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Estimating the Volume Transport of Kuroshio Extension Based on Satellite Altimetry and Hydrographic DataAbstract
In this study, an effective method of estimating the volume transport of the Kuroshio Extension (KE) is proposed using surface geostrophic flow inferred from satellite altimetry and vertical stratification derived from climatological temperature/salinity (T/S) profiles. Based on velocity measurements by a subsurface mooring array across the KE, we found that the vertical structure of horizontal flow in this region is dominated by the barotropic and first baroclinic normal modes, which is commendably described by the leading mode of empirical orthogonal functions (EOFs) of the observed velocity profiles as well. Further analysis demonstrates that the projection coefficient of moored velocity onto the superimposed vertical normal mode can be represented by the surface geostrophic velocity as derived from satellite altimetry. Given this relationship, we proposed a dynamical method to estimate the volume transport across the KE jet, which is well verified with both ocean reanalysis and repeated hydrographic data. This finding implicates that, in the regions where the currents render quasi-barotropic structure, it takes only satellite altimetry observation and climatological T/S to estimate the volume transport across any section.
Significance Statement
The Kuroshio Extension (KE) plays an important role in the midlatitude North Pacific climate system. To better understand the KE dynamic and its influences, it is very important to estimate the KE transport. However, direct observation is very difficult in this area. Combining a subsurface mooring array and climatological temperature/salinity data, the vertical structure of the KE is explored in this study using mode decomposition methods. The relationship between the vertical structure of the zonal velocity and surface geostrophic flow observed by satellite altimetry in the KE region is further investigated. Based on this relationship, the KE transport can be well estimated by using satellite altimetry observation and historical hydrographic observation.
Abstract
In this study, an effective method of estimating the volume transport of the Kuroshio Extension (KE) is proposed using surface geostrophic flow inferred from satellite altimetry and vertical stratification derived from climatological temperature/salinity (T/S) profiles. Based on velocity measurements by a subsurface mooring array across the KE, we found that the vertical structure of horizontal flow in this region is dominated by the barotropic and first baroclinic normal modes, which is commendably described by the leading mode of empirical orthogonal functions (EOFs) of the observed velocity profiles as well. Further analysis demonstrates that the projection coefficient of moored velocity onto the superimposed vertical normal mode can be represented by the surface geostrophic velocity as derived from satellite altimetry. Given this relationship, we proposed a dynamical method to estimate the volume transport across the KE jet, which is well verified with both ocean reanalysis and repeated hydrographic data. This finding implicates that, in the regions where the currents render quasi-barotropic structure, it takes only satellite altimetry observation and climatological T/S to estimate the volume transport across any section.
Significance Statement
The Kuroshio Extension (KE) plays an important role in the midlatitude North Pacific climate system. To better understand the KE dynamic and its influences, it is very important to estimate the KE transport. However, direct observation is very difficult in this area. Combining a subsurface mooring array and climatological temperature/salinity data, the vertical structure of the KE is explored in this study using mode decomposition methods. The relationship between the vertical structure of the zonal velocity and surface geostrophic flow observed by satellite altimetry in the KE region is further investigated. Based on this relationship, the KE transport can be well estimated by using satellite altimetry observation and historical hydrographic observation.
Abstract
The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE).
Abstract
The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE).
Corrections for Geostationary Cloud Liquid Water Path Using Microwave ImageryAbstract
Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
Significance Statement
Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.
Abstract
Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
Significance Statement
Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.
