Ivana Aleksovska is one of the #DigitAg co-funded PhDs
Improve short- and medium-term predictions of agronomic models by better taking into account the uncertainty of weather forecasts
- Start Date: November 2017
- University: Université Toulouse III Paul Sabatier
- PhD School: SDU2E (Toulouse)
- Field(s): Mathematics
- Doctoral Thesis Advisor: Laure Raynaud (Météo France), Robert Faivre (INRA MIAT)
- Co-supervisors : François Brun (Acta)
- Funding: #DigitAg – Acta-les instituts techniques
- #DigitAg: Cofunded PhD – Challenge 1
meteorological conditions: the crop cycle, irrigation management, crop protection and fertilization. This sector is strongly demanding decision support systems to better assess these constraints. A range of services is developing to meet this need such as Météus proposed by Isagri or Taméo built by Météo-France and Arvalis – Institut du végétal.
Atmospheric flow is a chaotic phenomenon and the development of quality weather forecasts is a scientific challenge, as there are many uncertainties: estimation of the initial conditions over the globe, representation of the physical processes in numerical weather prediction systems. To cope with this problem, the meteorological centers, including Météo-France, have implemented ensemble prediction systems that provide an estimate of the uncertainty of the predicted meteorological conditions.
The objective of this thesis is to define methods to exploit these ensemble meteorological forecasts for agronomic applications. To this end, the partner organizations (ACTA, Arvalis, IFV, INRA et Météo-France) of this project have identified contrasted case studies for which they have agronomic models. The doctoral student will work successively on the definition of the junction between the different ensemble of forecasts covering different timeframes, on the evaluation of the resulting uncertainties, and the development of representations of the results to make them easily usable by agricultural users.
Contact: ivana.aleksovska [AT] inra.fr – Tél : 07 83 71 41 68