Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

Abstract. Worldwide expansion of agriculture is impacting Earth's climate by altering the carbon, water and energy fluxes, but climate in turn is impacting crop production. To study this two-way interaction and its impact on seasonal dynamics of carbon, water and energy fluxes, we implemented dynamic crop growth processes into a land surface model, the Integrated Science Assessment Model (ISAM). In particular, we implement crop specific phenology schemes, which account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem and grain pools; dynamic vegetation structure growth, which better simulate the LAI and canopy height; dynamic root distribution processes in the soil layers, which better simulate the root response of soil water uptake and transpiration; and litter fall due to fresh and old dead leaves to better represent the water and energy interception by both stem and brown leaves of the canopy during leaf senescence. Observational data for LAI, above and below ground biomass, and carbon, water and energy fluxes were compiled from two Ameri-Flux sites, Mead, NE and Bondville, IL, to calibrate and evaluate the model performance under corn (C4)-soybean (C3) rotation system over the period 2001–2004. The calibrated model was able to capture the diurnal and seasonal patterns of carbon assimilation, water and energy fluxes under the corn-soybean rotation system at these two sites. Specifically, the calculated GPP, net radiation fluxes at the top of canopy and latent heat fluxes compared well with observations. The largest bias in model results is in sensible heat flux (H) for corn and soybean at both sites. With dynamic carbon allocation and root distribution processes, model simulated GPP and latent heat flux (LH) were in much better agreement with observation data than for the without dynamic case. Modeled latent heat improved by 12–27% during the growing season at both sites, leading to the improvement in modeled GPP by 13–61% compared to the without dynamic case.

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Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

Biogeosciences ◽

10.5194/bg-10-8039-2013 ◽

2013 ◽

Vol 10 (12) ◽

pp. 8039-8066 ◽

Cited By ~ 29

Author(s):

Y. Song ◽

A. K. Jain ◽

G. F. McIsaac

Keyword(s):

Latent Heat ◽

Land Surface ◽

Root Distribution ◽

Carbon Allocation ◽

Land Surface Model ◽

Crop Growth ◽

Surface Model ◽

Energy Fluxes ◽

Growth Processes ◽

Dynamic Crop Growth

Abstract. Worldwide expansion of agriculture is impacting the earth's climate by altering carbon, water, and energy fluxes, but the climate in turn is impacting crop production. To study this two-way interaction and its impact on seasonal dynamics of carbon, water, and energy fluxes, we implemented dynamic crop growth processes into a land surface model, the Integrated Science Assessment Model (ISAM). In particular, we implemented crop-specific phenology schemes and dynamic carbon allocation schemes. These schemes account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem, and grain pools. The dynamic vegetation structure simulation better captured the seasonal variability in leaf area index (LAI), canopy height, and root depth. We further implemented dynamic root distribution processes in soil layers, which better simulated the root response of soil water uptake and transpiration. Observational data for LAI, above- and belowground biomass, and carbon, water, and energy fluxes were compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, to calibrate and evaluate the model performance. For the purposes of calibration and evaluation, we use a corn–soybean (C4–C3) rotation system over the period 2001–2004. The calibrated model was able to capture the diurnal and seasonal patterns of carbon assimilation and water and energy fluxes for the corn–soybean rotation system at these two sites. Specifically, the calculated gross primary production (GPP), net radiation fluxes at the top of the canopy, and latent heat fluxes compared well with observations. The largest bias in model results was in sensible heat flux (SH) for corn and soybean at both sites. The dynamic crop growth simulation better captured the seasonal variability in carbon and energy fluxes relative to the static simulation implemented in the original version of ISAM. Especially, with dynamic carbon allocation and root distribution processes, the model's simulated GPP and latent heat flux (LH) were in much better agreement with observational data than for the static root distribution simulation. Modeled latent heat based on dynamic growth processes increased by 12–27% during the growing season at both sites, leading to an improvement in modeled GPP by 13–61% compared to the estimates based on the original version of the ISAM.

