Roughness Parameterization Research Articles

Quantifying Hydraulic Roughness From Field Data: Can Dune Morphology Tell the Whole Story?

AbstractHydraulic roughness is a fundamental property in river research, as it directly affects water levels, flow strength and the associated sediment transport rates. However, quantification of roughness is challenging, as it is not directly measurable in the field. In lowland rivers, bedforms are a major source of hydraulic roughness. Decades of research have focused on dunes to allow parameterization of roughness, with relatively little focus on field verification. This study aims to establish the predictive capacity of current roughness predictors, and to identify reasons for the unexplained part of the variance in roughness. We quantified hydraulic roughness based on the Darcy‐Weisbach friction factor (f) calculated from hydraulic field data of a 78 km‐long trajectory of the river Rhine and river Waal in the Netherlands. This is compared to predicted roughness values based on dune geometry, and to the spatial trends in the local topographic leeside angle, both inferred from bathymetric field data. Results from both approaches show the same general trend and magnitude of roughness values (0.019 < f < 0.069). Roughness inferred from dune geometry explains at best 31% of the variance. Efforts to explain the remaining variance from statistics of the local topographic leeside angles, which supposedly control flow separation, were unsuccessful. Unexpectedly, multi‐kilometer depth oscillations explain 34% of the total roughness variations. We suggest that flow divergence associated with depth increase causes energy loss, which is reflected in an elevated hydraulic roughness. Multi‐kilometer depth variations occur in many rivers worldwide, which implies a cause of flow resistance that needs further study.

Impact of SST and Surface Waves on Hurricane Florence (2018): A Coupled Modeling Investigation

AbstractHurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir–Simpson category 4 to category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere–ocean–wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by ocean waves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r = 0.89) and strength (r = 0.95) coming from the fully coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean and wave features to the model further modified the fluxes due to more realistic cooling beneath the storm, which in turn modified the precipitation field. Our experiments highlight significant differences in how air–sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.

Impact of surface roughness parameterizations on tropical cyclone simulations over the Bay of Bengal using WRF-OML model

The impact of different surface roughness schemes in air-sea coupling on simulating tropical cyclones (TCs) over the Bay of Bengal is analyzed using Weather Research and Forecasting-Ocean Mixed layer (WRF-OML) modeling system. The sensitivity of three surface roughness schemes is tested by conducting three experiments for seven TCs namely Phailin (2013), Lehar (2013), Hudhud (2014), Vardah (2016), Gaja (2018), Fani (2019) and Amphan (2020). The first experiment (Opt0) is configured with surface drag and moist enthalpy from Garratt formulations, the second (Opt1) uses surface drag from Donelan and constant moist enthalpy formulations and the third experiment (Opt2) employs the modified moist enthalpy from Garratt formulations along with Donelan drag. Results of predicted track, intensities, precipitation and structure of the cyclones are highly sensitive to the surface exchange coefficients formulated through the roughness parameterization. The Opt2 followed by Opt1 experiments captured the deepening and mature phases of the storm close to the observed estimates for both pre- and post-monsoon TCs, by improving the ocean-atmosphere feedback of surface energy fluxes. Comparison of simulated moisture convergence, transport and precipitation with observations for all cyclones suggest that the Opt2 provides an improved representation of surface enthalpy fluxes playing a crucial role in the moisture transport and cloud microphysical processes. The improved simulation of TCs with Opt1 and Opt2 over Opt0 is attributed to the realistic simulation of mixed layer deepening and sea surface temperature cooling effects in the model. An interesting result found from the WRF-OML simulations with three roughness schemes is the maximum moisture transport is mainly concentrated on the right sectors while the moisture convergence and precipitation are distributed on the left sectors of the cyclones.

