The impact of the temporal spacing of observations on analysis errors in an idealised data assimilation system

2013 ◽  
Vol 140 (682) ◽  
pp. 1441-1452 ◽  
Author(s):  
J. R. Eyre ◽  
P. P. Weston
Keyword(s):  
Data Assimilation ◽  
The Impact ◽  
Analysis Errors ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Temporal Spacing

Assessment of the Impact of Simulated Satellite Lidar Wind and Retrieved 183 GHz Water Vapor Observations on a Global Data Assimilation System

1990 ◽  
Vol 118 (12) ◽  
pp. 2513-2542 ◽  
Author(s):  
Ross N. Hoffman ◽  
Christopher Grassotti ◽  
Ronald G. Isaacs ◽  
Jean-Francois Louis ◽  
Thomas Nehrkorn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Global Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Evaluating the Impact of the Number of Satellite Altimeters Used in an Assimilative Ocean Prediction System

2010 ◽  
Vol 27 (3) ◽  
pp. 528-546 ◽  
Author(s):  
Robert W. Helber ◽  
Jay F. Shriver ◽  
Charlie N. Barron ◽  
Ole Martin Smedstad
Keyword(s):  
Data Assimilation ◽  
Statistical Error ◽  
Global Ocean ◽  
Height Anomaly ◽  
Naval Research ◽  
Thermocline Depth ◽  
Dynamic Height ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001–03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 1° radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods.

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

scholarly journals A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Govindan Kutty ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Winter Season ◽  
Data Quality Control ◽  
Hybrid Data Assimilation ◽  
Environmental Prediction ◽  
Hemisphere Winter ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.

scholarly journals Assessing the impact of multiple altimeter missions and Argo in a global eddy permitting data assimilation system

2017 ◽  
Author(s):  
Simon Verrier ◽  
Pierre-Yves Le Traon ◽  
Elisabeth Remy
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Error Reduction ◽  
Altimeter Data ◽  
Error Level ◽  
Global Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract. A series of Observing System Simulation Experiments (OSSEs) is carried out with a global data assimilation system at 1/4° resolution using simulated data derived from a 1/12° resolution free run simulation. The objective is to quantify how well multiple altimeter missions and Argo profiling floats can constrain a global data assimilation system but also to better understand the sensitivity of results to data assimilation techniques used in Mercator Ocean operational systems. Impact of multiple altimeter data is clearly evidenced. Forecasts of sea level and ocean currents are significantly improved when moving from one altimeter to two altimeters. In high eddy energy regions, sea level and surface current forecast errors when assimilating one altimeter data set are respectively 20 % and 45 % of the error of the simulation without assimilation. Forecasts of sea level and ocean currents continue to be improved when moving from one altimeter to two altimeters with a relative error reduction of almost 30 %. The addition of a third altimeter still improves the forecasts even at this medium 1/4° resolution and brings an additional relative error reduction of about 10 %. The error level of the analysis with one altimeter is close to the forecast error level when two or three altimeter data sets are assimilated. Assimilating altimeter data also improves the representation of the 3D ocean fields. The addition of Argo has a major impact to improve temperature and demonstrates the essential role of Argo together with altimetry to constrain a global data assimilation system. Salinity fields are only marginally improved. Results derived from these OSSEs are consistent with those derived from experiments with real data (observing system evaluations/OSEs) but they allow a more detailed characterization of errors on analyses and forecasts. Both OSEs and OSSEs should be systematically used and intercompared to test data assimilation systems and quantify the impact of existing observing systems.

CRAMPON: A Particle Filter to assimilate sparse snowpack observations into a semi-distributed geometry

2020 ◽  
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
Marie Dumont ◽  
Emmanuel Cosme ◽  
Clément Albergel
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Global Analysis ◽  
Weather Conditions ◽  
Observation Error ◽  
Stochastic Perturbations ◽  
Modelling Uncertainties ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

<p>In mountainous areas, detailed snowpack models are essential to capture the high spatio-temporal variability of the snowpack. This task is highly challenging, and models suffer from large simulation errors. In these regions, in-situ observations are scarce, while remote sensing observations are generally patchy owing to complex physiographic features (steep slopes, forests, shadows,...) and weather conditions (clouds). This point is stressing the need for a spatially coherent data assimilation system able to propagate the informations into unobserved locations.</p><p>In this study, we present CRAMPON (CRocus with AssiMilation of snowPack ObservatioNs), an ensemble data assimilation system ingesting snowpack observations in a spatialized context. CRAMPON quantifies snowpack modelling uncertainties with an ensemble and reduces them using a Particle Filter. Stochastic perturbations of meteorological forcings and the multi-physical version of Crocus snowpack model (ESCROC) are used to build the ensemble. Two variants of the Sequential Importance Resampling Particle Filter (PF) were implemented to tackle the common PF degeneracy issue that arises when assimilating a large number of observations. In a first approach (so-called global approach), the observations information is spread across topographic conditions by looking for a global analysis. Degeneracy is mitigated by inflating the observation error covariance matrix, with the side effect of reducing the impact of the assimilation. In a second approach (klocal), we propagate the information and mitigate degeneracy by a localisation of the PF based on background correlation patterns between topographic conditions.</p><p>Here, we investigate the ability of CRAMPON to globally benefit from partial observations in a conceptual semi-distributed domain which accounts for the main features of topographic-induced snowpack variability. We compare simulations without assimilation with experiments assimilating synthetic observations of the Height of Snow and VIS/NIR reflectance. This setup demonstrates the ability of CRAMPON to spread the information of various snow observations into unobserved locations.</p>

scholarly journals The Inclusion of IWV Estimates from AIRS/AMSU and SSM/I Sensors into the CPTEC/INPE Global Data Assimilation System

2013 ◽  
Vol 141 (1) ◽  
pp. 93-111
Author(s):  
Luiz F. Sapucci ◽  
Dirceu L. Herdies ◽  
Renata W. B. Mendonça
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Southern Hemisphere ◽  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Range Forecasts

Abstract Water vapor plays a crucial role in atmospheric processes and its distribution is associated with cloud-cover fraction and rainfall. The inclusion of integrated water vapor (IWV) estimates in numerical weather prediction improves the vertical structure of the humidity analysis and consequently contributes to obtaining a more realistic atmospheric state. Currently, satellite remote sensing is the most important source of humidity measurements in the Southern Hemisphere, providing information with good horizontal resolution and global coverage. In this study, the inclusion of IWV retrieved from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU) and Special Sensor Microwave Imager (SSM/I) were investigated as additional information in the Physical-space Statistical Analysis System (PSAS), which is the operational data assimilation system at the Center for Weather Forecasting and Climate Studies of the Brazilian National Institute for Space Research (CPTEC/INPE). Experiments were carried out with and without the assimilation of IWV values from both sensors. Results show that, in general, the IWV assimilation reduces the error in short-range forecasts of humidity profile, particularly over tropical regions. In these experiments, an analysis of the impact of the inclusion of IWV values from SSM/I and AIRS/AMSU sensors was done. Results indicated that the impact of the SSM/I values is significant over high-latitude oceanic regions in the Southern Hemisphere, while the impact of AIRS/AMSU values is more significant over continental regions where surface measurements are scarce, such as the Amazonian region. In that area the assimilation of IWV values from the AIRS/AMSU sensor shows a tendency to reduce the overestimate of the precipitation in short-range forecasts.

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