Browse
Abstract
This article aims to improve the quality control (QC) of surface daily temperature observations over complex physical geography. A new QC method based on multiverse optimization algorithm, variational mode decomposition, and kernel extreme learning machine (MVO–VMD–KELM) was eomployed to identify potential outliers. For the selected six regions with complex physical geography, the inverse distance weighting (IDW), the spatial regression test (SRT), KELM, and the empirical mode decomposition improved KELM (EMD–KELM) methods were employed to test the proposed method. The results indicate that the MVO–VMD–KELM method outperformed other methods in all the cases. The MVO–VMD–KELM method yielded better mean absolute error (MAE), root-mean-square error (RMSE), index of agreement (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC) values than others via the analysis of evaluation metrics for different cases. The comparison results led to the recommendation that the proposed method is an effective quality control method in identifying the seeded errors for the surface daily temperature observations.
Abstract
This article aims to improve the quality control (QC) of surface daily temperature observations over complex physical geography. A new QC method based on multiverse optimization algorithm, variational mode decomposition, and kernel extreme learning machine (MVO–VMD–KELM) was eomployed to identify potential outliers. For the selected six regions with complex physical geography, the inverse distance weighting (IDW), the spatial regression test (SRT), KELM, and the empirical mode decomposition improved KELM (EMD–KELM) methods were employed to test the proposed method. The results indicate that the MVO–VMD–KELM method outperformed other methods in all the cases. The MVO–VMD–KELM method yielded better mean absolute error (MAE), root-mean-square error (RMSE), index of agreement (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC) values than others via the analysis of evaluation metrics for different cases. The comparison results led to the recommendation that the proposed method is an effective quality control method in identifying the seeded errors for the surface daily temperature observations.
Estimates of Baroclinic Tidal Sea Level and Currents from Lagrangian Drifters and Satellite AltimetryAbstract
Internal waves generated by the interaction of the surface tides with topography are known to propagate long distances and lead to observable effects such as sea level variability, ocean currents, and mixing. In an effort to describe and predict these waves, the present work is concerned with using geographically distributed data from satellite altimeters and drifting buoys to estimate and map the baroclinic sea level associated with the M2, S2, N2, K1, and O1 tides. A new mapping methodology is developed, based on a mixed L 1/L 2-norm optimization, and compared with previously developed methods for tidal estimation from altimeter data. The altimeter and drifter data are considered separately in their roles for estimating tides and for cross-validating estimates obtained with independent data. Estimates obtained from altimetry and drifter data are found to agree remarkably well in regions where the drifter trajectories are spatially dense; however, heterogeneity of the drifter trajectories is a disadvantage when they are considered alone for tidal estimation. When the different data types are combined by using geodetic mission altimetry to cross validate estimates obtained with either exact-repeat altimetry or drifter data, and subsequently averaging the latter estimates, the estimates significantly improve on the previously published HRET8.1 model, as measured by their utility for predicting sea level and surface currents in the open ocean. The methodology has been applied to estimate the annual modulations of M2, which are found to have much smaller amplitudes compared to those reported in HRET8.1, and suggest that the latter estimates of these tides were not reliable.
Significance Statement
The mechanical and thermodynamic forcing of the ocean occurs primarily at very large scales associated with the gravitational perturbations of the sun and moon (tides), atmospheric wind stress, and solar insolation, but the frictional forces within the ocean act on very small scales. This research addresses the question of how the large-scale tidal forcing is transformed into the smaller-scale motion capable of being influenced by friction. The results show where internal waves are generated and how they transport energy across ocean basins to eventually be dissipated by friction. The results are useful to scientists interested in mapping the flows of mechanical energy in the ocean and predicting their influences on marine life, ocean temperature, and ocean currents.
