Coupling the Town Energy Balance (TEB) Scheme with the COSMO Atmospheric Model: Evaluation Against a Bulk Parameterization (TERRA_URB) for the Moscow Megacity
https://doi.org/10.24057/2071-9388-2025-3975
Abstract
Numerical weather prediction (NWP) models, coupled with urban parameterizations, play a crucial role in understanding and forecasting meteorological conditions within urban environments. In the mesoscale NWP model COSMO, only one urban parameterization, TERRA_URB, is available in the model’s operational version. TERRA_URB describes the city as a flat surface with modified physical properties in accordance with the urban canyon geometry. In this study, we have coupled the latest version 6.0 of the COSMO atmospheric model with a more sophisticated urban canopy model, TEB (Town Energy Balance), which explicitly simulates the energy exchange between the facets of the urban canyon. Here, we present the coupling approach and assessment of the model’s sensitivity to urban schemes of different complexity (TEB and TERRA_URB) over the Moscow region for August 2022. Despite using the same external parameters for both schemes, simulations demonstrate notable differences in modeled temperature, with TEB generally producing lower nighttime and morning temperatures. This leads to a greater underestimation of the urban heat island intensity in TEB when compared with the observations but improves the modeled diurnal cycle of the urban temperature. We attribute the observed temperature discrepancies to the different descriptions of heat conductivity and storage within urban surfaces. Although there are no clear advantages to using a more complex parameterization in terms of model air temperature errors, TEB offers more options to fine-tune input parameters and takes into account additional processes, in particular those associated with building heating and cooling, as well as with urban green infrastructure.
Keywords
urban parameterizations,
urban climate,
atmospheric models,
urban heat island,
Moscow agglomeration,
COSMO
Supporting agencies: Adaptation and testing of the TEB urban canopy model for conditions of Moscow was supported by the Russian Science Foundation № 24-17-00155. Coupling between TEB and COSMO was supported under the state assignment of Lomonosov Moscow State University. Preparation of the input parameters for urban canopy models was carried out with the financial support of the Russian Ministry of Science and Higher Education, agreement № 075-15-2019- 1621. Supercomputer simulations and model evaluation were supported by the Federal Service for Hydrometeorology and Environmental Monitoring of Russia (topic № 125032004255-7).
About the Authors
Maria A. Tarasova
Faculty of Geography, Lomonosov Moscow State University; Hydrometeorological Research Center of Russia; Research Computing Center, Lomonosov Moscow State University; Moscow Center for Fundamental and Applied Mathematics
Russian Federation
Russian Federation
Leninskie Gory, 1, Moscow, 119991
B. Predtechenskiy Pereulok, 11-13, Moscow, 123242
Leninskie Gory, 1, bld.4, Moscow, 119991
Leninskie Gory, 1, bld.4, Moscow, 119991
Mikhail I. Varentsov
Faculty of Geography, Lomonosov Moscow State University; Hydrometeorological Research Center of Russia; Research Computing Center, Lomonosov Moscow State University; Moscow Center for Fundamental and Applied Mathematics
Russian Federation
Russian Federation
Leninskie Gory, 1, Moscow, 119991
B. Predtechenskiy Pereulok, 11-13, Moscow, 123242
Leninskie Gory, 1, bld.4, Moscow, 119991
Leninskie Gory, 1, bld.4, Moscow, 119991
Andrey V. Debolskiy
Research Computing Center, Lomonosov Moscow State University; Moscow Center for Fundamental and Applied Mathematics; A.M. Obukhov Institute of Atmospheric Physics
Russian Federation
Russian Federation
Leninskie Gory, 1, bld.4, Moscow, 119991
Leninskie Gory, 1, bld.4, Moscow, 119991
3 Pyzhyovskiy Pereulok, Moscow, 119017
Victor M. Stepanenko
Faculty of Geography, Lomonosov Moscow State University; Research Computing Center, Lomonosov Moscow State University
Russian Federation
Russian Federation
Leninskie Gory, 1, Moscow, 119991
References
1. Arya S.P. (1988). Introduction to Micrometeorology, Academic, New York.
2. Best M. J. (2005). Representing urban areas within operational numerical weather predictionmodels. Bound.-Layer Meteor., 114, 91–109.
