Soil Initialization Strategy for Use in Limited-Area Weather Prediction Systems

Abstract

Three diverse methods of initializing soil moisture and temperature in limited-area numerical weather prediction models are compared and assessed through the use of nonstandard surface observations to identify the approach that best combines ease of implementation, improvement in forecast skill, and realistic estimations of soil parameters. The first method initializes the limited-area model soil prognostic variables by a simple interpolation from a parent global model that is used to provide the lateral boundary conditions for the forecasts, thus ensuring that the limited-area model's soil field cannot evolve far from the host model. The second method uses the soil properties generated by a previous limited-area model forecast, allowing the soil moisture to evolve over time to a new equilibrium consistent with the regional model's hydrological cycle. The third method implements a new local soil moisture variational analysis system that uses screen-level temperature to adjust the soil water content, allowing the use of high-resolution station data that may be available to a regional meteorological service.The methods are tested in a suite of short-term weather forecasts performed with the Consortium for Small Scale Modeling (COSMO) model over the period September-November 2008, using the ECMWF Integrated Forecast System (IFS) model to provide the lateral boundary conditions. Extensive comparisons to observations show that substantial improvements in forecast skills are achievable with improved soil temperature initialization while a smaller additional benefit in the prediction of surface fluxes is possible with the soil moisture analysis. The analysis suggests that keeping the model prognostic variables close to equilibrium with the soil state, especially for temperature, is more relevant than correcting the soil moisture initial values. In particular, if a local soil analysis system is not available, it seems preferable to adopt an ''open loop'' strategy rather than the interpolation from the host global model analysis. This appears to be especially true for the COSMO model in its current operational configuration since the soil-vegetation-atmosphere transfer (SVAT) scheme of the ECMWF global host model and that of COSMO are radically diverse.

First assimilation of temperature lidar data into an NWP model: impact on the simulation of the temperature field, inversion strength and PBL depth

“…Self spin‐up is possible for land–atmosphere model systems, and may lead to a more realistic representation of the soil variables, if the model does not show larger biases for precipitation. If done correctly, the self spin‐up enhances the surface fluxes and therefore the representation of the PBL in the model (Di Giuseppe et al ; Angevine et al ). The length of the surface spin‐up period depends on local weather conditions, but 18 h of spin‐up for the surface seems to be too short for this case‐study.…”

Land surface spinup for episodic modeling

“…We call the method "self-spinup"; it is equivalent to the "open loop" method of Di Giuseppe et al (2011). The somewhat arbitrary choice of a 1-month spinup period (repeated twice) is vindicated by the results.…”

“…In case such a system and data are not available, spinning up the soil with the atmospheric model itself can improve results. This "open loop" approach was shown by Di Giuseppe et al (2011) to improve the results of 3-month simulations.…”

The relevance of background‐error covariance matrix localization: an application to the variational retrieval of vertical profiles from SEVIRI observations

“…The daily temperature cycle is therefore too weak. The causes of these errors have been clearly identified in deficiencies in the soil temperature and humidity initialization (Di Giuseppe et al, 2010) and are therefore difficult to correct in this context. In fact, the assimilation of 1D-Var profiles is confined to sea areas and analysis increments are applied to upper-air variables.…”