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Abstract
This work tests a methodology for estimating the ocean stratification gradient using remotely sensed, high temporal and spatial resolution field measurements of internal wave propagation speeds. The internal wave (IW) speeds were calculated from IW tracks observed using a shore-based, X-band marine radar deployed at a field site on the south-central coast of California. An inverse model, based on the work of Kar and Guha (2020), that utilizes the linear internal wave dispersion relation assuming a constant vertical density gradient is the basis for the inverse model. This allows the vertical gradient of density to be expressed as a function of the internal wave phase speed, local water depth, and a background average density. The inputs to the algorithm are the known cross-shore bathymetry, the background ocean density, and the remotely-sensed cross-shore profiles of IW speed. The estimated density gradients are then compared to the synchronously measured vertical density profiles collected from an in situ instrument array. The results show a very good agreement offshore in deeper water (∼50m-30m) but more significant discrepancies in shallow water (20-10m) closer to shore. In addition, a sensitivity analysis is conducted that relates errors in measured speeds to errors in the estimated density gradients.
Abstract
This work tests a methodology for estimating the ocean stratification gradient using remotely sensed, high temporal and spatial resolution field measurements of internal wave propagation speeds. The internal wave (IW) speeds were calculated from IW tracks observed using a shore-based, X-band marine radar deployed at a field site on the south-central coast of California. An inverse model, based on the work of Kar and Guha (2020), that utilizes the linear internal wave dispersion relation assuming a constant vertical density gradient is the basis for the inverse model. This allows the vertical gradient of density to be expressed as a function of the internal wave phase speed, local water depth, and a background average density. The inputs to the algorithm are the known cross-shore bathymetry, the background ocean density, and the remotely-sensed cross-shore profiles of IW speed. The estimated density gradients are then compared to the synchronously measured vertical density profiles collected from an in situ instrument array. The results show a very good agreement offshore in deeper water (∼50m-30m) but more significant discrepancies in shallow water (20-10m) closer to shore. In addition, a sensitivity analysis is conducted that relates errors in measured speeds to errors in the estimated density gradients.
Abstract
Tides are often non-stationary due to non-astronomical influences. Investigating variable tidal properties implies a tradeoff between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a Matlab code that: a) uses CWT linearity (via the “Response Coefficient Method”) to implement super-resolution (Munk and Hasselman 1964); b) provides a Munk-Hasselman constituent-selection criterion; and c) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time-scales of days and multiple constituents per species with fortnightly filters. It outputs astronomical phase-lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s) and reconstruction(s), confidence limits, and signal-to-noise ratios. Artificial data and water-levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time-resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power-peaking (with a 24-hour period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify impacts of high flows and a salt-barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to non-stationary data.
Abstract
Tides are often non-stationary due to non-astronomical influences. Investigating variable tidal properties implies a tradeoff between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a Matlab code that: a) uses CWT linearity (via the “Response Coefficient Method”) to implement super-resolution (Munk and Hasselman 1964); b) provides a Munk-Hasselman constituent-selection criterion; and c) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time-scales of days and multiple constituents per species with fortnightly filters. It outputs astronomical phase-lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s) and reconstruction(s), confidence limits, and signal-to-noise ratios. Artificial data and water-levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time-resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power-peaking (with a 24-hour period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify impacts of high flows and a salt-barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to non-stationary data.
Abstract
Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler-frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in-situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method.
Abstract
Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler-frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in-situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method.
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.
Abstract
Near-bottom pressure sensors are widely used to measure surface gravity waves. Pressure spectra are usually converted to sea surface elevation spectra with a linear-theory transfer function assuming constant depth. This methodology has been validated over smooth sandy beaches, but not over complex bathymetry of coral or rocky environments. Bottom-mounted pressure sensors co-located with wave buoys in 10–13 m water depth from a 5-week rocky-shorelines experiment are used to quantify the error of pressure-based surface gravitywave statistics and develop correction methods. The rough bathymetry has O(1) m vertical variability on O(1–10) m horizontal scales, much shorter than the 90–40 m wavelength of sea-band (0.1-0.2 Hz). For sensor stability, pressure sensors were deployed by divers in bathymetric lows. When using the local depth measured by the pressure sensor, significant wave height squared overestimates the direct wave buoy measurements (up to 21%) in the sea band. An effective depth hypothesis is proposed where a spatially averaged water depth provides more accurate wave height statistics than the local depth at the pressure sensor. An optimal depth correction, estimated by minimizing the wave height error, varies from 0.1–1.6 m. A bathymetry averaging scale of 13 m is found by minimizing the median bathymetry deviation relative to the optimal. The optimal and averaged bathymetry depth corrections are similar across locations and, using linear theory, significantly reduce wave statistics errors. Therefore, pressure-based wave measurements require a correction that depends on the spatially averaged bathymetry around the instrument. The larger errors when using the local depth suggest that approximately linear surface waves are not strongly modified by abrupt depth changes over O(1) m horizontal scales.