Abstract
Optical lightning observations from low-Earth orbit play an important role in our understanding of long-term global lightning trends. Lightning Imaging Sensors (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite (1997–2015) and International Space Station (2017–present) capture optical emissions produced by lightning. This study uses the well-documented TRMM LIS performance to determine if the ISS LIS performs well enough to bridge the gap between TRMM LIS and the new generation of Geostationary Lightning Mappers (GLMs). The average events per group and groups per flash for ISS LIS are 3.6 and 9.9, which are 18% and 10% lower than TRMM LIS, respectively. ISS LIS has 30% lower mean group energy density and 30%–50% lower mean flash energy density than TRMM LIS in their common (±38°) latitude range. These differences are likely the result of larger pixel areas for ISS LIS over most of the field of view due to off-nadir pointing, combined with viewing obstructions and possible engineering differences. For both instruments, radiometric sensitivity decreases radially from the center of the array to the edges. ISS LIS sensitivity falls off faster and more variably, contributed to by the off-nadir pointing. Event energy density analysis indicate some anomalous hotspot pixels in the ISS LIS pixel array that were not present with the TRMM LIS. Despite these differences, ISS LIS provides similar parameter values to TRMM LIS with the expectation of somewhat lower lightning detection capability. In addition, recalculation of the event, group, and flash areas for both LIS datasets are strongly recommended since the archived values in the current release versions have significant errors.
Abstract
Optical lightning observations from low-Earth orbit play an important role in our understanding of long-term global lightning trends. Lightning Imaging Sensors (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite (1997–2015) and International Space Station (2017–present) capture optical emissions produced by lightning. This study uses the well-documented TRMM LIS performance to determine if the ISS LIS performs well enough to bridge the gap between TRMM LIS and the new generation of Geostationary Lightning Mappers (GLMs). The average events per group and groups per flash for ISS LIS are 3.6 and 9.9, which are 18% and 10% lower than TRMM LIS, respectively. ISS LIS has 30% lower mean group energy density and 30%–50% lower mean flash energy density than TRMM LIS in their common (±38°) latitude range. These differences are likely the result of larger pixel areas for ISS LIS over most of the field of view due to off-nadir pointing, combined with viewing obstructions and possible engineering differences. For both instruments, radiometric sensitivity decreases radially from the center of the array to the edges. ISS LIS sensitivity falls off faster and more variably, contributed to by the off-nadir pointing. Event energy density analysis indicate some anomalous hotspot pixels in the ISS LIS pixel array that were not present with the TRMM LIS. Despite these differences, ISS LIS provides similar parameter values to TRMM LIS with the expectation of somewhat lower lightning detection capability. In addition, recalculation of the event, group, and flash areas for both LIS datasets are strongly recommended since the archived values in the current release versions have significant errors.
Abstract
A retrospective tropical Indian Ocean dipole mode (IOD) hindcast for 1958–2014 was conducted using 20 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with a model-based analog forecast (MAF) method. In the MAF approach, forecast ensembles are extracted from preexisting model simulations by finding the states that initially best match an observed anomaly and tracking their subsequent evolution, with no additional model integrations. By optimizing the key factors in the MAF method, we suggest that the optimal domain for the analog criteria should be concentrated in the tropical Indian Ocean region for IOD predictions. Including external forcing trends improves the skills of the east and west poles of the IOD, but not the IOD prediction itself. The MAF IOD prediction showed comparable skills to the assimilation-initialized hindcast, with skillful predictions corresponding to a 4- and 3-month lead, respectively. The IOD forecast skill had significant decadal variations during the 55-yr period, with low skill after the early 2000s and before 1985 and high skill during 1985–2000. This work offers a computational efficient and practical approach for seasonal prediction of the tropical Indian Ocean sea surface temperature.
Abstract
A retrospective tropical Indian Ocean dipole mode (IOD) hindcast for 1958–2014 was conducted using 20 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with a model-based analog forecast (MAF) method. In the MAF approach, forecast ensembles are extracted from preexisting model simulations by finding the states that initially best match an observed anomaly and tracking their subsequent evolution, with no additional model integrations. By optimizing the key factors in the MAF method, we suggest that the optimal domain for the analog criteria should be concentrated in the tropical Indian Ocean region for IOD predictions. Including external forcing trends improves the skills of the east and west poles of the IOD, but not the IOD prediction itself. The MAF IOD prediction showed comparable skills to the assimilation-initialized hindcast, with skillful predictions corresponding to a 4- and 3-month lead, respectively. The IOD forecast skill had significant decadal variations during the 55-yr period, with low skill after the early 2000s and before 1985 and high skill during 1985–2000. This work offers a computational efficient and practical approach for seasonal prediction of the tropical Indian Ocean sea surface temperature.
