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Data Assimilation System (data + assimilation_system)

Distribution by Scientific Domains

Engineering	14%

Selected Abstracts

Global Daily Reference Evapotranspiration Modeling and Evaluation,

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2008
G.B. Senay
Abstract:, Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro-climatic locations. Since 2001, the National Oceanic and Atmospheric Administration's Global Data Assimilation System (GDAS) has been producing six-hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1-degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station-based reference ET estimates, we evaluated the GDAS-based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS-based reference ET at different spatial and temporal scales using five-year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ,100 km grid cell) between the two datasets, the correlations between station-based ET and GDAS-ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter-based reference ET for regional water and energy balance studies in many parts of the world. While the study revealed the potential of GDAS ETo for large-scale hydrological applications, site-specific use of GDAS ETo in complex hydro-climatic regions such as coastal areas and rugged terrain may require the application of bias correction and/or disaggregation of the GDAS ETo using downscaling techniques. [source]

Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 651 2010
Thibaut Montmerle
Abstract This study focuses on diagnosing variations of background-error covariances between precipitating and non-precipitating areas, and on presenting a heterogeneous covariance formulation to represent these variations in a variational framework. The context of this work is the assimilation of observations linked to precipitation (radar data especially) in the AROME model, which has been running operationally at Météo-France since December 2008 over French territory with a 2.5 km horizontal resolution. This system uses multivariate background-error covariances deduced from an ensemble-based method. At first, such statistics have been computed for 17 precipitating cases using an ensemble of AROME forecasts coupled with an ALADIN ensemble assimilation. Results, obtained from 3 h forecast differences performed separately for non-precipitating and precipitating columns, display large discrepancies in error variances, correlation lengths and the correlations between humidity, temperature and divergence errors. These results argue in favour of including heterogeneous background-error covariances in AROME incremental 3D-Var, allowing different covariances to be used in regions with different meteorological patterns. Such a method enables us to get increments more adequately structured in those regions, and thus potentially to make better use of observations in a data assimilation system. The implementation consists of expressing the analysis increment as the sum of two terms, one for precipitating areas and the other for non-precipitating areas, making use of a mask that defines rainy regions. This implies a doubling in the size of the control variable and of the gradient of the cost function. The feasibility of this method is shown through experiments with four isolated observations. Copyright © 2010 Royal Meteorological Society [source]

Simultaneous state estimation and attenuation correction for thunderstorms with radar data using an ensemble Kalman filter: tests with simulated data

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 643 2009
Ming Xue
Abstract A new approach to dealing with attenuated radar reflectivity data in the data assimilation process is proposed and tested with simulated data using the ensemble square-root Kalman filter. This approach differs from the traditional method where attenuation is corrected in observation space first before observations are assimilated into numerical models. We build attenuation correction into the data assimilation system by calculating the expected attenuation within the forward observation operators using the estimated atmospheric state. Such a procedure does not require prior assumption about the types of hydrometeor species along the radar beams, and allows us to take advantage of knowledge about the hydrometeors obtained through data assimilation and state estimation. Being based on optimal estimation theory, error and uncertainty information on the observations and prior estimate can be effectively utilized, and additional observed parameters, such as those from polarimetric radar, can potentially be incorporated into the system. Tests with simulated reflectivity data of an X-band 3 cm wavelength radar for a supercell storm show that the attenuation correction procedure is very effective,the analyses obtained using attenuated data are almost as good as those obtained using unattenuated data. The procedure is also robust in the presence of moderate dropsize-distribution-related observation operator error and when systematic radar calibration error exists. The analysis errors are very large if no attenuation correction is applied. The effect of attenuation and its correction when radial velocity data are also assimilated is discussed as well. In general, attenuation correction is equally important when quality radial velocity data are also assimilated. Copyright © 2009 Royal Meteorological Society [source]

Monitoring the observation impact on the short-range forecast

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 638 2009
Carla Cardinali
Abstract This paper describes the use of forecast sensitivity to observations as a diagnostic tool to monitor the observation impact on the 24-hour forecast range. In particular, the forecast error is provided by the control experiments (using all observations available) of two sets of observing system experiments performed at ECMWF, a month in summer 2006 and a month in winter 2007, respectively. In such a way, the observation data impact obtained with the forecast sensitivity is compared with the observing system experiment's data impact; differences and similarities are highlighted. Globally, the assimilated observations decrease the forecast error; locally, some poor performances are detected that are related either to the data quality or to the suboptimality of the data assimilation system. It is also found that the synoptic situation can affect the measurements or can produce areas of large field variability that the assimilation system cannot model correctly. Copyright © 2009 Royal Meteorological Society [source]

A review of forecast error covariance statistics in atmospheric variational data assimilation.

