مطالعه غلظت ستونی CH4 روی ایران: بکارگیری مشاهدات ماهواره‌ای GOSAT و شبیه‌سازی‌های عددی WRF-GHG

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری هواشناسی، دانشکده علوم و فنون دریایی، دانشگاه هرمزگان، بندر عباس، ایران

2 استاد، گروه علوم غیر زیستی جوی و اقیانوسی، دانشگاه هرمزگان، بندر عباس، ایران

3 استادیار، گروه کاوش‌های جوی، پژوهشکده هواشناسی، تهران، ایران

4 دانشیار، گروه پژوهشی هواشناسی سینوپتیکی و دینامیکی، پژوهشکده هواشناسی، تهران، ایران

چکیده

متان (CH4)، پس ازCO2، مهمترین گاز گلخانه‌ای انسانی است که اثر آن به 18 درصد نسبت واداشت تابشی جو و به نرخ افزایش نسبت اختلاط این گازها در جو کمک می‌کند. از این رو ردیابی کمی از میزان گسیل گازهای گلخانه‌ای در مناطق با منشا انسانی و شهری، با هدف ارزیابی دقیق میزان پخش از اهمیت بسیاری برخوردار است. در این مقاله، به منظور درک بهتر سهم منابع مختلف متان، از مدل WRF-GHG برای مدل سازی بر روی منطقه خاورمیانه به عنوان دامنه اول و ایران به عنوان دامنه دوم استفاده شده است. مهمترین منابع گسیل متان شامل، سوختن زیست توده، گسیل مصنوعی انسانی و گسیل تالاب‌ها، پسماندها می‌باشد. از مقایسه میدان‌های شبیه‌سازی شده متغییر‌های هواشناسی با اندازه‌گیری‌های ایستگاه‌های همدیدی، در سال 2010 در مناطق شهری اصلی می‌توان دریافت که، مدل قادر است تغییرات زمانی دمای سطح، رطوبت نسبی و باد را بازتولید کند. نتایج خطای اریبی در شبیه‌سازی غلظت متان، به طور متوسط در هر دو فصل گرم و سرد به ترتیب، 46.05 و 15.16 ppb می‌باشد. مقدار غلظت متان شبیه‌سازی شده برای فوریه و اوت عموماً در مقایسه با اندازه‌گیری‌های GOSAT بیش‌برآورد شده است و نتایج ارزیابی نشان داد که مدل WRF-Chem در فصل سرد (ماه فوریه) با توجه به خطاهای آماری بهتر از فصل گرم (ماه اوت) عمل می‌کند. نمای کلی بودجه‌های گسیل منابع مختلف متان به صورت متوسط ماهانه برای حوزه مورد مطالعه به ترتیب، گسیل انسانی با بودجه 68.8% و 63.5 برای دو ماه اوت و فوریه، تالاب‌ها با بودجه 24.4% و 33.1% در ماه‌های اوت و فوریه و سوختن زیست توده با بودجه گسیل 6.5% و 3.2% به ترتیب در تابستان و زمستان می‌باشد. تفاوت موجود بین غلظت‌های شبیه‌سازی‌شده و مشاهدات XCH4 از ماهواره‌ی GOSAT ‌می‌تواند ناشی از دست کم گرفتن گسیل ناشی از تالاب‌ها، فعالیت‌های‌کشاورزی و بهره برداری از سوخت‌های فسیلی باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Study of CH4 column concentration on Iran: Application of GOSAT satellite observations and WRF-GHG numerical simulations

نویسندگان [English]

  • Samira Karbasi 1
  • Hossein Malakooti 2
  • Mehdi Rahnama 3
  • Majid Azadi 4
1 Ph.D. candidate of Meteorology, Department of Marine and Atmospheric Science (non-Biologic), University of Hormozgan
2 Department of Marine and Atmospheric Science (non-Biologic), University of Hormozgan
3 Associate professor of Meteorology, Atmospheric Science and Meteorological Research Center (ASMERC)
4 Associate professor of Meteorology, Atmospheric Science and Meteorological Research Center (ASMERC)
چکیده [English]

