A Feature Track Correction (FTC) Observation Operator applied to Aeolus-AMV Collocations

2021 ◽  
Author(s):  
Ross N. Hoffman ◽  
Katherine Lukens ◽  
Kayo Ide ◽  
Kevin Garrett
Keyword(s):  
Data Assimilation ◽  
Degrees Of Freedom ◽  
Weighted Average ◽  
Horizontal Line ◽  
Null Solution ◽  
Observation Operator ◽  
Realistic Representation ◽  
Wind Profiles ◽  
Height Assignment ◽  
Atmospheric Motion Vectors

<p>In this study we propose and test a feature track correction (FTC) observation operator for atmospheric motion vectors (AMVs).  The FTC has four degrees of freedom corresponding to wind speed multiplicative and additive corrections (γ and δ<em><strong>V</strong></em>), a vertical height assignment correction (<em>h</em>), and an estimate of the depth of the layer that contributes to the AMV (Δ<em>z</em>).  Since the effect of the FTC observation operator is to add a bias correction to a weighted average of the profile of background winds an alternate formulation is in terms of a profile of weights (<em>w<sub>k</sub></em>) and δ<em><strong>V </strong></em>.</p><p>The FTC observation operator is tested in the context of a collocation study between AMVs projected onto the collocated Aeolus horizontal line-of-sight (HLOS) and the Aeolus HLOS wind profiles.  This is a prototype for an implementation in a variational data assimilation system and here the Aeolus profiles act as the background in the FTC observation operator.  Results were obtained for ten days of data using modest QC.  The overall OMB or collocation difference SD for a global solution applied to the independent sample is 5.49 m/s with negligible mean.  For comparison the corresponding simple (or pure) collocation SD is 7.85 m/s, and the null solution, which only interpolates the Aeolus profile to the reported height of the AMV and removes the overall bias, has an OMB SD of 7.23 m/s. These values correspond to reductions of variance of 51.0% and 42.3%, due to the FTC observation operator in comparison to the simple collocation and null solution, respectively.</p><p>These preliminary tests demonstrate the potential for the FTC observation operator for </p><ul><li>Improving AMV collocations (including triple collocation) with profile wind data.</li> <li>Characterizing AMVs. For example, summary results for the HLOS winds show that AMVs compare best with wind profiles averaged over a 4.5 km layer centered 0.5 km above the reported AMV height.</li> <li>Improving AMV observation usage within data assimilation (DA) systems. Lower estimated error and more realistic representation of AMVs with variational FTC (VarFTC) should result in greater information extracted.  The FTC observation operator accomplishes this by accounting for the effects of <em>h</em> and Δ<em>z</em>. </li> </ul>

scholarly journals How much information do extinction and backscattering measurements contain about the chemical composition of atmospheric aerosol?

2016 ◽  
Author(s):  
Michael Kahnert ◽  
Emma Andersson
Keyword(s):  
Inverse Problem ◽  
Standard Deviation ◽  
Chemical Composition ◽  
Data Assimilation ◽  
Information Content ◽  
Shannon Entropy ◽  
Degrees Of Freedom ◽  
Singular Values ◽  
Related Model ◽  
Observation Operator