Abstract
Internal waves generated by the interaction of the surface tides with topography are known to propagate long distances and lead to observable effects such as sea level variability, ocean currents, and mixing. In an effort to describe and predict these waves, the present work is concerned with using geographically distributed data from satellite altimeters and drifting buoys to estimate and map the baroclinic sea level associated with the M2, S2, N2, K1, and O1 tides. A new mapping methodology is developed, based on a mixed L 1/L 2-norm optimization, and compared with previously developed methods for tidal estimation from altimeter data. The altimeter and drifter data are considered separately in their roles for estimating tides and for cross-validating estimates obtained with independent data. Estimates obtained from altimetry and drifter data are found to agree remarkably well in regions where the drifter trajectories are spatially dense; however, heterogeneity of the drifter trajectories is a disadvantage when they are considered alone for tidal estimation. When the different data types are combined by using geodetic mission altimetry to cross validate estimates obtained with either exact-repeat altimetry or drifter data, and subsequently averaging the latter estimates, the estimates significantly improve on the previously published HRET8.1 model, as measured by their utility for predicting sea level and surface currents in the open ocean. The methodology has been applied to estimate the annual modulations of M2, which are found to have much smaller amplitudes compared to those reported in HRET8.1, and suggest that the latter estimates of these tides were not reliable.
Significance Statement
The mechanical and thermodynamic forcing of the ocean occurs primarily at very large scales associated with the gravitational perturbations of the sun and moon (tides), atmospheric wind stress, and solar insolation, but the frictional forces within the ocean act on very small scales. This research addresses the question of how the large-scale tidal forcing is transformed into the smaller-scale motion capable of being influenced by friction. The results show where internal waves are generated and how they transport energy across ocean basins to eventually be dissipated by friction. The results are useful to scientists interested in mapping the flows of mechanical energy in the ocean and predicting their influences on marine life, ocean temperature, and ocean currents.
An Automated Approach to Estimating Convective Boundary Layer Depth from Dual-Polarization WSR-88D Radar ObservationsAbstract
Convective boundary layer (CBL) depth can be estimated from dual-polarization WSR-88D radars using the product differential reflectivity Z DR because the CBL top is collocated with a local Z DR minimum produced by Bragg scatter at the interface of the CBL and the free troposphere. Quasi-vertical profiles (QVPs) of Z DR are produced for each radar volume scan and profiles from successive times are stitched together to form a time–height plot of Z DR from sunrise to sunset. QVPs of Z DR often show a low-Z DR channel that starts near the ground and rises during the morning and early afternoon, identifying the CBL top. Unfortunately, results show that this channel within the QVP can occasionally be misleading. This motivated creation of a new variable DVar, which combines Z DR with its azimuthal variance and is particularly helpful at identifying the CBL top during the morning hours. Two methods are developed to track the CBL top from QVPs of Z DR and DVar. Although each method has strengths and weaknesses, the best results are found when the two methods are combined using inverse variance weighting. The ability to detect CBL depth from routine WSR-88D radar scans rather than from rawinsondes or lidar instruments would vastly improve our understanding of CBL depth variations in the daytime by increasing the temporal and spatial frequencies of the observations.
Significance Statement
The daytime convective boundary layer (CBL) can increase in depth from a few hundred to a few thousand meters between sunrise and sunset and is strongly connected to temperature changes at Earth’s surface. Unfortunately, current observations of CBL depth primarily consist of measurements from twice daily rawinsonde launches at 97 locations across the United States. As a result, CBL depth observations lack fine spatial and temporal resolution and miss the daily cycle of CBL growth. This study seeks to fill the gaps in CBL depth observations by developing an automated method to estimate CBL depth from dual-polarization WSR-88D radar observations with a temporal resolution as fine as 5–10 min. These observations will greatly enhance our ability to observe and monitor CBL depth in real time.