Abstract
The 2010–12 Acoustic Technology for Observing the Interior of the Arctic Ocean (ACOBAR) experiment provided acoustic tomography data along three 167–301-km-long sections in Fram Strait between Greenland and Spitsbergen. Ocean sound speed data were assimilated into a regional numerical ocean model using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system. The resulting state estimate matched the assimilated sound speed time series; the root-mean-squared (RMS) error of the sound speed estimate (∼0.4 m s−1) is smaller than the uncertainty of the measurement (∼0.8 m s−1). Data assimilation improved modeled range- and depth-averaged ocean temperatures at the 78°50′N oceanographic mooring section in Fram Strait. The RMS error of the state estimate (0.21°C) is comparable to the uncertainty of the interpolated mooring section (0.23°C). Lack of depth information in the assimilated ocean sound speed measurements caused an increased temperature bias in the upper ocean (0–500 m). The correlations with the mooring section were not improved as short-term variations in the mooring measurements and the ocean state estimate do not always coincide in time. This is likely due to the small-scale eddying and nonlinearity of the ocean circulation in Fram Strait. Furthermore, the horizontal resolution of the state estimate (4.5 km) is eddy permitting, rather than eddy resolving. Thus, the state estimate cannot represent the full ocean dynamics of the region. This study is the first to demonstrate the usefulness of large-scale acoustic measurements for improving ocean state estimates at high latitudes.
Significance Statement
Acoustic tomography measurements allow one to observe ocean temperature in large ocean volumes under the Arctic sea ice by measuring sound speed, which is hard to synoptically observe by other methods. This study has established methods for assimilation of depth- and range-averaged ocean sound speed from an acoustic tomography experiment in Fram Strait. For the first time, a 2-yr time series of ocean sound from acoustic tomography has been assimilated into an ocean state estimate. The results highlight the use of ocean tomography in ice-covered regions to improve state estimates of ocean temperature.
Abstract
The 2010–12 Acoustic Technology for Observing the Interior of the Arctic Ocean (ACOBAR) experiment provided acoustic tomography data along three 167–301-km-long sections in Fram Strait between Greenland and Spitsbergen. Ocean sound speed data were assimilated into a regional numerical ocean model using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system. The resulting state estimate matched the assimilated sound speed time series; the root-mean-squared (RMS) error of the sound speed estimate (∼0.4 m s−1) is smaller than the uncertainty of the measurement (∼0.8 m s−1). Data assimilation improved modeled range- and depth-averaged ocean temperatures at the 78°50′N oceanographic mooring section in Fram Strait. The RMS error of the state estimate (0.21°C) is comparable to the uncertainty of the interpolated mooring section (0.23°C). Lack of depth information in the assimilated ocean sound speed measurements caused an increased temperature bias in the upper ocean (0–500 m). The correlations with the mooring section were not improved as short-term variations in the mooring measurements and the ocean state estimate do not always coincide in time. This is likely due to the small-scale eddying and nonlinearity of the ocean circulation in Fram Strait. Furthermore, the horizontal resolution of the state estimate (4.5 km) is eddy permitting, rather than eddy resolving. Thus, the state estimate cannot represent the full ocean dynamics of the region. This study is the first to demonstrate the usefulness of large-scale acoustic measurements for improving ocean state estimates at high latitudes.
