An observation system simulation experiment for the impact of land surface heterogeneity on AMSR-E soil moisture retrieval

2001 ◽  
Vol 39 (8) ◽  
pp. 1622-1631 ◽  
Author(s):  
W.T. Crow ◽  
M. Drusch ◽  
E.F. Wood
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Simulation Experiment ◽  
System Simulation ◽  
Surface Heterogeneity ◽  
Observation System ◽  
Land Surface Heterogeneity ◽  
The Impact

Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment

2019 ◽  
Vol 20 (1) ◽  
pp. 155-173 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Marco L. Carrera ◽  
Chris Derksen ◽  
Bernard Bilodeau ◽  
...  
Keyword(s):  
Land Surface ◽  
Simulation Experiment ◽  
Snow Water Equivalent ◽  
System Simulation ◽  
Surface Model ◽  
Trade Offs ◽  
Wet Snow ◽  
Observing System Simulation Experiment ◽  
The Impact ◽  
Snow Mass

Abstract Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

The Effect of Land Surface Heterogeneity and Background Wind on Shallow Cumulus Clouds and the Transition to Deeper Convection

2019 ◽  
Vol 76 (2) ◽  
pp. 401-419 ◽  
Author(s):  
Jungmin M. Lee ◽  
Yunyan Zhang ◽  
Stephen A. Klein
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Surface Heterogeneity ◽  
Secondary Circulation ◽  
Length Scale ◽  
Background Wind ◽  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Land Surface Heterogeneity ◽  
The Impact

Abstract Idealized large-eddy simulations (LESs) with prescribed heterogeneous land surface heat fluxes are performed to study the impact of the heterogeneity length scale and background wind speed on the development of shallow cumulus and the subsequent transition to congestus/deep convection. We study the impact of land surface heterogeneity in an atmosphere that favors shallow convection but is also conditionally unstable with respect to deeper convection. We find that before the convection transition, larger and thicker shallow cumulus clouds are attached to moisture pools near the PBL top over patches with low evaporative fraction (referred to as “DRY”). This feature is attributable to a surface-induced secondary circulation whose development depends on the heterogeneity size and the background wind speed. With large patches (≥5 km) under zero ambient wind, the secondary mesoscale circulation promotes the vertical transport of moisture forming a moisture pool over DRY patches, while with smaller patches, no such circulation develops. The influence of the background wind on the secondary circulation is strong such that any wind stronger than 2 m s−1 effectively eliminates the impact of surface heterogeneity on the PBL and brings no secondary circulation. This is because the triggered secondary circulation is not strong enough to withstand the imposed background wind. Based on these, we propose two criteria for the convection transition, namely, that the heterogeneity length scale is greater than 5 km and that the background wind speed is less than Uc0, where Uc0 is the near-surface cross-patch wind speed of the secondary circulation under zero background wind for a given patch size and is about 1.5 m s−1 in our cases.

scholarly journals Potential soil moisture products from the aquarius radiometer and scatterometer using an observing system simulation experiment

2013 ◽  
Vol 2 (1) ◽  
pp. 113-120 ◽  
Author(s):  
Y. Luo ◽  
X. Feng ◽  
P. Houser ◽  
V. Anantharaj ◽  
X. Fan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Simulation Experiment ◽  
System Simulation ◽  
Temporal Variations ◽  
Vegetation Coverage ◽  
Surface Model ◽  
L Band ◽  
Observing System Simulation Experiment ◽  
Mean Square Errors

Abstract. Using an observing system simulation experiment (OSSE), we investigate the potential soil moisture retrieval capability of the National Aeronautics and Space Administration (NASA) Aquarius radiometer (L-band 1.413 GHz) and scatterometer (L-band, 1.260 GHz). We estimate potential errors in soil moisture retrievals and identify the sources that could cause those errors. The OSSE system includes (i) a land surface model in the NASA Land Information System, (ii) a radiative transfer and backscatter model, (iii) a realistic orbital sampling model, and (iv) an inverse soil moisture retrieval model. We execute the OSSE over a 1000 × 2200 km2 region in the central United States, including the Red and Arkansas river basins. Spatial distributions of soil moisture retrieved from the radiometer and scatterometer are close to the synthetic truth. High root mean square errors (RMSEs) of radiometer retrievals are found over the heavily vegetated regions, while large RMSEs of scatterometer retrievals are scattered over the entire domain. The temporal variations of soil moisture are realistically captured over a sparely vegetated region with correlations 0.98 and 0.63, and RMSEs 1.28% and 8.23% vol/vol for radiometer and scatterometer, respectively. Over the densely vegetated region, soil moisture exhibits larger temporal variation than the truth, leading to correlation 0.70 and 0.67, respectively, and RMSEs 9.49% and 6.09% vol/vol respectively. The domain-averaged correlations and RMSEs suggest that radiometer is more accurate than scatterometer in retrieving soil moisture. The analysis also demonstrates that the accuracy of the retrieved soil moisture is affected by vegetation coverage and spatial aggregation.

