Variogram as a tool for assessing the quality of climate models
DOI:
https://doi.org/10.26512/ripe.v2i16.21612Palavras-chave:
Variogram. Geostatistics. Climate model. Model quality assessment.Resumo
Climate models are very sensitive to spatial resolution. Their skill must always be verified, as they involve several phenomena which take place in different scales. For that reason, some of those phenomena must be adequately parameterized, with appropriate techniques of upscaling. The proposal of this work is to present the variogram as a tool for assessing the quality of climate models, based on comparison of model results with different spatial discretization. Results of the ETA Model from INPE are presented in two different levels of discretisation: for resolutions higher than 5 km, to which non-hydrostatic models must be taken into account, and for resolution lower than 8 km, to which hydrostatic models are suited. Variograms for 36 km, 18 km, 4 km, 2 km and 1 km are calculated and their results are discussed, together with other metrics for quality assessment of forecast models. Variograms showed that there is an impact of grid coarseness over these numerical models, which was less noticeable in plots of precipitations for coarser grids.
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Referências
Armstrong, M., 1998, Basic Linear Geostatistics, Springer Verlag, Berlin.
Betts, A. K. & Miller, M. J., 1986, A new convective adjustment scheme. part ii: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets, Quarterly Journal of the Royal Meteorological Society, vol. 112, pp. 693”“709.
Bras, R. L. & Rodríguez-Iturbe, I., 1993, Random Functions and Hydrology, Dover Publications, New York.
Chou, S. C., Lyra, A., Mourão, C., Dereczynski, C., Pilotto, I., J. Gomes, J. B., Tavares, P., Silva, A., Rodrigues, D., Campos, D., Chagas, D., Sueiro, G., Siqueira, G., Nobre, P. & Marengo, J., 2014, Evaluation of the eta simulations nested in three global climate models, American Journal of Climate Change, vol. 3, n. 5, pp. 438”“454.
Cook, J., Oreskes, N., Doran, P. T., Anderegg,W. R. L., Verheggen, B., Maibach, E.W., Carlton, J. S., Lewandowsky, S., Skuce, A. G., Green, S. A., Nuccitelli, D., Jacobs, P., Richardson, M., Winkler, B., Painting, R. & Rice, K., 2016, Consensus on consensus: a synthesis of consensus estimates on human-caused global warming, Environmental Research Letters, vol. 11, n. 4, pp. 048002. URL: http://stacks.iop.org/1748-9326/11/i=4/a=048002
Cox, P. M., 2001, Description of the “TRIFFID” dynamic global vegetation model. Hadley Centre Technical Note 24.
Ek, M., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G. & 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, vol. 108, pp. 1”“16.
Fels, S. B. & Schwarzkopf, M. D., 1975, The simplified exchange approximation: A new method for radiative transfer calculations, Journal of the Atmospheric Sciences, vol. 32, pp. 1475”“1488.
Janji´c, Z. I., 1984, Nonlinear advection schemes and energy cascade on semi-staggered grids, Monthly Weather Review, vol. 112, pp. 1234”“1245.
Janji´c, Z. I., 1994, The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes, Monthly Weather Review, vol. 122, pp. 927”“945.
Jolliffe, I. T. & Stephenson, D. B., 2003, Forecast verification: a practitioner’s guide in atmospheric science, John Wiley & Sons, Chichester, UK.
Kalnay, E., 2003, Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, Cambridge, UK.
Lacis, A. A. & Hansen, J., 1974, A parameterization for the absorption of solar radiation in the earth’s atmosphere, Journal of the Atmospheric Sciences, vol. 31, pp. 118”“133.
Lantuéjoul, C., 1994, Nonconditional simulation of stationary isotropic multigaussian random functions, in M. Armstrong & P. Dowd (eds), Geostatistical Simulations, Kluwer Academic Publishers, Dordrecht, pp. 147”“177.
Lou, A. P. F., 2004, Modelagem geoestatística aplicada à integração entre dados de postos pluviométricos e radar meteorológico, Master’s thesis, COPPE-UFRJ, Rio de Janeiro.
Martin, G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., Hinton, T. J., Jones, C. D., McDonald, R. E., McLaren, A. J., O’Connor, F. M., Roberts, M. J., Rodriguez, J. M., Woodward, S., Best, M. J., Brooks, M. E., Brown, A. R., Butchart, N., Dearden, C., Derbyshire, S. H., Dharssi, I., Doutriaux-Boucher, M., Edwards, J. M., Falloon, P. D., Gedney, N., Gray, L. J., Hewitt, H. T., Hobson, M., Huddleston, M. R., Hughes, J., Ineson,
S., Ingram,W. J., James, P. M., Johns, T. C., Johnson, C. E., Jones, A., Jones, C. P., Joshi, M. M., Keen, A. B., Liddicoat, S., Lock, A. P., Maidens, A. V., Manners, J. C., Milton, S. F., Rae, J. G. L., Ridley, J. K., Sellar, A., Senior, C. A., Totterdell, I. J., Verhoef, A., Vidale, P. L., & Wiltshire, A., 2011, The HadGEM2 family of met office unified model climate configurations. geoscientific model development, , vol. 4, pp. 723”“757.
Matheron, G., 1965, Les variables régionalisées et leur estimation: une application de la théorie des fonctions aléatoires aux sciences de la nature, Masson, Paris.
Mesinger, F., 1984, A blocking technique for representation of mountains in atmospheric models, Rivista di Meteorologia Aeronautica, vol. 44, pp. 195”“202.
Mesinger, F., Chou, S. C., Gomes, J. L., Jovic, D., Bastos, P., Bustamante, J. F., Lazic, L., Lyra, A. A., Morelli, S., Ristic, I. & Veljovic, K., 2012, An upgraded version of the eta model, Meteorology and Atmospheric Physics, vol. 116, pp. 63”“79.
Nobre, P., Siqueira, L. S. P., de Almeida, R. A. F., Malagutti, M., Giarolla, E., Castelão, G. P., Bottino, . J., Kubota, P., Figueroa, S. N., Costa, M. C., Baptista, M., Irber, L. & Marcondes, G. G., 2013, Climate simulation and change in the brazilian climate model, Journal of Climate, vol. 26, pp. 6716”“6732.
Wackernagel, H., 2003, Multivariate Geostatistics ”“ An Introduction with Applications, 3rd edn, Springer Verlag, Berlin.
Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., Takemura, T., Chikira, M., Ogura, T., Sekiguchi, M., Takata, K., Yamazaki, D., Yokohata, T., Nozawa, T., Hasumi, H., Tatebe, H. & Kimoto, M., 2010, Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity, Journal of Climate, vol. 23, pp. 6312”“6335.
Zhao, Q., Black, T. L. & Baldwin, M. E., 1997, Implementation of the cloud prediction scheme in the eta model at NCEP, Weather and Forecasting, vol. 12, pp. 697”“712.
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