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1.
Due to the high cost of ocean observation system, the scientific design of observation network becomes much important. The current network of the high frequency radar system in the Gulf of Thailand has been studied using a three-dimensional coastal ocean model. At first, the observations from current radars have been assimilated into this coastal model and the forecast results have improved due to the data assimilation. But the results also show that further optimization of the observing network is necessary. And then, a series of experiments were carried out to assess the performance of the existing high frequency ground wave radar surface current observation system. The simulated surface current data in three regions were assimilated sequentially using an efficient ensemble Kalman filter data assimilation scheme. The experimental results showed that the coastal surface current observation system plays a positive role in improving the numerical simulation of the currents. Compared with the control experiment without assimilation, the simulation precision of surface and subsurface current had been improved after assimilated the surface currents observed at current networks. However, the improvement for three observing regions was quite different and current observing network in the Gulf of Thailand is not effective and a further optimization is required. Based on these evaluations, a manual scheme has been designed by discarding the redundant and inefficient locations and adding new stations where the performance after data assimilation is still low. For comparison, an objective scheme based on the idea of data assimilation has been obtained. Results show that all the two schemes of observing network perform better than the original network and optimal scheme-based data assimilation is much superior to the manual scheme that based on the evaluation of original observing network in the Gulf of Thailand. The distributions of the optimal network of radars could be a useful guidance for future design of observing system in this region. 相似文献
2.
The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind–ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997–2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event. 相似文献
3.
Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction system drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the northern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; however, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis increments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altimeter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed. 相似文献
4.
Medium-term prediction of sediment transport and morphological behaviour in the coastal zone is becoming increasingly important as a result of human interference and changing environmental conditions. The interaction of waves and tides is shown to play a pivotal role in the net (annual) sediment transport and morphodynamics of the coastal zone. The Telemac Modelling System has been applied to the Dyfi Estuary and neighbouring coastline, mid Wales, to recreate the annual wave–current conditions and the resulting sediment fluxes. ‘Input reduction’ methods have been required to produce realistic schematisations of events in practical computation times. A field campaign carried out in 2006 provided data for validation of the flow module (Telemac-2D) and also observations to verify the patterns predicted by the wave module (Tomawac). To improve model accuracy refinements were implemented with regard to the sand transport formulation used in the sand transport module (Sisyphe). Here, a parameterisation of the results from the UWB 1DV sand transport ‘research’ model, for the conditions in the Dyfi Estuary, has been introduced, allowing Sisyphe to provide greater realism in the morphological predictions. The model predictions are presented along with a discussion of the success/failure and limitations of the modelling methods applied. 相似文献
5.
Rama Rao Karri Abhijit Badwe Xuan Wang Ghada El Serafy Julius Sumihar Vladan Babovic Herman Gerritsen 《Ocean Dynamics》2013,63(1):43-61
Hydrodynamic models are commonly used for predicting water levels and currents in the deep ocean, ocean margins and shelf seas. Their accuracy is typically limited by factors, such as the complexity of the coastal geometry and bathymetry, plus the uncertainty in the flow forcing (deep ocean tide, winds and pressure). In Southeast Asian waters with its strongly hydrodynamic characteristics, the lack of detailed marine observations (bathymetry and tides) for model validation is an additional factor limiting flow representation. This paper deals with the application of ensemble Kalman filter (EnKF)-based data assimilation with the purpose of improving the deterministic model forecast. The efficacy of the EnKF is analysed via a twin experiment conducted with the 2D barotropic Singapore regional model. The results show that the applied data assimilation can improve the forecasts significantly in this complex flow regime. 相似文献
6.
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
7.
Based on the thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM), a thermospheric-ionospheric data assimilation and forecast system is developed. Using this system, we estimated the oxygen ions, neutral temperature, wind, and composition by assimilating the simulated data from Formosa Satellite 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) occultation electron density profiles to evaluate their effects on the ionospheric forecast. An ensemble Kalman filter data assimilation scheme and combined state and parameter estimation methods are used to estimate the unobserved parameters in the model. The statistical results show that the neutral and ion compositions are more effective than the neutral temperature and wind for improving the forecast of the ionospheric electron density, whose root mean square errors in the assimilation period decreased by approximately 40%, 30%, and 10% due to the estimations of the neutral composition, oxygen ions, and neutral temperature, respectively. Due to the different physical and chemical processes that these parameters primarily affect, their e-folding times differ greatly from longer than 12 h for neutral composition to approximately 6 h for oxygen ions and 3 h for neutral temperature. The effect of estimating the neutral composition on improving the ionospheric forecast is greater than that of estimating the oxygen ions, which can be also be seen in an actual data assimilation experiment. This indicates that the neutral composition is the most important thermospheric parameter in ionospheric data assimilations and forecasts. 相似文献
8.
