Wavefield separation and estimation of near surface properties using densely deployed 3C single sensor groups in land surface seismic recordings

2000 ◽  
Author(s):  
Johan O. A. Robertsson ◽  
Andrew Curtis
Keyword(s):  
Surface Properties ◽  
Land Surface ◽  
Near Surface ◽  
Wavefield Separation ◽  
Single Sensor

scholarly journals An Empirical Investigation of Convective Planetary Boundary Layer Evolution and Its Relationship with the Land Surface

2005 ◽  
Vol 44 (6) ◽  
pp. 917-932 ◽  
Author(s):  
Joseph A. Santanello ◽  
Mark A. Friedl ◽  
William P. Kustas
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Surface Properties ◽  
Planetary Boundary Layer ◽  
Time Scales ◽  
Land Surface ◽  
Specific Humidity ◽  
Near Surface ◽  
Convective Planetary Boundary Layer ◽  
Daily Time

Abstract Relationships among convective planetary boundary layer (PBL) evolution and land surface properties are explored using data from the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed in the southern Great Plains. Previous attempts to infer surface fluxes from observations of the PBL have been constrained by difficulties in accurately estimating and parameterizing the conservation equation and have been limited to multiday averages or small samples of daily case studies. Using radiosonde and surface flux data for June, July, and August of 1997, 1999, and 2001, a conservation approach was applied to 132 sets of daily observations. Results highlight the limitations of using this method on daily time scales caused by the diurnal variability and complexity of entrainment. A statistical investigation of the relationship among PBL and both land surface and near-surface properties that are not explicitly included in conservation methods indicates that atmospheric stability in the layer of PBL growth is the most influential variable controlling PBL development. Significant relationships between PBL height and soil moisture, 2-m potential temperature, and 2-m specific humidity are also identified through this analysis, and it is found that 76% of the variance in PBL height can be explained by observations of stability and soil water content. Using this approach, it is also possible to use limited observations of the PBL to estimate soil moisture on daily time scales without the need for detailed land surface parameterizations. In the future, the general framework that is presented may provide a means for robust estimation of near-surface soil moisture and land surface energy balance over regional scales.

Wavefield separation using densely deployed three‐component single‐sensor groups in land surface‐seismic recordings

Geophysics ◽  
2002 ◽  
Vol 67 (5) ◽  
pp. 1624-1633 ◽  
Author(s):  
Johan O. A. Robertsson ◽  
Andrew Curtis
Keyword(s):  
Free Surface ◽  
Seismic Data ◽  
Land Surface ◽  
Material Constant ◽  
Normal Incidence ◽  
S Wave ◽  
P Waves ◽  
Wavefield Separation ◽  
Single Sensor ◽  
Spatial Derivatives

Surface seismic data are usually acquired by placing receivers on the earth's free surface. This is exactly the surface at which all up‐coming wave energy is reflecting and converting into down‐going energy, so the wavefield recorded is the sum of up‐coming, down‐going reflected, and down‐going converted waves. In order to anaylze up‐coming (from the reservoir) energy only (e.g., for “true” amplitude analysis), it is necessary to separate and remove all down‐going waves from the recorded data. We present a new approach for wavefield separation of land surface‐seismic data based on receiver groups with densely deployed single‐sensor recordings. By converting vertical spatial derivatives to horizontal derivatives using the free‐surface condition, the methodology only requires locally dense measurements of the wavefield at the free surface to calculate all spatial derivatives of the wavefield. These can in turn be used to compute divergence (giving P‐wave potential) and curl (giving S‐wave potential) of the wavefield at the free surface. The effects of the free surface are removed through an up/down separation step using the elastodynamic representation theorem. This results in infinite spatial‐filter expressions that are appropriate for homogeneous media. The filter for P‐waves depends on both P‐ and S‐velocity at the receivers, whereas the S‐wave filters only depend on the S‐velocity. These velocities can be estimated using the techniques in the companion paper by Curtis and Robertsson in this issue. Spatially compact filters are chosen to approximate the analytical filter expressions. The filters are designed so that they can be applied within a densely deployed, spatially limited group of three‐component (3C) receivers. By assuming that the earth is locally homogeneous (no significant variations within the near‐surface region of the group), wavefield separation can be carried out also in areas with significant statics variations over the survey area. In particular, the simplest approximate expression for P‐waves consists of two terms. The first term corresponds to divergence in the presence of the free surface scaled by a material constant. The second term is a time derivative of the recorded vertical component scaled by a material constant. Hence, the first term is a correction that is added to the “traditional” P‐interpretation—the second term—which improves accuracy for incidence angles other than normal incidence. The proposed methodology is tested on synthetic data. By comparing “traditional” P‐sections to those obtained using the new methodology, we demonstrate that a significant improvement in amplitudes and phases of arrivals is obtained using the new methodology. By using the simplest possible filter which only involves first‐order derivatives in time and space, we obtained sufficiently accurate results up to incidence angles of around 30° away from normal incidence.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Analysis of Urban Effects in Oklahoma City using a Dense Surface Observing Network

