döll Hannes Müller Schmied 《Surveys in Geophysics》2014,35(6):1285-1309
We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pattern by rigorous error propagation from the monthly GRACE solutions, and (3) allows us to integrate model parameter calibration and data assimilation within a unified framework. We investigate the formal contribution of GRACE observations to the Kalman filter update by analysis of the Kalman gain matrix. We then present first model runs, calibrated via data assimilation, for two different experiments: the first one assimilates GRACE basin averages of total water storage and the second one introduces gridded GRACE data at \(5^\circ\) resolution into the assimilation. We finally validate the assimilated model by running it in free mode (i.e., without adding any further GRACE information) for a period of 3 years following the assimilation phase and comparing the results to the GRACE observations available for this period. 相似文献
15.
This paper investigates the feasibility of an ocean data assimilation system to analyze the salinity variability associated with the barrier layer in the equatorial Pacific. In order to validate reproducibility of the temperature and salinity fields, we perform an assimilation run where some temperature and salinity observations by TRITON buoys and Argo floats are withheld. The assimilation run reproduces interannual variability of salinity in the equatorial Pacific exhibited in the data that are withheld. Statistics shows that salinity values and variations in the assimilation run are closer to the data than the climatology and in the model free run. We also confirm that zonal currents in the equatorial Pacific in the reanalysis, where all available temperature and salinity data are assimilated, are consistent with an observation-based mapping and the data of the Acoustic Doppler Current Profiler mounted on TAO buoys. Variability of the barrier layer and relevant salinity field in the reanalysis is consistent with former studies. A thick barrier layer area generally exists west of the equatorial salinity front and is displaced zonally with the migration of the front in the response to El Niño-Southern Oscillation, although the area moved to the east over the front in the 1997 El Niño. It is confirmed that the barrier layer thickness is closely correlated with the near-surface temperature in the equatorial Pacific. 相似文献
16.
A series of numerical experiments for data assimilation with the Ensemble Kalman Filter (EnKF) in a shallow water model are
reported. Temperature profiles measured at a North Sea location, 55°30ˊ North and 0°55ˊ East (referred to as the CS station
of the NERC North Sea project), are assimilated in 1-D simulations. Comparison of simulations without assimilation to model
results obtained when assimilating data with the EnKF allows us to assess the filter performance in reproducing features of
the observations not accounted for by the model. The quality of the model error sampling is tested as well as the validity
of the Gaussian hypothesis underlying the analysis scheme of the EnKF. The influence of the model error parameters and the
frequency of the data assimilation are investigated and discussed. From these experiments, a set of optimal parameters for
the model error sampling are deduced and used to test the behavior of the EnKF when propagating surface information into the
water column. 相似文献
17.
Takehiko Nose Adrean Webb Takuji Waseda Jun Inoue Kazutoshi Sato 《Ocean Dynamics》2018,68(10):1383-1402
A predictability study on wave forecast of the Arctic Ocean is necessary to help identify hazardous areas and ensure sustainable shipping along the trans-Arctic routes. To assist with validation of the Arctic Ocean wave model, two drifting wave buoys were deployed off Point Barrow, Alaska for two months in September 2016. Both buoys measured significant wave heights exceeding 4 m during two different storm events on 19 September and 22 October. The NOAA-WAVEWATCH III? model with 16-km resolution was forced using wind and sea ice reanalysis data and obtained general agreement with the observation. The September storm was reproduced well; however, model accuracy deteriorated in October with a negative wave height bias of around 1 m during the October storm. Utilising reanalysis data, including the most up-to-date ERA5, this study investigated the cause: grid resolution, wind and ice forcing, and in situ sea level pressure observations assimilated for reanalysis. The analysis has found that there is a 20% reduction of in situ SLP observations in the area of interest, presumably due to fewer ships and deployment options during the sea ice advance period. The 63-member atmospheric ensemble reanalysis, ALERA2, has shown that this led to a larger ensemble spread in the October monthly mean wind field compared to September. Since atmospheric physics is complex during sea ice advance, it is speculated that the elevated uncertainty of synoptic-scale wind caused the negative wave model bias. This has implications for wave hindcasts and forecasts in the Arctic Ocean. 相似文献
18.
The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km2) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
19.
Caleb DechantHamid Moradkhani 《Advances in water resources》2011,34(3):351-364
Estimation of seasonal snowpack, in mountainous regions, is crucial for accurate streamflow prediction. This paper examines the ability of data assimilation (DA) of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center (NWSRFC) are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the ensemble Kalman filter (EnKF) and the particle filter (PF), is made using a coupled SNOW17 and the microwave emission model for layered snow pack (MEMLS) model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the advanced microwave scanning radiometer-earth observing system (AMSR-E) at the 36.5 GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. 相似文献
20.
The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g., oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater tetrachloroethylene (TCE) plume that underlies the Tooele Army Depot‐North (TEAD‐N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF‐based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well‐calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging‐based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation. 相似文献