*Email: a.a.ejigu@pgr.reading.ac.uk*

An accurate estimate of soil moisture has a vital role in a number of scientific research areas. It is important for day to day numerical weather prediction, extreme weather event forecasting such as for flooding and droughts, crop suitability to a particular region and crop yield estimation to mention a few. However, in-situ measurements of soil moisture are generally expensive to obtain, labour intensive and have sparse spatial coverage. To assist this, satellite measurements and models are used as a proxy of the ground measurement. Satellite missions such as SMAP (Soil Moisture Active Passive) observe the soil moisture content for the top few centimetres from the surface of the earth. On the other hand, soil moisture estimates from models are prone to errors due to model errors in representing the physics or the parameter values used.

Data assimilation is a method of combining numerical models with observed data and its error statistics. In principle, the state estimate after data assimilation is expected to be better than the standalone numerical model estimate of the state or the observations. There are a variety of data assimilation methods: Variational, Sequential, Monte Carlo methods and a combination of them. The Joint UK Land Environment Simulator (JULES) is a community land surface model which calculates several land surface processes such as surface energy balance and carbon cycle and used by the Met Office – the UK’s national weather service.

My PhD aims to improve the estimate of soil moisture from the JULES model using satellite data from SMAP and the Four-Dimensional Ensemble Variational (4DEnVar) data assimilation method introduced by Liu et al. (2008) and implemented by Pinnington (2019; under review), a combination of Variational and Ensemble data assimilation methods. In addition to satellite soil moisture data assimilation, ground measurement soil moisture data from Oklahoma Mesoscale Networks (Mesonet) are also assimilated.

The time series of soil moisture from the JULES model (prior), soil moisture obtained after assimilation (posterior) and observed soil moisture for Antlers station in Mesonet are depicted in Figure 1. Figure 2 shows the distance of prior soil moisture estimates and posterior soil moisture estimates from the assimilated observations. The smaller the distance is the better as the primary objective of data assimilation is to optimally fit the model trajectory into the observations and background. From Figure 1 and Figure 2 we can conclude that posterior soil moisture estimates are closer to the observations compared to the prior. Looking at particular months, prior soil moisture is closer to observations compared to the posterior around January and October. This is due to the fact that 4DEnVar considers all the observations to calculate an optimal trajectory which fits observations and background. Hence, it is not surprising to see the prior being closer to the observations than the posterior in some places.

Data assimilation experiments are repeated for different sites in Mesonet with varying soil type, topography and different climate and with different soil moisture dataset. In all the experiments, we have observed that posterior soil moisture estimates are closer to the observations than the prior soil moisture estimates. As a verification, soil moisture reanalysis is calculated for the year 2018 and compared to the observations. Figure 3 is SMAP soil moisture data assimilated into the JULES model and hind-casted for the following year.

**References**

Liu, C., Q. Xiao, and B. Wang, 2008: An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test. *Mon. Weather Rev.*, 136 (9), 3363–3373., https://doi.org/10.1175/2008MWR2312.1

Pinnington, E., T. Quaife, A. Lawless, K. Williams, T. Arkebauer, and D. Scoby, 2019: The Land Variational Ensemble Data Assimilation fRamework:

LaVEnDAR. *Geosci.* *Model Dev. Discuss.* https://doi.org/10.5194/gmd-2019-60