3. Bornstein R.D. (1968). Observations of the Urban Heat Island Effect in New York City. J. Appl. Meteorol., 7, 575–582.
4. Buchhorn M., Lesiv M., Tsendbazar N.E., Herold M., Bertels L., and Smets B. (2020). Copernicus global land cover layers-collection 2. Remote Sens., 12, 1–14, DOI: 10.3390/rs12061044.
5. Bueno B., Pigeon G., Norford L. K., Zibouche K., and Marchadier C. (2012). Development and evaluation of a building energy model integrated in the TEB scheme. Geoscientific Model Development, 5(2), 433–448, DOI: 10.5194/gmd-5-433-2012.
6. de Munck C., Lemonsu A., Bouzouidja R., Masson, V., and Claverie R. (2013). The GREENROOF module (v7.3) for modelling green roof hydrological and energetic performances within TEB. Geoscientific Model Development Discussions, 6, DOI: 10.5194/gmdd-6-1127-2013.
7. Doms G. et al. (2021). A description of the Nonhydrostatic Regional COSMO-Model. Part II: physical parameterizations. Deutscher Wetterdienst Publisher, 171 pp.
8. Ek M. B., Mitchell K. E., Lin Y., Rogers E., Grunmann P., Koren V., Gayno G., and Tarpley J. D. (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. Journal of Geophysical Research, 108(D22), 8851, DOI: 10.1029/2002JD003296.
9. Fortuniak K. (2007). Numerical estimation of the effective albedo of an urban canyon. Theor. Appl. Climatol., 91, 245–258, DOI: 10.1007/s00704-007-0312-6.
10. Frolkis V.A., Evsikov I.A., Ginzburg, A.S. (2024). Modeling Anthropogenic Heat Flux during the Heating Season in Large Cities of the Russian Federation. Izv. Atmos. Ocean. Phys., 60, 407–420, DOI: 10.1134/S0001433824700361.
11. Garbero V., Milelli M., Bucchignani E., Mercogliano P., Varentsov M., Rozinkina I., Rivin G., Blinov D., Wouters H., Schulz J.–P., Schättler U., Bassani F., Demuzere M., and Repola F. (2021). Evaluating the urban canopy scheme TERRA_URB in the COSMO model for selected European cities. Atmosphere, 12(2), DOI: 10.3390/atmos12020237.
12. Garuma G. (2018). Review of urban surface parameterizations for numerical climate models. Urban Climate, 24, 830–851, DOI: 10.1016/j.uclim.2017.10.006.
13. Ginzburg A.S., Dokukin S.A. (2021). Influence of Thermal Air Pollution on the Urban Climate (Estimates Using the COSMO-CLM Model). Izv. Atmos. Ocean. Phys., 57, 47–59, DOI: 10.1134/S0001433821010059.
14. Grimmond C. S. B. and Oke T. (1991). An Evapotranspiration–Interception Model for Urban Areas. Water Resour. Res., 27, 1739–1755, DOI: 10.1029/91WR00557.
15. Grimmond C.S.B., Blackett M., Best M., Barlow J., Baik J.–J., Belcher S., Bohnenstengel S.I., Calmet I., Chen F., Dandou A., Fortuniak K., Gouvea M.L., Hamdi R., Hendry M., Kondo H., Krayenhoff S., Lee S.–H., Loridan T., Martilli A., Masson V., Miao S., Oleson K., Pigeon G., Porson A., Salamanca F., Shashua–Bar L., Steeneveld G.–J., Tombrou M., Voogt J., and Zhang N. (2010). The international urban energy balance models comparison project: First results from Phase 1. Journal of Applied Meteorology and Climatology, 49, 1268–1292, DOI: 10.1175/2010JAMC2354.1.
