Land surface temperature estimation to improve the assimilation of SEVIRI radiances over land

Abstract

[1] The aim of this work is to estimate the land surface temperature from satellite observations in order to improve the assimilation of surface-sensitive infrared observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). To date, only a few SEVIRI observations are assimilated over land in limited area models; this is due to the still inappropriate description of the land surface in these systems (in both emissivity and surface temperature). In this paper, we demonstrate that the use of land emissivity climatologies at infrared wavelengths together with land surface temperature (LST) retrievals improve the assimilation of SEVIRI radiances against this operational configuration. Emissivity climatologies from the EUMETSAT Land Surface Analysis-Satellite Application Facilities (Land-SAF) were used, and LSTs were retrieved using one SEVIRI window channel and the radiative transfer equation. Retrieved LSTs were evaluated against independent observations/products. Some differences, due to instrumental specifications, were found when comparing SEVIRI and MODIS LSTs, but a good agreement was found between retrieved LSTs and the Land-SAF surface temperature product. The comparison of SEVIRI LSTs and LST analyses from ALADIN/France have pointed out warm (cold) biases during daytime (nighttime), which may be explained by an overall underestimation of the diurnal cycle by the model. The emissivity atlas combined with different LST retrievals were used to simulate SEVIRI radiances using the RTTOV radiative transfer model. A comparison was made between SEVIRI radiance simulations and observations. A significant improvement of the forward model statistics was noticed as well as an increase in the amount of data that could be potentially assimilated in ALADIN/France, compared to the operational setup. These developments were then tested in a context of data assimilation, thus enabling the use of more SEVIRI data over land. Two assimilation experiments were run over a 3 month period during summer 2009, one of which is representative of the operational model while the other differs by the assimilation of more SEVIRI data over land through a better representation of the emissivity and surface temperature. We show that the forecast impact is generally neutral to positive. In particular, SEVIRI data point to positive impact over southern Europe. SEVIRI data are also shown to improve the quality of analyses, particularly those of total column water vapor, and this is substantiated through comparisons with independent GPS measurements.Citation: Guedj, S., F. Karbou, and F. Rabier (2011), Land surface temperature estimation to improve the assimilation of SEVIRI radiances over land,

Assimilation of zenith total delays in the AROME France convective scale model: a recent assessment

“…Horizontal winds derived from the imagery of geostationary and polar orbiting satellites and from the ASCAT and OSCAT scatterometers are also included. The infra-red radiances from SEVIRI on board METEOSAT-10 are assimilated (Montmerle et al, 2007) at 50 km resolution with a recent increased usage of channels sensitive to the surface over land (Guedj et al, 2011). AROME assimilates radial velocities from Doppler radars of the French network (Montmerle and Faccani, 2009).…”

The Impact of Satellite‐Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts

“…For instance, Pavelin and Candy () reject infrared sounding data over land for some arid surfaces where the global model NWP forecast LST appears to have a large cold bias, particularly during daytime in the Sahara. This is also an issue for limited area models; Guedj et al () report a similar cold bias for the ALADIN/France NWP model simulations (when compared to geostationary LSTs) over the Spanish Peninsular.…”

Impact Assessment of Daily Satellite-Derived Surface Albedo in a Limited-Area NWP Model

“…As initiated by Gu et al (2006), other land surface parameters such as the LAI and the vegetation fraction that are produced by the Land SAF could be used for near-real-time NWP applications in simple land data assimilation systems to enhance consistency between the variables and the products, reduce some model biases, better describe short-term variability, and improve specification of land surface that could in turn help the assimilation of satellite atmospheric sounding channels over continental areas (Guedj et al 2011). enthusiasm and inspiration on the use of satellitederived products from the Land SAF in NWP models that led to the study described in this paper. The FLUXNET data providers are also acknowledged.…”