Introduction

One of the consequences of the increase and accumulation of greenhouse gases in the atmosphere is kown as global warming, which is undoubtedly one of the most important environmental challenges in the world, especially in the Middle East. Given the scarcity of water resources in recent years, the consequences of global warming and climate change in various countries have reached a very worrying level. Carbon dioxide and Methane are known as two of the most important human greenhouse gases in the atmosphere, accounting for 64% and 18%, respectively, of long-lived radiation induction (LLGHGs) (Forster et al, 2007). Methane is considered as the second most important anthropogenic greenhouse gas after Carbon Dioxide.

The most important sources of Methane emissions include: biomass incineration, artificial human emissions, wetland emissions, and wastes. Despite the importance of Methane for physical and atmospheric conditions, the spatial distribution of global resources and Methane sinks is not well understood. With the launch of Methane measurement from satellites, knowledge about the global distribution of Methane in the atmosphere greatly increased.

The Japanese Greenhouse Gas Satellite (GOSAT) is the only satellite that measures the column mixing ratio of atmospheric Methane. Since the 1990, various global models have been used to simulate CH4 concentrations. High-resolution simulation of CH4 at hourly intervals on Earth, with diverse ecosystems, due to the lack of intensive spatial and temporal measurements and the impossibility of reliable validations for chemical simulations are known as a serious challenge. The main purpose of this study is to understand the performance of the WRF-GHG model in simulation of Methane concentration and validation the results of medium-scale modeling output in total Methane concentration in comparison with GOSAT satellite observations over Iran.

Materials and methods

Iran and some area of its surroundings is considered as a study area. This study focuses on two case periods of hot and dry (August 31-2010) and cold and wet (February 1-28, 2010). In order to provide the initial and boundary conditions of meteorological fields, ERA5 reanalysis data were used with a horizontal resolution of 0.25 ° with a time resolution of 6 hours. Different emission input data from three different global greenhouse gas emission databases EDGAR_v5.0 (anthropogenic emissions), GFAS emissions (fire emissions), and datasets (CMS_V01) (wetland emissions) have been used.

Preliminary and boundary conditions for the chemical fields taken from atmospheric monitoring service data (CAMS) with a spatial resolution of 0.8º with 137 vertical levels and with 6 hours time resolution. To investigate and quantify the validity of meteorological fields simulated by the WRF-Chem numerical model, a set of observations of selective synoptic stations is used. For validation of CH4 WRF_Chem column concentration and statistical analysis, in the points that include remote sensing data (GOSAT sensor data), is used the set of level 2 products generated by the NIES algorithm. The local transit time of the GOSAT satellite Is approximated around 13: 00_9: 00, so the simulated concentration for this time is applied in the analysis. The first 15 days of the simulation are omitted to take into account the spin-up time. Statistical parameters of mean bias error (MBE), mean absolute error value (MAE), root mean square error (RMSE), and Pearson correlation coefficient (R) in meteorological and chemical variables are studied for validation of numerical simulations and quantification of error levels.

Results and discussion

The model has been able to calculate temporal changes in surface temperature, relative humidity, and wind speed to some extent correctly. The general tendency of the model to simulate the observed temperature and relative humidity for the selected time period is evaluated. In general, model values are closer to summer observations than all thirty days in two selective months. The wind speed forecast is often consistent with the wind speed values obtained from the measurements, and in most cases, the wind speed is overestimated at around 1.2 m/s.

