Evaluation of the Noah land-surface model using data from a fair-weather IHOP_2002 day with heterogeneous surface fluxes

The ECMWF operational land surface model, based on the Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) is the baseline for global weather, climate and environmental applications at ECMWF. In order to expedite its progress and benefit from international collaboration, an ECLand platform has been designed to host advanced and modular schemes. ECLand is paving the way toward a land model that could support a wider range of modelling applications, facilitating global kilometer scales testing as envisaged in the Copernicus and Destination Earth programmes. This presentation&#160;introduces the CHTESSEL and its recent new developments that aims at hosting new research applications.These new improvements touch upon different components of the model: (i) vegetation, (ii) snow, (iii) soil hydrology, (iv) open water/lakes (v) rivers and (vi) urban areas. The developments are evaluated separately with either offline simulations or coupled experiments, depending on their level of operational readiness, illustrating the benchmarking criteria for assessing process fidelity with regards to land surface fluxes and reservoirs involved in water-energy-carbon exchange, and within the Earth system prediction framework, as foreseen to enter upcoming ECMWF operational cycles.Reference: Souhail Boussetta, Gianpaolo Balsamo*, Anna Agust&#236;-Panareda, Gabriele Arduini, Anton Beljaars, Emanuel Dutra, Glenn Carver, Margarita Choulga, Ioan Hadade, Cinzia Mazzetti, Joaqu&#236;n Mun&#245;z-Sabater, Joe McNorton, Christel Prudhomme, Patricia De Rosnay, Irina Sandu, Nils Wedi, Dai Yamazaki, Ervin Zsoter, 2021: ECLand: an ECMWF land surface modelling platform, MDPI Atmosphere, (in prep).

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Impact of Land Surface and Forcing Parameters on the Spin-up Behaviour of Noah Land Surface Model over the Indian Sub-Continent

Pure and Applied Geophysics ◽

10.1007/s00024-017-1645-4 ◽

2017 ◽

Vol 175 (1) ◽

pp. 389-401

Author(s):

A. Bhattacharya ◽

A. N. V. Satyanarayana ◽

M. Mandal

Keyword(s):

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Noah Land Surface Model

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Improvement of the Noah land surface model for warm season processes: evaluation of water and energy flux simulation

Hydrological Processes ◽

10.1002/hyp.9214 ◽

2012 ◽

Vol 27 (2) ◽

pp. 297-303 ◽

Cited By ~ 42

Author(s):

Helin Wei ◽

Youlong Xia ◽

Kenneth E. Mitchell ◽

Michael B. Ek

Keyword(s):

Energy Flux ◽

Land Surface ◽

Land Surface Model ◽

Warm Season ◽

Surface Model ◽

Noah Land Surface Model

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Modeling Land Surface Fluxes from Uncertain Rainfall: A Case Study in the Sahel with Field-Driven Stochastic Rainfields

Atmosphere ◽

10.3390/atmos11050465 ◽

2020 ◽

Vol 11 (5) ◽

pp. 465

Author(s):

Bernard Cappelaere ◽

Denis Feurer ◽

Théo Vischel ◽

Catherine Ottlé ◽

Hassane Bil-Assanou Issoufou ◽

...

Keyword(s):

Land Surface ◽

Monsoon Season ◽

Land Surface Model ◽

Surface Fluxes ◽

Energy Budgets ◽

Substantial Part ◽

Surface Model ◽

Time Space ◽

Flux Variability ◽

Sahel Region

In distributed land surface modeling (LSM) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. This is particularly true for applications in the African Sahel region, where weak knowledge of highly time/space-variable convective rainfall in a poorly monitored region is a considerable obstacle to such developments. In this study, we used a field-based stochastic rainfield generator to analyze the propagation of the rainfall uncertainty through a distributed land surface model simulating water and energy fluxes in Sahelian ecosystems. Ensemble time/space rainfields were generated from field observations of the local AMMA-CATCH-Niger recording raingauge network. The rainfields were then used to force the SEtHyS-Savannah LSM, yielding an ensemble of time/space simulated fluxes. Through informative graphical representations and innovative diagnosis metrics, these outputs were analyzed to separate the different components of flux variability, among which was the uncertainty represented by ensemble-wise variability. Scale dependence was analyzed for each flux type in the water and energy budgets, producing a comprehensive picture of uncertainty propagation for the various flux types, with its relationship to intrinsic space/time flux variability. The study was performed over a 2530 km2 domain over six months, covering an entire monsoon season and the subsequent dry-down, using a kilometer/daily base resolution of analysis. The newly introduced dimensionless uncertainty measure, called the uncertainty coefficient, proved to be more effective in describing uncertainty patterns and relationships than a more classical measure based on variance fractions. Results show a clear scaling relationship in uncertainty coefficients between rainfall and the dependent fluxes, specific to each flux type. These results suggest a higher sensitivity to rainfall uncertainty for hydrological than for agro-ecological or meteorological applications, even though eddy fluxes do receive a substantial part of that source uncertainty.