Abstract
Convective boundary layer (CBL) depth can be estimated from dual-polarization WSR-88D radars using the product differential reflectivity Z DR because the CBL top is collocated with a local Z DR minimum produced by Bragg scatter at the interface of the CBL and the free troposphere. Quasi-vertical profiles (QVPs) of Z DR are produced for each radar volume scan and profiles from successive times are stitched together to form a time–height plot of Z DR from sunrise to sunset. QVPs of Z DR often show a low-Z DR channel that starts near the ground and rises during the morning and early afternoon, identifying the CBL top. Unfortunately, results show that this channel within the QVP can occasionally be misleading. This motivated creation of a new variable DVar, which combines Z DR with its azimuthal variance and is particularly helpful at identifying the CBL top during the morning hours. Two methods are developed to track the CBL top from QVPs of Z DR and DVar. Although each method has strengths and weaknesses, the best results are found when the two methods are combined using inverse variance weighting. The ability to detect CBL depth from routine WSR-88D radar scans rather than from rawinsondes or lidar instruments would vastly improve our understanding of CBL depth variations in the daytime by increasing the temporal and spatial frequencies of the observations.
Significance Statement
The daytime convective boundary layer (CBL) can increase in depth from a few hundred to a few thousand meters between sunrise and sunset and is strongly connected to temperature changes at Earth’s surface. Unfortunately, current observations of CBL depth primarily consist of measurements from twice daily rawinsonde launches at 97 locations across the United States. As a result, CBL depth observations lack fine spatial and temporal resolution and miss the daily cycle of CBL growth. This study seeks to fill the gaps in CBL depth observations by developing an automated method to estimate CBL depth from dual-polarization WSR-88D radar observations with a temporal resolution as fine as 5–10 min. These observations will greatly enhance our ability to observe and monitor CBL depth in real time.
Abstract
Knowledge of the directional distribution of a wave field is crucial for a better understanding of complex air–sea interactions. However, the dynamic and unpredictable nature of ocean waves, combined with the limitations of existing measurement technologies and analysis techniques, makes it difficult to obtain precise directional information, leading to a poor understanding of this important quantity. This study investigates the potential use of a wavelet-based method applied to GPS buoy observations as an alternative approach to the conventional methods for estimating the directional distribution of ocean waves. The results indicate that the wavelet-based estimations are consistently good when compared to the framework of widely used parameterizations for the directional distribution. The wavelet-based method presents advantages in comparison with the conventional methods, including being purely data-driven and not requiring any assumptions about the shape of the distribution. In addition, it was found that the wave directional distribution is narrower at the spectral peak and broadens asymmetrically at higher and lower scales, particularly sharply for frequencies below the peak. The directional spreading appears to be independent of the wave age across the entire range of frequencies, implying that the angular width of the directional spectrum is primarily controlled by nonlinear wave–wave interactions rather than by wind forcing. These results support the use of the wavelet-based method as a practical alternative for the estimation of the wave directional distribution. In addition, this study highlights the need for continued innovation in the field of ocean wave measuring technologies and analysis techniques to improve our understanding of air–sea interactions.
Significance Statement
This study presents a wavelet-based technique for obtaining the directional distribution of ocean waves applied to GPS buoy. This method serves as an alternative to conventional methods and is relatively easy to implement, making it a practical option for researchers and engineers. The study was conducted in a highly energetic environment characterized by high wind speeds and large waves, providing a valuable dataset for understanding the dynamics of marine environment in extreme conditions. This research has implications for improving our understanding of directional characteristics of ocean waves, which is crucial for navigation, offshore engineering, weather forecasting, and coastal hazard mitigation. This study also highlights the challenges associated with understanding wave directionality and emphasizes a need for further observations.