Rough Surface Scattering Model Comparisons for Radar Altimetry of Sea Ice

Currently, sea-ice thickness is often estimated using microwave radar altimetry. It is well known that radar measurements are highly dependent on the roughness characteristics of the observed scene and can have a profound effect on altimetry-based thickness estimates. This simulation-based study highlights the importance of sea-ice roughness parameterization in the context of radar altimetry. This work provides a new sea-ice roughness parameterization in the form of an elevation spectrum developed from field campaign data. The formulation of this elevation spectrum is partly based on the generalized power law. Simulations have been developed to predict electromagnetic scattering from surfaces described by the new spectrum, with a focus on the X-band (10 GHz) scattering with nadir incidence. In this work, both the analytical small slope approximation and numerically exact Method of Moments are applied to various sea-ice surface spectra, representing a wide range of surface roughness conditions. The results show that the normalized radar cross section of the sea-ice interface varies significantly depending on the surface elevation spectrum and the statistics of the rough interface. Comparisons with numerically exact methods show that analytical approximations to rough surface scattering work reasonably well for the cases considered here, except at large scattering angles.

A Novel Sea Surface Roughness Parameterization Based on Wave State and Sea Foam

A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

Improving Regional Model Skills During Typhoon Events: A Case Study for Super Typhoon Lingling Over the Northwest Pacific Ocean

The ability of forecasting systems to simulate tropical cyclones is still insufficient, and currently, there is an increased interest in improving model performance for intense tropical cyclones. In this study, the impact of reducing surface drag at high wind speeds on modeling wind and wave conditions during the super Typhoon Lingling event over the northwest Pacific Ocean in 2019 is investigated. The model response with respect to the parameterization for momentum exchange at the ocean surface is demonstrated using a fully coupled regional atmosphere model (the Consortium for Small-Scale Modeling-Climate Limited-area Modeling, CCLM) and a wind wave model (WAM). The active two-way coupling between the atmosphere and ocean waves model is enabled through the introduction of sea state-dependent surface drag into the CCLM and updated winds into the WAM. The momentum exchange with the sea surface is modeled via the dependency of the roughness length (Z0) on the surface stress itself and, when applicable, on the wind speed. Several high-resolution runs are performed using one-way or two-way fully coupled regional atmosphere-wave (CCLM-WAM) models. The model simulations are assessed against the best track data as well as against buoy and satellite observations. The results show that the spectral nudging technique can improve the model’s ability to capture the large-scale circulation, track and intensity of Typhoon Lingling at regional scales. Under the precondition of large-scale constraining, the two-way coupling simulation with the proposed new roughness parameterization performs much better than the simulations used in older studies in capturing the maximum wind speed of Typhoon Lingling due to the reduced drag at extreme wind conditions for the new Z0.

An Air-to-Soil Transition Model for Discrete Scattering-Emission Modelling at L-Band

Topsoil structures and inhomogeneous distribution of moisture in the soil volume will induce dielectric discontinuities from air to bulk soil, which in turn may induce multiple and volume scattering and affect the microwave surface emission. In situ ELBARA-III L-band radiometer observations of brightness temperature T B p ( p =H or V polarization) at the Maqu site on the Eastern Tibetan Plateau are exploited to understand the effect of surface roughness on coherent and incoherent emission processes. Assisted with in situ soil moisture (SM) and temperature profile measurements, this study develops an air-to-soil transition (ATS) model that incorporates the dielectric roughness (i.e., resulted from fine-scale topsoil structures and the soil volume) characterized by SM and geometric roughness effects, and demonstrates the necessity of the ATS model for modelling L-band T B p . The Wilheit (1978) coherent and Lv et al. (2014) incoherent models are compared for determining the dielectric constant of bulk soil in the ATS zone and for calculating soil effective temperature T eff . The Tor Vergata discrete scattering model (TVG) integrated with the advanced integral equation model (AIEM) is used as the baseline model configuration for simulating L-band T B p . Whereafter, the ATS model is integrated with the foregoing model for assessing its performance. Results show the ATS-based models reduce the underestimation of T B p (≈20-50 K) by the baseline simulations. Being dynamic in nature, the proposed dielectric roughness parameterization in the ATS model significantly improves the ability in interpreting T B p dynamics, which is important for improving SM retrieval at the global scale.

