Mason EGSalekin, SerajisMorgenroth J2018-02-012018-02-012017Mason EG, Salekin S, Morgenroth J (2017). Comparison between meteorological data from the New Zealand National Institute of Water and Atmospheric Research (NIWA) and data from independent meteorological stations. New Zealand Journal of Forestry Science. 47(7).0048-01341179-5395http://hdl.handle.net/10092/14989Background Hybrid eco-physiological/mensurational models of forest production generally require monthly meteorological estimates at local points in the landscape as inputs. Where to obtain these estimates and how best to localise them are important questions for modellers. Data collected from nine independent meteorological stations were compared with estimates from the nearest grid points of the Virtual Climate Station Network created by the New Zealand National Institute of Water and Atmospheric Research (NIWA) and also to estimates from NIWA’s nearest actual meteorological stations. Findings Localisation of temperature estimates was attempted through simple adiabatic adjustments of NIWA’s data and also adjustments that use elevation above sea level, latitude and distance from the sea. The latter adjustment was found to be slightly better than simple adiabatic adjustment. Results showed that useable local estimates can be obtained from absolute global solar radiation and adjusted mean daily maximum and minimum temperatures although there were small amounts of bias. Rainfall and relative humidity were not as well estimated for local points as the other variables and these poorer estimates may constrain our ability to model forest productivity in drier regions of New Zealand. Conclusions Monthly mean global radiation, and suitably adjusted estimates of mean daily maximum and minimum temperature from the Virtual Climate Station Network were found to estimate these properties for points in the landscape with reasonable precision and small bias. Rainfall, however, was imprecisely estimated.en© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Growth and yieldhybrid modellingclimateweatherJournal Article2017-08-16Fields of Research::30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environmentFields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370108 - Meteorologyhttps://doi.org/10.1186/s40490-017-0088-0