scholarly journals Land surface spinup for episodic modeling

2014 ◽  
Vol 14 (4) ◽  
pp. 4723-4744 ◽  
Author(s):  
W. M. Angevine ◽  
E. Bazile ◽  
D. Legain ◽  
D. Pino
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Land Surface ◽  
Large Scale ◽  
Layer Structure ◽  
Numerical Models ◽  
Surface Fluxes ◽  
Boundary Layer Structure ◽  
Soil Variables

Abstract. Soil moisture strongly controls the surface fluxes in mesoscale numerical models, and thereby influences the boundary layer structure. Proper initialization of soil moisture is therefore critical for faithful simulations. In many applications, such as air quality or process studies, the model is run for short, discrete periods (a day to a month). This paper describes one method for soil initialization in these cases, self-spinup. In self-spinup, the model is initialized with a coarse-resolution operational model or reanalysis output, and run for a month, cycling its own soil variables. This allows the soil variables to develop appropriate spatial variability, and may improve the actual values. The month (or other period) can be run more than once if needed. The case shown is for the Boundary Layer Late Afternoon and Sunset Turbulence experiment, conducted in France in 2011. Self-spinup adds spatial variability, which improves the representation of soil moisture patterns around the experiment location, which is quite near the Pyrenees Mountains. The self-spinup also corrects a wet bias in the large-scale analysis. The overall result is a much-improved simulation of boundary layer structure, evaluated by comparison with soundings from the field site. Self-spinup is not recommended as a substitute for multi-year spinup with an offline land data assimilation system in circumstances where the data sets required for such spinup are available at the required resolution. Self-spinup may fail if the modeled precipitation is poorly simulated. It is an expedient for cases when resources are not available to allow a better method to be used.

scholarly journals Land surface spinup for episodic modeling

2014 ◽  
Vol 14 (15) ◽  
pp. 8165-8172 ◽  
Author(s):  
W. M. Angevine ◽  
E. Bazile ◽  
D. Legain ◽  
D. Pino
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Land Surface ◽  
Large Scale ◽  
Layer Structure ◽  
Numerical Models ◽  
Surface Fluxes ◽  
Boundary Layer Structure ◽  
Soil Variables

Abstract. Soil moisture strongly controls the surface fluxes in mesoscale numerical models, and thereby influences the boundary layer structure. Proper initialization of soil moisture is therefore critical for faithful simulations. In many applications, such as air quality or process studies, the model is run for short, discrete periods (a day to a month). This paper describes one method for soil initialization in these cases – self-spinup. In self-spinup, the model is initialized with a coarse-resolution operational model or reanalysis output, and run for a month, cycling its own soil variables. This allows the soil variables to develop appropriate spatial variability, and may improve the actual values. The month (or other period) can be run more than once if needed. The case shown is for the Boundary Layer Late Afternoon and Sunset Turbulence experiment, conducted in France in 2011. Self-spinup adds spatial variability, which improves the representation of soil moisture patterns around the experiment location, which is quite near the Pyrenees Mountains. The self-spinup also corrects a wet bias in the large-scale analysis. The overall result is a much-improved simulation of boundary layer structure, evaluated by comparison with soundings from the field site. Self-spinup is not recommended as a substitute for multi-year spinup with an offline land data assimilation system in circumstances where the data sets required for such spinup are available at the required resolution. Self-spinup may fail if the modeled precipitation is poorly simulated. It is an expedient for cases when resources are not available to allow a better method to be used.

scholarly journals Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions

2013 ◽  
Vol 17 (3) ◽  
pp. 1177-1188 ◽  
Author(s):  
B. Li ◽  
M. Rodell
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Land Surface ◽  
Large Scale ◽  
Warm Season ◽  
Scale Dependency ◽  
Convex Shape ◽  
Spatial Moments ◽  
Environmental Controls

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications

2009 ◽  
Vol 48 (2) ◽  
pp. 349-368 ◽  
Author(s):  
Dev Niyogi ◽  
Kiran Alapaty ◽  
Sethu Raman ◽  
Fei Chen
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Gas Exchange ◽  
Surface Energy ◽  
Land Surface ◽  
Weather Forecasting ◽  
Stomatal Resistance ◽  
Surface Fluxes ◽  
Surface Model ◽  
Area Index