Abstract. We theoretically and numerically investigate the problem of assimilating lidar observations of extinction and backscattering coefficients of aerosols into a chemical transport model. More specifically, we consider the inverse problem of determining the chemical composition of aerosols from these observations. The main questions are how much information the observations contain to constrain the particles' chemical composition, and how one can optimise a chemical data assimilation system to make maximum use of the available information. We first quantify the information content of the measurements by computing the singular values of the observation operator. From the singular values we can compute the number of signal degrees of freedom and the reduction in Shannon entropy. For an observation standard deviation of 10 %, it is found that simultaneous measurements of extinction and backscattering allows us to constrain twice as many model variables as extinction measurements alone. The same holds for measurements at two wavelengths compared to measurements at a single wavelength. However, when we extend the set of measurements from two to three wavelengths then we observe only a small increase in the number of signal degrees of freedom, and a minor change in the Shannon entropy. The information content is strongly sensitive to the observation error; both the number of signal degrees of freedom and the reduction in Shannon entropy steeply decrease as the observation standard deviation increases in the range between 1 and 100 %. The right singular vectors of the observation operator can be employed to transform the model variables into a new basis in which the components of the state vector can be divided into signal-related and noise-related components. We incorporate these results in a chemical data assimilation algorithm by introducing weak constraints that restrict the assimilation algorithm to acting on the signal-related model variables only. This ensures that the information contained in the measurements is fully exploited, but not over-used. Numerical experiments confirm that the constrained data assimilation algorithm solves the inverse problem in a way that automatises the choice of control variables, and that restricts the minimisation of the costfunction to the signal-related model variables.

scholarly journals An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System

2017 ◽  
Vol 145 (12) ◽  
pp. 4937-4947 ◽  
Author(s):  
Kevin J. Mueller ◽  
Junjie Liu ◽  
Will McCarty ◽  
Ron Gelaro
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Motion Vector ◽  
Earth Observing System ◽  
Forecasting System ◽  
Height Assignment ◽  
Sensitivity Method ◽  
Atmospheric Motion Vectors ◽  
Cross Track ◽  
Modern Era

This study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground track, as opposed to when equal uncertainties were assigned to the eastward and northward components as in previous studies. Assimilating only the cross-track component provided 60% of the benefit of both components. When optimally assimilated, MISR CMV proved broadly beneficial throughout the Earth, with the greatest benefit evident at high latitudes where there is a confluence of more frequent CMV coverage and gaps in coverage from other MERRA-2 wind observations. Globally, MISR represented 1.6% of the total forecast benefit, whereas regionally that percentage was as large as 3.7%.

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

scholarly journals Forward modelling of tree-ring width and comparison with a global network of tree-ring chronologies

2014 ◽  
Vol 10 (2) ◽  
pp. 437-449 ◽  
Author(s):  
P. Breitenmoser ◽  
S. Brönnimann ◽  
D. Frank
Keyword(s):  
Data Assimilation ◽  
Tree Ring ◽  
Ring Width ◽  
Global Network ◽  
Threshold Temperature ◽  
Tree Ring Width ◽  
Data Set ◽  
Observation Operator ◽  
Tree Ring Data ◽  
Wide Range

Abstract. We investigate relationships between climate and tree-ring data on a global scale using the process-based Vaganov–Shashkin Lite (VSL) forward model of tree-ring width formation. The VSL model requires as inputs only latitude, monthly mean temperature, and monthly accumulated precipitation. Hence, this simple, process-based model enables ring-width simulation at any location where monthly climate records exist. In this study, we analyse the growth response of simulated tree rings to monthly climate conditions obtained from the CRU TS3.1 data set back to 1901. Our key aims are (a) to assess the VSL model performance by examining the relations between simulated and observed growth at 2287 globally distributed sites, (b) indentify optimal growth parameters found during the model calibration, and (c) to evaluate the potential of the VSL model as an observation operator for data-assimilation-based reconstructions of climate from tree-ring width. The assessment of the growth-onset threshold temperature of approximately 4–6 °C for most sites and species using a Bayesian estimation approach complements other studies on the lower temperature limits where plant growth may be sustained. Our results suggest that the VSL model skilfully simulates site level tree-ring series in response to climate forcing for a wide range of environmental conditions and species. Spatial aggregation of the tree-ring chronologies to reduce non-climatic noise at the site level yielded notable improvements in the coherence between modelled and actual growth. The resulting distinct and coherent patterns of significant relationships between the aggregated and simulated series further demonstrate the VSL model's ability to skilfully capture the climatic signal contained in tree-ring series. Finally, we propose that the VSL model can be used as an observation operator in data assimilation approaches to reconstruct past climate.

