In current state-of-the-art Numerical Weather Prediction (NWP) models Leaf Area Index (LAI) is considered as an external parameter where monthly values are derived from long-term averages. Such an approach is not capable of describing vegetation anomalies e.g. during severe droughts, when LAI values (especially over non-irrigated grasslands and croplands) could be considerably lower than long-year averages of the selected month. A solution for this inaccuracy is presented in this study, with the aim to use satellite LAI observations in the NWP model. The main difficulty with such an approach is that high resolution (e.g. that of Sentinel) satellite vegetation products have a time lag of 10-15 days. To overcome this the following system was developed: satellite vegetation observations are assimilated in an offline land data assimilation system which is capable to deliver a soil and vegetation state analysis 10 days prior the actual date (T-10d). From T-10d we integrate the offline surface model with prognostic vegetation until the current date; and the resulting vegetation state (at time T) could be merged with the operational analyses of the NWP model.
In the present study the AROME-Hungary NWP system is used, which is run operationally at the Hungarian Meteorological Service (OMSZ) at 2.5 km horizontal resolution. For the simulation of surface processes the offline SURFEX land surface model is applied, which in its present version utilizes the ISBA-Ags simplified photosynthesis scheme to simulate LAI prognostically. In SURFEX-offline an Extended Kalman Filter method is used to assimilate Leaf Area Index satellite measurements, from the OLCI sensor of Sentinel-3.
The capabilities of the system and the impact of LAI change on the weather forecast produced by AROME-Hungary are presented during summer 2021. In 2021 a cold spring was followed by very hot and dry summer in the Carpathian Basin. During July and August a severe drought occurred over Southern Hungary and Northern Serbia, and consequently maize fields over large areas were significantly underdeveloped (irrigation is very limited in the region). This LAI anomaly was well captured by the SURFEX-offline system. Results show that LAI can have an impact on the weather forecast produced by AROME in summer anticyclonic cases. Most affected variables are 2 m temperature and precipitation.
Based on the encouraging results it is planned that this system for the improvement of LAI fields in AROME is going to be implemented in the operational NWP chain of OMSZ in near future.
How to cite: Szintai, B., Tóth, H., and Kullmann, L.: Introducing a daily updated Leaf Area Index in a mesoscale Numerical Weather Prediction model, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-453, https://doi.org/10.5194/ems2022-453, 2022.