Evaluation of drag coefficients under medicane conditions: Coupling waves, sea spray and surface friction

This work represents an initial step toward the coupled ocean-atmosphere-aerosols modeling of severe storms in the Mediterranean basin. This is realized extending the classical Charnock formulation of surface roughness over sea and consequently the determination of air-sea momentum fluxes in numerical models. To improve the forecast of severe Mediterranean storm systems, like the so-called Medicanes, it is essential to consider the wave-state and sea spray effects on the sea surface roughness explicitly. This is implemented in the present work by coupling the third-generation WAVEWATCH III model (WWIII) and the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) model configured with the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol package. The communication between the two models is realized through an offline coupling scheme, which utilizes a regridding algorithm between the numerical domains of the two models. In this context, the wave peak frequency is passed from WWIII to WRF-Chem model permitting the calculation of the wave-age and the sea spray source functions as well as the modification of the overseas drag coefficients. Two different surface roughness parameterizations are tested and compared with ERA5 reanalysis data from the ECMWF - Copernicus Climate Change Service. A single case study is performed considering the cyclone Rolf that occurred between 5 and 9 November 2011 in the western Mediterranean basin. It is considered one of the longest-lasting and most intense tropical-like cyclones in the Mediterranean with more than 48-h occurrence of tropical-like features and a wind speed peak reaching 30 m s−1 in its mature phase. The role of sea spray is evaluated considering two basic strategies, namely the limited-saturation layer approach and the “sea spray force” on the momentum balance. It is demonstrated that (i) the formulation based on the limited-saturation layer approach is better suited for describing the drag in the cyclonic region, where velocities are highest and consequently there is a very high concentration of sea-spray spume droplets; (ii) the momentum drag level-off at the highest wind speed. Our results give a contribution on the understanding of the air-sea flux parameterizations for a range of wind speed above 25–35 m s−1, indicating that sea-spray generation should be accounted for in coupled numerical models for ocean and atmosphere.

Multiscale Surface Roughness for Improved Soil Moisture Estimation

Surface roughness parameterization plays an important role in passive microwave soil moisture (SM) retrieval. This article proposes a new formulation for estimating surface roughness. The proposed model incorporates the field-scale (micro) roughness as well as topographic (macro) roughness. The performance of the model is evaluated by inverting the traditional tau–omega model for retrieving SM. The study focuses on the passive active L-band system (PALS) radiometer data collected as a part of two Soil Moisture Active Passive Validation Experiment (SMAPVEX), i.e., SMAPVEX12 (humid Manitoba, Canada) and SMAPVEX15 (semiarid Arizona, USA) with highly different microroughness and macroroughness. The measured surface roughness is observed to increase exponentially with clay fraction (CF). This behavior is minimized with increase in leaf area index (LAI). In the absence of vegetation, the contribution of topography toward surface roughness increases. A higher surface roughness value is estimated for SMAPVEX12, which positively correlate with LAI and CF and negatively correlate with wetness conditions. On the other hand, due to the high topographic variability in SMAPVEX15 region, the contribution of topography (surface curvature) toward total surface roughness is significant. Also, consistently dry SM resulted in high microroughness for SMAPVEX15. Nevertheless, a total surface roughness estimated for SMAPVEX15 region is less than for SMAPVEX12. The surface roughness formulation presented in this study can be extrapolated to any spatial resolution.

Uncertainty in Parameterizing Floodplain Forest Friction for Natural Flood Management, Using Remote Sensing

One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM.

The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling

Water level or water surface elevation (WSE) is an important state variable of rivers, lakes, and wetlands. Hydrodynamic models of rivers and streams simulate WSE and can benefit from spatially distributed WSE observations, to increase model reliability and predictive skill. This has been partially addressed by satellite radar altimetry, but satellite altimetry is unable to deliver useful data for small rivers. To overcome such limitations, we deployed a radar altimetry system on an unmanned aerial vehicle (UAV), to map spatially distributed WSE. We showed that UAV altimetry can provide observations of WSE with a very high spatial resolution (ca. 0.5 m) and accuracy (ca. 3 cm), in a time-saving and cost-effective way. Furthermore, we investigated the value of this dataset for the calibration and validation of hydrodynamic models. Specifically, we introduced spatially distributed roughness parameters in a hydrodynamic model and estimated these parameters, using the observed WSE profiles along the stream as input. A case study was conducted in the Åmose stream, Denmark. The results showed that UAV-borne WSE can identify significant variations of the Manning–Strickler coefficients, along this small and highly vegetated stream and over time. Moreover, the model performed extremely well using distributed roughness coefficients, but it could not reproduce WSE satisfactorily using uniform roughness. We concluded that distributed roughness coefficients should be considered, especially for small vegetated rivers, to improve model performance, both locally and globally. Spatially distributed parameterizations of the effective channel roughness could be constrained with UAV-borne WSE. This study demonstrated for the first time that UAV-borne WSE can help to understand the variations of hydraulic roughness, and can support efficient river management and maintenance.

