Forest climatology: Estimation of missing values for Bavaria, Germany

Agricultural and Forest Meteorology

Volumn 96, Issue 1-3, 1999, Pages 131-144

  Xia, Youlong ^a   Fabian, Peter ^a   Stohl, Andreas ^a   Winterhalter, Martin ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Forest climate stations; Mean absolute errors (MAE); Missing data; Weather stations

Indexed keywords

CLIMATOLOGY; ENVIRONMENTAL MONITORING; GERMANY; PRECIPITATION; REGRESSION ANALYSIS; TEMPERATURE; WATER VAPOR;

FOREST; MICROMETEOROLOGY; TIME SERIES ANALYSIS;

GERMANY;

EID: 0033618894     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0168-1923(99)00056-8     Document Type: Article

References (46)

1. Bardossy, A., Muster, H., 1993. Comparison of interpolation methods for daily rainfall amounts under different meteorological conditions. WMO/TD-No. 558, A/72.
2. Barnes, S.L., 1973. Mesoscale objective map analysis using weighted time-series observations. NOAA Tech. Memo. ERL NSSL-62. US Dept. of Commerce, 60 pp.
3. BayForKlim, 1996. Bavarian Climate Atlas. Munich, Germany (in German).
4. Bussieres N., Hogg W. The objective analysis of daily rainfall by distance weighting schemes on a mesoscale grid. Atmosphere-Ocean. 27:1989;521-541.
5. Cannell M.G.R. Spring frost damage on young picea stitchensis: 1. Occurrence of damaging frosts in Scotland compared with Western North America. Forestry. 57:1984;159-175.
6. Cannell M.G.R., Sheppard L.J., Smith R.I., Murray M.B. Autumn frost damage on young picea stichensis: 2. Shoot frost hardening, and the probability of frost damage in Scotland. Forestry. 58:1985;145-166.
7. Cressman G.P. An operational objective analysis system. Monthly Weather Rev. 87:1959;367-374.
8. Degaetano A.T., Eggleston K.L., Knapp W.W. A method to estimate missing maximum and minimum temperature observations. J. Appl. Meteorol. 34:1995;371-380.
9. Eischeid J.K., Baker C.B., Karl T.R., Diaz H.F. The quality control of long-term climatological data using objective data analysis. J. Appl. Meteorol. 34:1995;2787-2795.
10. Hevesi J.A., Istok J.D., Flint A.L. Precipitation estimation in mountainous terrain using multivariate geostatistics Part 1. Structural analysis. J. Appl. Meteorol. 31:1992;661-675.
11. Hevesi J.A., Flint A.L., Istok J.D. Precipitation estimation in mountainous terrain using multivariate geostatistics Part 2. Isohyetal maps. J. Appl. Meteorol. 31:1992;677-688.
12. Hubbard K.G. Spatial variability of daily weather variables in the high plains of the USA. Agric. Forest Meteorol. 68:1994;29-41.
13. Hutchinson M.F. Stochastic space-time weather models from ground-based data. Agric. Forest Meteorol. 73:1995;237-264.
14. Huth R., Nemesova I. Estimation of missing daily temperature: can a weather categorization improve its accuracy. J. Appl. Meteorol. 34:1995;1901-1916.
15. Ishida T., Kawashima S. Use of cokriging to estimate surface air temperature from elevation. Theoretical Appl. Climatol. 47:1993;147-157.
16. Kemp W.P., Burnell D.G., Everson D.O., Thomson A.J. Estimating missing daily maximum and minimum temperatures. J. Climate Appl. Meteorol. 22:1983;1587-1593.
17. Kim J.W., Chang J.T., Baker N.L., Wilks D.S., Gates W.L. The statistical problem of climate inversion: determination of the relationship between local and large-scale climate. Monthly Weather Rev. 112:1984;2069-2077.
18. Kimmins J.P., Comeau P.G., Kurz W. Modeling the interactions between moisture and nutrients in the control of forest growth. Forest Ecol. Manage. 30:1990;361-379.
19. Klink K., Willmott C.J. Principal components of the surface wind field in the United States: a comparison of analysis based upon wind velocity, direction, and speed. Int. J. Climatol. 9:1989;293-308.
20. Kramer K., Friend A., Leinonen I. Modeling comparison to evaluate the importance of phenology and spring damage for the effects of climate change on growth of mixed temperate-zone deciduous forests. Climate Res. 7:1996;31-41.
