- Short report
- Open Access
Comparison between meteorological data from the New Zealand National Institute of Water and Atmospheric Research (NIWA) and data from independent meteorological stations
© The Author(s). 2017
- Received: 23 September 2016
- Accepted: 3 February 2017
- Published: 24 February 2017
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.
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.
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.
- Growth and yield
- Hybrid modelling
Eco-physiological modelling of forest production relies heavily on local meteorological data in order to calculate constraints of photosynthesis, and we need to clearly identify the precision and bias associated with sources of such data.
A typical eco-physiological or “hybrid” model of forest growth and yield exploits a linear relationship between intercepted radiation and forest net primary productivity (Monteith 1972, 1977). The slope of the relationship has been labelled “quantum efficiency,” and it is influenced by air temperature, soil moisture status, vapour pressure deficit (VPD), soil nutrition and plant physiological age. The idea of reducing maximum achievable quantum efficiency with modifiers that represent these influences is the basis of the 3-PG model (Landsberg and Waring 1997). Modifiers vary between 0 and 1 and are generally calculated using models of sub-processes such as water balance models or predictions of the impact of VPD on stomatal conductance. In order to work effectively, sub-models require accurate inputs of meteorological data, particularly rainfall, daily maximum temperature, daily minimum temperature, VPD and daily global or (if available) photosynthetically active radiation.
Eco-physiological models have been made to operate at a variety of temporal scales (McMurtrie and Wolf 1983a, 1983b; McMurtrie and Landsberg 1992; McMurtrie et al. 1990), but hybrid models of forest production used for management purposes usually employ a monthly time step (Mason et al. 2011; Mason et al. 2007).
Determine the precision and bias of available estimates of meteorological data for particular points in the New Zealand landscape by comparing the estimates with measurements at independent meteorological stations
Identify any adjustments that might be made using other information, such as elevation, that might improve estimates for those points
Data from the nearest available NIWA meteorological station were adjusted for differences in elevation, latitude and distance from the sea. Such adjustments were made using simple adiabatic adjustments and using equations reported by Norton (1985).
Estimates from the National Institute of Water and Atmospheric Research’s (NIWA) “Virtual Climate Station Network” (VCSN) (Cichota et al. 2008; Tait et al. 2006), a grid of points at approximately 5 km spacing across New Zealand where daily estimates of weather variables are modelled. These were also adjusted to localise them using equations reported by Norton (1985).
Experimental meteorological stations, their locations, locations of the virtual climate stations points closest to them and distance between them
Experimental station name
Distance to coast (km)
VCSN elevation (m)
VCSN distance to coast (km)
Distance between experimental station and VCSN (m)
Details of NIWA stations used for comparison with experimental station estimates of temperature, rainfall and radiation
NIWA Temp station number
Distance to NIWA station (km)
NIWA rainfall station number
Distance to NIWA rainfall station (km)
NIWA radiation station number
Distance to NIWA radiation station (km)
Staff at NIWA interpolate between meteorological stations to provide daily estimates of weather at 5 km by 5 km grid points throughout New Zealand (Tait et al. 2006), and this is known as the Virtual Climate Station Network (VCSN). The nearest grid points to the experimental stations were selected, and their data were kindly supplied to us by Dr Andrew Tait, Principal Scientist with the National Climate Centre. The locations of these grid points are shown in Fig. 1, and the distances between our stations and the points are shown in Table 1.
Data from all sources were summarised by year and month, with averages for all variables except rainfall, which was summed.
Mean daily maximum and minimum temperature estimates from NIWA were localised to our stations in two ways: (a) An adiabatic adjustment was made based on the difference between NIWA estimate point elevations and experimental station elevations (hereafter called “lapsed”) and (b) equations predicting long-term monthly temperature means from elevation, latitude and distance from the sea (Norton 1985) (hereafter called “Norton-adjusted”) were employed for both experimental station locations and the NIWA estimate point locations and the difference was added to the NIWA station estimates.
Tables of correlations were prepared between both raw and adjusted NIWA estimates and actual recorded estimates of monthly weather statistics.
Graphs of observed versus estimated meteorological statistics were prepared with points coloured and labelled by station.
Graphs of residuals versus predicted values, differences in elevation and distances between our stations and NIWA estimate points were prepared.
Correlations (expressed as R values) between observed mean daily maximum and minimum temperatures averaged by month, and estimates from either VCSN points or nearest NIWA meteorological stations, including some alternative localisations
Raw NIWA station
Lapsed NIWA station
Norton NIWA station
Plots (not shown) of residuals versus (a) distances between estimate points and stations and (b) differences in elevation between estimate points and station points were created. Residuals of raw maximum VCSN and nearest NIWA station estimates were correlated with elevation difference, but adjusted estimates were less clearly correlated with elevation difference. Residuals of raw VCSN estimates tended to have a higher variance with distance, but not those of adjusted estimates nor did NIWA estimates vary with distance.
Residuals increased with distance between our stations and estimate points. Note also that despite having a higher correlation with observed values, the nearest NIWA station estimates were biased overall, with observed values generally larger than NIWA station values. Bias did not appear to be related to any particular feature, such as distance from, station or differences between NIWA station elevation and elevation of sample point.
Vapour pressure deficit
Discussion and conclusions
Rainfall was the most poorly estimated variable, and in some cases, the error may become very important in models, particularly in dry areas where water supply is the dominant factor influencing growth. NIWA has far more rainfall stations than stations that measure other variables, and clearly, NIWA’s focus on rainfall measurements is justified as rainfall appears to be far more local than other variables.
We have concerns regarding the overall bias of NIWA station estimates of radiation because a consistent bias can accumulate errors. However, differences may reflect the particular locations and low level of replication of the experimental stations and corresponding NIWA radiation stations. Future studies with more stations may be able to identify better ways to localise estimates from VCSN points, thereby reducing bias.
There was a tendency for both VCSN and NIWA station estimates to be biased with respect to individual stations, and as expected, this bias was often related to distance between our stations and the estimate points. Adjustments of temperature using simple lapse adjustments for elevation differences or Norton adjustments for elevation, distance from the sea and latitude often reduced temperature estimate bias, particularly for maximum temperatures. Minimum temperatures are quite well estimated from VCSN points, and local adjustment offered little, if any, improvement in estimates.
The study reported here was enabled by grants from the Agmardt fund and also the Forestry Sector Levy fund. Experiments associated with the study were established by the New Zealand Dryland Forestry Initiative and the University of Canterbury. Help with establishment of meteorological stations by the members of the New Zealand Dryland Forestry Initiative is gratefully acknowledged. We are very grateful to the land owners, the Atkinsons, the Dillons, the Lawsons, the Averys, the Cuddons, the McNeils, Juken New Zealand Ltd., the Selwyn District Council and the Christchurch City Council.
EM designed the study, helped with the setup and service meteorological stations, assembled the data, did the analysis and wrote the report. SS helped with the data collection. JM helped with the GIS aspects of the study and with the editorial comments on the report. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis 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.
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