Estimation of Turbulent Heat Fluxes via Assimilation of Air Temperature and Specific Humidity into an Atmospheric Boundary Layer Model

AbstractA number of studies have used time series of air temperature and specific humidity observations to estimate turbulent heat fluxes. These studies require the specification of surface roughness lengths for heat and momentum (that are directly related to the neutral bulk heat transfer coefficientCHN) and/or ground heat flux, which are often unavailable. In this study, sequences of air temperature and specific humidity are assimilated into an atmospheric boundary layer model within a variational data assimilation (VDA) framework to estimateCHN, evaporative fraction (EF), turbulent heat fluxes, and atmospheric boundary layer (ABL) height, potential temperature, and humidity. The developed VDA approach needs neither the surface roughness parameterization (as it is optimized by the VDA approach) nor ground heat flux measurements. The VDA approach is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summers of 1987 and 1988. The results indicate that the estimated sensible and latent heat fluxes agree fairly well with the corresponding measurements. For FIFE 1987 (1988), the daily sensible and latent heat fluxes estimates have a root-mean-square error of 25.72 W m−2(27.77 W m−2) and 53.63 W m−2(48.22 W m−2), respectively. In addition, the ABL height, specific humidity, and potential temperature estimates from the VDA system are in good agreement with those inferred from the radiosondes both in terms of magnitude and diurnal trend.

Quantifying the relative impact of hydrological and hydraulic modelling parameterizations on uncertainty of inundation maps

ABSTRACTFlood risk management strongly relies on inundation models for river basin zoning in flood-prone and risk-free areas. Floodplain zoning is significantly affected by the diverse and concurrent uncertainties that characterize the modelling chain used for producing inundation maps. In order to quantify the relative impact of the uncertainties linked to a lumped hydrological (rainfall–runoff) model and a FLO-2D hydraulic model, a Monte Carlo procedure is proposed in this work. The hydrological uncertainty is associated with the design rainfall estimation method, while the hydraulic model uncertainty is associated with roughness parameterization. This uncertainty analysis is tested on the case study of the Marta coastal catchment in Italy, by comparing the different frequency, extent and depth of inundation simulations associated with varying rainfall forcing and/or hydraulic model roughness realizations. The results suggest a significant predominance of the hydrological uncertainty with respect to the hydraulic one on the overall uncertainty associated with the simulated inundation maps.

Impact of different sea surface roughness on surface gravity waves using a coupled atmosphere–wave model: a case of Hurricane Isaac (2012)

The complicated pattern of the chaotic ocean surface depends strongly on the interaction between wind and waves. An accurate representation of momentum and energy exchange at air–sea interface is very important for ocean modeling and climate studies. The exchange of momentum, heat, and moisture at the air–sea interface considered a fundamental process in the development of mesoscale atmospheric phenomena such as hurricanes and precipitating systems. Since these exchanges take place in the wave boundary layer, one cannot neglect the importance of ocean surface waves in modifying air–sea interaction processes. In this paper, the sensitivity of a regional coupled atmosphere–wave model to sea surface roughness parameterizations is discussed. In order to see the impact of sea spray in modifying the surface gravity waves, we have introduced a sea-spray parameterization in surface layer scheme of the atmospheric model. We have taken the case of Hurricane Isaac, formed in the Gulf of Mexico in 2012 for this study. It is observed that the newly added sea-spray scheme shows a better performance compared with three existing, well-known formulations. Thus, during extreme events, sea spray can play an important role in modifying the wind and wave height.

