Maps of the long-term mean precipitation involving local landscape variables were generated for the Baltic countries, and the effectiveness of seven modelling methods was compared. The precipitation data were recorded in 245 meteorological stations in 1966–2005, and 51 location-related explanatory variables were used. The similarity-based reasoning in the Constud software system outperformed other methods according to the validation fit, except for spring. The multivariate adaptive regression splines (MARS) was another effective method on average. The inclusion of landscape variables, compared to reverse distance-weighted interpolation, highlights the effect of uplands, larger water bodies and forested areas. The long-term mean amount of precipitation, calculated as the station average, probably underestimates the real value for Estonia and overestimates it for Lithuania due to the uneven distribution of observation stations.
Algar, A. C., Kharouba, H. M., Young, E. R. & Kerr, J. T. 2009. Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods. Ecography, 32, 22–33.
http://dx.doi.org/10.1111/j.1600-0587.2009.05832.x
Ali, A. 1998. Nonparametric spatial rainfall characterization using adaptive kernel estimator. Journal of Geographic Information and Decision Analysis, 2, 34–43.
Basist, A., Bell, G. D. & Meentemeyer, V. 1994. Statistical relationships between topography and precipitation patterns. Journal of Climate, 7, 1305–1315.
http://dx.doi.org/10.1175/1520-0442(1994)007<1305:SRBTAP>2.0.CO;2
Bergström, S. & Carlsson, B. 1994. River runoff to the Baltic Sea: 1950–1990. Ambio, 23, 280–287.
Daly, C., Neilson, R. P. & Phillips, D. L. 1994. A statistical topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology, 33, 140–158.
http://dx.doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2
Dibike, Y. B. & Coulibaly, P. 2006. Temporal neural networks for downscaling climate variability and extremes. Neural Networks Archive, 19, 135–144.
http://dx.doi.org/10.1016/j.neunet.2006.01.003
Elith, J., Leathwick, J. R. & Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.
http://dx.doi.org/10.1111/j.1365-2656.2008.01390.x
Galvonaitė, A., Misiūnienė, M., Valiukas, D. & Buitkuvienė, M. S. 2007. Lietuvos klimatas [Lithuanian Climate]. Lietuvos hidrometeorologijos tarnyba, Vilnius, 180 pp. [in Lithuanian].
Goodale, C. L., Aber, J. D. & Ollinger, S. V. 1998. Mapping monthly precipitation, temperature, and solar radiation for Ireland with polynomial regression and a digital elevation model. Climate Research, 10, 35–49.
http://dx.doi.org/10.3354/cr010035
Hastie, T., Tibshirani, R. & Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer, 745 pp.
Jaagus, J. 1999. New data about the climate of Estonia. Publicationes Instituti Geographici Universitatis Tartuensis, 85, 28–38 [in Estonian, with English summary].
Jaagus, J. & Tarand, A. 1988. Spatial distribution of precipitation in Estonia. Yearbook of the Estonian Geographical Society, 24, 5–16 [in Estonian, with English summary].
Jaagus, J., Briede, A., Rimkus, E. & Remm, K. 2010. Precipitation pattern in the Baltic countries under the influence of large-scale atmospheric circulation and local landscape factors. International Journal of Climatology, 29, 705–720.
Kondrashov, D., Shen, J., Berk, R., D’Andrea, F. & Ghil, M. 2007. Predicting weather regime transitions in Northern Hemisphere datasets. Climate Dynamics, 29, 535–551.
http://dx.doi.org/10.1007/s00382-007-0293-2
Linder, M., Jakobson, L. & Absalon, E. 2010. The effect of distance correction factor in case-based predictions of vegetation classes in Karula, Estonia. In International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. Proceedings of the Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science, Hong Kong 26.–28. May 2010 (Guilbert, E., Lees, B. & Leung, Yee, eds), pp. 570–574. International Society of Photogrammetry and Remote Sensing.
Liu, H., Chandrasekar, V. & Xu, G. 2001. An adaptive neural network scheme for radar rainfall estimation from WSR-88D observations. Journal of Applied Meteorology and Climatology, 40, 2038–2050.
http://dx.doi.org/10.1175/1520-0450(2001)040<2038:AANNSF>2.0.CO;2
Lucio, P. S., Conde, F. C., Cavalcanti, I. F. A., Serrano, A. I., Ramos, A. M. & Cardoso, A. O. 2007. Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil. Advances in Geosciences, 10, 67–76.
http://dx.doi.org/10.5194/adgeo-10-67-2007
Maimon, O. & Rokach, L. 2005. Data Mining and Knowledge Discovery Handbook. Springer, New York, 1383 pp.
http://dx.doi.org/10.1007/b107408
Michaelides, S. C., Pattichis, C. S. & Kleovoulou, G. 2001. Classification of rainfall variability by using artificial neural networks. International Journal of Climatology, 21, 1401–1414.
http://dx.doi.org/10.1002/joc.702
Moral, F. J. 2010. Comparison of different geostatistical approaches to map climate variables: application to precipitation. International Journal of Climatology, 30, 620–631.