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Distinguishing between early- and late-covering crops in the land surface model Noah-MP: impact on simulated surface energy fluxes and temperature

Biogeosciences ◽

10.5194/bg-17-2791-2020 ◽

2020 ◽

Vol 17 (10) ◽

pp. 2791-2805

Author(s):

Kristina Bohm ◽

Joachim Ingwersen ◽

Josipa Milovac ◽

Thilo Streck

Keyword(s):

Land Use ◽

Heat Flux ◽

Surface Energy ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Energy Fluxes ◽

Surface Energy Fluxes

Abstract. Land surface models are essential parts of climate and weather models. The widely used Noah-MP land surface model requires information on the leaf area index (LAI) and green vegetation fraction (GVF) as key inputs of its evapotranspiration scheme. The model aggregates all agricultural areas into a land use class termed “cropland and pasture”. In a previous study we showed that, on a regional scale, the GVF has a bimodal distribution formed by two crop groups differing in phenology and growth dynamics: early-covering crops (ECC; e.g., winter wheat, winter rapeseed, winter barley) and late-covering crops (LCC; e.g., corn, silage maize, sugar beet). That result can be generalized for central Europe. The present study quantifies the effect of splitting the land use class cropland and pasture of Noah-MP into ECC and LCC on surface energy fluxes and temperature. We further studied the influence of increasing the LCC share, which in the study area (the Kraichgau region, southwest Germany) is mainly the result of heavily subsidized biomass production, on energy partitioning at the land surface. We used the GVF dynamics derived from high-resolution (5 m × 5 m) RapidEye satellite data and measured LAI data for the simulations. Our results confirm that the GVF and LAI strongly influence the partitioning of surface energy fluxes, resulting in pronounced differences between simulations of ECC and LCC. Splitting up the generic crop into ECC and LCC had the strongest effect on land surface exchange processes in July–August. During this period, ECC are at the senescence growth stage or already harvested, while LCC have a well-developed ground-covering canopy. The generic crop resulted in humid bias, i.e., an increase in evapotranspiration by +0.5 mm d−1 (latent heat flux is 1.3 MJ m−2 d−1), decrease in sensible heat flux (H) by 1.2 MJ m−2 d−1 and decrease in surface temperature by −1 ∘C. The bias increased as the shares of ECC and LCC became similar. The observed differences will impact the simulations of processes in the planetary boundary layer. Increasing the LCC share from 28 % to 38 % in the Kraichgau region led to a decrease in latent heat flux (LE) and a heating up of the land surface in the early growing season. Over the second part of the season, LE increased and the land surface cooled down by up to 1 ∘C.

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The interactions between soil–biosphere–atmosphere (ISBA) land surface model multi-energy balance (MEB) option in SURFEXv8 – Part 2: Introduction of a litter formulation and model evaluation for local-scale forest sites

Geoscientific Model Development ◽

10.5194/gmd-10-1621-2017 ◽

2017 ◽

Vol 10 (4) ◽

pp. 1621-1644 ◽

Cited By ~ 10

Author(s):

Adrien Napoly ◽

Aaron Boone ◽

Patrick Samuelsson ◽

Stefan Gollvik ◽

Eric Martin ◽

...

Keyword(s):