An Analysis of the Importance of a Fully-Coupled Atmosphere and Land-Surface When Considering the Impact of Multi-Scale Land Spatial Heterogeneity on Cloud Development

2021 ◽  
Author(s):  
Jason Simon ◽  
Tyler Waterman ◽  
Finley Hay-Chapman ◽  
Paul Dirmeyer ◽  
Andrew Bragg ◽  
...  
Keyword(s):  
Land Surface ◽  
Wrf Model ◽  
Surface Heterogeneity ◽  
Land Surface Models ◽  
Bottom Boundary ◽  
Surface Models ◽  
Land Surface Heterogeneity ◽  
Land Surfaces ◽  
The Impact ◽  
Fully Coupled

<p><span>Land-surface heterogeneity is known to play an important role in land-surface hydrology, which drives the bottom boundary condition for atmospheric models in numerical weather prediction (NWP) applications. However, the ultimate impact of land-surface heterogeneity on atmospheric boundary layer (ABL) development is still an open problem with implications for sub-grid scale (SGS) parameterizations for both NWP and climate modeling. Large-eddy simulation (LES) is often used to study the effects of land-surface heterogeneity on ABL development, most typically via specified surface fields which are not influenced by the atmosphere (i.e. semi-coupled). Heterogeneous land surfaces have been seen in previous studies to have a significant influence on ABL dynamics, particularly cloud production, in certain cases when semi-coupled to the atmosphere. </span></p><p><span>Here we use the Weather Research and Forecasting (WRF) model as an LES with both semi-coupled and fully-coupled land surfaces to investigate the impact of two-way coupling on the interaction between heterogeneous land surfaces and daytime ABLs. For semi-coupled simulations, the HydroBlocks land-surface model is run offline, drive</span><span>n by 4-km NLDAS-2 meteorology with Stage-IV radar rainfall data, and then used to specify the bottom boundary in WRF. The WRF-Hydro model is used for cases where the land surface is fully coupled to the WRF model. Both land-surface models use the Noah-MP model as their underlying physics package and add both subsurface and overland flow routing. </span><span>The WRF model uses a 100-m horizontal resolution, and the land-surface models use </span><span>high resolution (30 m) datasets that were upscaled to match the LES resolution for elevation, landcover, and soil type using NED, NLCD, and POLARIS respectively. </span><span>These LES experiments are performed over the ARM Southern Great Plains Site</span><span> atmospheric observatory in Oklahoma during the Summer of 2017 with a grid size of 100 km x 100 km to imitate a single cell in a modern climate model. </span><span>The impact of land-surface heterogeneity on the atmosphere is evaluated by comparing simulations using the fully heterogeneous land surfaces with simulations where the land surface is homogenized at each timestep, taking a domain-wide spatial mean value at every grid cell. </span><span>Results are evaluated primarily by the differences in the development of clouds and evolution of turbulent kinetic energy in the ABL. </span></p>

scholarly journals On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

2020 ◽  
Vol 12 (16) ◽  
pp. 2645
Author(s):  
Maheshwari Neelam ◽  
Binayak P. Mohanty
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface ◽  
Surface Heterogeneity ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Land Surface Heterogeneity ◽  
Vegetation Characteristics ◽  
Physical Variables

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

scholarly journals Potential soil moisture products from the Aquarius radiometer and scatterometer using an observing system simulation experiment

2012 ◽  
Vol 2 (2) ◽  
pp. 457-476 ◽  
Author(s):  
Y. Luo ◽  
X. Feng ◽  
P. Houser ◽  
V. Anantharaj ◽  
X. Fan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Simulation Experiment ◽  
System Simulation ◽  
Temporal Variations ◽  
Vegetation Coverage ◽  
Surface Model ◽  
L Band ◽  
Observing System Simulation Experiment ◽  
Mean Square Errors

Abstract. Using an Observing System Simulation Experiment (OSSE), we investigate the potential soil moisture retrieval capability of the National Aeronautics and Space Administration (NASA) Aquarius radiometer (L-band 1.413 GHz) and scatterometer (L-band, 1.260 GHz). We estimate potential errors in soil moisture retrievals and identify the sources that could cause those errors. The OSSE system includes: (i) a land surface model in the NASA Land Information System, (ii) a radiative transfer and backscatter model, (iii) a realistic orbital sampling model and (iv) an inverse soil moisture retrieval model. We execute the OSSE over a 1000 × 2200 km2 region in the central US, including the Red and Arkansas river basins. Spatial distributions of soil moisture retrieved from the radiometer and scatterometer are close to the synthetic truth. High root mean square errors (RMSEs) of radiometer retrievals are found over the heavily vegetated regions, while large RMSE of scatterometer retrievals are scattered over the entire domain. The temporal variations of soil moisture are realistically captured over a sparely vegetated region with correlations 0.98 and 0.63, and RMSEs 1.28% and 8.23% vol vol−1 for radiometer and scatterometer, respectively. Over the densely vegetated region, soil moisture exhibits larger temporal variation than the truth, leading to correlation 0.70 and 0.67 respectively, and RMSEs 9.49% and 6.09% vol vol−1 respectively. The domain averaged correlations and RMSEs suggest that radiometer is more accurate than scatterometer in retrieving soil moisture. The analysis also demonstrates that the accuracy of the retrieved soil moisture is affected by vegetation coverage and spatial aggregation.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion

2009 ◽  
Vol 133 (3) ◽  
Author(s):  
Yuling Wu ◽  
Udaysankar S. Nair ◽  
Roger A. Pielke ◽  
Richard T. McNider ◽  
Sundar A. Christopher ◽  
...  
Keyword(s):  
Land Surface ◽  
Atmospheric Dispersion ◽  
Surface Heterogeneity ◽  
Land Surface Heterogeneity

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

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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