Abstract Current land surface schemes used for mesoscale weather forecast models use the Jarvis-type stomatal resistance formulations for representing the vegetation transpiration processes. The Jarvis scheme, however, despite its robustness, needs significant tuning of the hypothetical minimum-stomatal resistance term to simulate surface energy balances. In this study, the authors show that the Jarvis-type stomatal resistance/transpiration model can be efficiently replaced in a coupled land–atmosphere model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange–based surface evapotranspiration model (GEM) as a land surface scheme for mesoscale weather forecasting model applications. The GEM was dynamically coupled with a prognostic soil moisture–soil temperature model and an atmospheric boundary layer (ABL) model. This coupled system was then validated over different natural surfaces including temperate C4 vegetation (prairie grass and corn field) and C3 vegetation (soybean, fallow, and hardwood forest) under contrasting surface conditions (such as different soil moisture and leaf area index). Results indicated that the coupled model was able to realistically simulate the surface fluxes and the boundary layer characteristics over different landscapes. The surface energy fluxes, particularly for latent heat, are typically within 10%–20% of the observations without any tuning of the biophysical–vegetation characteristics, and the response to the changes in the surface characteristics is consistent with observations and theory. This result shows that photosynthesis-based transpiration/stomatal resistance models such as GEM, despite various complexities, can be applied for mesoscale weather forecasting applications. Future efforts for understanding the different scaling parameterizations and for correcting errors for low soil moisture and/or wilting vegetation conditions are necessary to improve model performance. Results from this study suggest that the GEM approach using the photosynthesis-based soil vegetation atmosphere transfer (SVAT) scheme is thus superior to the Jarvis-based approaches. Currently GEM is being implemented within the Noah land surface model for the community Weather Research and Forecasting (WRF) Advanced Research Version Modeling System (ARW) and the NCAR high-resolution land data assimilation system (HRLDAS), and validation is under way.

scholarly journals Sensitivity of Numerical Weather Forecasts to Initial Soil Moisture Variations in CFSv2

2016 ◽  
Vol 31 (6) ◽  
pp. 1973-1983 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Subhadeep Halder
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Time Scales ◽  
Land Surface ◽  
Forecast Model ◽  
Surface Fluxes ◽  
Near Surface ◽  
Weather Forecasts ◽  
Initial Soil Moisture ◽  
Initial Soil

Abstract When initial soil moisture is perturbed among ensemble members in the operational NWS global forecast model, surface latent and sensible fluxes are immediately affected much more strongly, systematically, and over a greater area than conventional land–atmosphere coupling metrics suggest. Flux perturbations are likewise transmitted to the atmospheric boundary layer more formidably than climatology-based metrics would indicate. Impacts are not limited to the traditional land–atmosphere coupling hot spots, but extend over nearly all ice-free land areas of the globe. Key to isolating this effect is that initial atmospheric states are identical among quantities correlated, pinpointing soil moisture and snow cover. A consequence of this high sensitivity is that significant positive impacts of realistic land surface initialization on the skill of deterministic near-surface temperature and humidity forecasts are also immediate and nearly universal during boreal spring and summer (the period investigated) and persist for at least 3 days over most land areas. Land surface initialization may be more broadly important for weather forecasts than previously realized, as the research focus historically has been on subseasonal-to-seasonal time scales. This study attempts to bridge the gap between climate studies with their associated coupling assessments and weather forecast time scales. Furthermore, errors in land surface initialization and shortcomings in the parameterization of atmospheric processes sensitive to surface fluxes may have greater consequences than previously recognized, the latter exemplified by the lack of impact on precipitation forecasts even though the simulation of boundary layer development is shown to be greatly improved with realistic soil moisture initialization.

scholarly journals Trends in Land–Atmosphere Interactions from CMIP5 Simulations

2013 ◽  
Vol 14 (3) ◽  
pp. 829-849 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Yan Jin ◽  
Bohar Singh ◽  
Xiaoqin Yan
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Lower Troposphere ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Future Climate Change ◽  
Boreal Winter ◽  
The Future

Abstract Data from 15 models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) for preindustrial, historical, and future climate change experiments are examined for consensus changes in land surface variables, fluxes, and metrics relevant to land–atmosphere interactions. Consensus changes in soil moisture and latent heat fluxes for past-to-present and present-to-future periods are consistent with CMIP3 simulations, showing a general drying trend over land (less soil moisture, less evaporation) over most of the globe, with the notable exception of high northern latitudes during winter. Sensible heat flux and net radiation declined from preindustrial times to current conditions according to the multimodel consensus, mainly due to increasing aerosols, but that trend reverses abruptly in the future projection. No broad trends are found in soil moisture memory except for reductions during boreal winter associated with high-latitude warming and diminution of frozen soils. Land–atmosphere coupling is projected to increase in the future across most of the globe, meaning a greater control by soil moisture variations on surface fluxes and the lower troposphere. There is also a strong consensus for a deepening atmospheric boundary layer and diminished gradients across the entrainment zone at the top of the boundary layer, indicating that the land surface feedback on the atmosphere should become stronger both in absolute terms and relative to the influence of the conditions of the free atmosphere. Coupled with the trend toward greater hydrologic extremes such as severe droughts, the land surface seems likely to play a greater role in amplifying both extremes and trends in climate on subseasonal and longer time scales.

Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production

2019 ◽  
pp. 0309524X1988092
Author(s):  
Mohamed Marouan Ichenial ◽  
Abdellah El-Hajjaji ◽  
Abdellatif Khamlichi
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farm ◽  
Layer Structure ◽  
Atmospheric Stability ◽  
Weather Conditions ◽  
Boundary Layer Structure

The assessment of climatological site conditions, airflow characteristics, and the turbulence affecting wind turbines is an important phase in developing wake engineering models. A method of modeling atmospheric boundary layer structure under atmospheric stability effects is crucial for accurate evaluation of the spatial scale of modern wind turbines, but by themselves, they are incapable to account for the varying large-scale weather conditions. As a result, combining lower atmospheric models with mesoscale models is required. In order to realize a reasonable approximation of initial atmospheric inflow condition used for wake identification behind an NREL 5-MW wind turbine, different vertical wind profile models on equilibrium conditions are tested and evaluated in this article. Wind farm simulator solvers require massive computing resources and forcing mechanisms tendencies inputs from weather forecast models. A three-dimensional Flow Redirection and Induction in Steady-state engineering model was developed for simulating and optimizing the wake losses of different rows of wind turbines under different stability stratifications. The obtained results were compared to high-fidelity simulation data generated by the famous Simulator for Wind Farm Applications. This work showed that a significant improvement related to atmospheric boundary layer structure can be made to develop accurate engineering wake models in order to reduce wake losses.

scholarly journals Two perspectives on the coupled carbon, water, and energy exchange in the planetary boundary layer

2014 ◽  
Vol 11 (4) ◽  
pp. 5275-5325 ◽  
Author(s):  
M. Combe ◽  
J. Vilà-Guerau de Arellano ◽  
H. G. Ouwersloot ◽  
C. M. J. Jacobs ◽  
W. Peters
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Carbon Storage ◽  
Crop Yield ◽  
Land Surface ◽  
Surface Fluxes ◽  
Point Of View ◽  
Upper Atmosphere ◽  
Atmospheric State

Abstract. Understanding the interactions between the land surface and the atmosphere is key to model boundary-layer meteorology and cloud formation, as well as carbon cycling and crop yield. In this study we explore these interactions in the exchange of water, heat, and CO2 in a cropland–atmosphere system at the diurnal and local scale. We thereto couple an atmospheric mixed-layer model (MXL) to two land-surface schemes, developed from two different perspectives: while one land-surface scheme (A-gs) simulates vegetation from an atmospheric point of view, the other (GECROS) simulates vegetation from a carbon-storage point of view. We calculate surface fluxes of heat, moisture and carbon, as well as the resulting atmospheric state and boundary-layer dynamics, over a maize field in the Netherlands, for a day on which we have a rich set of observations available. Particular emphasis is placed on understanding the role of upper atmosphere conditions like subsidence, in comparison to the role of surface forcings like soil moisture. We show that the atmospheric-oriented model (MXL-A-gs) outperforms the carbon storage-oriented model (MXL-GECROS) on this diurnal scale. This performance strongly depends on the sensitivity of the modelled stomatal conductance to water stress, which is implemented differently in each model. This sensitivity also influences the magnitude of the surface fluxes of CO2, water and heat (surface control), and subsequently impacts the boundary-layer growth and entrainment fluxes (upper atmosphere control), which alter the atmospheric state. These findings suggest that observed CO2 mole fractions in the boundary layer can reflect strong influences of both the surface and upper atmospheric conditions, and the interpretation of CO2 mole fraction variations depends on the assumed land-surface coupling. We illustrate this with a sensitivity analysis where increased subsidence, typical for periods of drought, can induce a change of 12 ppm in atmospheric CO2 mole fractions, solely by decreasing the boundary-layer volume. The effect of such high subsidence on the Bowen ratio is of the same magnitude as induced by the depletion of soil moisture that would typically occur during a corresponding drought event. Correctly including such two-way land-surface interactions on the diurnal scale can thus potentially improve our understanding and interpretation of observed variations in atmospheric CO2, as well as improve crop yield forecasts by better describing the water loss and carbon gain.

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