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Performance Evaluation of Force Transducer for the Observation of Sediment Entrainment in Rapidly Varied FlowsAbstract
Direct measurement of forces within the rough bed layer have been limited by previous spatial-averaging shear force studies. A highly sensitive force transducer assembled with a target sphere was used to measure and record the instantaneous three-dimensional forces of sediment at incipient motion. In the current study, a laser Doppler anemometer, ultrasonic displacement meter, and a force transducer accompanied by video recordings were used to experimentally investigate the incipient motion of sediment. The developed experimental setup have the potential to resolve and improve fundamental classical hypotheses regarding the incipient sediment motion. Experiments conducted in a large recirculating flume verified that the force transducer detects instantaneous forces at incipient motion under varies hydrodynamic conditions. Depth time series, instantaneous horizontal, vertical, and lateral forces are presented for dam-break and tidal breaking bores. Evidence suggests that the uplift vertical force plays an important role in destabilizing and in the incipient motion of particles. A sudden decrease in horizontal force was observed in tidal breaking bore due to flow reversal; however, a rapid rise was observed due to initial impact of dam-break bore. Bore velocity seems to have a larger effect on dam-break force than bore height. Furthermore, lateral force has the least influence during tidal breaking bore, while sediment particles are subjected to additional lateral force during dam-break bore.
Abstract
Direct measurement of forces within the rough bed layer have been limited by previous spatial-averaging shear force studies. A highly sensitive force transducer assembled with a target sphere was used to measure and record the instantaneous three-dimensional forces of sediment at incipient motion. In the current study, a laser Doppler anemometer, ultrasonic displacement meter, and a force transducer accompanied by video recordings were used to experimentally investigate the incipient motion of sediment. The developed experimental setup have the potential to resolve and improve fundamental classical hypotheses regarding the incipient sediment motion. Experiments conducted in a large recirculating flume verified that the force transducer detects instantaneous forces at incipient motion under varies hydrodynamic conditions. Depth time series, instantaneous horizontal, vertical, and lateral forces are presented for dam-break and tidal breaking bores. Evidence suggests that the uplift vertical force plays an important role in destabilizing and in the incipient motion of particles. A sudden decrease in horizontal force was observed in tidal breaking bore due to flow reversal; however, a rapid rise was observed due to initial impact of dam-break bore. Bore velocity seems to have a larger effect on dam-break force than bore height. Furthermore, lateral force has the least influence during tidal breaking bore, while sediment particles are subjected to additional lateral force during dam-break bore.
Abstract
High-frequency radars (HFR) remotely measure ocean surface currents based on the Doppler shift of electromagnetic waves backscattered by surface gravity waves with one-half of the electromagnetic wavelength, called Bragg waves. Their phase velocity is affected by their interactions with the mean Eulerian currents and with all of the other waves present at the sea surface. Therefore, HFRs should measure a quantity related to the Stokes drift in addition to mean Eulerian currents. However, different expressions have been proposed for this quantity: the filtered surface Stokes drift, one-half of the surface Stokes drift, and the weighted depth-averaged Stokes drift. We evaluate these quantities using directional wave spectra measured by bottom-mounted acoustic wave and current (AWAC) profilers in the lower Saint Lawrence Estuary, Quebec, Canada, deployed in an area covered by four HFRs: two Wellen radars (WERA) and two coastal ocean dynamics applications radars (CODAR). Since HFRs measure the weighted depth-averaged Eulerian currents, we extrapolate the Eulerian currents measured by the AWACs to the sea surface assuming linear Ekman dynamics to perform the weighted depth averaging. During summer 2013, when winds are weak, correlations between the AWAC and HFR currents are stronger (0.93) than during winter 2016/17 (0.42–0.62), when winds are high. After adding the different wave-induced quantities to the Eulerian currents measured by the AWACs, however, correlations during winter 2016/17 significantly increase. Among the different expressions tested, the highest correlations (0.80–0.96) are obtained using one-half of the surface Stokes drift, suggesting that HFRs measure the latter in addition to mean Eulerian currents.