Ashish Routray U. C. Mohanty Krishna K. Osuri S. Kiran Prasad 《Pure and Applied Geophysics》2013,170(12):2329-2350
An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the performance of the model simulation for the intense convective system which influences the large scale monsoonal flow is significantly improved after assimilation of the Indian DWR data from even one coastal locale within the MDs track. 相似文献
9.
Hirose Nariaki Usui Norihisa Sakamoto Kei Tsujino Hiroyuki Yamanaka Goro Nakano Hideyuki Urakawa Shogo Toyoda Takahiro Fujii Yosuke Kohno Nadao 《Ocean Dynamics》2019,69(11):1333-1357
We developed a new system to monitor and forecast coastal and open-ocean states around Japan for operational use by the Japan Meteorological Agency. The system consists of an eddy-resolving analysis model based on four-dimensional variational assimilation and a high (2-km) resolution forecast model covering Japanese coastal areas that incorporates an initialization scheme with temporal and spatial filtering. Assimilation and forecast experiments were performed for 2008 to 2017, and the results were validated against various observation datasets. The assimilation results captured well the observed variability in sea surface temperature, coastal sea level, volume transport, and sea ice. Furthermore, the volume budget for the Japan Sea was significantly improved by the use of the 2-km resolution forecast model compared with the 10-km resolution analysis model. The forecast results indicate that this system has a predictive limit longer than 1 month in many areas, including in the Kuroshio current area south of Japan and the southern Japan Sea. In the forecast results of case studies, the 2017 Kuroshio large meander was well predicted, and warm water intrusions accompanying Kuroshio path variations south of Japan were also successfully reproduced. Sea ice forecasts for the Sea of Okhotsk largely captured the evolution of sea ice in late winter, but sea ice in early winter included relatively large errors. This system has high potential to meet operational requirements for monitoring and forecasting ocean phenomena at both meso- and coastal scales.
相似文献10.
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
11.
Jianzhong Ge Pingxing Ding Changsheng Chen Song Hu Gui Fu Lunyu Wu 《Ocean Dynamics》2013,63(8):881-900
A high-resolution numerical model system is essential to resolve multi-scale coastal ocean dynamics. So a multi-scale unstructured grid-based finite-volume coastal ocean model (FVCOM) system has been established for the East China Sea and Changjiang Estuary (ECS–CE) with the aim at resolving coastal ocean dynamics and understanding different physical processes. The modeling system consists of a three-domain-nested weather research and forecasting model, FVCOM model with the inclusion of FVCOM surface wave model in order to understand the wave–current interactions. The ECS–CE system contains three different scale models: a shelf-scale model for the East China Sea, an estuarine-scale model for the Changjiang Estuary and adjacent region, and a fine-scale model for the deep waterway regions. These three FVCOM-based models guarantee the conservation of mass and momentum transferring from outer domain to inner domain using the one-way common-grid nesting procedure. The model system has been validated using data from various observation data, including surface wind, tides, currents, salinity, and wave to accurately reveal the multi-scale dynamics of the East China Sea and Changjiang Estuary. This modeling system has been demonstrated via application to the seasonal variations of Changjiang diluted water and the bottom saltwater intrusion in the North Passage, and it shows strong potential for estuarine and coastal ocean dynamics and operational forecasting. 相似文献
12.
Mohammad Hadi Moeini Amir Etemad-Shahidi Vahid Chegini Iraj Rahmani 《Ocean Dynamics》2012,62(5):785-797
The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in
the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data
with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches.
It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not
result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating
of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were
used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output
variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the
best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave
parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further
improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN. 相似文献
13.
On the assimilation of total-ozone satellite data 总被引：1，自引：0，他引：1
A two-dimensional model for advection and data assimilation of total-ozone data has been developed. The Assimilation Model KNMI (AMK) is a global model describing the transport of the column amounts of ozone, by a wind field at a single pressure level, assuming that total ozone behaves as a passive tracer. In this study, ozone column amounts measured by the TIROS Operational Vertical Sounder (TOVS) instrument on the National Oceanic and Atmospheric Administration (NOAA) polar satellites and wind fields from the Meteorological Archive and Retrieval System (MARS) archives at ECMWF have been used. By means of the AMK, the incomplete space-time distribution of the TOVS measurements is filled in and global total-ozone maps at any given time can be obtained. The choice of wind field to be used for transporting column amounts of ozone is extensively discussed. It is shown that the 200-hPa wind field is the optimal single-pressure-level wind field for advecting total ozone. Assimilated ozone fields are the basic information for research on atmospheric chemistry and dynamics, but are also important for the validation of ozone measurements. 相似文献
14.
Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations. 相似文献
15.
V. Yesubabu C. V. Srinivas K. B. R. R. Hariprasad R. Baskaran 《Pure and Applied Geophysics》2014,171(8):2023-2042
In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72 h predictions, respectively, for the two cases using assimilation of all observations. Simulated rainfall estimates indicate that while the assimilation of scatterometer wind data improves the location of the rainfall, the assimilation of MODIS profiles produces a realistic pattern and amount of rainfall, close to the observational estimates. 相似文献
16.
A three-dimensional finite volume unstructured mesh model of the west coast of Britain, with high resolution in the coastal regions, is used to investigate the role of wind wave turbulence and wind and tide forced currents in producing maximum bed stress in the eastern Irish Sea. The spatial distribution of the maximum bed stress, which is important in sediment transport problems, is determined, together with how it is modified by the direction of wind forced currents, tide–surge interaction and a surface source of wind wave turbulence associated with wave breaking. Initial calculations show that to first order the distribution of maximum bed stress is determined by the tide. However, since maximum sediment transport occurs at times of episodic events, such as storm surges, their effects upon maximum bed stresses are examined for the case of strong northerly, southerly and westerly wind forcing. Calculations show that due to tide–surge interaction both the tidal distribution and the surge are modified by non-linear effects. Consequently, the magnitude and spatial distribution of maximum bed stress during major wind events depends upon wind direction. In addition calculations show that a surface source of turbulence due to wind wave breaking in shallow water can influence the maximum bed stress. In turn, this influences the wind forced flow and hence the movement of suspended sediment. Calculations of the spatial variability of maximum bed stress indicate the level of measurements required for model validation. 相似文献
17.
18.
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
19.
Alexander Barth Aida Alvera-Azcárate Jean-Marie Beckers Joanna Staneva Emil V. Stanev Johannes Schulz-Stellenfleth 《Ocean Dynamics》2011,61(5):599-610
Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency
radar surface currents are assimilated using an ensemble scheme which aims to obtain improved surface winds taking into account
European Centre for Medium-Range Weather Forecasts winds as a first guess and surface current measurements. The objective
of this study is to show that wind forcing can be improved using an approach similar to parameter estimation in ensemble data
assimilation. Like variational assimilation schemes, the method provides an improved wind field based on surface current measurements.
However, the technique does not require an adjoint, and it is thus easier to implement. In addition, it does not rely on a
linearization of the model dynamics. The method is validated directly by comparing the analyzed wind speed to independent
in situ measurements and indirectly by assessing the impact of the corrected winds on model sea surface temperature (SST)
relative to satellite SST. 相似文献
20.
C. E. Synolakis E. N. Bernard V. V. Titov U. Kânoğlu F. I. González 《Pure and Applied Geophysics》2008,165(11-12):2197-2228
In the aftermath of the 26 December, 2004 tsunami, several quantitative predictions of inundation for historic events were presented at international meetings differing substantially from the corresponding well-established paleotsunami measurements. These significant differences attracted press attention, reducing the credibility of all inundation modeling efforts. Without exception, the predictions were made using models that had not been benchmarked. Since an increasing number of nations are now developing tsunami mitigation plans, it is essential that all numerical models used in emergency planning be subjected to validation—the process of ensuring that the model accurately solves the parent equations of motion—and verification—the process of ensuring that the model represents geophysical reality. Here, we discuss analytical, laboratory, and field benchmark tests with which tsunami numerical models can be validated and verified. This is a continuous process; even proven models must be subjected to additional testing as new knowledge and data are acquired. To date, only a few existing numerical models have met current standards, and these models remain the only choice for use for real-world forecasts, whether short-term or long-term. Short-term forecasts involve data assimilation to improve forecast system robustness and this requires additional benchmarks, also discussed here. This painstaking process may appear onerous, but it is the only defensible methodology when human lives are at stake. Model standards and procedures as described here have been adopted for implementation in the U.S. tsunami forecasting system under development by the National Oceanic and Atmospheric Administration, they are being adopted by the Nuclear Regulatory Commission of the U.S. and by the appropriate subcommittees of the Intergovernmental Oceanographic Commission of UNESCO. 相似文献