scholarly journals Land-atmosphere interactions in an high resolution atmospheric simulation coupled with a surface data assimilation scheme

2009 ◽  
Vol 9 (5) ◽  
pp. 1613-1624 ◽  
Author(s):  
L. Campo ◽  
F. Castelli ◽  
D. Entekhabi ◽  
F. Caparrini
Keyword(s):  
Energy Balance ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Budget ◽  
Land Surface ◽  
Surface States ◽  
Atmospheric Model ◽  
Turbulent Fluxes ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract. A valid tool for the retrieving of the turbulent fluxes that characterize the surface energy budget is constituted by the remote sensing of land surface states. In this study sequences of satellite-derived observations (from SEVIRI sensors aboard the Meteosat Second Generation) of Land Surface Temperature have been used as input in a data assimilation scheme in order to retrieve parameters that describe energy balance at the ground surface in the Tuscany region, in central Italy, during summer 2005. A parsimonious 1-D multiscale variational assimilation procedure has been followed, that requires also near surface meteorological observations. A simplified model of the surface energy balance that includes such assimilation scheme has been coupled with the limited area atmospheric model RAMS, in order to improve in the latter the accuracy of the energy budget at the surface. The coupling has been realized replacing the assimilation scheme products, in terms of surface turbulent fluxes and temperature and humidity states during the meteorological simulation. Comparisons between meteorological model results with and without coupling with the assimilation scheme are discussed, both in terms of reconstruction of surface variables and of vertical characterization of the lower atmosphere. In particular, the effects of the coupling on the moisture feedback between surface and atmosphere are considered and estimates of the precipitation recycling ratio are provided. The results of the coupling experiment showed improvements in the reconstruction of the surface states by the atmospheric model and considerable influence on the atmospheric dynamics.

scholarly journals A dual-pass data assimilation scheme for estimating surface energy fluxes with FY3A-VIRR land surface temperature

2013 ◽  
Vol 10 (3) ◽  
pp. 3927-3972
Author(s):  
T. R. Xu ◽  
S. M. Liu ◽  
Z. W. Xu ◽  
S. Liang ◽  
L. Xu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Surface Energy Flux ◽  
Data Assimilation Scheme ◽  
Dual Pass ◽  
Assimilation Scheme

Abstract. A dual-pass data assimilation scheme is developed to improve predictions of surface energy fluxes. Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at the weekly temporal scale and pass 2 optimizes soil moisture at the daily temporal scale. Based on the ensemble Kalman filter (EnKF), land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) is assimilated into common land model (CoLM) for the first time. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun that include alpine meadow, grass, crop, and orchard land cover types. The results are compared with data set generated by a multi-scale surface energy flux observation system that includes an automatic weather station (AWS), an eddy covariance (EC) and a large aperture scintillometer (LAS). Results indicate that the CoLM can simulate the diurnal variations of surface energy flux, but usually overestimates sensible heat flux and underestimates latent heat flux and evaporation fraction (EF). With FY3A-VIRR LST data, the dual-pass data assimilation scheme can reduce model uncertainties and improve predictions of surface energy flux. Compared with EC measurements, the average model biases (BIAS) values change from 37.8 to 7.7 W m−2 and from −27.6 to 18.8 W m−2; the root mean square error (RMSE) values drop from 74.7 to 39.1 W m−2 and from 95.1 to 62.7 W m−2 for sensible and latent heat fluxes respectively. For evaporation fraction (EF), the average BIAS values change from −0.29 to 0.0 and the average RMSE values drop from 0.38 to 0.12. To compare the results with LAS-measured sensible heat flux, the source areas are calculated using a footprint model and overlaid with FY3A pixels. The four sites averaged BIAS values drop from 63.7 to −8.5 W m−2 and RMSE values drop from 118.2 to 69.8 W m−2. Ultimately, the error sources in surface energy flux predictions are investigated, and the results show that both soil moisture and vegetation parameters caused the big model biases in surface energy flux predictions. With Pass 1 and Pass 2, the dual-pass data assimilation scheme can cut down the surface energy flux prediction biases (BIAS) to nearly zero.

scholarly journals A dual-pass data assimilation scheme for estimating turbulent fluxes with FY3A data

2012 ◽  
Vol 9 (7) ◽  
pp. 8493-8534
Author(s):  
T. R. Xu ◽  
S. M. Liu ◽  
Z. W. Xu ◽  
S. Liang ◽  
L. Xu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Latent Heat ◽  
Turbulent Flux ◽  
Turbulent Fluxes ◽  
Temporal Scale ◽  
Data Assimilation Scheme ◽  
Vegetation Parameters ◽  
Dual Pass ◽  
Assimilation Scheme

Abstract. A dual-pass data assimilation scheme is developed to improve predictions of turbulent fluxes with FY3A land surface temperature (LST) data. This scheme is constructed based on the ensemble Kalman filter (EnKF) and common land model (CoLM). Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at a long temporal scale and pass 2 optimizes soil moisture at a short temporal scale. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun in the People's Republic of China (PRC) that include grass, alpine meadow, crop, and orchard land cover types. The results are compared with data generated by a multi-scale turbulent flux observation system that includes an eddy covariance (EC) and a large aperture scintillometer (LAS) system. Results indicate that the CoLM can simulate the diurnal variations of turbulent flux, but usually underestimates the latent heat flux and evaporation fraction (EF) and overestimates sensible heat flux. With the assimilation of FY3A LST data, the dual-pass data assimilation scheme can improve the predictions of turbulent flux. The average root mean square error (RMSE) values drop from 81.2 to 39.6 W m−2 and from 101.7 to 58.9 W m−2 (the RMSE values drop 51.2% and 42.1%) for sensible and latent heat fluxes, respectively. To compare the results with LAS measurements, the source areas are calculated using a footprint model and overlaid with FY3A pixels since the LAS cover more than one FY3A pixel. The comparisons show that the assimilation results are closer to LAS measurements. With the dual-pass data assimilation scheme, the estimated soil moistures are generally closer to observations. Furthermore, the vegetation parameters are retrieved and incorporated into CoLM which enhanced the model's predictive abilities.

