NWP-Based Forecasts

DNICast Advances

- Assimilation of satellite radiance generally improves the forecast.

- Initialization of clouds using Nowcasting Satellite Application Facilities brings forecasted model clouds closte to these observations.

The HARMONIE system for Numerical Weather Prediction

Data assimilation in Numerical Weather Prediction (NWP) optimally blends observations with atmospheric model data in order to obtain the best possible initial state for an atmospheric model prediction. It was early realized (Lorenz, 1965) that the forecast quality is strongly dependent on an accurate description of the initial state and hence on the abilities of the data assimilation system and the observations used. Different data assimilation methods exist. One method well suited to handle meteorological observations that are non-linearly related to the model state variables, such as is the case for satellite radiances, is variational data assimilation. Here, the HARMONIE NWP system is used to produce short-range forecasts.

The HARMONIE NWP model system is a non-hydrostatic meso-scale forecasting system developed within the HIRLAM-ALADIN consortia. Presently HARMONIE consists of initial condition generation through data assimilation for the upper air, and the AROME forecast model with two model components (Seity et al., 2011). The current HARMONIE data assimilation system consists of surface data assimilation (Giard and Bazile, 2000) based on optimal interpolation and upper-air 3-dimensional variational data assimilation (3D-Var), with climatological background error statistics (Seity et al., 2011).

Assimilation of SEVIRI Radiances

The SEVIRI instrument (Schmetz et al., 2002), on board the geostationary Meteosat Second Generation weather satellites, is an optical imaging spectrometer measuring top-of-the-atmosphere (TOA) radiances in 12 spectral channels. The spatial resolution of the measurements is about 3 km over the HARMONIE Iberian Peninsula DNICAST domain. The instrument scans the atmosphere every 15 minutes. Here, two of eight IR channels were used, which are spectrally located around 6.25 and 7.35 ?m (also referred to as water-vapor channels). To assure that only radiances not affected by clouds are used we also incorporate SEVIRI cloud-retrieval products processed with a software package (Le Gleau and Derrien, 2002) developed in the framework of the EUMETSAT Satellite Application Facility on Nowcasting & Very Short Range Forecasting.

To evaluate the impact of assimilation of SEVIRI radiances on the forecast initial state and on the following forecasts parallel data assimilation and forecast experiment are set-up. There are two parallel experiments run for a one-month period over the HARMONIE DNICast Iberian Peninsula domain. In one of the parallel experiments only a baseline set of meteorological observation types will be used in the 3-dimensional variational data assimilation for the atmosphere. The other parallel experiment will be similar, except for that also SEVIRI radiances are used in the 3-dimensional variational data assimilation for the atmosphere. The time period of the parallel experiments is from 1 to 30 of April 2013. The assimilation period is preceded by a two-week period during which the SEVIRI variational bias correction coefficients are spun up. Surface initial conditions and upper air initial states for the beginning of the spin-up period were taken from one of the extended standard HARMONIE DNICast runs. In the evaluation of the parallel experiments we will focus on the forecast quality in terms of humidity, clouds and DNI.

Cloud initialization with MSG data

In addition to assimilation of satellite radiances an approach based on ideas of van der Veen (2013) is examined to use SAFNWC products for improving the NWP initial state and the following short-range cloud forecasts. Two-dimensional fields of cloud mask, cloud top height and cloud base are used to modify the HARMONIE model 3-dimensional initial state. Not only the model cloud fields will be modified in accordance with the satellite based products, but also the temperature and humidity fields are modified. The humidity modifications are based on model relations between cloud amount and humidity. In order to preserve the same buoyancy after the cloud and humidity information were modified; also the temperature profile needs to be adjusted. With these physical constraints, humidity and temperature fields enable the cloud modifications to be in balance and, as a result, the introduced cloud information will have the potential to influence the forecast during several hours of model integration.


Dee, D., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323–3343.

Giard, D., and E. Bazile, 2000: Implementation of a new assimilation scheme for soil and surface variables in a global nwp model. Mon. Wea. Rev., 128, 997–1015.

Le Gleau H, Derrien M., 2002: ‘User manual for the PGE01-02-03 of the SAFNWC/MSG: Scientific part’, EUMETSAT documentation SAF NWC/IOP/MFL/SCI/SUM/01. Centre de Meteorologie Spatiale: Toulouse, France.

Lorenz, E., 1965: A study of the predictability of a 28-variable atmospheric model. Tellus, 17, 321-333. MACC-RAD, 2015: McClear service, http://www.soda-pro.com/web-services/radiation/mcclear.

Schmetz J, Pili P, Tjemkes S, Just D, Kerkmann J, Rota S, Ratier A., 2002: An introduction to Meteosat Second Generation (MSG). Bull. Am. Meteorol. Soc. 83: 977–992.

Seity, Y., P. Brousseau, S.Malardel, G. Hello, P. Benard, F. Bouttier, C. Lac, and V.Masson, 2011: The arome-france convective-scale operational model. Mon. Wea. Rev., 139, 976–991.

Van der Veen, S.H., 2013: Improving NWP Model Cloud Forecasts Using Meteosat Second-Generation Imagery. Mon. Wea. Rev., 141, 1545–1557. doi: http://dx.doi.org/10.1175/MWR-D-12-00021.1.