Abstract
Knowledge of the directional distribution of a wave field is crucial for a better understanding of complex air–sea interactions. However, the dynamic and unpredictable nature of ocean waves, combined with the limitations of existing measurement technologies and analysis techniques, makes it difficult to obtain precise directional information, leading to a poor understanding of this important quantity. This study investigates the potential use of a wavelet-based method applied to GPS buoy observations as an alternative approach to the conventional methods for estimating the directional distribution of ocean waves. The results indicate that the wavelet-based estimations are consistently good when compared to the framework of widely used parameterizations for the directional distribution. The wavelet-based method presents advantages in comparison with the conventional methods, including being purely data-driven and not requiring any assumptions about the shape of the distribution. In addition, it was found that the wave directional distribution is narrower at the spectral peak and broadens asymmetrically at higher and lower scales, particularly sharply for frequencies below the peak. The directional spreading appears to be independent of the wave age across the entire range of frequencies, implying that the angular width of the directional spectrum is primarily controlled by nonlinear wave–wave interactions rather than by wind forcing. These results support the use of the wavelet-based method as a practical alternative for the estimation of the wave directional distribution. In addition, this study highlights the need for continued innovation in the field of ocean wave measuring technologies and analysis techniques to improve our understanding of air–sea interactions.
Significance Statement
This study presents a wavelet-based technique for obtaining the directional distribution of ocean waves applied to GPS buoy. This method serves as an alternative to conventional methods and is relatively easy to implement, making it a practical option for researchers and engineers. The study was conducted in a highly energetic environment characterized by high wind speeds and large waves, providing a valuable dataset for understanding the dynamics of marine environment in extreme conditions. This research has implications for improving our understanding of directional characteristics of ocean waves, which is crucial for navigation, offshore engineering, weather forecasting, and coastal hazard mitigation. This study also highlights the challenges associated with understanding wave directionality and emphasizes a need for further observations.
SWIRL: The First Australian Operational Radar-Based 3D Wind Analysis SystemAbstract
In this paper, the first Australian operational radar-based three-dimensional (3D) wind analysis system named Synthetic Wind Information from Radar and Lidar (SWIRL) is described and evaluated. SWIRL employs a variational minimization formulation to combine results from four individual wind retrieval techniques of varied complexity to derive 3D winds in single-Doppler and multi-Doppler radar regions: a variational version of the traditional velocity azimuth display (VVAD) and double VAD (DVAD) techniques, a single-Doppler wind retrieval technique using optical flow horizontal wind proxies, and a multi-Doppler 3D wind retrieval technique. The SWIRL 3D wind components are evaluated against wind profiler observations and radar simulations using a very high-resolution (50 m) numerical simulation of a supercell thunderstorm. We find that SWIRL can retrieve very accurate horizontal winds, especially below 2-km height in the multi-Doppler regions, with mean absolute errors on wind speed and direction < 2 m s−1 and 10° on average and <2.5 m s−1 and 15°–20° 90% of the time. These errors do not increase noticeably with wind speed, highlighting the suitability of these retrieved winds to be used for damaging and destructive wind detection and nowcasting. The single-Doppler retrieval using optical flow is also found to provide reasonably accurate winds at these heights. The accurate retrieval of convective-scale updrafts and downdrafts, even using multi-Doppler information, is still a major challenge, with mean absolute errors of vertical velocity of about 50% on average. This can be attributed to the limitations of the current radar technology used operationally, imposing slow antenna speeds.
Significance Statement
Damaging and destructive winds have the potential to inflict significant damage to properties and assets and, tragically, result in loss of life. Efficient direction of emergency services to affected areas is essential for a prompt return to normal conditions. Wind farm operators require precise information on anticipated wind shifts to reduce the risk of energy grid failures. Strong winds also contribute to compound weather events, such as water ingress through hail-damaged roofs or structural damage to buildings caused by hailstones. The purpose of this work was to equip Australia with the first operational wind monitoring system, based on operational radar observations, to serve all these critical applications (and more).