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 637 2008
II: Modelling the forecast error covariance statistics
Abstract This article reviews a range of leading methods to model the background error covariance matrix (the B -matrix) in modern variational data assimilation systems. Owing partly to its very large rank, the B -matrix is impossible to use in an explicit fashion in an operational setting and so methods have been sought to model its important properties in a practical way. Because the B -matrix is such an important component of a data assimilation system, a large effort has been made in recent years to improve its formulation. Operational variational assimilation systems use a form of control variable transform to model B. This transform relates variables that exist in the assimilation's ,control space' to variables in the forecast model's physical space. The mathematical basis on which the control variable transform allows the B-matrix to be modelled is reviewed from first principles, and examples of existing transforms are brought together from the literature. The method allows a large rank matrix to be represented by a relatively small number of parameters, and it is shown how information that is not provided explicitly is filled in. Methods use dynamical properties of the atmosphere (e.g. balance relationships) and make assumptions about the way that background errors are spatially correlated (e.g. homogeneity and isotropy in the horizontal). It is also common to assume that the B -matrix is static. The way that these, and other, assumptions are built into systems is shown. The article gives an example of how a current method performs. An important part of this article is a discussion of some new ideas that have been proposed to improve the method. Examples include how a more appropriate use of balance relations can be made, how errors in the moist variables can be treated and how assumptions of homogeneity/isotropy and the otherwise static property of the B -matrix can be relaxed. Key developments in the application of dynamics, wavelets, recursive filters and flow-dependent methods are reviewed. The article ends with a round up of the methods and a discussion of future challenges that the field will need to address. Copyright © 2008 Royal Meteorological Society [source]

Accounting for an imperfect model in 4D-Var

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 621 2006
Yannick Tr'emolet
Abstract In most operational implementations of four-dimensional variational data assimilation (4D-Var), it is assumed that the model used in the data assimilation process is perfect or, at least, that errors in the model can be neglected when compared to other errors in the system. In this paper, we study how model error could be accounted for in 4D-Var. We present three approaches for the formulation of weak-constraint 4D-Var: estimating explicitly a model-error forcing term, estimating a representation of model bias or, estimating a four-dimensional model state as the control variable. The consequences of these approaches with respect to the implementation and the properties of 4D-Var are discussed. We show that 4D-Var with an additional model-error representation as part of the control variable is essentially an initial-value problem and that its characteristics are very similar to that of strong constraint 4D-Var. Taking the four-dimensional state as the control variable, however, leads to very different properties. In that case, weak-constraint 4D-Var can be interpreted as a coupling between successive strong-constraint assimilation cycles. A possible extension towards long-window 4D-Var and possibilities for evolutions of the data assimilation system are presented. Copyright © 2006 Royal Meteorological Society [source]

Construction and application of covariance functions with variable length-fields

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 619 2006
Gregory Gaspari
Abstract This article focuses on construction, directly in physical space, of three-dimensional covariance functions parametrized by a length-field, and on an application of these functions to improve the representation of the Quasi-Biennial Oscillation (QBO) in the Goddard Earth Observing System, Version 4 (GEOS-4) data assimilation system. The covariance functions are obtained by fusing collections of auto-covariance functions having different constant length-scales with their associated cross-covariance functions. This construction yields covariance functions with length-scales that can vary arbitrarily over any finite partition of the spatial domain. A simple, and also motivating application of these functions is to the case where the length-scale varies in the vertical direction only. The class of covariance functions with variable length-fields constructed in this article will be called multi-level to associate them with this application. The multi-level covariance functions extend well-known single-level covariance functions depending only on a constant length-scale. Generalizations of the familiar first-and third-order autoregressive covariances in three dimensions are given, providing multi-level covariances with zero and four continuous derivatives at zero separation, respectively. Multi-level piecewise rational covariances with two continuous derivatives at zero separation are also provided. Multi-level power-law covariances are constructed with continuous derivatives of all orders. Additional multi-level covariance functions are constructed using the Schur product of single- and multi-level covariance functions. A multi-variate, multi-level power-law covariance with a large troposphere-to-stratosphere length-field gradient is employed to reproduce the QBO from sparse radiosonde wind observations in the tropical lower stratosphere. This covariance model is described along with details of the assimilation experiments. The new covariance model is shown to represent the vertical wind shear associated with the QBO much more effectively than the multi-variate, multi-level covariance model in the baseline GEOS-4 system. Copyright © 2006 Royal Meteorological Society [source]