Heat Flux ◽

Energy Balance ◽

Soil Temperature ◽

Latent Heat ◽

Land Surface ◽

Forest Floor ◽

Land Surface Model ◽

Local Scale ◽

Surface Model ◽

Model Parameters

Abstract. Land surface models (LSMs) need to balance a complicated trade-off between computational cost and complexity in order to adequately represent the exchanges of energy, water and matter with the atmosphere and the ocean. Some current generation LSMs use a simplified or composite canopy approach that generates recurrent errors in simulated soil temperature and turbulent fluxes. In response to these issues, a new version of the interactions between soil–biosphere–atmosphere (ISBA) land surface model has recently been developed that explicitly solves the transfer of energy and water from the upper canopy and the forest floor, which is characterized as a litter layer. The multi-energy balance (MEB) version of ISBA is first evaluated for three well-instrumented contrasting local-scale sites, and sensitivity tests are performed to explore the behavior of new model parameters. Second, ISBA-MEB is benchmarked against observations from 42 forested sites from the global micro-meteorological network (FLUXNET) for multiple annual cycles.It is shown that ISBA-MEB outperforms the composite version of ISBA in improving the representation of soil temperature, ground, sensible and, to a lesser extent, latent heat fluxes. Both versions of ISBA give comparable results in terms of simulated latent heat flux because of the similar formulations of the water uptake and the stomatal resistance. However, MEB produces a better agreement with the observations of sensible heat flux than the previous version of ISBA for 87.5 % of the simulated years across the 42 forested FLUXNET sites. Most of this improvement arises owing to the improved simulation of the ground conduction flux, which is greatly improved using MEB, especially owing to the forest litter parameterization. It is also shown that certain processes are also modeled more realistically (such as the partitioning of evapotranspiration into transpiration and ground evaporation), even if certain statistical performances are neutral. The analyses demonstrate that the shading effect of the vegetation, the explicit treatment of turbulent transfer for the canopy and ground, and the insulating thermal and hydrological effects of the forest floor litter turn out to be essential for simulating the exchange of energy, water and matter across a large range of forest types and climates.

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The Interactions between Soil-Biosphere-Atmosphere (ISBA) land surface model Multi-Energy Balance (MEB) option in SURFEX – Part 2: Model evaluation for local scale forest sites

10.5194/gmd-2016-270 ◽

2016 ◽

Cited By ~ 2

Author(s):

Adrien Napoly ◽

Aaron Boone ◽

Patrick Samuelsson ◽

Stefan Gollvik ◽

Eric Martin ◽

...

Keyword(s):

Heat Flux ◽

Energy Balance ◽

Soil Temperature ◽

Latent Heat ◽

Land Surface ◽

Forest Floor ◽

Land Surface Model ◽

Local Scale ◽

Surface Model ◽

Model Parameters

Abstract. Land surface models (LSMs) need to balance a complicated trade-off between computational cost and complexity in order to adequately represent the exchanges of energy, water and matter with the atmosphere and the ocean. Some current generation LSMs use a simplified or composite canopy approach that generates recurrent errors in simulated soil temperature and turbulent fluxes. In response to these issues, a new version of the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model has recently been developed which explicitly solves the transfer of energy and water from the upper canopy and the forest floor which is characterized as a litter layer. The so-called Multi Energy Balance (MEB) version of ISBA is first evaluated for three well-instrumented contrasting local scale sites, and sensitivity tests are performed to explore the behavior of new model parameters. Second, ISBA-MEB is benchmarked against observations from 42 forested sites from the global micro-meteorological network (FluxNet) for multiple annual cycles. It is shown that ISBA-MEB outperforms the composite version of ISBA in improving the representation of soil temperature, ground, sensible and to a lesser extent latent heat fluxes. Both versions of ISBA give comparable results in terms of simulated latent heat flux because of the similar formulations of the water uptake and the stomatal resistance. However, MEB produces a better agreement with the observations of sensible heat flux than the previous version of ISBA for 87.5 % of the simulated years across the 42 forested FluxNet sites. Most of this improvement arises owing to the improved simulation of the ground conduction flux, which is greatly improved using MEB, especially owing to the forest litter parameterization. It is also shown that certain processes are also modeled more realistically (such as the partitioning of evapotranspiration into transpiration and ground evaporation), even if certain statistical performances are neutral. The analyses demonstrate that shading effect of the vegetation, the explicit treatment of turbulent transfer for the canopy and ground, and the insulating thermal and hydrological effects of the forest floor litter turn out to be essential for simulating the exchange of energy, water and matter across a large range of forest types and climates.