16. Heise E., Ritter B., and Schrodin R. (2006). Operational implementation of the multilayer soil model. COSMO Technical Reports No. 9, Consortium for Small-Scale Modelling, 19pp.
17. Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc., 146. 1999–2049, DOI: 10.1002/qj.3803.
18. Kanda M., Kanega M., Kawai T., Moriwaki R., and Sugawara H. (2007). Roughness lengths for momentum and heat derived from outdoor urban scale models. J. Appl. Meteorol. Climatol., 46(7), 1067–1079, DOI: 10.1175/JAM2500.1.
19. Khaikine M.N. Kuznetsova I.N. Kadygrov E.N. Miller E.A. (2006). Investigation of temporal-spatial parameters of an urban heat island on the basis of passive microwave remote sensing. Theor. Appl. Climatol., 84, 161–169.
20. Kusaka H., Kondo H., Kikegawa Y., and Kimura F. (2001). A simple single–layer urban canopy model for atmospheric models: Comparison with multi–layer and slab models. Boundary–Layer Meteorology, 101(3), 329–358, DOI: 10.1023/A:1019207923078.
21. Kuznetsova I. N., Semutnikova E. G., Lezina E. A., Zakharova P. V., Tkacheva Yu. V., Varentsov M. I., Tarasova M. A., Rivin G. S., and Khrykina E. A. (2024). Characteristics of the Moscow Heat Island and Quality of Its Simulation with the COSMO-Ru1-MSK Model Based on the Mosecomonitoring Observations. Russ. Meteorol. Hydrol., 49(9), 795–810.
22. Lean H. W., Theeuwes N. E., Baldauf M., Barkmeijer J., Bessardon G., Blunn L., et al. (2024). The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100-m scale weather and climate models. Quarterly Journal of the Royal Meteorological Society, 150(765), 4671–4708. DOI: 10.1002/QJ.4858.
23. Lemonsu A., Grimmond C. S. B., and Masson V. (2004). Modeling the Surface Energy Balance of the Core of an Old Mediterranean City: Marseille. J. Appl. Meteorol., 43, 312–327, DOI: 10.1175/1520-0450(2004)043<0312:MTSEBO>2.0.CO;2.
24. Lemonsu A., Masson V., Shashua–Bar L., Erell E., and Pearlmutter D. (2012). Inclusion of vegetation in the Town Energy Balance model for modelling urban green areas. Geosci. Model Dev., 5, 1377–1393, DOI: 10.5194/gmd–5–1377–2012.
25. Liu Y., Chen F., Warner T., and Basara J. (2006). Verification of a mesoscale data assimilation and forecasting system for the Oklahoma city area during the joint urban 2003 field project. J. Appl. Meteorol., 45, 912–929.
26. Lokoshchenko M.A., Korneva I.A., Kochin A.V., Dubovetsky A.Z., Novitsky M.A., and Razin P.Ye. (2016). Vertical extension of the urban heat island above Moscow. Dokl. Earth Sc., 466, 70–74, DOI: 10.1134/S1028334X16010128.
27. Lokoshchenko M. A., Enukova E. A., Alekseeva L. I. (2023). Modern changes of the urban heat island in Moscow. Doklady Earth Sciences, 511(2), 716–725.
28. Martilli A., Clappier A., and Rotach M. W. (2002). An Urban Surface Exchange Parameterisation for Mesoscale Models. Bound.–Lay. Meteorol., 104, 261–304, DOI: 10.1023/A:1016099921195.
29. Masson V. A (2000). Physically–Based Scheme For The Urban Energy Budget In Atmospheric Models. Bound.–Lay. Meteorol., 94, 357–397, DOI: 10.1023/A:1002463829265.