Impact on Sea-Surface Electromagnetic Scattering and Emission Modeling of Recent Progress on the Parameterization of Ocean Surface Roughness, Drag Coefficient, and Whitecap Coverage in High Wind Conditions

Ocean surface wind stress is the force generating ocean surface roughness and whitecaps. The surface wind stress is related to the surface wind speed by the drag coefficient. Consequently, the magnitude of roughness and whitecaps calculated from wind speed input depends on the applied drag coefficient formula, which remains uncertain especially for high wind conditions. Because roughness and whitecaps are two major components of the ocean surface response in microwave remote sensing, a clear understanding of the drag coefficient, ocean surface roughness, and whitecap coverage is critical to interpreting the microwave signals emitted or scattered from the ocean surface. In reverse, the microwave signals represent precious data sources of these ocean surface properties [surface wind stress (or equivalently the drag coefficient), surface roughness, and whitecaps] that are difficult to measure with traditional oceanographic instruments, especially in severe weather conditions. This article describes the improvement of ocean surface properties achieved by incorporating microwave remote sensing measurements. In essence, the global data of microwave monostatic and bistatic radar cross sections contribute to the revision of ocean surface roughness spectrum model, and microwave radiometer measurements in tropical cyclones provide clarification regarding the dependence on wind speed of the drag coefficient and whitecap coverage. In return, the calculated excess emissivities and scattering radar cross sections are improved from implementing the updated results of drag coefficient, whitecap coverage, and surface roughness in microwave emission and scattering models.

Effects of the Sea Surface Roughness and Sea Spray‐Induced Flux Parameterization on the Simulations of a Tropical Cyclone

AbstractA tropical cyclone (TC) interacts with the ocean through the interchange of surface fluxes of heat and momentum at the air‐sea interface. The sustainability and intensification of TCs are maintained by the enhancement in the latent heat flux in the high wind speed regime. An accurate calculation of heat fluxes is vital to understand the dynamics of the TC. The sea surface roughness is a crucial parameter that modulates the air‐sea fluxes. A coupled ocean‐atmosphere‐wave model is used to simulate TC Vardah over the Bay of Bengal during 10–15 December 2016. The study focuses on the influence of sea surface roughness on the heat fluxes, track, and intensity of TC Vardah. Four numerical experiments are performed with different parameterization schemes for surface roughness on the basis of (i) frictional velocity (Charnock), (ii) wave age, (iii) wave steepness, and (iv) a combination of wave steepness and wave age. A change in ocean surface roughness results in the modification of the cyclone track and heat fluxes. However, the effect of sea spray on the simulation of wind intensity of TC Vardah is only marginal. Out of the four experiments, the wave age‐based roughness parameterization was found to improve cyclone track, wind, and, hence, the generation of wind waves on the sea surface. In the case study of TC Vardah, the dynamic sea spray reduces surface wind speed up to 1.5 m/s which is within the uncertainty range of the model. An intercomparison of model experiments reveals the impact of surface heat fluxes on the TC Vardha characteristics.

Assessment of Soil Moisture SMAP Retrievals and ELBARA-III Measurements in a Tibetan Meadow Ecosystem

This letter presents the results evaluating retrievals of liquid water content (θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> ) performed with a zero-order radiative transfer (τ-ω) model under frozen and thawed soil conditions from Soil Moisture Active Passive (SMAP) and ELBARA-III brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> ) measurements collected over a Tibetan meadow ecosystem. A good agreement is found between time series of the SMAP and ELBARA-III measured T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> resulting in a Pearson product-moment coefficient (R) larger than 0.87. Differences noted between the two data sets can be associated with discrepancies in θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> measured in the specific footprints, whereby the SMAP measurements are best explained by the in situ θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> . Furthermore, the in situ θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> has a better agreement with the horizontally polarized SMAP and ELBARA-III measurements (THB ) in the cold season, whereas the vertically polarized measurements (TVB ) are1111better correlated with θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> in the warm season. With the implementation of new vegetation and surface roughness parameterizations for the τ-ω model, the dynamics of in situ θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> is better reproduced by corresponding retrievals for both frozen and thawed soil conditions, leading to the reduction in the unbiased root-mean-square error (ubRMSE) by more than 31% in comparison with these retrievals using SMAP default parameterizations. Notably, the single-channel algorithm configured with the new parameterizations using SMAP TVB measured during the ascending overpass provides the best θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> retrievals with a ubRMSE of 0.035 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> that is well within the SMAP mission requirements.