Significance Statement
Acoustic tomography measurements allow one to observe ocean temperature in large ocean volumes under the Arctic sea ice by measuring sound speed, which is hard to synoptically observe by other methods. This study has established methods for assimilation of depth- and range-averaged ocean sound speed from an acoustic tomography experiment in Fram Strait. For the first time, a 2-yr time series of ocean sound from acoustic tomography has been assimilated into an ocean state estimate. The results highlight the use of ocean tomography in ice-covered regions to improve state estimates of ocean temperature.
Abstract
In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially mixed estuary. These experiments included surface drifter and synthetic aperture radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with biologging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially mixed estuary by inverting surface current and gravity wave observations with a 3D hydrostatic ocean model. Bathymetry estimation skill depends on two factors: location (i.e., differing estimation quality inside versus outside the MCR) and observation type (e.g., surface currents versus significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.
Abstract
In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially mixed estuary. These experiments included surface drifter and synthetic aperture radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with biologging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially mixed estuary by inverting surface current and gravity wave observations with a 3D hydrostatic ocean model. Bathymetry estimation skill depends on two factors: location (i.e., differing estimation quality inside versus outside the MCR) and observation type (e.g., surface currents versus significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.
Detection of Rain in Tropical Cyclones by Underwater Ambient SoundAbstract
Rain in tropical cyclones is studied using eight time series of underwater ambient sound at 40–50 kHz with wind speeds up to 45 m s−1 beneath three tropical cyclones. At tropical cyclone wind speeds, rain- and wind-generated sound levels are comparable, and therefore rain cannot be detected by sound level alone. A rain detection algorithm that is based on the variations of 5–30-kHz sound levels with periods longer than 20 s and shorter than 30 min is proposed. Faster fluctuations (<20 s) are primarily due to wave breaking, and slower ones (>30 min) are due to overall wind variations. Higher-frequency sound (>30 kHz) is strongly attenuated by bubble clouds. This approach is supported by observations that, for wind speeds < 40 m s−1, the variation in sound level is much larger than that expected from observed wind variations and is roughly comparable to that expected from rain variations. The hydrophone results are consistent with rain estimates by the Tropical Rainfall Measuring Mission (TRMM) satellite and with Stepped-Frequency Microwave Radiometer (SFMR) and radar estimates by surveillance flights. The observations indicate that the rain-generated sound fluctuations have broadband acoustic spectra centered around 10 kHz. Acoustically detected rain events usually last for a few minutes. The data used in this study are insufficient to produce useful estimation of rain rate from ambient sound because of limited quantity and accuracy of the validation data. The frequency dependence of sound variations suggests that quantitative rainfall algorithms from ambient sound may be developed using multiple sound frequencies.
Significance Statement
Rain is an indispensable process in forecasting the intensity and path of tropical cyclones. However, its role in the air–sea interaction is still poorly understood, and its parameterization in numerical models is still in development. In this work, we analyzed sound measurements made by hydrophones on board Lagrangian floats beneath tropical cyclones. We find that wind, rain, and breaking waves each have distinctive signatures in underwater ambient sound. We suggest that the air–sea dynamic processes in tropical cyclones can be explored by listening to ambient sound using hydrophones beneath the sea surface.
Abstract
Rain in tropical cyclones is studied using eight time series of underwater ambient sound at 40–50 kHz with wind speeds up to 45 m s−1 beneath three tropical cyclones. At tropical cyclone wind speeds, rain- and wind-generated sound levels are comparable, and therefore rain cannot be detected by sound level alone. A rain detection algorithm that is based on the variations of 5–30-kHz sound levels with periods longer than 20 s and shorter than 30 min is proposed. Faster fluctuations (<20 s) are primarily due to wave breaking, and slower ones (>30 min) are due to overall wind variations. Higher-frequency sound (>30 kHz) is strongly attenuated by bubble clouds. This approach is supported by observations that, for wind speeds < 40 m s−1, the variation in sound level is much larger than that expected from observed wind variations and is roughly comparable to that expected from rain variations. The hydrophone results are consistent with rain estimates by the Tropical Rainfall Measuring Mission (TRMM) satellite and with Stepped-Frequency Microwave Radiometer (SFMR) and radar estimates by surveillance flights. The observations indicate that the rain-generated sound fluctuations have broadband acoustic spectra centered around 10 kHz. Acoustically detected rain events usually last for a few minutes. The data used in this study are insufficient to produce useful estimation of rain rate from ambient sound because of limited quantity and accuracy of the validation data. The frequency dependence of sound variations suggests that quantitative rainfall algorithms from ambient sound may be developed using multiple sound frequencies.