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ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-4653-2015 ◽

2015 ◽

Vol 8 (6) ◽

pp. 4653-4696 ◽

Cited By ~ 6

Author(s):

X. Wu ◽

N. Vuichard ◽

P. Ciais ◽

N. Viovy ◽

N. de Noblet-Ducoudré ◽

...

Keyword(s):

Winter Wheat ◽

Latent Heat ◽

Aboveground Biomass ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Sensible Heat ◽

Energy Fluxes ◽

Crop Phenology

Abstract. The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0–20.1 and ~9.4–22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model.

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ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe

Geoscientific Model Development ◽

10.5194/gmd-9-857-2016 ◽

2016 ◽

Vol 9 (2) ◽

pp. 857-873 ◽

Cited By ~ 30

Author(s):

X. Wu ◽

N. Vuichard ◽

P. Ciais ◽

N. Viovy ◽

N. de Noblet-Ducoudré ◽

...

Keyword(s):

Winter Wheat ◽

Latent Heat ◽

Crop Yield ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Good Match ◽

Energy Fluxes ◽

Crop Phenology

Abstract. The response of crops to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water, and energy fluxes, causing feedbacks to the climate. To simulate the response of temperate crops to changing climate and [CO2], which accounts for the specific phenology of crops mediated by management practice, we describe here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module, and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but is tested here using maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of ∼  9.0–20.1 and ∼  9.4–22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was ∼  8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements, which themselves showed an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to unrealistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.

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Evaluation of the Soil, Vegetation, and Snow (SVS) Land Surface Model for the Simulation of Surface Energy Fluxes and Soil Moisture under Snow-Free Conditions

Atmosphere ◽

10.3390/atmos11030278 ◽

2020 ◽

Vol 11 (3) ◽

pp. 278 ◽

Cited By ~ 2

Author(s):

Gonzalo Leonardini ◽

François Anctil ◽

Maria Abrahamowicz ◽

Étienne Gaborit ◽

Vincent Vionnet ◽

...

Keyword(s):

Heat Flux ◽

Soil Moisture ◽

Land Surface ◽

Sensible Heat Flux ◽

Land Surface Model ◽

Soil Heat Flux ◽

Heat Fluxes ◽

Surface Model ◽

Point Scale ◽

Energy Fluxes

The recently developed Soil, Vegetation, and Snow (SVS) land surface model is being progressively implemented at Environment and Climate Change Canada (ECCC) for operational numerical weather and hydrological predictions. The objective of this study is to evaluate the ability of SVS, in offline point-scale mode and under snow-free conditions, to simulate the surface heat fluxes and soil moisture when compared to flux tower observations and simulations from the Canadian Land Surface Scheme (CLASS), used here as a benchmark model. To do this, we performed point-scale simulations of between 4 and 12 years of data records at six selected sites of the FLUXNET network under arid, Mediterranean and tropical climates. At all sites, SVS shows realistic simulations of latent heat flux, sensible heat flux and net radiation. Soil heat flux is reasonably well simulated for the arid sites and one Mediterranean site and poorly simulated for the tropical sites. On the other hand, surface soil moisture was reasonably well simulated at the arid and Mediterranean sites and poorly simulated at the tropical sites. SVS performance was comparable to CLASS not only for energy fluxes and soil moisture, but also for more specific processes such as evapotranspiration and water balance.