Abstract
In this paper, the first Australian operational radar-based three-dimensional (3D) wind analysis system named Synthetic Wind Information from Radar and Lidar (SWIRL) is described and evaluated. SWIRL employs a variational minimization formulation to combine results from four individual wind retrieval techniques of varied complexity to derive 3D winds in single-Doppler and multi-Doppler radar regions: a variational version of the traditional velocity azimuth display (VVAD) and double VAD (DVAD) techniques, a single-Doppler wind retrieval technique using optical flow horizontal wind proxies, and a multi-Doppler 3D wind retrieval technique. The SWIRL 3D wind components are evaluated against wind profiler observations and radar simulations using a very high-resolution (50 m) numerical simulation of a supercell thunderstorm. We find that SWIRL can retrieve very accurate horizontal winds, especially below 2-km height in the multi-Doppler regions, with mean absolute errors on wind speed and direction < 2 m s−1 and 10° on average and <2.5 m s−1 and 15°–20° 90% of the time. These errors do not increase noticeably with wind speed, highlighting the suitability of these retrieved winds to be used for damaging and destructive wind detection and nowcasting. The single-Doppler retrieval using optical flow is also found to provide reasonably accurate winds at these heights. The accurate retrieval of convective-scale updrafts and downdrafts, even using multi-Doppler information, is still a major challenge, with mean absolute errors of vertical velocity of about 50% on average. This can be attributed to the limitations of the current radar technology used operationally, imposing slow antenna speeds.
Significance Statement
Damaging and destructive winds have the potential to inflict significant damage to properties and assets and, tragically, result in loss of life. Efficient direction of emergency services to affected areas is essential for a prompt return to normal conditions. Wind farm operators require precise information on anticipated wind shifts to reduce the risk of energy grid failures. Strong winds also contribute to compound weather events, such as water ingress through hail-damaged roofs or structural damage to buildings caused by hailstones. The purpose of this work was to equip Australia with the first operational wind monitoring system, based on operational radar observations, to serve all these critical applications (and more).
Abstract
One of the most widely used systems for wind speed and direction observations at meteorological sites is based on Doppler wind lidar (DWL) technology. The wind vector derivation strategies of these instruments rely on the assumption of stationary and homogeneous horizontal wind, which is often not the case over heterogeneous terrain. This study focuses on the validation of two DWL systems, operated by the German Weather Service [Deutscher Wetterdienst (DWD)] and installed at the boundary layer field site Falkenberg (Lindenberg, Germany), with respect to measurements from a small, fixed-wing uncrewed aircraft system (UAS) of the type Multi-Purpose Airborne Sensor Carrier (MASC-3). A wind vector intercomparison at an altitude range from 100 to 500 m between DWL and UAS is performed, after a quality control of the aircraft’s data accuracy against a cup anemometer and wind vane mounted on a meteorological mast also operating at the location. Both DWL systems exhibit an overall root-mean-square difference in the wind vector retrieval of less than 22% for wind speed and lower than 18° for wind direction. The enhancement or deterioration of these statistics is analyzed with respect to scanning height and atmospheric stability. The limitations of this type of validation approach are highlighted and accounted for in the analysis.
Abstract
One of the most widely used systems for wind speed and direction observations at meteorological sites is based on Doppler wind lidar (DWL) technology. The wind vector derivation strategies of these instruments rely on the assumption of stationary and homogeneous horizontal wind, which is often not the case over heterogeneous terrain. This study focuses on the validation of two DWL systems, operated by the German Weather Service [Deutscher Wetterdienst (DWD)] and installed at the boundary layer field site Falkenberg (Lindenberg, Germany), with respect to measurements from a small, fixed-wing uncrewed aircraft system (UAS) of the type Multi-Purpose Airborne Sensor Carrier (MASC-3). A wind vector intercomparison at an altitude range from 100 to 500 m between DWL and UAS is performed, after a quality control of the aircraft’s data accuracy against a cup anemometer and wind vane mounted on a meteorological mast also operating at the location. Both DWL systems exhibit an overall root-mean-square difference in the wind vector retrieval of less than 22% for wind speed and lower than 18° for wind direction. The enhancement or deterioration of these statistics is analyzed with respect to scanning height and atmospheric stability. The limitations of this type of validation approach are highlighted and accounted for in the analysis.