Statistical evaluations of the WRF-Chem model, together with the GHG gas-phase chemistry mechanism, show the simulation of Methane concentration versus observations by the GOSAT satellite, and the estimation of the average monthly concentration in February and August 2010. The values of MAE, RMSE, RMSE_u, RMSE_s, BIAS and R are calculated equal to 42.92, 46.05, 7.82, 44.60, -24.99 and 0.63, for hot and equal to 12.01, 13.94, 7.09, 11.68, 7.50 and 0.76 ppb, for cold periods respectively. It can be seen that the WRF-Chem model performed better in Methane simulation in cold and wet periods (January) compared to the hot and dry seasons (August).

Conclusion

In this study, the WRF-Chem model was used to simulate meteorological variables and air pollutants (methane greenhouse gas) concentrations in the Middle East-Iran region during the study period of February and August 2010. The sensitivity of the model is considered using the GHG gas-phase chemistry scheme. The main findings of this study are:

The model is able to reproduce temporal changes in surface temperature, relative humidity, and wind. The model underestimates the air temperature and relative humidity respectively around, 1/05 ⸰C -5% in the study area (Iran).

In simulating of Methane concentration, and examining the results with related GOSAT satellite observations, the model overestimates around15 ppb of the Methane concentrations. The evaluation results show that the WRF-Chem model performs better in the cold season (January) than in the warm season (August).

This uncertainty in CH4 simulation can be attributed to a deficiency in various input components of the CH4 emission in different categories. Improving the simulation for the various parameters reported to the model as the primary CH4 emission can generally help to improve the CH4 simulations.

کلیدواژه‌ها [English]