30. Masson V., Grimmond C. S. B., and Oke T. R. (2002). Evaluation of the Town Energy Balance (TEB) Scheme with Direct Measurements from Dry Districts in Two Cities. J. Appl. Meteorol., 41, 1011– 1026, DOI: 10.1175/1520-0450(2002)041<1011:EOTTEB>2.0.CO;2.
31. Masson, V. (2006). Urban surface modeling and the meso–scale impact of cities. Theor. Appl. Climatol., 84, 35–45, DOI: 10.1007/s00704-005-0142-3.
32. Masson V., Le Moigne P., Martin E., Faroux S., Alias A., Alkama R., Belamari S., Barbu A., Boone A., Bouyssel F., Brousseau P., Brun E., Calvet J.–C., Carrer D., Decharme B., Delire C., Donier S., Essaouini K., Gibelin A.–L., Giordani H., Habets F., Jidane M., Kerdraon G., Kourzeneva E., Lafaysse M., Lafont S., Lebeaupin Brossier C., Lemonsu A., Mahfouf J.–F., Marguinaud P., Mokhtari M., Morin S., Pigeon G., Salgado R., Seity Y., Taillefer F., Tanguy G., Tulet P., Vincendon B., Vionnet V., and Voldoire A. (2013). The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geoscientific Model Development, 6, 929–960, DOI: 10.5194/gmd–6–929–2013.
33. Masson V., Bonhomme M., Salagnac J.‐L., Briottet X., and Lemonsu A. (2014). Solar panels reduce both global warming and urban heat island. Frontiers in Environmental Science, 2, DOI: 10.3389/fenvs.2014.00014.
34. Meyer D., Schoetter R., Riechert M., Verrelle A., Tewari M., Dudhia J., Masson V., van Reeuwijk M., and Grimmond S. (2020). WRF-TEB: Implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model. Journal of Advances in Modeling Earth Systems, 12, e2019MS001961, DOI: 10.1029/2019MS001961.
35. Mussetti G., Brunner D., Henne S., Allegrini J., Krayenhoff E., Schubert S., Feigenwinter, C., Vogt R., Wicki A., and Carmeliet J. (2020). COSMO–BEP–Tree v1.0: a coupled urban climate model with explicit representation of street trees. Geoscientific Model Development, 13(3), 1685–1710, DOI: 10.5194/gmd-13-1685-2020.
36. Nunez M. and Oke T. R. (1977). The Energy Balance of an Urban Canyon. Journal of Applied Meteorology, 16, 11–19, DOI: 10.1175/1520-0450(1977)016<0011:TEBOAU>2.0.CO;2.
37. Oke T., Mills G., Christen A., and Voogt J. (2017). Urban Climates. Cambridge: Cambridge University Press., 548 p. DOI: 10.1017/9781139016476.
38. Platonov V.S., Varentsov M.I., Yarinich Y.I., Shikhov A.N., and Chernokulsky A.V. (2024). A large mid-latitude city intensifies severe convective events: Evidence from long-term high-resolution simulations. Urban Climate, 54, DOI: 10.1016/j.uclim.2024.101837.
39. Porson A., Clark P. A., Harman I. N., Best, M. J., and Belcher, S. E. (2010). Implementation of a new urban energy budget scheme in the MetUM. Part I: Description and idealized simulations. Quarterly Journal of the Royal Meteorological Society, 136(651), 1514–1529, DOI: 10.1002/qj.668.
40. Ritter B. and Geleyn J. (1992). A Comprehensive Radiation Scheme for Numerical Weather Prediction Models with Potential Applications in Climate Simulations. Mon. Wea. Rev., 120, 303–325, DOI: 10.1175/1520-0493(1992)120<0303:ACRSFN>2.0.CO;2.