Evaluating Existing Ocean Glider Sampling Strategies for Submesoscale DynamicsAbstract
Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesoscale variability in the ocean mixed layer of the Weddell Sea. A 1/48° regional simulation of the Southern Ocean is sampled with virtual “bow-tie” glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases is comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find that small reductions in the number of dive–climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates that the bias is not dependent on the direction of travel in our deep ocean study site.
Significance Statement
Recent observational campaigns have focused on using autonomous vehicles to better understand processes responsible for mixing in the surface region of the ocean. There exists uncertainty around how effective these missions are at returning reliable and representative information. This study seeks to quantify the performance of existing strategies in observing mixing processes, and we confirm that strategies are biased to underestimate indicators of mixing. Furthermore, compensating for the bias by increasing the number of resources or changing the manner in which resources are used has limited reward. Our findings are important for decision-making during the planning phase of an observational campaign and display that further innovations are required to account for the sampling bias.
Abstract
Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesoscale variability in the ocean mixed layer of the Weddell Sea. A 1/48° regional simulation of the Southern Ocean is sampled with virtual “bow-tie” glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases is comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find that small reductions in the number of dive–climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates that the bias is not dependent on the direction of travel in our deep ocean study site.
Significance Statement
Recent observational campaigns have focused on using autonomous vehicles to better understand processes responsible for mixing in the surface region of the ocean. There exists uncertainty around how effective these missions are at returning reliable and representative information. This study seeks to quantify the performance of existing strategies in observing mixing processes, and we confirm that strategies are biased to underestimate indicators of mixing. Furthermore, compensating for the bias by increasing the number of resources or changing the manner in which resources are used has limited reward. Our findings are important for decision-making during the planning phase of an observational campaign and display that further innovations are required to account for the sampling bias.
Abstract
Generally, sea ice prediction skills can be improved by assimilating available observations of the sea ice concentration (SIC) and sea ice thickness (SIT) into a numerical forecast model to update the initial conditions. However, due to inadequate daily SIT satellite observations in the Arctic melting season, the SIC fields in forecast models are usually directly updated, which causes mismatch of SIC and SIT in dynamics and affects the model prediction accuracy. In this study, a statistically based bivariate regression model of SIT (BRMT) is tentatively established based on the grid reanalysis data of SIC and SIT to reconstruct daily Arctic SIT data. The results show that the BRMT can reproduce the spatial and temporal changes in the SIT in the melting season and capture the variation trend of SIT in some periods. Compared with the SIT observations from buoy and satellite, the reconstructed SIT shows better performance in the central Arctic than other datasets. Furthermore, when the reconstructed SIT is added to the forecast model with only assimilated SIC, the forecast accuracy of SIC, sea ice extent, and SIT in the Arctic melting season is improved and does not weaken with the increase in the forecast time. Especially in the central Arctic, the average absolute deviation between 24-h SIT forecast results and observations is only 0.16 m. The results indicate great potential for applying the reconstructed SIT to the operational forecast of Arctic sea ice during the melting season in the future.
Significance Statement
To improve the prediction skills of Arctic sea ice, it is necessary to assimilate the sea ice observation into the dynamic model to generate a more realistic initial prediction field. At present, the observation data of daily sea ice thickness (SIT) during the Arctic melting season are few, which cannot well meet the demand of operational SIT forecast. In this study, a bivariate regression model is put forward to construct SIT using the sea ice concentration (SIC) observation. Benefitting from the joint assimilation of the observed SIC and the reconstructed SIT, the forecast accuracy of sea ice variables is greatly improved. The reconstructed SIT is expected to provide an available dataset for further research on the Arctic sea ice forecast.
