Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model

Sassi, Mohamed Zied; Fourrié, Nadia; Guidard, Vincent; Birman, Camille

doi:10.16993/tellusa.48

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…(2022) of passive microwave vegetation optical depth in Meteo‐France's LDAS‐Monde (Albergel et al ., 2018) which led to improved evapotranspiration over the United States; the inclusion by Sassi et al . (2023) of land‐surface temperature retrievals from radiances measured by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) in the AROME‐France model LDAS which had a positive impact on short‐range night‐time forecasts at the surface and in the PBL; the development by Fairbairn et al . (2019) of a stand‐alone version of ECMWF's LDAS (de Rosnay et al ., 2014) which can be useful for development and testing; and the application by Kumar et al .…”

Section: Introductionmentioning

confidence: 99%

“…A few examples of recent innovations and developments on major LDAS include: the assimilation by Mucia et al (2022) of passive microwave vegetation optical depth in Meteo-France's LDAS-Monde (Albergel et al, 2018) which led to improved evapotranspiration over the United States; the inclusion by Sassi et al (2023) of land-surface temperature retrievals from radiances measured by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) in the AROME-France model LDAS which had a positive impact on short-range night-time forecasts at the surface and in the PBL; the development by Fairbairn et al (2019) of a stand-alone version of ECMWF's LDAS (de Rosnay et al, 2014) which can be useful for development and testing; and the application by Kumar et al (2019) of the US National Aeronautics and Space Administration (NASA)'s Land Information System (LIS, see Kumar et al, 2006) to provide information that could benefit the USA National Climate Assessment.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Bélair

Alavi

Carrera

et al. 2023

Quart J Royal Meteoro Soc

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In this study screen‐level analyses of air temperature and humidity are used to objectively evaluate the impact of a new land‐surface package being considered for implementation in Environment and Climate Change Canada (ECCC)'s medium‐range global deterministic numerical weather prediction (NWP) system. Through its control of heat, moisture, and momentum fluxes to the atmosphere, the land surface has a substantial impact on near‐surface meteorology and on the atmospheric boundary layer. The approach examined in this study is based on the comparison between model forecasts and screen‐level analyses. It demonstrates the impact on medium‐range NWP of a new land‐surface package that includes (i) a new set of databases to specify soils and land‐cover characteristics, (ii) improved land‐surface initial conditions obtained by the assimilation of space‐based remote‐sensing observations, and (iii) a more sophisticated scheme for land‐surface modelling. The evaluation method is shown to provide useful information on the impact of the new land‐surface package, including lead‐time‐averaged difference maps as well as plots showing the evolution with lead time of the standard deviation of errors (STDE) and of the temporal correlation between forecasts and analyses. The new land‐surface package has a positive impact on near‐surface forecasts of air temperature and humidity for a summertime period, with smaller STDE and larger temporal correlation for both variables. The improvement is greater for humidity than for air temperature. The maximum impact is found around seven‐day lead time, with substantial gains in absolute and relative values for STDE and temporal correlation. The positive impact is also quantified in terms of prediction hours, with gains of about one day at the medium range. Details of the pros and cons for this objective evaluation approach are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Bélair

Alavi

Carrera

et al. 2023

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

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Towards a 2DEnVar surface data assimilation approach within the convective scale numerical weather prediction model AROME‐France

Marimbordes,

Birman,

Arbogast

et al. 2024

Quart J Royal Meteoro Soc

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Surface data assimilation (DA) plays a key role in the initialisation of soil variables in coupled surface–atmosphere numerical weather prediction (NWP) models. Météo‐France, as many other NWP centres, uses an operational surface DA method based on an optimal interpolation (OI), implemented more than 30 years ago. The current surface DA system implemented at Météo‐France in the operational limited‐area AROME‐France model operates in two steps. First, a two‐dimensional OI method analyses the diagnostic screen‐level parameters, which are then used to initialise the prognostic soil variables according to a one‐dimensional OI method. This article presents the implementation and the results of a new two‐dimensional ensemble‐based variational (2DEnVar) system, using the object‐oriented prediction system framework, for the diagnostic screen‐level variable analyses (2‐m temperature and 2‐m relative humidity) within the AROME‐France model. These analysed variables are then used to initialise the soil temperature and soil moisture of the first and second soil layers. A two‐dimensional variational system is initially implemented and compared with an equivalent system that uses an OI surface analysis. The comparison of 2‐m temperature increments shows that the two systems behave in a similar way. From this two‐dimensional variational approach, a 2DEnVar scheme is developed and compared with the OI screen‐level analysis used operationally (reference system). The results are encouraging even though a degradation in forecast quality is noticed at short ranges for 2‐m temperature and 2‐m relative humidity. This is mainly due to a low spread near the surface in the ensemble‐based data assimilation that is used to compute background‐error covariances in 2DEnVar. Sensitivity tests have been performed to improve the initial version of the 2DEnVar scheme. A positive and significant impact is noticed when a multiplicative inflation is applied to the background perturbations coming from the ensemble‐based data assimilation for the computation of 2‐m temperature and 2‐m relative humidity background‐error covariances. Finally, when comparing the inflated 2DEnVar system with the reference, the two systems behave similarly. This suggests that the 2DEnVar scheme, based on ensemble statistics, is capable of properly reconstructing background‐error structures derived from the empirical correlations described in OI.

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Physical and Statistical Links between Errors at the Surface, in the Boundary Layer, and in the Free Atmosphere in Medium-Range Numerical Weather Predictions

Bélair,

Alavi,

Leroyer

et al. 2024

Atmosphere

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The adequate representation of interactions between the land surface and the atmosphere is of crucial importance in modern numerical weather prediction (NWP) systems. In this context, this study examines how errors in the planetary boundary layer (PBL) depend on the quality of near-surface prediction over land for medium-range NWP. Two series of 10-day forecasts from Environment and Climate Change Canada (ECCC)’s global deterministic prediction system were evaluated: one similar to what is currently used in ECCC’s operational systems and the other with improved land surface modeling and land data assimilation. An objective evaluation was performed for the 2019 summer season in North America, with a special emphasis on three specific areas: northern Canada, the central US, and the southeastern US. The results indicate that the impact of the new land surface package is more difficult to interpret in the PBL than it is at the screen level. The error differences between the two experiments are quite distinct for the three regions examined. As expected, random errors (standard deviations) for air temperature and specific humidity in the PBL are directly linked with their own random errors at the screen level, with correlation coefficients decreasing from a value of one at the surface to values of about 0.2–0.3 a few kilometers above the surface. Less expected, however, is the fact that random errors in the lower atmosphere also strongly depend on changes in air temperature biases at the surface. Warmer near-surface conditions lead to increased random errors for air temperature in the lower atmosphere, in association with the development of the deeper PBL, with greater spatial variability. This finding is of particular interest when evaluating new configurations of NWP systems for implementation in national meteorological and environmental prediction centers.

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Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model

Cited by 3 publications

References 33 publications

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Towards a 2DEnVar surface data assimilation approach within the convective scale numerical weather prediction model AROME‐France

Physical and Statistical Links between Errors at the Surface, in the Boundary Layer, and in the Free Atmosphere in Medium-Range Numerical Weather Predictions

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