Semi-coupling of a Field-scale Resolving Land-surface Model and WRF-LES to Investigate the Influence of Land-surface Heterogeneity on Cloud Development

Land-surface heterogeneity is known to play an important role in land surface hydrology and thus the boundary conditions for numerical weather prediction (NWP) and climate modeling. For this reason, there have been considerable efforts over the past two decades to improve its representation in large scale models. However, to date, the inclusion of sub-grid heterogeneity in modeling land-atmosphere interactions in regional and global models has been limited to sub-grid spatial means and thus have almost entirely disregarded its multi-scale impact on the simulated atmospheric dynamics. To begin to address this challenge, here we use large-eddy simulations (LES) coupled to a land-surface model to gain a more complete understanding of its role in the coupled land-atmosphere system. In this work, we illustrate its impact over the Southern Great Plains (SGP) site in the United States and present a path forward for using these modeling experiments to guide the development of a complementary coupling parameterization within climate models.More specifically, over the SGP site, we use high-resolution LES to investigate the impact of SGS land heterogeneity under different atmospheric and surface conditions to inform the development of land-surface and planetary boundary layer (PBL) parameterizations for coarser, operational-scale weather and climate modeling efforts. The experiment methodology uses a high-resolution land-surface model (WRF-Hydro), spun-up over multiple years using reanalysis data, which is then coupled to the Weather Research and Forecasting (WRF) model for high-resolution LES. Cases are considered using both the fully heterogeneous land model as well as using a homogeneous surface with domain-averaged flux values at all grid points, allowing the dynamical effects of land-surface heterogeneity on the atmosphere to be isolated, and the land/atmospheric conditions under which land-surface heterogeneity plays a role to be studied. Results are evaluated primarily by the differences in the development of the planetary boundary layer and the extent, duration and intensity of developing rainfall events.

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High-resolution spatially explicit land surface model calibration using field-scale satellite-based daily evapotranspiration product

Journal of Hydrology ◽

10.1016/j.jhydrol.2020.125730 ◽

2020 ◽

pp. 125730

Author(s):

Yi Yang ◽

Kaiyu Guan ◽

Bin Peng ◽

Ming Pan ◽

Chongya Jiang ◽

...

Keyword(s):

High Resolution ◽

Land Surface ◽

Model Calibration ◽

Land Surface Model ◽

Spatially Explicit ◽

Surface Model ◽

Field Scale ◽

Daily Evapotranspiration

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Surface soil moisture data assimilation for irrigation amounts and timing estimation in semi-arid regions

10.5194/egusphere-egu21-14594 ◽

2021 ◽

Author(s):

Nadia Ouaadi ◽

Lionel Jarlan ◽

Saïd Khabba ◽

Jamal Ezzahar ◽

Olivier Merlin

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Land Surface Model ◽

Surface Model ◽

Surface Soil Moisture ◽

Field Scale ◽

In Situ Data ◽

Statistical Metrics

Irrigation is the largest consumer of water in the world, with more than 70% of the world's fresh water dedicated to agriculture. In this context, we developed and evaluated a new method to predict daily to seasonal irrigation timing and amounts at the field scale using surface soil moisture (SSM) data assimilated into a simple&#160; land surface model through a particle filter technique. The method is first tested using in situ SSM before using SSM products retrieved from Sentinel-1. Data collected on different wheat fields grown &#160;in Morocco, for both flood and drip irrigation techniques, are used to assess the performance of the proposed method. With in situ data, the results are good. Seasonal amounts are retrieved with R > 0.98, RMSE <42 mm and bias<2 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation where more than 70% of the irrigation events are detected with a time difference from actual irrigation events shorter than 4 days, when assimilating SSM observation every 6 days to mimics Sentinel-1 revisit time. Over the drip irrigated fields, the statistical metrics are R = 0.70, RMSE =28.5 mm and bias= -0.24 mm for irrigation amounts cumulated over 15 days. The approach is then evaluated using SSM products derived from Sentinel-1 data; statistical metrics are R= 0.64, RMSE= 28.78 mm and bias = 1.99 mm for irrigation amounts cumulated over 15 days. In addition to irrigated fields, the applicationof the developed methodover rainfed fieldsdid not detect any irrigation. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

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

Journal of Hydrometeorology ◽

10.1175/2011jhm1315.1 ◽

2011 ◽

Vol 12 (5) ◽

pp. 787-804 ◽

Cited By ~ 13

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.

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Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

Hydrology and Earth System Sciences ◽

10.5194/hess-25-2445-2021 ◽

2021 ◽

Vol 25 (5) ◽

pp. 2445-2458

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Soil Physics ◽

Field Scale

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

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Improving soil moisture predictions from a land surface model by optimisation of pedotransfer functions.

10.5194/ismc2021-81 ◽

2021 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Richard Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Pedotransfer Functions ◽

Small Scale ◽

Surface Model ◽

Land Surface Models ◽

Field Scale ◽

Surface Models

Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.

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