Impact study of the 2003 North Atlantic THORPEX Regional Campaign

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 615 2006
Nadia Fourrié
Abstract An experiment took place during autumn 2003 with the aim of testing the feasibility of an operational targeting of observations over the North Atlantic Ocean in the context of the international programme THORPEX. The purpose of this paper is to evaluate the impact of these additional observations in the French operational model ARPEGE during the last three weeks of the campaign. Results are shown for large regions over and around the North Atlantic Ocean and for specific verification areas. Over Europe, the addition of observations is slightly beneficial for the forecast, mostly in the low troposphere over wide areas and above 100 hPa. However, the impact of extra data is more significant but also more mixed for the dedicated verification areas: they are case, forecast-range and level dependent. In addition, the information content is studied with the Degrees of Freedom for Signal (DFS) for the evaluation of the observation impact on the analysis of one case of December 2003. Firstly, the variations of the DFS have been illustrated in a simplified data assimilation system. It has been found for that case that satellite data have the most important global contribution to the overall analysis, especially the humidity sensitive infrared radiances. For the conventional data, the wind measurements of the aircraft and from the geostationary satellites are the most informative. For the targeted area, the data from aircraft and the dropsondes have the largest DFS. It has been noted that the DFS of the dropsondes located in the sensitivity maximum is larger than the other one even if there is no link between the DFS and the forecast. However, the impact of the dropsondes grows with respect to the forecast range and leads to an improvement of the forecast for this case. Copyright © 2006 Royal Meteorological Society [source]

The ERA-40 re-analysis

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 612 2005
S. M. Uppala
Abstract ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re-analysis period, with assimilable data provided by a succession of satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean-buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA-40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA-40. This benefited from many of the changes introduced into operational forecasting since the mid-1990s, when the systems used for the 15-year ECMWF re-analysis (ERA-15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized. A number of results indicative of the overall performance of the data assimilation system, and implicitly of the observing system, are presented and discussed. The comparison of background (short-range) forecasts and analyses with observations, the consistency of the global mass budget, the magnitude of differences between analysis and background fields and the accuracy of medium-range forecasts run from the ERA-40 analyses are illustrated. Several results demonstrate the marked improvement that was made to the observing system for the southern hemisphere in the 1970s, particularly towards the end of the decade. In contrast, the synoptic quality of the analysis for the northern hemisphere is sufficient to provide forecasts that remain skilful well into the medium range for all years. Two particular problems are also examined: excessive precipitation over tropical oceans and a too strong Brewer-Dobson circulation, both of which are pronounced in later years. Several other aspects of the quality of the re-analyses revealed by monitoring and validation studies are summarized. Expectations that the ,second-generation' ERA-40 re-analysis would provide products that are better than those from the firstgeneration ERA-15 and NCEP/NCAR re-analyses are found to have been met in most cases. © Royal Meteorological Society, 2005. The contributions of N. A. Rayner and R. W. Saunders are Crown copyright. [source]

A reduced-order simulated annealing approach for four-dimensional variational data assimilation in meteorology and oceanography

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 11 2008
I. Hoteit
Abstract Four-dimensional variational data assimilation in meteorology and oceanography suffers from the presence of local minima in the cost function. These local minima arise when the system under study is strongly nonlinear. The number of local minima further dramatically increases with the length of the assimilation period and often renders the solution to the problem intractable. Global optimization methods are therefore needed to resolve this problem. However, the huge computational burden makes the application of these sophisticated techniques unfeasible for large variational data assimilation systems. In this study, a Simulated Annealing (SA) algorithm, complemented with an order-reduction of the control vector, is used to tackle this problem. SA is a very powerful tool of combinatorial minimization in the presence of several local minima at the cost of increasing the execution time. Order-reduction is then used to reduce the dimension of the search space in order to speed up the convergence rate of the SA algorithm. This is achieved through a proper orthogonal decomposition. The new approach was implemented with a realistic eddy-permitting configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) of the tropical Pacific Ocean. Numerical results indicate that the reduced-order SA approach was able to efficiently reduce the cost function with a reasonable number of function evaluations. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Assimilation of satellite-derived soil moisture from ASCAT in a limited-area NWP model