Abstract
High-frequency radars (HFR) remotely measure ocean surface currents based on the Doppler shift of electromagnetic waves backscattered by surface gravity waves with one-half of the electromagnetic wavelength, called Bragg waves. Their phase velocity is affected by their interactions with the mean Eulerian currents and with all of the other waves present at the sea surface. Therefore, HFRs should measure a quantity related to the Stokes drift in addition to mean Eulerian currents. However, different expressions have been proposed for this quantity: the filtered surface Stokes drift, one-half of the surface Stokes drift, and the weighted depth-averaged Stokes drift. We evaluate these quantities using directional wave spectra measured by bottom-mounted acoustic wave and current (AWAC) profilers in the lower Saint Lawrence Estuary, Quebec, Canada, deployed in an area covered by four HFRs: two Wellen radars (WERA) and two coastal ocean dynamics applications radars (CODAR). Since HFRs measure the weighted depth-averaged Eulerian currents, we extrapolate the Eulerian currents measured by the AWACs to the sea surface assuming linear Ekman dynamics to perform the weighted depth averaging. During summer 2013, when winds are weak, correlations between the AWAC and HFR currents are stronger (0.93) than during winter 2016/17 (0.42–0.62), when winds are high. After adding the different wave-induced quantities to the Eulerian currents measured by the AWACs, however, correlations during winter 2016/17 significantly increase. Among the different expressions tested, the highest correlations (0.80–0.96) are obtained using one-half of the surface Stokes drift, suggesting that HFRs measure the latter in addition to mean Eulerian currents.
Abstract
Reconstructing tidal signals is indispensable for verifying altimetry products, forecasting water levels, and evaluating long-term trends. Uncertainties in the estimated tidal parameters must be carefully assessed to adequately select the relevant tidal constituents and evaluate the accuracy of the reconstructed water levels. Customary harmonic analysis uses ordinary least squares (OLS) regressions for their simplicity. However, the OLS may lead to incorrect estimations of the regression coefficient uncertainty due to the neglect of the residual autocorrelation. This study introduces two residual resamplings (moving-block and semiparametric bootstraps) for estimating the variability of tidal regression parameters and shows that they are powerful methods to assess the effects of regression errors with nontrivial autocorrelation structures. A Monte Carlo experiment compares their performance to four analytical procedures selected from those provided by the RT_Tide, UTide, and NS_Tide packages and the robustfit.m MATLAB function. In the Monte Carlo experiment, an iteratively reweighted least squares (IRLS) regression is used to estimate the tidal parameters for hourly simulations of one-dimensional water levels. Generally, robustfit.m and the considered RT_Tide method overestimate the tidal amplitude variability, while the selected UTide and NS_Tide approaches underestimate it. After some substantial methodological corrections the selected NS_Tide method shows adequate performance. As a result, estimating the regression variance–covariance with the considered RT_Tide, UTide, and NS_Tide methods may lead to the erroneous selection of constituents and underestimation of water level uncertainty, compromising the validity of their results in some applications.
Significance Statement
At many locations, the production of reliable water level predictions for marine navigation, emergency response, and adaptation to extreme weather relies on the precise modeling of tides. However, the complicated interaction between tides, weather, and other climatological processes may generate large uncertainties in tidal predictions. In this study, we investigate how different statistical methods may lead to different quantification of tidal model uncertainty when using data with completely known properties (e.g., knowing the tidal signal, as well as the amount and structure of noise). The main finding is that most commonly used statistical methods may estimate incorrectly the uncertainty in tidal parameters and predictions. This inconsistency is due to some specific simplifying assumptions underlying the analysis and may be reduced using statistical techniques based on data resampling.