41. Rivin G.S., Vil’fand R.M., Kiktev D.B., Rozinkina I.A., Tudriy K.O., Blinov D.V., Varentsov M.I., Samsonov T.E., Bundel’ A.Y., Kirsanov A.A., and Zakharchenko D.I. (2019). The system for numerical prediction of weather events (including severe ones) for Moscow megacity: The prototype development. Russian Meteorology and Hydrology, 44(11), 33–45, DOI: 10.3103/S1068373919110025.
42. Rivin G.S., Rozinkina I.A., Vil’fand R.M., Kiktev D.B., Tudrii K.O., Blinov D.V., Varentsov M.I., Zakharchenko D.I., Samsonov T.E., Repina I.A., and Artamonov A. Yu. (2020). Development of the High–Resolution Operational System for Numerical Prediction of Weather and Severe Weather Events for The Moscow Region. Russ. Meteorol. Hydrol., 45, 455–465, DOI: 10.3103/s1068373920070018.
43. Rockel B., Will A., and Hense A. (2008). The regional climate model COSMO-CLM (CCLM). Meteorol. Z., 17, 347–348, DOI: 10.1127/0941-2948/2008/0309.
44. Rotach M. (1995). Profiles of turbulence statistics in and above an urban street canyon. Atmos. Environ., 29, 1473–1486.
45. Rowley F.B., Algren A.B., and Blackshaw J.L. (1930). Surface Conductances as Affected by Air Velocity, Temperature and Character of Surface. ASHRAE Trans., 36, 429–446.
46. Rowley F.B., and Eckley W. (1932). A. Surface Coefficients as Affected by Wind Direction. ASHRAE Trans., 38, 33–46.
47. Salamanca F., Georgescu M., Mahalov A., Moustaoui M., and Wang M. (2014). Anthropogenic heating of the urban environment due to air conditioning. J. Geophys. Res. Atmos., 119, 5949–5965, DOI:10.1002/2013JD021225.
48. Samsonov T.E., and Varentsov M.I. (2020). Computation of City-descriptive parameters for high-resolution numerical weather prediction in Moscow megacity in the framework of the COSMO model. Russ. Meteorol. Hydrol., 45, 515–521, DOI: 10.3103/S1068373920070079.
49. Sarkar A. and De Ridder K. (2010). The Urban Heat Island Intensity of Paris: A Case Study Based on a Simple Urban Surface Parametrization. Bound.-Lay. Meteorol., 138, 511–520, DOI: 10.1007/s10546-010-9568-y.
50. Schoetter R., Kwok Y., de Munck C., Lau K., Wong W., and Masson V. (2020). Multi–layer coupling between SURFEX–TEB–V9.0 and Meso–NH–v5.3 for modelling the urban climate of high–rise cities. Geoscientific Model Development, 13(11), 5609–5643, DOI: 10.5194/gmd-13-5609-2020.
51. Schubert S., Grossman–Clarke S., and Martilli A. (2012). A Double–Canyon Radiation Scheme for Multi–Layer Urban Canopy Models. Boundary–Layer Meteorology, 145(3), 439–468, DOI: 10.1007/s10546-012-9728-3.
52. Schubert S. and Grossman–Clarke S. (2014). Evaluation of the coupled COSMO–CLM/DCEP model with observations from BUBBLE. Q. J. R. Meteorol Soc., 140(685), 2465–2483, DOI: 10.1002/qj.2311.
53. Schrodin R. and Heise E. (2001). The Multi-Layer Version of the DWD Soil Model TERRA LM. Tech. Rep. 2, Consortium for Small-Scale Modelling, 1–16.
54. Schulz J. and Vogel G. (2020). Improving the Processes in the Land Surface Scheme TERRA: Bare Soil Evaporation and Skin Temperature. Atmosphere, 11, 513, DOI: 10.3390/atmos11050513.
55. Stewart I.D. and Oke T.R. (2012). Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc., 93, 1879–1900, DOI: 10.1175/BAMS-D-11-00019.1.