  • Global Warming
  • Greenhouse Gas
  • Methane (CH4)
  • WRF-GHG Model
  • GOSAT Satellite
  1. Ahmadov, R., C. Gerbig, R. Kretschmer, S. Körner, C. Rödenbeck, P. Bousquet, and M. Ramonet, 2009: Comparing high resolution WRF-VPRM simulations and two global CO2 transport models with coastal tower measurements of CO2. Biogeosciences, 6, 807–817.
  2. Ahmadov, R., C. Gerbig, R. Kretschmer, S. Körner, C. Rödenbeck, P. Bousquet, and M. Ramonet. "Comparing high resolution WRF-VPRM simulations and two global CO 2 transport models with coastal tower measurements of CO 2." Biogeosciences 6, no. 5 (2009): 807-817.
  3. Alijani, Behlool, Tulabi Nejad, and Karbalaei Dari. "Behavior measurement of the effect of global warming on subtropical high pressure." Natural Geography Research 51, no. 1 (2019): 33-50.
  4. Archer, D., Eby, M., Brovkin, V., Ridgwell, A., Cao, L., Mikolajewicz, U., Caldeira, K., Matsumoto, K., Munhoven, G., Montenegro, A., et al. (2009). Atmospheric lifetime of fossil fuel carbon dioxide. Annual review of earth and planetary sciences, 37:117–134.
  5. Ballav, Srabanti, Prabir K. Patra, Masayuki Takigawa, Sarbari Ghosh, Utpal K. De, Shamil Maksyutov, Shohei Murayama, Hitoshi Mukai, and Shigeru Hashimoto. "Simulation of CO2 concentration over East Asia using the regional transport model WRF-CO2." Journal of the Meteorological Society of Japan. Ser. II 90, no. 6 (2012): 959-976.
  6. Bousquet, P., Ciais, P., Miller, J. B., Dlugokencky, E. J., Hauglustaine, D. A., Prigent, C., Van der Werf, G. R., Peylin, P., Brunke, E.-G., Carouge, C., Langenfelds, R. L., Lathière, J., Papa, F., Ramonet, M., Schmidt, M., Steele, L. P., Tyler, S. C., and White, J. (2006). Contribution of anthropogenic and natural sources to atmospheric methane variability. Nature, 443(7110): 439–443.
  7. Bovensmann, H., Burrows, J., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V., Chance, K., and Goede, A. (1999). Sciamachy: Mission objectives and measurement modes. Journal of the atmospheric sciences, 56(2):127–150.
  8. Butz, André, Otto P. Hasekamp, Christian Frankenberg, and Ilse Aben. "Retrievals of atmospheric CO 2 from simulated space-borne measurements of backscattered near-infrared sunlight: accounting for aerosol effects." Applied optics 48, no. 18 (2009): 3322-3336.
  9. Chanton, J. P. and Smith, L. K. (1993). Seasonal variations in the isotopic composition of methane associated with aquatic macrophytes. In Biogeochemistry of Global Change, pages 619–632. Springer.
  10. Christensen, T. R., Ekberg, A., Ström, L., Mastepanov, M., Panikov, N., Öquist, M., Svensson, B. H., Nykänen, H., Martikainen, P. J., and Oskarsson, H. (2003). Factors controlling large scale variations in methane emissions from wetlands. Geophysical Research Letters, 30(7).
  11. Corbin, K. D., A. S. Denning, E. Y. Lokupitiya, A. E. Schuh, N. L. Miles, K. J. Davis, S. Richardson, and I. T. Baker, 2010: Assessing the impact of crops on regional CO2 fluxes and atmospheric concentrations. Tellus B, 62, 521–532.
  12. Dlugokencky, E. J. (2019). Trends in Atmospheric Methane (www.esrl.noaa.gov/gmd/ccgg/trends_ch4/). NOAA/ESRL.
  13. Dlugokencky, E. J., S. Houweling, L. Bruhwiler, K. A. Masarie, P. M. Lang, J. B. Miller, and Tans, P. P. (2003). Atmospheric methane levels off: Temporary pause or a new steady‐state?, Geophysical Research Letters, 30, no. 19.
  14. Duncan, B. N., Martin, R. V., Staudt, A. C., Yevich, R., and Logan, J. A. (2003). Interannual and seasonal variability of biomass burning emissions constrained by satellite observations. Journal of Geophysical Research: Atmospheres, 108(D2): ACH–1.
  15. Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D., Haywood, J., Lean, J., Lowe, D., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Van Dorland, R. (2007). Climate change 2007: the physical science basis: contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  16. Frankenberg, C., D. Wunch, G. Toon, Camille Risi, R. Scheepmaker, J-E. Lee, P. Wennberg, and J. Worden. "Water vapor isotopologue retrievals from high-resolution GOSAT shortwave infrared spectra." Atmospheric Measurement Techniques 6, no. 2 (2013): 263-274.