scholarly journals Diurnal land surface energy balance partitioning estimated from the thermodynamic limit of a cold heat engine

2018 ◽  
Author(s):  
Axel Kleidon ◽  
Maik Renner
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Thermodynamic Limit ◽  
Land Surface ◽  
Radiative Forcing ◽  
Surface Energy Balance ◽  
Heat Engine ◽  
Turbulent Fluxes ◽  
Heat Fluxes ◽  
Turbulent Heat

Abstract. Turbulent fluxes strongly shape the conditions at the land surface, yet they are typically formulated in terms of semi-empirical parameterisations that make it difficult to derive theoretical estimates of how global change impacts land surface functioning. Here, we describe these turbulent fluxes as the result of a thermodynamic process that generates work to sustain convective motion and thus maintains the turbulent exchange between the land surface and the atmosphere. We first derive a limit from the second law of thermodynamics that is equivalent to the Carnot limit, but which explicitly accounts for diurnal heat storage changes in the lower atmosphere. We then use this limit of a cold heat engine together with the surface energy balance to infer the maximum power that can be derived from the turbulent fluxes for a given solar radiative forcing. The surface energy balance partitioning estimated from this thermodynamic limit requires no empirical parameters and compares very well with the observed partitioning of absorbed solar radiation into radiative and turbulent heat fluxes across a range of climates, with correlation coefficients r2 ≥ 95 % and slopes near one. These results suggest that turbulent heat fluxes on land operate near their thermodynamic limit on how much convection can be generated from the local radiative forcing. It implies that this type of approach can be used to derive first-order estimates of global change that are solely based on physical principles.

An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim–Xiu Land Surface Model

2009 ◽  
Vol 48 (7) ◽  
pp. 1362-1376 ◽  
Author(s):  
Jonathan E. Pleim ◽  
Robert Gilliam
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Soil ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract The Pleim–Xiu land surface model (PX LSM) has been improved by the addition of a second indirect data assimilation scheme. The first, which was described previously, is a technique in which soil moisture is nudged according to the biases in 2-m air temperature and relative humidity between the model- and observation-based analyses. The new technique involves nudging the deep soil temperature in the soil temperature force–restore (FR) model according to model bias in 2-m air temperature only during nighttime. While the FR technique is computationally efficient and very accurate for the special conditions for which it was derived, it is very dependent on the deep soil temperature that drives the restoration term of the surface soil temperature equation. Thus, adjustment of the deep soil temperature to optimize the 2-m air temperature during the night, when surface forcing is minimal, provides significant advantages over other methods of deep soil moisture initialization. Simulations of the Weather Research and Forecasting Model (WRF) using the PX LSM with and without the new deep soil temperature nudging scheme demonstrate substantial benefits of the new scheme for reducing error and bias of the 2-m air temperature. The effects of the new nudging scheme are most pronounced in the winter (January 2006) during which the model’s cold bias is greatly reduced. Air temperature error and bias are also reduced in a summer simulation (August 2006) with the greatest benefits in less vegetated and more arid regions. Thus, the deep temperature nudging scheme complements the soil moisture nudging scheme because it is most effective for conditions in which the soil moisture scheme is least effective, that is, when evapotranspiration is not important (winter and arid climates).

scholarly journals A Weak-Constraint-Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes

2007 ◽  
Vol 4 (4) ◽  
pp. 649-653 ◽  
Author(s):  
Jun Qin ◽  
Shunlin Liang ◽  
Ronggao Liu ◽  
Hao Zhang ◽  
Bo Hu
Keyword(s):  
Data Assimilation ◽  
Turbulent Fluxes ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

scholarly journals Diurnal land surface energy balance partitioning estimated from the thermodynamic limit of a cold heat engine

2018 ◽  
Vol 9 (3) ◽  
pp. 1127-1140 ◽  
Author(s):  
Axel Kleidon ◽  
Maik Renner
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Thermodynamic Limit ◽  
Land Surface ◽  
Radiative Forcing ◽  
Surface Energy Balance ◽  
Heat Engine ◽  
Turbulent Fluxes ◽  
Heat Fluxes ◽  
Turbulent Heat

Abstract. Turbulent fluxes strongly shape the conditions at the land surface, yet they are typically formulated in terms of semiempirical parameterizations that make it difficult to derive theoretical estimates of how global change impacts land surface functioning. Here, we describe these turbulent fluxes as the result of a thermodynamic process that generates work to sustain convective motion and thus maintains the turbulent exchange between the land surface and the atmosphere. We first derive a limit from the second law of thermodynamics that is equivalent to the Carnot limit but which explicitly accounts for diurnal heat storage changes in the lower atmosphere. We call this the limit of a “cold” heat engine and use it together with the surface energy balance to infer the maximum power that can be derived from the turbulent fluxes for a given solar radiative forcing. The surface energy balance partitioning estimated from this thermodynamic limit requires no empirical parameters and compares very well with the observed partitioning of absorbed solar radiation into radiative and turbulent heat fluxes across a range of climates, with correlation coefficients r2≥95 % and slopes near 1. These results suggest that turbulent heat fluxes on land operate near their thermodynamic limit on how much convection can be generated from the local radiative forcing. It implies that this type of approach can be used to derive general estimates of global change that are solely based on physical principles.

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