The Global Analysis of the Ionospheric Correlation Time and Its Implications for Ionospheric Data Assimilation

Radio Science ◽  
2020 ◽  
Vol 55 (12) ◽  
Author(s):  
Victoriya V. Forsythe ◽  
Irfan Azeem ◽  
Geoff Crowley ◽  
Roman A. Makarevich ◽  
Cheng Wang
Keyword(s):  
Data Assimilation ◽  
Correlation Time ◽  
Global Analysis ◽  
Ionospheric Data Assimilation

scholarly journals Can a Moderate-Resolution Limited-Area Data Assimilation System Add Value to the Global Analysis of Tropical Cyclones?

2013 ◽  
Vol 141 (6) ◽  
pp. 1866-1883 ◽  
Author(s):  
Christina R. Holt ◽  
Istvan Szunyogh ◽  
Gyorgyi Gyarmati
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Global Analysis ◽  
Flow Analysis ◽  
Horizontal Resolution ◽  
Limited Area ◽  
Northwest Pacific ◽  
Accurate Analysis ◽  
Analysis System ◽  
Area Data

Abstract This study investigates the benefits of employing a limited-area data assimilation (DA) system to enhance lower-resolution global analyses in the northwest Pacific tropical cyclone (TC) basin. Numerical experiments are carried out with a global analysis system at horizontal resolution T62 and a limited-area analysis system at resolutions from 200 to 36 km. The global and limited-area DA systems, which are both based on the local ensemble transform Kalman filter algorithm, are implemented using a unique configuration, in which the global DA system provides information about the large-scale analysis and background uncertainty to the limited-area DA system. The limited-area analyses of the storm locations are, on average, more accurate than those from the global analyses, but increasing the resolution of the limited-area system beyond 100 km has little benefit. Two factors contribute to the higher accuracy of the limited-area analyses. First, the limited-area system improves the accuracy of the location estimates for strong storms, which is introduced when the background is updated by the global assimilation. Second, it improves the accuracy of the background estimate of the storm locations for moderate and weak storms. Improvements in the steering flow analysis as a result of increased resolution are modest and short lived in the forecasts. Limited-area track forecasts are more accurate, on average, than global forecasts, independently of the strength of the storms up to five days. This forecast improvement is due to the more accurate analysis of the initial position of storms and the better representation of the interactions between the storms and their immediate environment.

Effect of inaccurate specification of time-correlated model error in an Ensemble Smoother

2020 ◽  
Author(s):  
Haonan Ren ◽  
Peter Jan Van Leeuwen ◽  
Javier Amezcua
Keyword(s):  
Data Assimilation ◽  
Time Scale ◽  
Correlation Time ◽  
Time Window ◽  
Model Error ◽  
Multiple Time ◽  
Model Errors ◽  
Time Step ◽  
Single Observation ◽  
Kalman Smoother

<p>Data assimilation has been often performed under the perfect model assumption known as the strong-constraint setting. There is an increasing number of researches accounting for the model errors, the weak-constrain setting, but often with different degrees of approximation or simplification without knowing their impact on the data assimilation results. We investigate what effect inaccurate model errors, in particular, the an inaccurate time correlation, can have on data assimilation results, with a Kalman Smoother and the Ensemble Kalman Smoother.<br>We choose a linear auto-regressive model for the experiment. We assume the true state of the system has the correct and fixed correlation time-scale ω<sub>r</sub> in the model errors, and the prior or the background generated by the model contains the model error with the fixed, guessed time-scale ω<sub>g</sub> which differs from the correct one and is also used in the data assimilation process. There are 10 variables in the system and we separate the simulation period into multiple time-windows. And we use a fairly large ensemble size (up to 200 ensemble members) to improve the accuracy of the data assimilation results. In order to evaluate the performance of the EnKS with auto-correlated model errors, we calculate the ratio of root-mean-square error over the spread of all ensemble members.<br>The results with a single observation at the end of the simulation time-window show that, using an underestimated correlation time-scale leads to overestimated spread of the ensemble, and with an overestimated time-scale, the results show underestimation in the ensemble spread. However, with very dense observation frequency, observing every time-step for instance, the results are completely opposite to the results with a single observation. In order to understand the results, we derive the expression for the true posterior state covariance and the posterior covariance using the incorrect decorrelation time-scale. We do this for a Kalman Smoother to avoid the sampling uncertainties. The results are richer than expected and highly dependent on the observation frequency. From the analytical solution of the analysis, we find that the RMSE is a function of both ω<sub>r</sub><sub> </sub>and ω<sub>g</sub>, and the spread or the variance only depends on ω<sub>g</sub>. We also find that the analyzed variance is not always a monotonically increasing function of ω<sub>g</sub>, and it also depends on the observation frequency. In general, the results show the effect of the correlated model error and the incorrect correlation time-scale on data assimilation result, which is also affected by the observation frequency.</p>

Ionospheric data assimilation: recovery of strong mid-latitudinal density gradients

2003 ◽  
Vol 65 (10) ◽  
pp. 1087-1097 ◽  
Author(s):  
Jan J. Sojka ◽  
Donald C. Thompson ◽  
Robert W. Schunk ◽  
J.Vincent Eccles ◽  
Jonathan J. Makela ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Density Gradients ◽  
Ionospheric Data Assimilation

scholarly journals Current status of the ability of the GEMS/MACC models to reproduce the tropospheric CO vertical distribution as measured by MOZAIC