56. Tarasova M. A., Varentsov M. I., and Stepanenko V. M. (2023). Parameterization of the interaction between the atmosphere and the urban surface: Current state and prospects. Izvestiya - Atmospheric and Oceanic Physics, 59(2), 127–148, DOI: 10.1134/S0001433823020068.
57. Tarasova M.A., Debolskiy A.V., Mortikov E.V., Varentsov M.I., Glazunov A.V., Stepanenko V.M. (2024). On the Parameterization of the Mean Wind Profile for Urban Canopy Models. Lobachevskii Journal of Mathematics, 45(7), 3198–3210.
58. Tiedke M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Weath. Rev., 117, 1779–1800, DOI: 10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
59. Trusilova K., Früh B., Brienen S., Walter A., Masson V., Pigeon G., and Becker P. (2013). Implementation of an Urban Parameterization Scheme into the Regional Climate Model COSMO–CLM. Journal of Applied Meteorology and Climatology, 52(10), 2296–2311, DOI: 10.1175/JAMC-D-12-0209.1.
60. Varentsov M.I., Samsonov T.E., Kislov A.V., and Konstantinov P.I. (2017). Simulations of Moscow agglomeration heat island within the framework of the regional climate model COSMO-CLM. Moscow University Vestnik. Series 5. Geography, 6, 25-37 (in Russian with English summary).
61. Varentsov M., Wouters H., Platonov V., and Konstantinov P. (2018). Megacity–Induced Mesoclimatic Effects in the Lower Atmosphere: A Modeling Study for Multiple Summers over Moscow, Russia. Atmosphere, 9(2), 50, DOI: 10.3390/atmos9020050.
62. Varentsov M. I., Grishchenko M. Y., and Wouters H. (2019). Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY, 12(4), 74–95, DOI: 10.24057/2071-9388-2019-10.
63. Varentsov M., Samsonov T., and Demuzere M. (2020). Impact of urban canopy parameters on a megacity’s modelled thermal environment. Atmosphere, 11(2), 1349, DOI: 10.3390/atmos11121349.
64. Varentsov M., Vasenev V., Dvornikov Y., Samsonov T., and Klimanova O. (2023) Does size matter? Modelling the cooling effect of green infrastructures in a megacity during a heat wave. Science of The Total Environment, 902, 165966, DOI: 10.1016/j.scitotenv.2023.165966.
65. Wouters H., Demuzere M., De Ridder K., and van Lipzig N.P. (2015). The impact of impervious water–storage parametrization on urban climate modeling. Urban Climate, 11, 24–50, DOI: 10.1016/j.uclim.2014.11.005.
66. Wouters H., Demuzere M., Blahak U., Fortuniak K., Maiheu B., Camps J., Tielemans D., and van Lipzig N.P. (2016). The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modeling: description and application with the COSMO–CLM model for a Belgian summer. Geoscientific Model Development, 9(9), 3027–3054, DOI: 10.5194/gmd-9-3027-2016.
67. Zanaga D., Van De Kerchove R., De Keersmaecker W., Souverijns N., Brockmann C., Quast R., Wevers J., Grosu A., Paccini A., Vergnaud S., Cartus O., Santoro M., Fritz S., Georgieva I., Lesiv M., Carter S., Herold M., Li Linlin, Tsendbazar N.E., Ramoino F., and Arino O. (2012). ESA WorldCover 10 m 2020 v100. DOI: 10.5281/zenodo.5571936.
Review
For citations:
Tarasova M.A., Varentsov M.I., Debolskiy A.V., Stepanenko V.M. Coupling the Town Energy Balance (TEB) Scheme with the COSMO Atmospheric Model: Evaluation Against a Bulk Parameterization (TERRA_URB) for the Moscow Megacity. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(3):118-134. https://doi.org/10.24057/2071-9388-2025-3975
Views:
8
Email this article 