Abstract
Reconstructing tidal signals is indispensable for verifying altimetry products, forecasting water levels, and evaluating long-term trends. Uncertainties in the estimated tidal parameters must be carefully assessed to adequately select the relevant tidal constituents and evaluate the accuracy of the reconstructed water levels. Customary harmonic analysis uses ordinary least squares (OLS) regressions for their simplicity. However, the OLS may lead to incorrect estimations of the regression coefficient uncertainty due to the neglect of the residual autocorrelation. This study introduces two residual resamplings (moving-block and semiparametric bootstraps) for estimating the variability of tidal regression parameters and shows that they are powerful methods to assess the effects of regression errors with nontrivial autocorrelation structures. A Monte Carlo experiment compares their performance to four analytical procedures selected from those provided by the RT_Tide, UTide, and NS_Tide packages and the robustfit.m MATLAB function. In the Monte Carlo experiment, an iteratively reweighted least squares (IRLS) regression is used to estimate the tidal parameters for hourly simulations of one-dimensional water levels. Generally, robustfit.m and the considered RT_Tide method overestimate the tidal amplitude variability, while the selected UTide and NS_Tide approaches underestimate it. After some substantial methodological corrections the selected NS_Tide method shows adequate performance. As a result, estimating the regression variance–covariance with the considered RT_Tide, UTide, and NS_Tide methods may lead to the erroneous selection of constituents and underestimation of water level uncertainty, compromising the validity of their results in some applications.
Significance Statement
At many locations, the production of reliable water level predictions for marine navigation, emergency response, and adaptation to extreme weather relies on the precise modeling of tides. However, the complicated interaction between tides, weather, and other climatological processes may generate large uncertainties in tidal predictions. In this study, we investigate how different statistical methods may lead to different quantification of tidal model uncertainty when using data with completely known properties (e.g., knowing the tidal signal, as well as the amount and structure of noise). The main finding is that most commonly used statistical methods may estimate incorrectly the uncertainty in tidal parameters and predictions. This inconsistency is due to some specific simplifying assumptions underlying the analysis and may be reduced using statistical techniques based on data resampling.
Abstract
Horizontal velocity gradients of a flow field and the related kinematic properties (KPs) of divergence, vorticity, and strain rate can be estimated from dense drifter deployments, e.g., the spatiotemporal average divergence (and other KPs) over a triangular area defined by three drifters and over a given time interval can be computed from the initial and final areas of said triangle. Unfortunately, this computation can be subject to large errors, especially when the triangle shape is far from equilateral. Therefore, samples with small aspect ratios are generally discarded. Here we derive the thresholds on two shape metrics that optimize the balance between retention of good and removal of bad divergence estimates. The primary tool is a high-resolution regional ocean model simulation, where a baseline for the average divergence can be established, so that actual errors are available. A value of 0.2 for the scaled aspect ratio Λ and a value of 0.86π for the largest interior angle θ are found to be equally effective thresholds, especially at scales of 5 km and below. While discarding samples with low Λ or high θ values necessarily biases the distribution of divergence estimates slightly toward positive values, this bias is small compared to (and in the opposite direction of) the Lagrangian sampling bias due to drifters preferably sampling convergence regions. Errors due to position uncertainty are suppressed by the shape-based subsampling. The subsampling also improves the identification of the areas of extreme divergence or convergence. An application to an observational dataset demonstrates that these model-derived thresholds can be effectively used on actual drifter data.
Significance Statement
Divergence in the ocean indicates how fast floating objects in the ocean spread apart, while convergence (negative divergence) captures how fast they accumulate. Measuring divergence in the ocean, however, remains challenging. One method is to estimate divergence from the trajectories of drifting buoys. This study provides guidance under what circumstances these estimates should be discarded because they are too likely to have large errors. The criteria proposed here are less stringent than some of the ad hoc criteria previously used. This will allow users to retain more of their estimates. We consider how position uncertainty affects the reliability of the divergence estimates. An observational dataset collected in the Mediterranean is used to illustrate an application of these reliability criteria.