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WRF v.3.9 sensitivity to land surface model and horizontal resolution changes over North America

10.5194/gmd-2021-243 ◽

2021 ◽

Author(s):

Almudena García-García ◽

Francisco José Cuesta-Valero ◽

Hugo Beltrami ◽

Fidel González-Rouco ◽

Elena García-Bustamante

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Land Surface Model ◽

Horizontal Resolution ◽

Convective Precipitation ◽

Surface Model ◽

Near Surface ◽

Low Latitudes

Abstract. Understanding the differences between regional simulations of land-atmosphere interactions and near-surface conditions is crucial for a more reliable representation of past and future climate. Here, we explore the effect of changes in the model's horizontal resolution on the simulated energy balance at the surface and near-surface conditions using the Weather Research and Forecasting (WRF) model. To this aim, an ensemble of twelve simulations using three different horizontal resolutions (25 km, 50 km and 100 km) and four different Land Surface Model (LSM) configurations over North America from 1980 to 2013 is developed. Our results show that finer resolutions lead to higher surface net shortwave radiation and maximum temperatures at mid- and high latitudes. At low latitudes over coastal areas, an increase in resolution leads to lower values of sensible heat flux and higher values of latent heat flux, as well as lower values of surface temperatures and higher values of precipitation and soil moisture in summer. The use of finer resolutions leads then to an increase in summer values of latent heat flux, convective and non-convective precipitation and soil moisture at low latitudes. The effect of the LSM choice is larger than the effect of horizontal resolution on the near-surface temperature conditions. By contrast, the effect of the LSM choice on the simulation of precipitation is weaker than the effect of horizontal resolution, showing larger differences among LSM simulations in summer and over regions with high latent heat flux. Comparison between observations and the simulation of daily maximum and minimum temperatures and accumulated precipitation indicates that the CLM4 LSM yields the lowest biases in maximum and minimum mean temperatures, but the highest biases in extreme temperatures. Increasing horizontal resolution leads to larger biases in accumulated precipitation over all regions particularly in summer. The reasons behind relate the partition between convective and non-convective precipitation, specially noticeable over western US.

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An Empirical Latent Heat Flux Parameterization for the Noah Land Surface Model

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2180.1 ◽

2010 ◽

Vol 49 (8) ◽

pp. 1696-1713 ◽

Cited By ~ 11

Author(s):

Christopher M. Godfrey ◽

David J. Stensrud

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Initial Conditions ◽

Land Surface Model ◽

Vegetation Coverage ◽

Surface Model ◽

Oklahoma Mesonet ◽

Surface Observations

Abstract Proper partitioning of the surface energy fluxes that drive the evolution of the planetary boundary layer in numerical weather prediction models requires an accurate representation of initial land surface conditions. Unfortunately, soil temperature and moisture observations are unavailable in most areas and routine daily estimates of vegetation coverage and biomass are not easily available. This gap in observational capabilities seriously hampers the evaluation and improvement of land surface parameterizations, since model errors likely relate to improper initial conditions as much as to inaccuracies in the parameterizations. Two unique datasets help to overcome these difficulties. First, 1-km fractional vegetation coverage and leaf area index values can be derived from biweekly maximum normalized difference vegetation index composites obtained from daily observations by the Advanced Very High Resolution Radiometer onboard NOAA satellites. Second, the Oklahoma Mesonet supplies multiple soil temperature and moisture measurements at various soil depths each hour. Combined, these two datasets provide significantly improved initial conditions for a land surface model and allow an evaluation of the accuracy of the land surface model with much greater confidence than previously. Forecasts that both include and neglect these unique land surface observations are used to evaluate the value of these two data sources to land surface initializations. The dense network of surface observations afforded by the Oklahoma Mesonet, including surface flux data derived from special sensors, provides verification of the model results, which indicate that predicted latent heat fluxes still differ from observations by as much as 150 W m−2. This result provides a springboard for assessing parameterization errors within the model. A new empirical parameterization developed using principal-component regression reveals simple relationships between latent heat flux and other surface observations. Periods of very dry conditions observed across Oklahoma are used advantageously to derive a parameterization for evaporation from bare soil. Combining this parameterization with an empirical canopy transpiration scheme yields improved sensible and latent heat flux forecasts and better partitioning of the surface energy budget. Surface temperature and mixing ratio forecasts show improvement when compared with observations.

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