2010 ◽  
Vol 3 (2) ◽  
pp. 501-518 ◽  
Author(s):  
N. Elguindi ◽  
H. Clark ◽  
C. Ordóñez ◽  
V. Thouret ◽  
J. Flemming ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Forest Fires ◽  
Global Analysis ◽  
Coupled Model ◽  
Current Status ◽  
Emissions Inventory ◽  
Fire Emissions ◽  
Potential Gain ◽  
The Us ◽  
Forecasting System

Abstract. Vertical profiles of CO taken from the MOZAIC aircraft database are used to globally evaluate the performance of the GEMS/MACC models, including the ECMWF-Integrated Forecasting System (IFS) model coupled to the CTM MOZART-3 with 4DVAR data assimilation for the year 2004. This study provides a unique opportunity to compare the performance of three offline CTMs (MOZART-3, MOCAGE and TM5) driven by the same meteorology as well as one coupled atmosphere/CTM model run with data assimilation, enabling us to assess the potential gain brought by the combination of online transport and the 4DVAR chemical satellite data assimilation. First we present a global analysis of observed CO seasonal averages and interannual variability for the years 2002–2007. Results show that despite the intense boreal forest fires that occurred during the summer in Alaska and Canada, the year 2004 had comparably lower tropospheric CO concentrations. Next we present a validation of CO estimates produced by the MACC models for 2004, including an assessment of their ability to transport pollutants originating from the Alaskan/Canadian wildfires. In general, all the models tend to underestimate CO. The coupled model and the CTMs perform best in Europe and the US where biases range from 0 to -25% in the free troposphere and from 0 to -50% in the surface and boundary layers (BL). Using the 4DVAR technique to assimilate MOPITT V4 CO significantly reduces biases by up to 50% in most regions. However none of the models, even the IFS-MOZART-3 coupled model with assimilation, are able to reproduce well the CO plumes originating from the Alaskan/Canadian wildfires at downwind locations in the eastern US and Europe. Sensitivity tests reveal that deficiencies in the fire emissions inventory and injection height play a role.

Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2339-2354 ◽  
Author(s):  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Stéphane Laroche ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Global Analysis ◽  
Variational Data Assimilation ◽  
Simultaneous Occurrence ◽  
Strong Wind ◽  
Surface Drag ◽  
Vertical Diffusion ◽  
Regional Forecasts ◽  
The Impact ◽  
Lagrangian Scheme

Abstract On 15 March 2005, the Meteorological Service of Canada (MSC) proceeded to the implementation of a four-dimensional variational data assimilation (4DVAR) system, which led to significant improvements in the quality of global forecasts. This paper describes the different elements of MSC’s 4DVAR assimilation system, discusses some issues encountered during the development, and reports on the overall results from the 4DVAR implementation tests. The 4DVAR system adopted an incremental approach with two outer iterations. The simplified model used in the minimization has a horizontal resolution of 170 km and its simplified physics includes vertical diffusion, surface drag, orographic blocking, stratiform condensation, and convection. One important element of the design is its modularity, which has permitted continued progress on the three-dimensional variational data assimilation (3DVAR) component (e.g., addition of new observation types) and the model (e.g., computational and numerical changes). This paper discusses some numerical problems that occur in the vicinity of the Poles where the semi-Lagrangian scheme becomes unstable when there is a simultaneous occurrence of converging meridians and strong wind gradients. These could be removed by filtering the winds in the zonal direction before they are used to estimate the upstream position in the semi-Lagrangian scheme. The results show improvements in all aspects of the forecasts over all regions. The impact is particularly significant in the Southern Hemisphere where 4DVAR is able to extract more information from satellite data. In the Northern Hemisphere, 4DVAR accepts more asynoptic data, in particular coming from profilers and aircrafts. The impact noted is also positive and the short-term forecasts are particularly improved over the west coast of North America. Finally, the dynamical consistency of the 4DVAR global analyses leads to a significant impact on regional forecasts. Experimentation has shown that regional forecasts initiated directly from a 4DVAR global analysis are improved with respect to the regional forecasts resulting from the regional 3DVAR analysis.

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 Assimilation of Tropical Cyclone Observations: Improving the Assimilation of TCVitals, Scatterometer Winds, and Dropwindsonde Observations

2015 ◽  
Vol 143 (10) ◽  
pp. 3956-3980 ◽  
Author(s):  
Christina Holt ◽  
Istvan Szunyogh ◽  
Gyorgyi Gyarmati ◽  
S. Mark Leidner ◽  
Ross N. Hoffman
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Statistical Model ◽  
Global Analysis ◽  
Second Order ◽  
Statistical Moments ◽  
Limited Area ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme ◽  
Normally Distributed

Abstract The standard statistical model of data assimilation assumes that the background and observation errors are normally distributed, and the first- and second-order statistical moments of the two distributions are known or can be accurately estimated. Because these assumptions are never satisfied completely in practice, data assimilation schemes must be robust to errors in the underlying statistical model. This paper tests simple approaches to improving the robustness of data assimilation in tropical cyclone (TC) regions. Analysis–forecast experiments are carried out with three types of data—Tropical Cyclone Vitals (TCVitals), DOTSTAR, and QuikSCAT—that are particularly relevant for TCs and with an ensemble-based data assimilation scheme that prepares a global analysis and a limited-area analysis in a TC basin simultaneously. The results of the experiments demonstrate that significant analysis and forecast improvements can be achieved for TCs that are category 1 and higher by improving the robustness of the data assimilation scheme.

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