2016 ◽  
Vol 55 (3) ◽  
pp. 723-741 ◽  
Author(s):  
Xiao-Ming Hu ◽  
Ming Xue ◽  
Petra M. Klein ◽  
Bradley G. Illston ◽  
Sheng Chen
Keyword(s):  
Metropolitan Area ◽  
Land Surface ◽  
Critical Role ◽  
Central Business District ◽  
Temperature Inversion ◽  
Oklahoma City ◽  
Near Surface ◽  
Open Questions ◽  
The Stability ◽  
Business District

AbstractMany studies have investigated urban heat island (UHI) intensity for cities around the world, which is normally quantified as the temperature difference between urban location(s) and rural location(s). A few open questions still remain regarding the UHI, such as the spatial distribution of UHI intensity, temporal (including diurnal and seasonal) variation of UHI intensity, and the UHI formation mechanism. A dense network of atmospheric monitoring sites, known as the Oklahoma City (OKC) Micronet (OKCNET), was deployed in 2008 across the OKC metropolitan area. This study analyzes data from OKCNET in 2009 and 2010 to investigate OKC UHI at a subcity spatial scale for the first time. The UHI intensity exhibited large spatial variations over OKC. During both daytime and nighttime, the strongest UHI intensity is mostly confined around the central business district where land surface roughness is the highest in the OKC metropolitan area. These results do not support the roughness warming theory to explain the air temperature UHI in OKC. The UHI intensity of OKC increased prominently around the early evening transition (EET) and stayed at a fairly constant level throughout the night. The physical processes during the EET play a critical role in determining the nocturnal UHI intensity. The near-surface rural temperature inversion strength was a good indicator for nocturnal UHI intensity. As a consequence of the relatively weak near-surface rural inversion, the strongest nocturnal UHI in OKC was less likely to occur in summer. Other meteorological factors (e.g., wind speed and cloud) can affect the stability/depth of the nighttime boundary layer and can thus modulate nocturnal UHI intensity.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

scholarly journals Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime

2021 ◽  
Author(s):  
Stefano Materia ◽  
Constantin Ardilouze ◽  
Chloé Prodhomme ◽  
Markus G. Donat ◽  
Marianna Benassi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Heat Waves ◽  
Climate Regime ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Surface Temperatures ◽  
The Mediterranean

AbstractLand surface and atmosphere are interlocked by the hydrological and energy cycles and the effects of soil water-air coupling can modulate near-surface temperatures. In this work, three paired experiments were designed to evaluate impacts of different soil moisture initial and boundary conditions on summer temperatures in the Mediterranean transitional climate regime region. In this area, evapotranspiration is not limited by solar radiation, rather by soil moisture, which therefore controls the boundary layer variability. Extremely dry, extremely wet and averagely humid ground conditions are imposed to two global climate models at the beginning of the warm and dry season. Then, sensitivity experiments, where atmosphere is alternatively interactive with and forced by land surface, are launched. The initial soil state largely affects summer near-surface temperatures: dry soils contribute to warm the lower atmosphere and exacerbate heat extremes, while wet terrains suppress thermal peaks, and both effects last for several months. Land-atmosphere coupling proves to be a fundamental ingredient to modulate the boundary layer state, through the partition between latent and sensible heat fluxes. In the coupled runs, early season heat waves are sustained by interactive dry soils, which respond to hot weather conditions with increased evaporative demand, resulting in longer-lasting extreme temperatures. On the other hand, when wet conditions are prescribed across the season, the occurrence of hot days is suppressed. The land surface prescribed by climatological precipitation forcing causes a temperature drop throughout the months, due to sustained evaporation of surface soil water. Results have implications for seasonal forecasts on both rain-fed and irrigated continental regions in transitional climate zones.

