Here is a short selection of the most useful papers regarding the comprehension of meteorological data spatialization for our walloon agricultural context :
In the coming months we plan to organize and share our full bibliography.
These books focus on the theory relative to the general principles of spatialization techniques :
Here is a list of european experts in terms of weather spatialization worth following.
The literature reveals that a lot of spatial interpolation methods have been developed the last decades. These techniques have been borrowed from other fields and transposed (oil prospection) in the field of meteorology where the comprehension and modelisation of the processes is much more technical due to the complexity and the spatial heterogeneity of weather events. In such, there is not an out-of-the box recipe to apply to each weather parameter. The choice of the right interpolation method depends of many factors such as the spatial distribution of the weather station network, the topography, the number of stations, local gradients such as global circulation effects, etc. Moreover, more attention has been ported on the spatialization of climate data rather than hourly meteorological data which is our concern. Therefore, an important phase of testing, benchmarking and tweaking of the processes described here above is required in order to efficiently produce useful and sensible gridded outputs that could be used profitably by agronomical models. These phase will require a deep knowledge of the principles of these geostatistical spatialization method combined with the development of programming skills required to explore the data and conduct practical analysis. The exploratory phases needed for the development of an adjusted data analysis technique able to deal with data scarcity should be performed in a way such as the the most simple solutions are evaluated first. Depending of the results of the evaluation of the investigated technique, we will decide if further investigations are required. If no significant extra-value is added by are more complex process, the later will not be retained.
The KNMI (KONINKELIJK NATIONAAL METEOROLOGISCH INSTITUUT) has developed what they call An operational R-based interpolation facility for climate and meteo data. In october 2017 we have organized a first knowledge exchange workshop with this partner.
They have found R-software to be the most appropriate tool for weather data spatialization. This opinion is also shared by Meteo Switzerland (Christopher Frei), Meteo Norway (Ole Einar Tveito) and the RMI (Michel Journée).
Raymond Sluiter has published the review paper Interpolation methods for climate data into which he details the various deterministic and stochastic spatilization methods available. This review is an excellent starting point for who wants to start in the field of weather data spatialization.
Their developments were conducted in the context of the creation of a new climate atlas rather than with agronomical purposes. According to their feedback, there is no out-of-the box solution. We must find the solution best suited to our purpose by proceeding from the simplest solution and progressively add more complexity while asserting the level of accuracy brougth by this additional complexity. A good balance must be found between complexity and operability since we aim to build an operational suite.
Their presentations are available in the KNMI supplementary materials
Arvalis (Institut du Végétal) has also conducted weather data spatialization research in an agricultural context (crop warning systems). We have organized a knowledge exchange workshop in January 2018. Like the KNMI they have tested various methods with an increasing level of complexity. Our contact Olivier Deudon also uses R-software to conduct his researches.
The key points of their research are detailed in the arvalis supplementary materials. Here we present a brief summary of their methodology and main findings. The aim of their work was to test various methods of weather data spatial interpolation and find the most efficient ones (in terms of accuracy) for various parameters (temperature, relative humidity, rainfall) in the context of their specific AWS network (> 400 stations in France).
Regarding temperature : * tested methods : Inverse distance, multiple regressions, various kriging methods * validation method : splitting the dataset in training set (355 stations) and test set (100 stations) * model evaluation criterion : RMSE * method with the lowest RMSE for T°: universal kriging * used covariates : elevation, surface solar irradiance
As mentioned above, our project is mainly inspired from the ZEPP (ZENTRALSTELLE DER LÄNDER FÜR EDV-GESTÜTZTE ENTSCHEIDUNGSHILFEN UND PROGRAMME IM PFLANZENSCHUTZ - Central Institute for Decision Support Systems in Crop Protection) work. Here we present the key points of our November 2017 workshop.
It is essential to keep in mind the agricultural scope of the platform. The objective is make the best predictions in cultural area. It is not a problem if the quality of the prediction is not as high in area were not crops are grown (e.g. Hautes-Fagnes).
What matters most are the quality of the decision support tools outputs based on our weather data rather than the weather data itself. Their comparison of various spatialization technique revealed that for their needs, the most efficient technique is the multiple regression based on elevation, latitude and longitude. This comparison is extensively discussed in the Zeuner PhD Thesis present in the ZEPP supplementary material
Here we present a brief summary of their method and main findings. The aim of their work was to provide an operationnal platform able to supply crop alert system models with hourly gridded datasets of temperature and relative humidity accross germany that present the highest accuracy.
Regarding temperature : * tested methods : krigin, IDW, spline, multiple regression * validation method : 570 stations * model evaluation criterion: difference hourly interpolated - measured at the location of the stations (+ boxplots) * used covaraites : elevation * choosen method : multiple regression
The RMI is our primary partner in terms of weather data spatialization with who we work in close collaboration. As the KNMI, they have an advanced expertise in terms of spatialization of weather data using R software.