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

scholarly journals Modeling of the Melting Layer. Part IV: Brightband Bulk Parameterization

2008 ◽  
Vol 65 (6) ◽  
pp. 1991-2001 ◽  
Author(s):  
Catherine Heyraud ◽  
Wanda Szyrmer ◽  
Stéphane Laroche ◽  
Isztar Zawadzki
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Melting Layer ◽  
Observation Operator ◽  
Data Assimilation Scheme ◽  
Bulk Model ◽  
Assimilation Scheme ◽  
Bulk Parameterization ◽  
Good Agreement ◽  
Extract Information

Abstract In this paper a simplified UHF-band backscattering parameterization for individual melting snowflakes is proposed. This parameterization is a function of the density, shape, and melted fraction, and is used here in a brightband bulk modeling study. A 1D bulk model is developed where aggregation and breakup are neglected. Model results are in good agreement with detailed bin-model results and simulate the radar brightband observations well. It is shown the model can be seen as an observation operator that could be introduced into a data assimilation scheme to extract information contained in the radar data measurements.

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

scholarly journals Sensitivities of Quantitative Precipitation Forecasts for Typhoon Soudelor (2015) near Landfall to Polarimetric Radar Data Assimilation

2020 ◽  
Vol 12 (22) ◽  
pp. 3711
Author(s):  
Chih-Chien Tsai ◽  
Kao-Shen Chung
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Differential Phase ◽  
Observation Operator ◽  
Quantitative Precipitation Forecasts ◽  
Series Of Experiments ◽  
Differential Reflectivity ◽  
Radar Data Assimilation

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating various combinations of radar variables are carried out for the purpose of improving a 6 h deterministic forecast for the most intense period. The results of the control simulation expose three sources of the observation operator errors, including the raindrop shape-size relation, the limitations for ice-phase hydrometeors, and the melting ice model. Nevertheless, polarimetric radar data assimilation with the unadjusted observation operator can still improve the analyses, especially rainwater, and consequent QPFs for this typhoon case. The different impacts of assimilating reflectivity, differential reflectivity, and specific differential phase are only distinguishable at the lower levels of convective precipitation areas where specific differential phase is found most helpful. The positive effect of radar data assimilation on QPFs can last three hours in this study, and further improvement can be expected by optimizing the observation operator in the future

scholarly journals Sensitivity of Atmospheric Motion Vectors Height Assignment Methods to Semitransparent Cloud Properties Using Simulated Meteosat-8 Radiances

2010 ◽  
Vol 49 (6) ◽  
pp. 1205-1218 ◽  
Author(s):  
Régis Borde ◽  
Philippe Dubuisson
Keyword(s):  
Initial Pressure ◽  
Cloud Microphysics ◽  
Atmospheric Parameters ◽  
Ice Clouds ◽  
Clear Sky ◽  
Cloud Properties ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Cloud Top Pressure ◽  
Atmospheric Motion Vectors

Abstract This paper presents the sensitivity to various atmospheric parameters of two height assignment methods that aim to retrieve the cloud-top height of semitransparent clouds. The use of simulated Meteosat-8 radiances has the advantage that the pressure retrieved by a given method can be compared to the initial pressure set to the cloud in the model, which is exactly known. The methods retrieve the pressure of a perfectly opaque cloud to within a few hectopascals. However, considering more realistic ice clouds, methods are sensitive to all of the tested atmospheric parameters and, especially, to the cloud microphysics, which can bias the results of the CO2-slicing method by several tens of hectopascals. The cloud-top pressure retrieval is especially difficult for thinner clouds with optical thicknesses smaller than 2, for which the errors can reach several tens of hectopascals. The methods have also been tested after introducing realistic perturbations in the temperature and humidity profiles and on the clear-sky surface radiances. The corresponding averages of errors on the retrieved pressures are also very large, especially for thin clouds. In multilayer cloud situations the height assignment methods do not work properly, placing the cloud-top height somewhere between the two cloud layers for most cirrus cloud layers with optical thicknesses between 0.1 and 10.

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