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This study evaluates the performance of the WRF-GHG model in analyzing CO2 and CH4 concentrations in Berlin. The study compares the model results to a case study in Berlin and explores the benefits of using the differential column methodology (DCM) for model analysis. The study helps understand the model results and provides insights into tracer emissions. The WRF-GHG model includes high-resolution meteorological and concentration fields, as well as concentration estimates from different emission processes.

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Questions of the study:

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  1. Analysis for Total Column CO2 and CH4 in Berlin using WRF-GHG combined with Differential Column Methodology (DCM) (1) Professorshipof Environmental Sensingand Modeling, Department ofElectricaland Computer Engineering, Technische Universität München (TUM), Munich, Germany (2) Max Planck Institute for Biogeochemistry, Department of Biogeochemical Systems, Jena, Germany (3) Leibniz Supercomputing Center (Leibniz-Rechenzentrum, LRZ) of Bavarian Academy of Sciences and Humanities, Garching, Germany Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany Xinxu Zhao (1), Julia Marschall (2), Stephan Hachinger (3), Christoph Gerbig (2), Jia Chen (1)

  2. Questions of the study: Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany • How good is the performance of the WRF-GHG model in general? • Comparing model to case study in Berlin (cf. Hase et al, 2015) • 2. Is it beneficial to use the differential column methodology (DCM) for the model analysis? • Comparing standard approach with DCM approach • Helping to understand the model results (e.g., features of tracer emissions) • Cancel out the bias…

  3. WRF-GHG Model: • High resolution; • Meteorologicalfields • Concentrationfields • Concentration estimates from different emission processes • etc.. From: WRF-GHG Technical Report Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  4. WRF-GHG Model: Model Domain: 3 domains Spatial resolutions: 9 km, 3 km & 1 km 26 Vertical layers, up to 50 hPa d01 Berlin External Data Sources: 1. VPRM tracer: MODIS satellite estimates 2. Anthropogenic tracer: EDGAR V.4.1 3. Meteorological fields: GFS 4. The initial and boundary conditions for concentration fields: CAMS Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  5. Measurement Information: Heili Lind Tegel Char Tempelhof Mahls Licht Schönefeld • Measuring CO2 and CH4 concentrations • Ground-based remote sensing (EM27/SUN) • Performed in July 2014 in Berlin (Hase et al, 2015) Hase, F. et al. Application of portable FTIR spectrometers for detecting greenhouse gas emissions of the major city Berlin. Atmos. Chem. Phys., 16, 10.5194/amt-8-3059-2015, 2015. Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  6. Evaluationofthe WRF-GHG model: • Comparisonof Wind Fields ( Wind Speeds & Wind Directions) •  Wind Speeds & Wind Directions at 10 m • ComparisonofConcentration Fields •  Pressureweightedaverage CO2 & CH4 • Tracer Analysis •  CO2: Anthropogenicemissions & Biogenicactivities  CH4: Anthropogenicemissions & Soiluptakeprocess Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  7. Comparison of modeled and observed wind (at 10 m):  Modeled wind speed is overlapping well with measurements  Measured wind directions show larger variability than modeled wind directions Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  8. Comparisonofmodeledandobserved XCO2and XCH4 XCH4 [ppb] XCO2 [ppm] 2.7% bias (+50 ppb) • Modeled XCH4 is overestimated compared with measurements (DCM will help)  Modeled XCO2 fits well with measurements Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  9. CO2:Influence of anthropogenic and biogenic activities •  XCO2 : • Biogenic activities are dominating • Anthropogenic influence is weak (see, however, DCM analysis) Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  10. CH4:Influence of humane activities and soil uptake process •  XCH4 : • Anthropogenic activities are dominating • Soil uptake process has almost no influence Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  11. Differential Column Methodology (DCM): • DCM is calculating differences between upwind and downwind sites • to cancel out the bias of XCH4 • to highlight the influence of CO2 anthropogenic activities  DCM is applied using this equation : Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany Chen, J. et al. Differential column measurements using compact solar-tracking spectrometers, Atmos. Chem. Phys., 16, 8479-8498, doi:10.5194/acp-16-8479-2016, 2016

  12. DCM result for CO2:  Wind directions and speeds are homogenous between upwind and downwind sites  Human activities are dominating the variations of ∆XCO2 within urban areas Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  13. DCM result for CH4:  Simulated ∆XCH4showsbetteragreementwithmeasurementscomparedtothestandardapproach Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  14. Conclusion and Outlook • WRF-GHG is a suitable tool for GHG transport analysis in urban areas (more cases, e.g., Munich, Hamburg) Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  15. Permanent Column Sensor Network Garching r = 20 km Markt Schwaben TUM Weßling Höhenkirchen Prevailing wind direction Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  16. Conclusion and Outlook • WRF-GHG is a suitable tool for GHG transport analysis in urban areas (more cases, e.g., Munich, Hamburg) • Differential Column Methodology (DCM) can be an effective method to cancel out the bias caused, e.g., from initialization conditions and highlight the regional emission sources • The WRF-GHG mesoscale simulation framework can be combined with microscale atmospheric transport models (CFD) for simulating crucial details of emission transport patterns Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

  17. Thank you for your attention! Zhao et al. | May 7th, 2019 | WRF Workshp| Munich, Germany

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