Development of stochastically perturbed parameterization scheme for the Noah Land Surface Model with the optimized random forcing parameters using the micro-genetic algorithm

2021 ◽  
Author(s):  
Sujeong Lim ◽  
Claudio Cassardo ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Surface Model ◽  
Ensemble Spread ◽  
Near Surface ◽  
Random Forcing ◽  
Micro Genetic Algorithm ◽  
Stochastically Perturbed

<p>The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature — a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm — the micro-genetic algorithm (micro-GA) — to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.</p>

Evaluating the role of soil physical properties on simulated land-atmosphere interactions over South Africa using coupled atmosphere-hydrological modeling

2021 ◽  
Author(s):  
Zhenyu Zhang ◽  
Patrick Laux ◽  
Joël Arnault ◽  
Jianhui Wei ◽  
Jussi Baade ◽  
...  
Keyword(s):  
South Africa ◽  
Physical Properties ◽  
Land Degradation ◽  
Land Surface ◽  
Soil Physical Properties ◽  
Near Surface ◽  
Soil Database ◽  
Global Soil ◽  
The Impact

<p>Land degradation with its direct impact on vegetation, surface soil layers and land surface albedo, has great relevance with the climate system. Assessing the climatic and ecological effects induced by land degradation requires a precise understanding of the interaction between the land surface and atmosphere. In coupled land-atmosphere modeling, the low boundary conditions impact the thermal and hydraulic exchanges at the land surface, therefore regulates the overlying atmosphere by land-atmosphere feedback processes. However, those land-atmosphere interactions are not convincingly represented in coupled land-atmosphere modeling applications. It is partly due to an approximate representation of hydrological processes in land surface modeling. Another source of uncertainties relates to the generalization of soil physical properties in the modeling system. This study focuses on the role of the prescribed physical properties of soil in high-resolution land surface-atmosphere simulations over South Africa. The model used here is the hydrologically-enhanced Weather Research and Forecasting (WRF-Hydro) model. Four commonly used global soil datasets obtained from UN Food and Agriculture Organization (FAO) soil database, Harmonized World Soil Database (HWSD), Global Soil Dataset for Earth System Model (GSDE), and SoilGrids dataset, are incorporated within the WRF-Hydro experiments for investigating the impact of soil information on land-atmosphere interactions. The simulation results of near-surface temperature, skin temperature, and surface energy fluxes are presented and compared to observational-based reference dataset. It is found that simulated soil moisture is largely influenced by soil texture features, which affects its feedback to the atmosphere.</p>

scholarly journals Dome effect of black carbon and its key influencing factors: A one-dimensional modelling study

2017 ◽  
Author(s):  
Zilin Wang ◽  
Xin Huang ◽  
Aijun Ding
Keyword(s):  
Boundary Layer ◽  
Air Pollution ◽  
Black Carbon ◽  
Rural Areas ◽  
Land Surface ◽  
Critical Role ◽  
Aerosol Layer ◽  
Near Surface ◽  
Haze Pollution ◽  
The Impact

Abstract. Black carbon (BC) has been identified to play a critical role in aerosol-planet boundary layer (PBL) interaction and further deterioration of near-surface air pollution in megacities, which has been named as its dome effect. However, the impacts of key factors that influence this effect, such as the vertical distribution and aging processes of BC, and also the underlying land surface, have not been quantitatively explored yet. Here, based on available in-situ measurements of meteorology and atmospheric aerosols together with the meteorology-chemistry online coupled model, WRF-Chem, we conduct a set of parallel simulations to quantify the roles of these factors in influencing the BC's dome effect and surface haze pollution, and discuss the main implications of the results to air pollution mitigation in China. We found that the impact of BC on PBL is very sensitive to the altitude of aerosol layer. The upper level BC, especially those near the capping inversion, is more essential in suppressing the PBL height and weakening the turbulence mixing. The dome effect of BC tends to be significantly intensified as BC aerosol mixed with scattering aerosols during winter haze events, resulting in a decrease of PBL height by more than 25 %. In addition, the dome effect is more substantial (up to 15 %) in rural areas than that in the urban areas with the same BC loading, indicating an unexpected regional impact of such kind of effect to air quality in countryside. This study suggests that China's regional air pollution would greatly benefit from BC emission reductions, especially those from the elevated sources from the chimneys and also the domestic combustions in rural areas, through weakening the aerosol-boundary layer interactions that triggered by BC.

scholarly journals Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region

2016 ◽  
Vol 10 (4) ◽  
pp. 1721-1737 ◽  
Author(s):  
Wenli Wang ◽  
Annette Rinke ◽  
John C. Moore ◽  
Duoying Ji ◽  
Xuefeng Cui ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Depth ◽  
Soil Depth ◽  
Permafrost Region ◽  
Simple Relationship ◽  
Land Surface Models ◽  
Cross Model ◽  
Near Surface ◽  
Surface Models

Abstract. A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

Sign in / Sign up

Export Citation Format

Share Document