Abstract
Horizontal velocity gradients of a flow field and the related kinematic properties (KPs) of divergence, vorticity, and strain rate can be estimated from dense drifter deployments, e.g., the spatiotemporal average divergence (and other KPs) over a triangular area defined by three drifters and over a given time interval can be computed from the initial and final areas of said triangle. Unfortunately, this computation can be subject to large errors, especially when the triangle shape is far from equilateral. Therefore, samples with small aspect ratios are generally discarded. Here we derive the thresholds on two shape metrics that optimize the balance between retention of good and removal of bad divergence estimates. The primary tool is a high-resolution regional ocean model simulation, where a baseline for the average divergence can be established, so that actual errors are available. A value of 0.2 for the scaled aspect ratio Λ and a value of 0.86π for the largest interior angle θ are found to be equally effective thresholds, especially at scales of 5 km and below. While discarding samples with low Λ or high θ values necessarily biases the distribution of divergence estimates slightly toward positive values, this bias is small compared to (and in the opposite direction of) the Lagrangian sampling bias due to drifters preferably sampling convergence regions. Errors due to position uncertainty are suppressed by the shape-based subsampling. The subsampling also improves the identification of the areas of extreme divergence or convergence. An application to an observational dataset demonstrates that these model-derived thresholds can be effectively used on actual drifter data.
Significance Statement
Divergence in the ocean indicates how fast floating objects in the ocean spread apart, while convergence (negative divergence) captures how fast they accumulate. Measuring divergence in the ocean, however, remains challenging. One method is to estimate divergence from the trajectories of drifting buoys. This study provides guidance under what circumstances these estimates should be discarded because they are too likely to have large errors. The criteria proposed here are less stringent than some of the ad hoc criteria previously used. This will allow users to retain more of their estimates. We consider how position uncertainty affects the reliability of the divergence estimates. An observational dataset collected in the Mediterranean is used to illustrate an application of these reliability criteria.
Abstract
The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated.
Significance Statement
This work illustrates a novel approach to improve the parameterization of vertical mixing in the upper ocean, which plays an important role in climate and ocean models. The approach utilizes the data from realistic turbulence simulation, called large-eddy simulation, as proxy observation data for upper ocean turbulence to analyze the parameterization, and the statistical method, called inverse estimation, to obtain the optimized empirical constants used in the parameterization. The same approach can be applied to improve other turbulence parameterization, and the new vertical mixing parameterization can be applied to improve climate and ocean models.
Abstract
The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated.
Significance Statement
This work illustrates a novel approach to improve the parameterization of vertical mixing in the upper ocean, which plays an important role in climate and ocean models. The approach utilizes the data from realistic turbulence simulation, called large-eddy simulation, as proxy observation data for upper ocean turbulence to analyze the parameterization, and the statistical method, called inverse estimation, to obtain the optimized empirical constants used in the parameterization. The same approach can be applied to improve other turbulence parameterization, and the new vertical mixing parameterization can be applied to improve climate and ocean models.
Abstract
The static and dynamic performances of the RBRargo 3 are investigated using a combination of laboratory-based and in situ datasets from floats deployed as part of an Argo pilot program. Temperature and pressure measurements compare well to co-located reference data acquired from shipboard CTDs. Static accuracy of salinity measurements is significantly improved using 1) a time lag for temperature, 2) a quadratic pressure dependence, and 3) a unit-based calibration for each RBRargo 3 over its full pressure range. Long-term deployments show no significant drift in the RBRargo 3 accuracy. The dynamic response of the RBRargo 3 demonstrates the presence of two different adjustment time scales: a long-term adjustment O(120) s, driven by the temperature difference between the interior of the conductivity cell and the water, and a short-term adjustment O(5–10) s, associated to the initial exchange of heat between the water and the inner ceramic. Corrections for these effects, including dependence on profiling speed, are developed.