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 648 2010
Jean-François Mahfouf
Abstract A simplified Extended Kalman Filter is developed for the assimilation of satellite-derived surface soil moisture from the Advanced Scatterometer (ASCAT) instrument (on board the polar-orbiting satellite METOP) in a limited-area NWP model where soil water vertical transfers are described by a force,restore method. An analytic formulation of the land surface scheme Jacobians is derived to simplify the coupling between land surface and atmospheric data assimilation systems. Various steps necessary before the assimilation of ASCAT products are defined: projection of satellite data on the model grid, screening based on various criteria, bias correction using a CDF matching technique, and specification of model and observation errors. Three-dimensional variational data assimilation experiments are then performed during a four-week period in May 2009 over western Europe. A control assimilation is also run where the soil moisture evolves freely. Forecasts from these analyses show that the assimilation of ASCAT data slightly reduces the daytime low-level relative humidity positive bias of the control run. Forecast skill scores with respect to other variables are rather neutral. A comparison of the control run with the operational system where soil moisture is corrected from short-range forecast errors of screen-level observations show similar improvements but are more pronounced. These differences come from the fact that the number of screen-level observations from the surface network over Europe is significantly larger than those provided by a polar-orbiting satellite. These results are consistent with those obtained at ECMWF using soil moisture products derived from other satellite instruments (X-band radiometer TMI and C-band scatterometer ERS). Several avenues for improving this preliminary methodology are proposed. Copyright © 2010 Royal Meteorological Society [source]

Ensemble data assimilation with the CNMCA regional forecasting system

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 646 2010
Massimo Bonavita
Abstract The Ensemble Kalman Filter (EnKF) is likely to become a viable alternative to variational methods for the next generation of meteorological and oceanographic data assimilation systems. In this work we present results from real-data assimilation experiments using the CNMCA regional numerical weather prediction (NWP) forecasting system and compare them to the currently operational variational-based analysis. The set of observations used is the same as the one ingested in the operational data stream, with the exception of satellite radiances and scatterometer winds. Results show that the EnKF-based assimilation cycle is capable of producing analyses and forecasts of consistently superior skill in the root mean square error metric than CNMCA operational 3D-Var. One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. To combat this problem a number of different parametrizations of the model error unaccounted for in the assimilation cycle have been proposed. In the CNMCA system a combination of adaptive multiplicative and additive background covariance inflations has been found to give adequate results and to be capable of avoiding filter divergence in extended assimilation trials. The additive component of the covariance inflation has been implemented through the use of scaled forecast differences. Following suggestions that ensemble square-root filters can violate the gaussianity assumption when used with nonlinear prognostic models, the statistical distribution of the forecast and analysis ensembles has been studied. No sign of the ensemble collapsing onto one or a few model states has been found, and the forecast and analysis ensembles appear to stay remarkably close to the assumed probability distribution functions. Copyright © 2010 Royal Meteorological Society [source]

A review of forecast error covariance statistics in atmospheric variational data assimilation.

Effects of data selection and error specification on the assimilation of AIRS data,

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 622 2007
J. Joiner
Abstract The Atmospheric InfraRed Sounder (AIRS), flying aboard NASA's Aqua satellite with the Advanced Microwave Sounding Unit-A (AMSU-A) and four other instruments, has been providing data for use in numerical weather prediction and data assimilation systems for over three years. The full AIRS data set is currently not transmitted in near-real-time to the prediction/assimilation centres. Instead, data sets with reduced spatial and spectral information are produced and made available within three hours of the observation time. In this paper, we evaluate the use of different channel selections and error specifications. We achieve significant positive impact from the Aqua AIRS/AMSU-A combination during our experimental time period of January 2003. The best results are obtained using a set of 156 channels that do not include any in the H2O band between 1080 and 2100 cm,1. The H2O band channels have a large influence on both temperature and humidity analyses. If observation and background errors are not properly specified, the partitioning of temperature and humidity information from these channels will not be correct, and this can lead to a degradation in forecast skill. Therefore, we suggest that it is important to focus on background error specification in order to maximize the impact from AIRS and similar instruments. In addition, we find that changing the specified channel errors has a significant effect on the amount of data that enters the analysis as a result of quality control thresholds that are related to the errors. However, moderate changes to the channel errors do not significantly impact forecast skill with the 156 channel set. We also examine the effects of different types of spatial data reduction on assimilated data sets and NWP forecast skill. Whether we pick the centre or the warmest AIRS pixel in a 3 × 3 array affects the amount of data ingested by the analysis but does not have a statistically significant impact on the forecast skill. Copyright © Published in 2007 by John Wiley & Sons, Ltd. [source]