Abstract
Generally, sea ice prediction skills can be improved by assimilating available observations of the sea ice concentration (SIC) and sea ice thickness (SIT) into a numerical forecast model to update the initial conditions. However, due to inadequate daily SIT satellite observations in the Arctic melting season, the SIC fields in forecast models are usually directly updated, which causes mismatch of SIC and SIT in dynamics and affects the model prediction accuracy. In this study, a statistically based bivariate regression model of SIT (BRMT) is tentatively established based on the grid reanalysis data of SIC and SIT to reconstruct daily Arctic SIT data. The results show that the BRMT can reproduce the spatial and temporal changes in the SIT in the melting season and capture the variation trend of SIT in some periods. Compared with the SIT observations from buoy and satellite, the reconstructed SIT shows better performance in the central Arctic than other datasets. Furthermore, when the reconstructed SIT is added to the forecast model with only assimilated SIC, the forecast accuracy of SIC, sea ice extent, and SIT in the Arctic melting season is improved and does not weaken with the increase in the forecast time. Especially in the central Arctic, the average absolute deviation between 24-h SIT forecast results and observations is only 0.16 m. The results indicate great potential for applying the reconstructed SIT to the operational forecast of Arctic sea ice during the melting season in the future.
Significance Statement
To improve the prediction skills of Arctic sea ice, it is necessary to assimilate the sea ice observation into the dynamic model to generate a more realistic initial prediction field. At present, the observation data of daily sea ice thickness (SIT) during the Arctic melting season are few, which cannot well meet the demand of operational SIT forecast. In this study, a bivariate regression model is put forward to construct SIT using the sea ice concentration (SIC) observation. Benefitting from the joint assimilation of the observed SIC and the reconstructed SIT, the forecast accuracy of sea ice variables is greatly improved. The reconstructed SIT is expected to provide an available dataset for further research on the Arctic sea ice forecast.
Abstract
Wind observations are a critical part of the current global observation system used for numerical weather prediction (NWP). Wind lidars have been cited as precise instruments that can provide three-dimensional wind measurements. Several studies have conducted observing system experiments (OSEs) with existing lidar observations or observing system simulation experiments (OSSEs) with simulated lidar observations highlighting the benefits of wind lidar measurements to NWP. Previous studies using simulated lidar observations have typically tied aerosol optical properties to functions of relative humidity instead of to aerosol properties. A methodology is presented for simulating wind measurements from a novel 2053-nm lidar using aerosol properties derived using the GEOS-5 nature run, along with estimating winds derived from cloud information. Some assumptions regarding aerosol scattering and the distribution of clouds are explored, along with the role of observation weighting and implications for representativeness error. Results from a preliminary OSSE are presented highlighting the importance of assumptions used to derive data from cloud returns and aerosol scattering. While a longer duration study is required, results show a general reduction in analysis error when lidar measurements are ingested.
Abstract
Wind observations are a critical part of the current global observation system used for numerical weather prediction (NWP). Wind lidars have been cited as precise instruments that can provide three-dimensional wind measurements. Several studies have conducted observing system experiments (OSEs) with existing lidar observations or observing system simulation experiments (OSSEs) with simulated lidar observations highlighting the benefits of wind lidar measurements to NWP. Previous studies using simulated lidar observations have typically tied aerosol optical properties to functions of relative humidity instead of to aerosol properties. A methodology is presented for simulating wind measurements from a novel 2053-nm lidar using aerosol properties derived using the GEOS-5 nature run, along with estimating winds derived from cloud information. Some assumptions regarding aerosol scattering and the distribution of clouds are explored, along with the role of observation weighting and implications for representativeness error. Results from a preliminary OSSE are presented highlighting the importance of assumptions used to derive data from cloud returns and aerosol scattering. While a longer duration study is required, results show a general reduction in analysis error when lidar measurements are ingested.
Abstract
The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure.
Significance Statement
The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.
Abstract
The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure.
Significance Statement
The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.
