scholarly journals Investigating the Impact of Soil Moisture and Atmospheric Stability on Cloud Development and Distribution Using a Coupled Large-Eddy Simulation and Land Surface Model

2011 ◽  
Vol 12 (5) ◽  
pp. 787-804 ◽  
Author(s):  
Hsin-Yuan Huang ◽  
Steven A. Margulis
Keyword(s):  
Thermal Stability ◽  
Soil Moisture ◽  
Land Surface ◽  
Atmospheric Stability ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface Fluxes ◽  
Surface Model ◽  
Eddy Simulation ◽  
Cloud Development

Abstract The influence of soil moisture and atmospheric thermal stability on surface fluxes, boundary layer characteristics, and cloud development are investigated using a coupled large-eddy simulation (LES)–land surface model (LSM) framework. The study day from the Cabauw site in the central part of the Netherlands has been studied to examine the soil moisture–cloud feedback using a parameterized single-column model (SCM) in previous work. Good agreement is seen in the comparison between coupled model results and observations collected at the Cabauw eddy-covariance tower. Simulation results confirm the hypothesis that both surface fluxes and atmospheric boundary layer (ABL) states are strongly affected by soil moisture and atmospheric stability, which was proposed by a previous study using an SCM with simple parameterization. While the ABL-top cloud development is a nonmonotonic function of surface water content under different thermal stability conditions, coupled model simulations find that weak thermal stability has significant impacts on both thermal and moisture fluxes and variances near the entrainment zone, especially for the dry surface cases. Additionally, the impacts of ABL-top stability on thermal and moisture entrainment processes are in a different magnitude. The explicitly resolved cloud cover fraction increases with increasing soil moisture only occurs in cases with strong atmospheric stability, and an opposite result is seen when weak atmospheric stability exists. The elevation of cloud base highly depends on the strength of sensible heat flux. However, results of cloud thickness show that a dry surface with weak thermal stability is able to form a large amount of cumulus cloud, even if the soil provides less water vapor.

scholarly journals An Intercomparison of ERS-Scat and AMSR-E Soil Moisture Observations with Model Simulations over France

2009 ◽  
Vol 10 (2) ◽  
pp. 431-447 ◽  
Author(s):  
Christoph Rüdiger ◽  
Jean-Christophe Calvet ◽  
Claire Gruhier ◽  
Thomas R. H. Holmes ◽  
Richard A. M. de Jeu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface Model ◽  
Monitoring Site ◽  
Remotely Sensed Data ◽  
Mountainous Regions ◽  
Vegetation Water Content ◽  
Vegetation Water

Abstract This paper presents a study undertaken in preparation of the work leading up to the assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the land surface model (LSM) Interaction Soil Biosphere Atmosphere (ISBA) at Météo-France. This study consists of an intercomparison experiment of different space-borne platforms providing surface soil moisture information [Advanced Microwave Scanning Radiometer for Earth Observing (AMSR-E) and European Remote Sensing Satellite Scatterometer (ERS-Scat)] with the reanalysis soil moisture predictions over France from the model suite of Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN), ISBA, and coupled model (MODCOU; SIM) of Météo-France for the years of 2003–05. Both modeled and remotely sensed data are initially validated against in situ observations obtained at the experimental soil moisture monitoring site Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) in southwestern France. Two different AMSR-E soil moisture products are compared in the course of this study—the official AMSR-E product from the National Snow and Ice Data Center (NSIDC) and a new product developed at the Vrije Universiteit Amsterdam and NASA (VUA–NASA)—which were obtained using two different retrieval algorithms. This allows for an additional assessment of the different algorithms while using identical brightness temperature datasets. This study shows that a good correlation generally exists between AMSR-E (VUA–NASA), ERS-Scat, and SIM for low altitudes and low-to-moderate vegetation covers (1.5–3 kg m−2 vegetation water content), with a reduction in the correlation in mountainous regions. It also shows that the AMSR-E (NSIDC) soil moisture product has significant differences when compared to the other datasets.

scholarly journals Convective Planetary Boundary Layer Interactions with the Land Surface at Diurnal Time Scales: Diagnostics and Feedbacks

2007 ◽  
Vol 8 (5) ◽  
pp. 1082-1097 ◽  
Author(s):  
Joseph A. Santanello ◽  
Mark A. Friedl ◽  
Michael B. Ek
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Planetary Boundary Layer ◽  
Mixed Layer ◽  
Land Surface ◽  
Atmospheric Stability ◽  
Surface Fluxes ◽  
Surface Model ◽  
Wide Range ◽  
Convective Planetary Boundary Layer

Abstract The convective planetary boundary layer (PBL) integrates surface fluxes and conditions over regional and diurnal scales. As a result, the structure and evolution of the PBL contains information directly related to land surface states. To examine the nature and magnitude of land–atmosphere coupling and the interactions and feedbacks controlling PBL development, the authors used a large sample of radiosonde observations collected at the southern Atmospheric Research Measurement Program–Great Plains Cloud and Radiation Testbed (ARM-CART) site in association with simulations of mixed-layer growth from a single-column PBL/land surface model. The model accurately predicts PBL evolution and realistically simulates thermodynamics associated with two key controls on PBL growth: atmospheric stability and soil moisture. The information content of these variables and their influence on PBL height and screen-level temperature can be characterized using statistical methods to describe PBL–land surface coupling over a wide range of conditions. Results also show that the first-order effects of land–atmosphere coupling are manifested in the control of soil moisture and stability on atmospheric demand for evapotranspiration and on the surface energy balance. Two principal land–atmosphere feedback regimes observed during soil moisture drydown periods are identified that complicate direct relationships between PBL and land surface properties, and, as a result, limit the accuracy of uncoupled land surface and traditional PBL growth models. In particular, treatments for entrainment and the role of the residual mixed layer are critical to quantifying diurnal land–atmosphere interactions.

ECLand: an ECMWF land surface modelling platform

2021 ◽  
Author(s):  
Gianpaolo Balsamo ◽  
Souhail Boussetta
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Land Surface Model ◽  
Open Water ◽  
Surface Fluxes ◽  
Surface Model ◽  
New Developments ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
New Research

<p>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 introduces the CHTESSEL and its recent new developments that aims at hosting new research applications.</p><p>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.</p><p>Reference: Souhail Boussetta, Gianpaolo Balsamo*, Anna Agustì-Panareda, Gabriele Arduini, Anton Beljaars, Emanuel Dutra, Glenn Carver, Margarita Choulga, Ioan Hadade, Cinzia Mazzetti, Joaquìn Munõz-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).</p>

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

scholarly journals Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

2017 ◽  
Author(s):  
Sibo Zhang ◽  
Jean-Christophe Calvet ◽  
José Darrozes ◽  
Nicolas Roussel ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Ground Surface ◽  
Satellite System ◽  
Surface Model ◽  
Global Navigation Satellite ◽  
Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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