  17. Frankenberg, C., Meirink, J. F., van Weele, M., Platt, U., and Wagner, T. (2005). Assessing methane emissions from global space-borne observations. Science, 308(5724):1010–1014
  18. Grell, Georg A., Steven E. Peckham, Rainer Schmitz, Stuart A. McKeen, Gregory Frost, William C. Skamarock, and Brian Eder. "Fully coupled “online” chemistry within the WRF model." Atmospheric Environment 39, no. 37 (2005): 6957-6975.
  19. Hansen, J. E. and Sato, M. (2001). Trends of measured climate forcing agents. Proceedings of the National Academy of Sciences, 98(26):14778– 14783.
  20. Houweling, S., F-M. Breon, I. Aben, C. Rödenbeck, M. Gloor, M. Heimann, and Ph Ciais. "Inverse modeling of CO 2 sources and sinks using satellite data: a synthetic inter-comparison of measurement techniques and their performance as a function of space and time." Atmospheric Chemistry and Physics 4, no. 2 (2004): 523-538.
  21. Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G., Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler, L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A., Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J., Krummel, P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C., Naik, V., O’Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R. G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T., Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa, S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F., Williams, J. E., and Zeng, G. (2013). Three decades of global methane sources and sinks. Nature Geoscience, 6(10):813–823.
  22. Kuze, Akihiko, Hiroshi Suto, Masakatsu Nakajima, and Takashi Hamazaki. "Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring." Applied optics 48, no. 35 (2009): 6716-6733.
  23. Law, R. M., P. J. Rayner, and et al., 1996: Variations in modelled atmospheric transport of carbon dioxide and the consequences for CO2 inversions. Global Biogeochem. Cyc. 16, GB1053, doi: 10.1029/96GB01892.
  24. Law, R. M., W. Peters, and et al., 2008: TransCom model simulations of hourly atmospheric CO2: Experimental overview and diurnal cycle results for 2002. Global Biogeochem. Cyc., 22, GB3009, doi:10.1029/ 2007GB003050.
  25. Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J.-X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., et al. (2019). Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. Atmospheric Chemistry and Physics, 19(11):7859–7881.
  26. Massart, S., Agusti-Panareda, A., Aben, I., Butz, A., Chevallier, F., Crevoisier, C., Engelen, R., Frankenberg, C., and Hasekamp, O. (2014). Assimilation of atmospheric methane products in the macc-ii system: from sciamachy to tanso and iasi. Atmospheric Chemistry and Physics, 1
  27. Miao, Ru, Ning Lu, Ling Yao, Yunqiang Zhu, Juanle Wang, and Jiulin Sun. "Multi-year comparison of carbon dioxide from satellite data with ground-based FTS measurements (2003–2011)." Remote Sensing 5, no. 7 (2013): 3431-3456.
  28. Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E., Biraud, S. C., Dlugokencky, E. J., Eluszkiewicz, J., Fischer, M. L., Janssens-Maenhout, G., et al. (2013). Anthropogenic emissions of methane in the United States. Proceedings of the National Academy of Sciences, 110(50):20018–20022.
  29. Mitsch, W. J., Nahlik, A., Wolski, P., Bernal, B., Zhang, L., and Ramberg, L. (2010). Tropical wetlands: seasonal hydrologic pulsing, carbon sequestration, and methane emissions. Wetlands ecology and management, 18(5):573–586.
  30. Mitsch, William J., Amanda Nahlik, Piotr Wolski, Blanca Bernal, Li Zhang, and Lars Ramberg. "Tropical wetlands: seasonal hydrologic pulsing, carbon sequestration, and methane emissions." Wetlands ecology and management 18, no. 5 (2010): 573-586.