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Assessment of Roughness Length Schemes Implemented within the Noah Land Surface Model for High-Altitude Regions

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0102.1 ◽

2014 ◽

Vol 15 (3) ◽

pp. 921-937 ◽

Cited By ~ 34

Author(s):

Donghai Zheng ◽

Rogier van der Velde ◽

Zhongbo Su ◽

Martijn J. Booij ◽

Arjen Y. Hoekstra ◽

...

Keyword(s):

Heat Flux ◽

High Altitude ◽

Land Surface ◽

Land Surface Model ◽

Surface Heat ◽

Heat Fluxes ◽

Surface Model ◽

Surface Heat Fluxes ◽

Noah Land Surface Model ◽

Better Than

ABSTRACT Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (Tsfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. In this study, the performance of various z0h and z0m schemes developed for the Noah land surface model is assessed for a high-altitude site (3430 m) on the northeastern part of the Tibetan Plateau. Based on the in situ surface heat fluxes and profile measurements of wind and temperature, monthly variations of z0m and diurnal variations of z0h are derived through application of the Monin–Obukhov similarity theory. These derived values together with the measured heat fluxes are utilized to assess the performance of those z0m and z0h schemes for different seasons. The analyses show that the z0m dynamics are related to vegetation dynamics and soil water freeze–thaw state, which are reproduced satisfactorily with current z0m schemes. Further, it is demonstrated that the heat flux simulations are very sensitive to the diurnal variations of z0h. The newly developed z0h schemes all capture, at least over the sparse vegetated surfaces during the winter season, the observed diurnal variability much better than the original one. It should, however, be noted that for the dense vegetated surfaces during the spring and monsoon seasons, not all newly developed schemes perform consistently better than the original one. With the most promising schemes, the Noah simulated sensible heat flux, latent heat flux, Tsfc, and soil temperature improved for the monsoon season by about 29%, 79%, 75%, and 81%, respectively. In addition, the impact of Tsfc calculation and energy balance closure associated with measurement uncertainties on the above findings are discussed, and the selection of the appropriate z0h scheme for applications is addressed.

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Evaluation of a Photosynthesis-Based Canopy Resistance Formulation in the Noah Land-Surface Model

Boundary-Layer Meteorology ◽

10.1007/s10546-010-9559-z ◽

2010 ◽

Vol 138 (2) ◽

pp. 263-284 ◽

Cited By ~ 28

Author(s):

Anil Kumar ◽

Fei Chen ◽

Dev Niyogi ◽

Joseph G. Alfieri ◽

Michael Ek ◽

...

Keyword(s):

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Canopy Resistance ◽

Noah Land Surface Model

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Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0075.1 ◽

2018 ◽

Vol 19 (12) ◽

pp. 1917-1933 ◽

Cited By ~ 1

Author(s):

Li Fang ◽

Xiwu Zhan ◽

Christopher R. Hain ◽

Jifu Yin ◽

Jicheng Liu

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Weather Prediction ◽

Wrf Model ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

Vegetation Fraction ◽

Data Products ◽

Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

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Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Geoscientific Model Development ◽

10.5194/gmd-9-2833-2016 ◽

2016 ◽

Vol 9 (8) ◽

pp. 2833-2852 ◽

Cited By ~ 20

Author(s):

Nina M. Raoult ◽

Tim E. Jupp ◽

Peter M. Cox ◽

Catherine M. Luke

Keyword(s):

Data Assimilation ◽

Land Surface ◽

Gross Primary Production ◽

Land Surface Model ◽

Weather Forecast ◽

Surface Model ◽

Land Surface Parameter ◽

Estimation System ◽

Using Data ◽

The Uk

Abstract. Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

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