Significance Statement
Rain is an indispensable process in forecasting the intensity and path of tropical cyclones. However, its role in the air–sea interaction is still poorly understood, and its parameterization in numerical models is still in development. In this work, we analyzed sound measurements made by hydrophones on board Lagrangian floats beneath tropical cyclones. We find that wind, rain, and breaking waves each have distinctive signatures in underwater ambient sound. We suggest that the air–sea dynamic processes in tropical cyclones can be explored by listening to ambient sound using hydrophones beneath the sea surface.
Removing Interfering Signals in Spaceborne Radar Data for Precipitation Detection at Very High AltitudesAbstract
This study investigated the effects of interfering signals on high-altitude precipitation extraction from spaceborne precipitation radar data. Data analyses were performed on the products of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and the Global Precipitation Measurement Core Observatory Dual-Frequency Precipitation Radar (GPM DPR) to clarify the effects of removing radio interferences and mirror images, particularly focusing on deep precipitation detection. The TRMM PR acquired precipitation data up to an altitude of approximately 20 km and occasionally captured interferences from artificial radio transmissions in specific areas. Artifacts could be distinguished as isolated profiles exhibiting almost constant radar reflectivity. The number of interferences affecting the TRMM PR gradually increased during the operation period of 1998–2013. A filter was introduced to separate the observed profiles into deep storms that reach the upper observation altitude and contamination caused by radio interference. The former frequently appeared over the Sahel area, where the observation upper limits are lowest. The removal of the latter, radio interference, improved the detection accuracy of the mean precipitation at high altitudes and considerably influenced specific low-precipitation areas such as the Middle East. This spatial feature–based filter allowed us to evaluate the results of screening based on noise limits that are implemented in standard algorithms. The GPM DPR Ku-band radar product contained other unwanted echoes due to the mirror images appearing as second-trip echoes contaminating the high-altitude statistics. Such second-trip echoes constitute a major portion of the echoes observed near the highest altitudes of deep storms.
Significance Statement
Understanding the current state of separation of naturally occurring precipitation signals from artificial interference signals in spaceborne radar data at altitudes of approximately 20 km is critical for gaining a comprehensive picture of the intensity and structure of precipitation systems. In the case of the TRMM PR data, artifacts could be distinguished as isolated profiles with an almost constant radar reflectivity, and interferences gradually increased during the operation period. The removal of radio interference considerably affects the statistics of extremely deep storms. Improved algorithms and observation techniques have expanded the observation coverage associated with the GPM DPR KuPR data, but there are interferences (mirror images) that should be removed for a thorough discussion of very high-altitude precipitation.
Abstract
This study investigated the effects of interfering signals on high-altitude precipitation extraction from spaceborne precipitation radar data. Data analyses were performed on the products of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and the Global Precipitation Measurement Core Observatory Dual-Frequency Precipitation Radar (GPM DPR) to clarify the effects of removing radio interferences and mirror images, particularly focusing on deep precipitation detection. The TRMM PR acquired precipitation data up to an altitude of approximately 20 km and occasionally captured interferences from artificial radio transmissions in specific areas. Artifacts could be distinguished as isolated profiles exhibiting almost constant radar reflectivity. The number of interferences affecting the TRMM PR gradually increased during the operation period of 1998–2013. A filter was introduced to separate the observed profiles into deep storms that reach the upper observation altitude and contamination caused by radio interference. The former frequently appeared over the Sahel area, where the observation upper limits are lowest. The removal of the latter, radio interference, improved the detection accuracy of the mean precipitation at high altitudes and considerably influenced specific low-precipitation areas such as the Middle East. This spatial feature–based filter allowed us to evaluate the results of screening based on noise limits that are implemented in standard algorithms. The GPM DPR Ku-band radar product contained other unwanted echoes due to the mirror images appearing as second-trip echoes contaminating the high-altitude statistics. Such second-trip echoes constitute a major portion of the echoes observed near the highest altitudes of deep storms.
