nach oben

Erschienen in:

2011 | OriginalPaper | Buchkapitel

13. Assessment of Source–Receptor Relations by Inverse Modelling and Chemical Data Assimilation

verfasst von : Hendrik Elbern, Achim Strunk, Elmar Friese, Lars Nieradzik

Erschienen in: Persistent Pollution – Past, Present and Future

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Data assimilation and Inverse Modelling may serve various purposes by use of manifold techniques. A reasonable definition reads as follows: extracts the signal from noisy observations (filtering) interpolates in space and time (interpolation) and reconstructs state variables that are not sampled by the observation network (completeness).

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Chemical Transport Modelling

Nächstes Kapitel Aerosols as Transport Vehicles of Persistent Pollutants

Ackermann, I.J., H. Hass, M. Memmesheimer, A. Ebel, F.S. Binkowski, and U. Shankar, 1998: Modal aerosol dynamics model for Europe: Development and first applications. Atmos. Environ. 32, 2981–2999.CrossRef

Benedetti, A., and M. Fisher, 2007: Background error statistics for aerosols. Q. J. R. Meteorol. Soc. 133, 391–405.CrossRef

Benedetti, A., J.-J. Mocrette, O. Boucher, A. Dethof, R. Engelen, M. Fisher, H. Flentje, N. Huneeus, L. Jones, J. Kaiser, S. Kinne, A. Mangold, M. Razinger, A. Simmons, and M. Suttie, 2009: Aerosol analysis and forecast in the european centre for medium–range weather forecasts integrated forecast system: 2. data assimilation. J. Geophys. Res. 114, D13205.CrossRef

Bennet, A., 1992: Inverse Methods in Physical Oceanography, Cambridge University Press, pp. 346.

Bernhofer, C., and B. Köstner (Eds.), 2005: Vertical Transport of Energy and Trace Gases at Anchor Stations and Their Spatial/Temporal Extrapolation under Complex Natural Conditions (VERTIKO): Project Summary, vol 12, Tharandter Klimaprotokolle, Technische Universität Dresden, Germany.

Boersma, K.F., H.J. Eskes, and E.J. Brinksma, 2004: Error analysis for tropospheric NO₂ retrieval from space. J. Geophys. Res. 109, 2004.CrossRef

Carmichael, G.R., A. Sandu, T. Chai, D.N. Daescu, E.M. Constantinescu, and Y. Tang, 2007: Predicting air quality: Improvements through advanced methods to integrate models and measurements. J. Comp. Phys. 227, 3540–3571.CrossRef

Chai, T.F., G.R. Carmichael, A. Sandu, Y.H. Tang, and D.N. Daescu, 2006: Chemical data assimilation of transport and chemical evolution over the Pacific (TRACE-P) aircraft measurements. J. Geophys. Res. 111, D02301.CrossRef

Collins, W., P. Rasch, B. Eaton, B. Khattatov, J.-F. Lamarque, and C. Zender, 2001: Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX. J. Geophys. Res. 106, 7313–7336.CrossRef

Constantinescu, E.M., A. Sandu, T. Chai, and G.R. Carmichael, 2007a: Ensemble-based chemical data assimilation. I: General approach. Q. J. R. Meteorol. Soc. 133, 1229–1243.CrossRef

Constantinescu, E.M., A. Sandu, T. Chai, and G.R. Carmichael, 2007b: Ensemble-based chemical data assimilation. II: Covariance localization. Q. J. R. Meteorol. Soc. 133, 1245–1256.CrossRef

Daley, R., 1991: Atmospheric Data Analysis, Cambridge University Press pp. 457.

Deeter, M.N. et al., 2003: Operational carbon monoxide retrieval algorithm and selected results for the MOPITT instrument. J. Geophys. Res. 108, 4399.CrossRef

Elbern, H., and H. Schmidt, 2001: Ozone episode analysis by four-dimensional variational chemistry data assimilation. J. Geophys. Res. 106, 3569–3590.CrossRef

Elbern, H., H. Schmidt, O. Talagrand, and A. Ebel, 2000: 4D–variational data assimilation with an adjoint air quality model for emission analysis. Environ. Model. Software 15, 539–548.CrossRef

Elbern, H., A. Strunk, H. Schmidt, and O. Talagrand, 2007: Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos. Chem. Phys. 7, 1–59.CrossRef

Eskes, H.J., and K.F. Boersma, 2003: Averaging kernels for DOAS total-column satellite retrievals. Atmos. Chem. Phys. 3, 1285–1291.CrossRef

Fedorov, V., 1998: Kriging and other estimators of spatial field characteristics (with special reference to environmental studies). Atmos. Environ. 23, 174–184.

Hamill, T.M., 2006: Ensemble-based atmospheric data assimilation, Chapter 6. Predictability of Weather and Climate, 124–156, Cambridge University Press, Cambridge.