Numerical modeling of tsunami inundation using upscaled urban roughness parameterization

This study develops and validates a numerical model of tsunami inundation using upscaled urban roughness parameterization and a Drag Force Model (DFM) to simulate the effect of structures as a drag force acting on flow. The DFM can deal with subgrid scale effects of structures using three upscaled parameters: the characteristic height of the structures, and the projected areas of the structures in the x− and y− directions. The validation is based on experimental results of the physical modeling of tsunami inundation conducted in the HyTOFU flume targeting the 2011 Tohoku Earthquake Tsunami; the physical model was a 1:250 scale idealization of the town of Onagawa in Japan. This study numerically simulated tsunami inundation by comparing the experimental results with numerical results reproduced by the DFM using roughness parameterizations. The validation of the DFM reveals that it can express the effect of the flow direction and inundation ratio, and it improves the estimation of inundated extent, maximum inundation depth, and arrival time when compared with other roughness parameterizations. This technique requires careful validation of inundation characteristics and mesh size dependency, and future work to improve estimates of the drag coefficient for the prototype scale and incident wave conditions is recommended.

Boundary Layer Turbulence over Surface Waves in a Strongly Forced Condition: LES and Observation

AbstractThe impact of sea state on air–sea momentum flux (or wind stress) is a poorly understood component of wind–wave interactions, particularly in high wind conditions. The wind stress and mean wind profile over the ocean are influenced by the characteristics of boundary layer turbulence over surface waves, which are strongly modulated by transient airflow separation events; however, the features controlling their occurrence and intensity are not well known. A large-eddy simulation (LES) for wind over a sinusoidal wave train is employed to reproduce laboratory observations of phase-averaged airflow over waves in strongly forced conditions. The LES and observation both use a wave-following coordinate system with a decomposition of wind velocity into mean, wave-coherent, and turbulent fluctuation components. The LES results of the mean wind profile and structure of wave-induced and turbulent stress components agree reasonably well with observations. Both LES and observation show enhanced turbulent stress and mean wind shear at the height of the wave crest, signifying the impact of intermittent airflow separation events. Disparities exist particularly near the crest, suggesting that airflow separation and sheltering are affected by the nonlinearity and unsteadiness of laboratory waves. Our results also suggest that the intensity of airflow separation is most sensitive to wave steepness and the surface roughness parameterization near the crest. These results clarify how the characteristics of finite-amplitude waves can control the airflow dynamics, which may substantially influence the mean wind profile, equivalent surface roughness, and drag coefficient.

Relevance of wind stress and wave-dependent ocean surface roughness on the generation of winter meteotsunamis in the Northern Gulf of Mexico

Meteotsunamis associated with passing squall lines are often observed ahead of cold fronts during the winter in the Northern Gulf of Mexico. Winter meteotsunamis occur simultaneously with wind speed variations (~5–20 m/s) and sea-level atmospheric pressure oscillations (~1–6 hPa) with periods between several minutes to 2 h. In order to enhance understanding of meteotsunami generation and propagation, the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system is applied to one of the most intense winter meteotsunamis measured in the Northern Gulf of Mexico during the decade 2009–2018. The model, verified with sea level and atmospheric observations, is able to reproduce the timing and intensity of the wind at 10 m elevation and sea level atmospheric pressure fluctuations. The mean bias between observed and measured wind speeds and atmospheric pressure are 1.73 m/s and 0.63 hPa, respectively. The maximum meteotsunami elevation and its timing are successfully captured with a 7% underestimation of the maximum elevation. The relative effect of atmospheric pressure and wind stress on meteotsunami generation is assessed with different numerical simulations. Results indicate that both wind stress and atmospheric pressure changes contributed to the generation of the meteotsunami. Wind stress was the dominant force in shallow waters (<15 m in this application), while the effects of atmospheric pressure disturbances dominated over areas with Froude number close to one (~40 m in this application). During the passage of the squall line, the sea surface became rougher in a sea state characterized by young and steep local ocean waves. Compared to a purely wind-speed-dependent roughness scheme, the application of a wave-dependent roughness parameterization improved the modeled meteotsunami maximum elevation by 37%.