  31. Neue, H., Gaunt, J., Wang, Z., Becker-Heidmann, P., and Quijano, C. (1997). Carbon in tropical wetlands. Geoderma, 79(1-4):163–185
  32. Nisbet, E. and Chappellaz, J. (2009). Shifting gear, quickly. Science, 324(5926):477–478.
  33. Nisbet, E. G., Dlugokencky, E. J., and Bousquet, P. (2014). Methane on the rise—again. Science, 343(6170):493–495.
  34. Nisbet, E. G., Dlugokencky, E. J., Manning, M. R., Lowry, D., Fisher, R. E., France, J. L., Michel, S. E., Miller, J. B., White, J. W. C., Vaughn, B., Bousquet, P., Pyle, J. A., Warwick, N. J., Cain, M., Brownlow, R., Zazzeri, G., Lanoisellé, M., Manning, A. C., Gloor, E., Worthy, D. E. J., Brunke, E.-G., Labuschagne, C., Wolff, E. W., and Ganesan, A. L. (2016). Rising atmospheric methane: 2007-2014 growth and isotopic shift. Global Biogeochemical Cycles, 30(9):1356–1370
  35. Niwano, M., M. Takigawa, M. Takahashi, H. Akimoto, M. Nakazato, T. Nagai, T. Sakai, and Y. Mano, 2007: Evaluation of vertical ozone profiles simulated by WRF/ Chem using Lidar-observed data. SOLA, 3, 133–136, doi:10.2151/sola.2007-034
  36. Oshchepkov, Sergey, Andrey Bril, Tatsuya Yokota, Paul O. Wennberg, Nicholas M. Deutscher, Debra Wunch, Geoffrey C. Toon et al. "Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space. Part 2: Algorithm intercomparison in the GOSAT data processing for CO2 retrievals over TCCON sites." Journal of Geophysical Research: Atmospheres 118, no. 3 (2013): 1493-1512.
  37. Pandey, S., Houweling, S., Krol, M., Aben, I., Monteil, G., Nechita-Banda, N., Dlugokencky, E. J., Detmers, R., Hasekamp, O., Xu, X., Riley, W. J., Poulter, B., Zhang, Z., McDonald, K. C., White, J. W. C., Bousquet, P., and Röckmann, T. (2017). Enhanced methane emissions from tropical wetlands during the 2011 La Niña. Scientific Reports, 7(1): 45759.
  38. Parker, R. "Boesch h, Cogan A, Fraser A, Feng L, Palmer Pi, et al. Methane observations from the Greenhouse Gases Observing SATellite: comparison to groundbased TCCON data and model calculations." Geophys Res Lett 38 (2011): 15.
  39. Patra, P. K., R. M. Law, W. Peters, et al., 2008: TransCom model simulations of hourly atmospheric CO2: Analysis of synoptic-scale variations for the period 2002–2003. Global Biogeochem. Cyc., 22, GB4013, doi:10.1029/2007GB00381
  40. Petrenko, V. V., Smith, A. M., Brook, E. J., Lowe, D., Riedel, K., Brailsford, G., Hua, Q., Schaefer, H., Reeh, N., Weiss, R. F., et al. (2009). 14CH4 measurements in Greenland ice: investigating last glacial termination CH4 sources. Science, 324(5926): 506–508.
  41. Peylin, Philippe, Sander Houweling, Maarten C. Krol, Ute Karstens, Christian Rödenbeck, Camilla Geels, Alex Vermeulen et al. "Importance of fossil fuel emission uncertainties over Europe for CO 2 modeling: model intercomparison." Atmospheric chemistry and physics 11, no. 13 (2011): 6607-6622.
  42. Rigby, M., Montzka, S. A., Prinn, R. G., White, J. W., Young, D., O’Doherty, S., Lunt, M. F., Ganesan, A. L., Manning, A. J., Simmonds, P. G., et al. (2017). Role of atmospheric oxidation in recent methane growth. Proceedings of the National Academy of Sciences, 114(21):5373–5377.
  43. Rigby, M., Prinn, R. G., Fraser, P. J., Simmonds, P. G., Langenfelds, R. L., Huang, J., Cunnold, D. M., Steele, L. P., Krummel, P. B., Weiss, R. F., O’Doherty, S., Salameh, P. K., Wang, H. J., Harth, C. M., Mühle, J., and Porter, L. W. (2008). Renewed growth of atmospheric methane. Geophysical Research Letters, 35(22):L22805.
  44. Ringeval, B., Houweling, S., Van Bodegom, P. M., Spahni, R., Van Beek, R., Joos, F., and Röckmann, T. (2014). Methane emissions from floodplains in the Amazon Basin: Challenges in developing a process-based model for global applications. Biogeosciences, 11(6):1519–1558.
  45. Ru Miao, Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011), Remote Sensing 5(7):3431-3456 · July 2013.