Significance Statement
Understanding the current state of separation of naturally occurring precipitation signals from artificial interference signals in spaceborne radar data at altitudes of approximately 20 km is critical for gaining a comprehensive picture of the intensity and structure of precipitation systems. In the case of the TRMM PR data, artifacts could be distinguished as isolated profiles with an almost constant radar reflectivity, and interferences gradually increased during the operation period. The removal of radio interference considerably affects the statistics of extremely deep storms. Improved algorithms and observation techniques have expanded the observation coverage associated with the GPM DPR KuPR data, but there are interferences (mirror images) that should be removed for a thorough discussion of very high-altitude precipitation.
Abstract
Accurate measurements of ocean waves underpin efficient offshore operations and optimal offshore structure design, helping to ensure the offshore industry can operate both safely and economically. Popular instruments used by the offshore industry are the Rosemount WaveRadar (Radar) and the Waverider Buoy. The Optech Laser has been used at some locations for specific studies. Recent reports indicate systematic differences of order 10% among the wave measurements made by these instruments. This paper examines the relative performance of these instruments based upon various time-domain comparisons, including results from a quality control (QC) procedure, capabilities of measuring the wave surface profile (skewness), and crest heights for varying wind sea and swell conditions. The QC check provides good-quality data that can be further investigated with an assurance of error-free data, suggesting that the Waverider produces the best-quality data with the lowest failure rate compared to the Laser and Radar. A significant number of the Waverider surface elevation records have negative skewness, particularly at higher sea states, affecting its crest height measurements, which are lower than those from the Laser and Radar. Additionally, the significant wave height (H 1/3) estimates of the Radar are lower than the Laser and Waverider, but its zero-crossing wave periods (TZ ), on average, are longer than the Laser and the Waverider. The significant heights (H 1/3) of Laser and Waverider are in good agreement for all three datasets, but the Waverider’s zero-crossing wave period (TZ ) estimates are significantly longer than the Laser.
Abstract
Accurate measurements of ocean waves underpin efficient offshore operations and optimal offshore structure design, helping to ensure the offshore industry can operate both safely and economically. Popular instruments used by the offshore industry are the Rosemount WaveRadar (Radar) and the Waverider Buoy. The Optech Laser has been used at some locations for specific studies. Recent reports indicate systematic differences of order 10% among the wave measurements made by these instruments. This paper examines the relative performance of these instruments based upon various time-domain comparisons, including results from a quality control (QC) procedure, capabilities of measuring the wave surface profile (skewness), and crest heights for varying wind sea and swell conditions. The QC check provides good-quality data that can be further investigated with an assurance of error-free data, suggesting that the Waverider produces the best-quality data with the lowest failure rate compared to the Laser and Radar. A significant number of the Waverider surface elevation records have negative skewness, particularly at higher sea states, affecting its crest height measurements, which are lower than those from the Laser and Radar. Additionally, the significant wave height (H 1/3) estimates of the Radar are lower than the Laser and Waverider, but its zero-crossing wave periods (TZ ), on average, are longer than the Laser and the Waverider. The significant heights (H 1/3) of Laser and Waverider are in good agreement for all three datasets, but the Waverider’s zero-crossing wave period (TZ ) estimates are significantly longer than the Laser.