Hanea, R.G., G.J.M. Velders, and A. Heemink, 2004: Data assimilation of ground level ozone in Europe with a Kalman filter and chemistry transport. J. Geophys. Res. 109, 1–19.CrossRef

Hanna, S.R., J.C. Chang, and M.E. Fernau, 1998: Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variables. Atmos. Environ. 32, 3619–3628.CrossRef

Hanna, S.R., Z.G. Lu, H.C. Frey, N. Wheeler, J. Vukovich, S. Arunachalam, M. Fernau, and D.A. Hansen, 2001: Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July1995 OTAG domain. Atmos. Environ. 35, 891–903.CrossRef

Heue, K.P., A. Richter, M. Bruns, J.P. Burrows, C. von Friedeburg, U. Platt, I. Pundt, P. Wang, and T. Wagner, 2005: Validation of SCIAMACHY tropospheric NO₂-columns with AMAXDOAS measurements. Atmos. Chem. Phys. 5, 1039–1051.CrossRef

Holzer-Popp, T., M. Schroedter, and G. Gesell, 2002: Retrieving aerosol optical depth and type in the boundary layer over land and ocean from simultaneous GOME spectrometer and ATSR-2 radiometer measurements, 1, Method description. J. Geophys. Res. 107, 4578.CrossRef

Lahoz, W.A., Q. Errera, R. Swinbank, and D. Fonteyn, 2007: Data assimilation of stratospheric constituents: a review. Atmos. Chem. Phys. 7, 5745–5773.CrossRef

Marenco, A. et al., 1998: Measurement of ozone and water vapor by Airbus in-service aircraft: The MOZAIC airborne program. An overview. J. Geophys. Res. 103, 25631–25642.CrossRef

Muller, J.F., and T. Stavrakou, 2005: Inversion of CO and NOx emissions using the adjoint of the IMAGES model. Atmos. Chem. Phys. 5, 1157–1186.CrossRef

Müller, M.D., A.K. Kaifel, M. Weber, S. Tellmann, J.P. Burrows, and D. Loyola, 2003: Ozone profile retrievel from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY)). J. Geophys. Res. 108, 1–19.

Nieradzik, L.P., and H. Elbern, 2006: Variational assimilation of combined satellite retrieved and in situ aerosol data in an advanced chemistry transport model, in: Proceedings of the ESA Atmospheric Science Conference, ESA, ESA-ESRIN, Frascati.

Quélo, D., V. Mallet, and B. Sportisse, 2005: Inverse modelling of nox emissions at regional scale over northern france. Preliminary investigation of the second-order sensitivity. J. Geophys. Res. 110, 1–12.CrossRef

Sander, R., A. Kerkweg, P. Jöckel, and J. Lelieveld, 2005: Technical note: The new comprehensive atmospheric chemistry module MECCA. Atmos. Chem. Phys. 5, 445–450.CrossRef

Schmidt, H., and D. Martin, 2003: Adjoint sensitivity of episodic ozone in the Paris area to emissions on the continental scale. J. Geophys. Res. 108, 1–16.

Thouret, V., J. Cho, R. Newell, A. Marenco, and H. Smit, 2000: General characteristics of tropospheric trace constituent layers observed in the mozaic program. J. Geophys. Res. 105, 17379–17392.CrossRef

van Loon, M., P.J.H. Builtjes, and A.J. Segers, 2000: Data assimilation of ozone in the atmospheric transport chemistry model LOTOS. Environ. Model. Software 15, 603–609.CrossRef

Verlaan, M., and A.W. Heemink, 1995: Reduced rank square root filters for large scale data assimilation problems, in: Second International Symposium on Assimilation of Observations in Meteorology and Oceanography, 1995, Tokyo, Japan

Volz-Thomas, A., H. Geiss, A. Hofzumahaus, and K.H. Becker, 2003: Introduction to special section: Photochemistry experiment in BERLIOZ. J. Geophys. Res. 108, 8252.CrossRef

Yumimoto, K., I. Uno, N. Sugimoto, A. Shimizu, Z. Liu, and D.M. Winker, 2008: Adjoint inversion modelling of Asian dust emission using lidar observations. Atmos. Chem. Phys. 8, 2869–2884.CrossRef

Zhang, S., J.E. Penner, and O. Torres, 2005: Inverse modelling of biomass burning emissions using Total Ozone Mapping Spectrometer aerosol index for 1997. J. Geophys. Res. 110, 1–21.

Zhang, J., J.S. Reid, D.L. Westphal, N.L. Baker, and E.J. Hyer, 2008: A system for operational aerosol optical depth data assimilation over global oceans. J. Geophys. Res. 113, D10208.CrossRef

Titel: Assessment of Source–Receptor Relations by Inverse Modelling and Chemical Data Assimilation
verfasst von: Hendrik Elbern
Achim Strunk
Elmar Friese
Lars Nieradzik
Verlag: Springer Berlin Heidelberg
Buch: Persistent Pollution – Past, Present and Future
Print ISBN: 978-3-642-17420-9

Electronic ISBN: 978-3-642-17419-3

Copyright-Jahr: 2011
DOI: https://doi.org/10.1007/978-3-642-17419-3_13