  46. Samira Karbasi, Hossein Malakooti, Mehdi Rahnama, Majid Azadi, (2021). Evaluation of CO2 Greenhouse Gas Estimation Algorithms Based on GOSAT Satellite Data and Ground-based Observation Stations, Iranian Journal of Remote Sencing & GIS, 12(3), 23-36. magiran.com/p2251001.
  47. Sarrat, C., J. Noilhan, A. J. Dolman, C. Gerbig, R. Ahmadov, L. F. Tolk, A. G. C. A Meesters, R. W. A. Hutjes, H. W. Ter Maat, G. Perez-Landa, and S. Donier, 2007: Atmospheric CO2 modelling at the regional scale: an intercomparison of 5 meso-scale atmospheric models. Biogeoscience, 4, 1115–1126
  48. Scarpelli, T., Jacob, D., Maasakkers, J., Sheng, J. X., Rose, K., Payer Sulprizio, M., and Worden, J. (2018). A Global Gridded Inventory of Methane Emissions from Fuel Exploitation including Oil, Gas, and Coal. AGU Fall Meeting Abstracts.
  49. Skamarock, William C. "A Description of the Advanced Research WRF Version 2, NCAR technical note, NCAR/TN-468+ STR." http://www. mmm. Ucar. Edu/wrf/users/docs/arw_v2. pdf (2005).
  50. Skamarock, William C., Joseph B. Klemp, Jimy Dudhia, David O. Gill, Dale M. Barker, Michael G. Duda, Xiang-Yu Huang, Wei Wang, and Jordan G. Powers. "A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+ STR." National Center for Atmospheric Research: Boulder, CO, USA 125 (2008).
  51. Taguchi, S., R. M. Law, C. Rödenbeck, P. K. Patra, S. Maksyutov, W. Zahorowski, H. Sartorius, and I. Levin, 2011: TransCom continuous experiment: comparison of 222Rn transport at hourly time scales at three stations in Germany. Atmos. Chem. Phys., 11, 10071–10084, doi:10.5194/acp-11-10071-2011.
  52. Takigawa, M., M. Niwano, H. Akimoto, and M. Takahashi, 2007: Development of one way nested global-regional air quality forecasting model. SOLA, 3, 81–84.
  53. Tolk, L. F., A. G. C. A. Meesters, A. J. Dolman, and W. Peters. "Modelling representation errors of atmospheric CO 2 mixing ratios at a regional scale." Atmospheric chemistry and physics 8, no. 22 (2008): 6587-6596.
  54. Tolk, L. F., A. J. Dolman, A. G. C. A. Meesters, and Wouter Peters. "A comparison of different inverse carbon flux estimation approaches for application on a regional domain." Atmospheric Chemistry and Physics 11, no. 20 (2011): 10349-10365.
  55. Turner, A. J., Frankenberg, C., Wennberg, P. O., and Jacob, D. J. (2017). Ambiguity in the causes for decadal trends in atmospheric methane and hydroxyl. Proceedings of the National Academy of Sciences, 114(21):5367–5372.
  56. Verkaik, Joost, and Laurens GANZEVELD. Evaluation of Colombian Methane Emissions Combining WRF-Chem and TROPOMI. 2019.
  57. Vogel, Felix R., Balendra Thiruchittampalam, Jochen Theloke, Roberto Kretschmer, Christoph Gerbig, Samuel Hammer, and Ingeborg Levin. "Can we evaluate a fine-grained emission model using high-resolution atmospheric transport modelling and regional fossil fuel CO2 observations?" Tellus B: Chemical and Physical Meteorology 65, no. 1 (2013): 18681.
  58. Walter, B. P. and Heimann, M. (2000). A process-based, climate-sensitive model to derive methane emissions from natural wetlands: Application to five wetland sites, sensitivity to model parameters, and climate. Global Biogeochemical Cycles, 14(3): 745–765.
  59. Walter, Bernadette P., and Martin Heimann. "A process‐based, climate‐sensitive model to derive methane emissions from natural wetlands: Application to five wetland sites, sensitivity to model parameters, and climate." Global Biogeochemical Cycles 14, no. 3 (2000): 745-765.
  60. Yokota, T., Y. Yoshida, N. Eguchi, Y. Ota, T. Tanaka, H. Watanabe, and S. Maksyutov. "Global concentrations of CO2 and CH4 retrieved from GOSAT: First preliminary results." Sola 5 (2009): 160-163.
  61. Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., and Maksyutov, S. (2009). Global concentrations of co2 and ch4 retrieved from gosat: First preliminary results. Sola, 5:160–163.
  62. Yoshida, Y., Y. Ota, N. Eguchi, N. Kikuchi, K. Nobuta, H. Tran, I. Morino, and T. Yokota. "Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite." Atmospheric Measurement Techniques 4, no. 4 (2011): 717-734.