21. Luo Z., Wahba G., Johnson D.R. Spatial-temporal analysis of temperature using smoothing spline ANOVA. J. Climate. 11:1998;18-28.
22. Meek D.W., Hatfield J.L. Data quality checking for single station meteorological databases. Agric. Forest Meteorol. 69:1994;85-109.
23. Nalder I.A., Wein R.W. Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric. Forest Meteorol. 92:1998;211-225.
24. Palomino I., Martin F. A simple method for spatial interpolation of the wind in complex terrain. J. Appl. Meteorol. 34:1995;1678-1693.
25. Paulhus J.L.H., Kohler M.A. Interpolation of missing precipitation records. Monthly Weather Rev. 80:1952;129-133.
26. Preuhsler, T., Gietl, G., Grimmeisen, W., 1995. Bayerische Waldklimastationen Jahrbuch 1993. Bayerische Landesanstalt fuer Wald und Forstwirtschaft. Sachgebiet Forsthydrologie, Freising, Germany.
27. Rudolf B., Hauschild H., Reiss M., Schneider U. Die Berechnung der Gebietsniederschläe im 25°-Raster durch ein objectives Analyseverfahren. Meteorologische Zeitschrift. 1:1992;32-50.
28. Russo J.M., Liebhold A.M., Kelley J.G. Mesoscale weather data as input to a gypsy moth (Lepidoptera: Lymantriidae) phenology model. J. Econ. Entomol. 86:1993;838-844.
29. Saborowski J., Stock R. Regionalization of precipitation data in the Harz mountains. Allgemeine Forst und Jagdzeitung. 165:1994;117-122.
30. Shepard, D., 1968. A two-dimensional interpolation function for irregularly spaced data. Proc. 1968 ACM Nat. Conf., pp.517-524.
31. Sokol Z., Stekl P. 3-D mesoscale objective analysis of selected elements from SYNOP and SYRED reports. Zeitschrift für Meteorologie. 3:1994;242-246.
32. Tabony R.C. The estimation of missing climatological data. J. Climatol. 3:1983;297-314.
33. Thiebaux, H.J., Pedder, M.A., 1987. Spatial Objective Analysis with Applications in Atmospheric Science. Academic Press, London, 455pp.
34. Tronci N., Molteni F., Bozzini M. A comparison of local approximation methods for the analysis of meteorological data. Arch. Meteorol., Geophys. Bioclimatol. 36:1986;189-211.
35. Van der Voet, P., Van Diepen, C.A., Oude Voshaar, J., 1994. Spatial interpolation of daily meteorological data. A knowledge-based procedure for the regions of the European Communities. Wageningen, DLO Winand Staring Centre for Integrated Land, soil, and Water Research, Report 53.3, 117pp.
36. Wallis J.R., LettenMayer D.P., Wood E.F. A daily hydroclimatological data set for the continental United States. Water Resources Res. 27:1991;1657-1663.
37. Ward M.N., Folland C.K. Prediction of seasonal rainfall in the north Nordeste of Brazil using eigenvectors of sea-surface temperature. Int. J. Climatol. 11:1991;711-743.
38. Wigley T.M.L., Jones P.D., Briffa K.R., Smith G. Obtaining sub-grid-scale information from coarse-resolution general circulation model output. J. Geophysical Res. 95:1990;1943-1953.
39. Willmott C.J., Rowe C.M., Philpot W.D. Small-scale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Am. Cartography. 12:1985;5-16.
40. Willmott C.J., Robeson S.M., Feddema J.J. Influence of spatially variable instrument network on climatic averages. Geophys. Res. Letters. 18:1991;2249-2251.
41. Willmott C.J., Robeson S.M., Feddema J.J. Estimating continental and terrestrial precipitation averages from rain-gauge network. Int. J. Climatol. 14:1994;403-414.
42. Willmott C.J., Matsuura K. Smart interpolation of annually averaged air temperature in the United States. J. Appl. Meteorol. 34:1995;2577-2586.
43. Willmott C.J., Robeson S.M. Climatologically aided interpolation (CAI) of terrestrial air temperature. Int. J. Climatol. 15:1995;221-229.
44. Xia, Y., Fabian, P., Stohl, A., Winterhalter, M., 1999. Forest climatology: reconstruction of mean climatological data for Bavaria, Germany. Agric. Forest Meteorol. (this issue).
45. Young K.C. A three-way model for interpolating monthly precipitation values. Monthly Weather Rev. 120:1992;2561-2569.
46. Young K.C., Gall R.L. A streamflow forecast model for central Arizona. J. Appl. Meteorol. 31:1992;465-479.