Abstract
The static and dynamic performances of the RBRargo 3 are investigated using a combination of laboratory-based and in situ datasets from floats deployed as part of an Argo pilot program. Temperature and pressure measurements compare well to co-located reference data acquired from shipboard CTDs. Static accuracy of salinity measurements is significantly improved using 1) a time lag for temperature, 2) a quadratic pressure dependence, and 3) a unit-based calibration for each RBRargo 3 over its full pressure range. Long-term deployments show no significant drift in the RBRargo 3 accuracy. The dynamic response of the RBRargo 3 demonstrates the presence of two different adjustment time scales: a long-term adjustment O(120) s, driven by the temperature difference between the interior of the conductivity cell and the water, and a short-term adjustment O(5–10) s, associated to the initial exchange of heat between the water and the inner ceramic. Corrections for these effects, including dependence on profiling speed, are developed.
Abstract
A series of laboratory experiments are carried out to demonstrate the impacts of instrumented bottom frame legs on flow and turbulence. The magnitudes of vertical velocity, turbulent kinetic energy, dissipation, and shear stress induced by the frame legs depend on several factors, including the diameter and number of the frame legs, distances between the legs and the observational location, and the magnitude of the incoming flow and its direction with respect to the layout of the frame. In situ observations were carried out near the mouth of the Yellow River using two acoustic Doppler velocimeters mounted on a bottom frame. The estimated vertical velocity and turbulent kinetic energy dissipation rate show a significant asymmetry with flood and ebb tidal flows. This asymmetry can be partly explained by the influences of the bottom frame legs. Finally, the design and deployment principles of bottom frames are discussed for the purpose of reducing the impacts of the frame legs.
Significance Statement
Instrumented bottom frames are widely used for observations in the oceanic bottom boundary layer and above. However, the impacts of frame legs on the observed flow and turbulence have rarely been investigated. A series of laboratory experiments demonstrate that frame legs can induce vertical flow and enhanced turbulence, and the magnitudes of these influences vary with the size and layout of the frame legs and the magnitude and direction of the background flow. The results of the laboratory experiments can partially explain an “asymmetry” behavior of the vertical flow and turbulent kinetic energy with the flood and ebb tidal flows, derived from in situ observations near the mouth of the Yellow River.
Abstract
A series of laboratory experiments are carried out to demonstrate the impacts of instrumented bottom frame legs on flow and turbulence. The magnitudes of vertical velocity, turbulent kinetic energy, dissipation, and shear stress induced by the frame legs depend on several factors, including the diameter and number of the frame legs, distances between the legs and the observational location, and the magnitude of the incoming flow and its direction with respect to the layout of the frame. In situ observations were carried out near the mouth of the Yellow River using two acoustic Doppler velocimeters mounted on a bottom frame. The estimated vertical velocity and turbulent kinetic energy dissipation rate show a significant asymmetry with flood and ebb tidal flows. This asymmetry can be partly explained by the influences of the bottom frame legs. Finally, the design and deployment principles of bottom frames are discussed for the purpose of reducing the impacts of the frame legs.
Significance Statement
Instrumented bottom frames are widely used for observations in the oceanic bottom boundary layer and above. However, the impacts of frame legs on the observed flow and turbulence have rarely been investigated. A series of laboratory experiments demonstrate that frame legs can induce vertical flow and enhanced turbulence, and the magnitudes of these influences vary with the size and layout of the frame legs and the magnitude and direction of the background flow. The results of the laboratory experiments can partially explain an “asymmetry” behavior of the vertical flow and turbulent kinetic energy with the flood and ebb tidal flows, derived from in situ observations near the mouth of the Yellow River.
Assessment of Retrieved GMI Emissivity over Land, Snow, and Sea Ice in the GEOS SystemAbstract
Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System–Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.
Abstract
Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System–Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.
GOES ABI Detection of Thin Cirrus over LandAbstract
This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.
Abstract
This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.