Moser, G. & Serpico, S. B. 2009. Automatic parameter optimization for support vector regression for land and sea surface temperature estimation from remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 47, 909–921.
http://dx.doi.org/10.1109/TGRS.2008.2005993
Ninyerola, M., Pons, X. & Roure, J. M. 2006. Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System. Theoretical and Applied Climatology, 89, 195–209.
http://dx.doi.org/10.1007/s00704-006-0264-2
Nisbet, R., Elder, I. V. J. & Miner, G. 2009. Handbook of Statistical Analysis and Data Mining Applications. Elsevier, Amsterdam, 824 pp.
Omstedt, A., Meuller, L. & Nyberg, L. 1997. Interannual, seasonal and regional variations of precipitation and evaporation over the Baltic Sea. Ambio, 26, 484–492.
Remm, K. 2005. Correlations between forest stand diversity and landscape pattern in Otepää NP, Estonia. Journal for Nature Conservation, 13, 137–145.
http://dx.doi.org/10.1016/j.jnc.2005.02.001
Remm, M. & Remm, K. 2008. Case-based estimation of the risk of enterobiasis. Artificial Intelligence in Medicine, 43, 167–177.
http://dx.doi.org/10.1016/j.artmed.2008.04.002
Remm, K. & Remm, L. 2009. Similarity-based large-scale distribution mapping of orchids. Biodiversity and Conservation, 18, 1629–1647.
Remm, K. & Remm, M. 2010. Geographical aspects of enterobiasis in Estonia. Health & Place, 16, 291–300.
http://dx.doi.org/10.1016/j.healthplace.2009.10.012
Remm, K., Linder, M. & Remm, L. 2009. Relative density of finds for assessing similarity-based maps of orchid occurrence. Ecological Modelling, 220, 294–309.
http://dx.doi.org/10.1016/j.ecolmodel.2008.10.017
Rutgersson, A., Bumke, K., Clemens, M., Foltescu, V., Lindau, R., Michelson, D. & Omstedt, A. 2001. Precipitation estimates over the Baltic Sea: present state of the art. Nordic Hydrology, 32, 285–314.
Sokol, Z. & Bližňák, V. 2009. Areal distribution and precipitation–altitude relationship of heavy short-term precipitation in the Czech Republic in the warm part of the year. Atmospheric Research, 94, 652–662.
http://dx.doi.org/10.1016/j.atmosres.2009.03.001
Tamm, T., Remm, K. & Proosa, H. 2010. LSTATS software and its application. In Proceedings of the Seventh IASTED International Conference: Signal Processing, Pattern Recognition and Applications; Innsbruck, Austria; 17.–19.02.2010 (Zagar, B., Kuijper, A. & Sahbi, H., eds), pp. 317–324. ACTA Press.
Tan, P.-N., Steinbach, M. & Kumar, V. 2006. Introduction to Data Mining. Pearson, Boston, 769 pp.
Temnikova, N. S. 1958. Klimat Latvijskoj SSR [Climate of the Latvian SSR]. Gidrometeoizdat, Riga, 232 pp. [in Russian].
Tripathi, S., Srinivas, V. V. & Nanjundiah, R. S. 2006. Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology, 330, 621–640.
http://dx.doi.org/10.1016/j.jhydrol.2006.04.030
Wei, H., Li, J.-L. & Liang, T.-G. 2005. Study on the estimation of precipitation resources for rainwater harvesting agriculture in semi-arid land of China. Agric Water Manage, 71, 33–45.
http://dx.doi.org/10.1016/j.agwat.2004.07.002
Weigel, A. P., Liniger, M. A. & Appenzeller, C. 2009. Seasonal ensemble forecasts: are recalibrated single models better than multimodels? Monthly Weather Review, 137, 1460–1479.
http://dx.doi.org/10.1175/2008MWR2773.1
Witton, I. H. & Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Kaufmann, San Francisco; Elsevier, Oxford, 528 pp.
Zanetti, S. S., Sousa, E. F., Oliveira, V. P. S., Almeida, F. T. & Bernardo, S. 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. Journal of Irrigation and Drainage Engineering, 133, 83–89.
http://dx.doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83)