Abstract
Near-bottom pressure sensors are widely used to measure surface gravity waves. Pressure spectra are usually converted to sea surface elevation spectra with a linear-theory transfer function assuming constant depth. This methodology has been validated over smooth sandy beaches, but not over complex bathymetry of coral or rocky environments. Bottom-mounted pressure sensors co-located with wave buoys in 10–13 m water depth from a 5-week rocky-shorelines experiment are used to quantify the error of pressure-based surface gravitywave statistics and develop correction methods. The rough bathymetry has O(1) m vertical variability on O(1–10) m horizontal scales, much shorter than the 90–40 m wavelength of sea-band (0.1-0.2 Hz). For sensor stability, pressure sensors were deployed by divers in bathymetric lows. When using the local depth measured by the pressure sensor, significant wave height squared overestimates the direct wave buoy measurements (up to 21%) in the sea band. An effective depth hypothesis is proposed where a spatially averaged water depth provides more accurate wave height statistics than the local depth at the pressure sensor. An optimal depth correction, estimated by minimizing the wave height error, varies from 0.1–1.6 m. A bathymetry averaging scale of 13 m is found by minimizing the median bathymetry deviation relative to the optimal. The optimal and averaged bathymetry depth corrections are similar across locations and, using linear theory, significantly reduce wave statistics errors. Therefore, pressure-based wave measurements require a correction that depends on the spatially averaged bathymetry around the instrument. The larger errors when using the local depth suggest that approximately linear surface waves are not strongly modified by abrupt depth changes over O(1) m horizontal scales.
Abstract
The Data Interpolation Empirical Orthogonal Function (DINEOF) algorithm is used to reconstruct datasets of geophysical and biological variables such as sea surface temperature (SST) and Chlorophyll a (Chl a). In this study, we analyze the impact of both the quantity and distribution of missing data on the performance of DINEOF demonstrating how DINEOF plus a connectivity mask can be used for future data reconstruction tasks. We propose an enhanced version of DINEOF (DINEOF+) by adding two steps: (1) Using a 75% threshold of missing data for reconstructing incomplete datasets and (2) Masking interpolated points that lacks sufficient space-time observations in the original dataset. We successfully apply DINEOF+ to the OC-CCI global daily Chl a dataset and validate the results using in situ datasets. We find that the recovery rate varies across ocean basins and years. In oligotrophic waters, the daily data coverage increased by 40–50% during the period from 2003 to 2020. Using DINEOF+ allows us to obtain a significantly higher temporal resolution of global Chl a data, which will improve understanding of marine phytoplankton dynamics in response to changing environments.
Abstract
The Data Interpolation Empirical Orthogonal Function (DINEOF) algorithm is used to reconstruct datasets of geophysical and biological variables such as sea surface temperature (SST) and Chlorophyll a (Chl a). In this study, we analyze the impact of both the quantity and distribution of missing data on the performance of DINEOF demonstrating how DINEOF plus a connectivity mask can be used for future data reconstruction tasks. We propose an enhanced version of DINEOF (DINEOF+) by adding two steps: (1) Using a 75% threshold of missing data for reconstructing incomplete datasets and (2) Masking interpolated points that lacks sufficient space-time observations in the original dataset. We successfully apply DINEOF+ to the OC-CCI global daily Chl a dataset and validate the results using in situ datasets. We find that the recovery rate varies across ocean basins and years. In oligotrophic waters, the daily data coverage increased by 40–50% during the period from 2003 to 2020. Using DINEOF+ allows us to obtain a significantly higher temporal resolution of global Chl a data, which will improve understanding of marine phytoplankton dynamics in response to changing environments.
Abstract
The density of newly fallen snow (ρ N) is an important parameter for assessing accumulated snowfall depth. We examined the relationships between polarimetric parameters of X-band radar and the ρ N in dry snow cases with ground temperatures less than 0 °C. Our study was based on observations at Niigata Prefecture, Japan, along the coastal region of the Sea of Japan. This region is subjected primarily to sea-effect snow during the winter monsoon season, and convective clouds and rimed snow are common. We assumed that snow particles that accumulated on the ground originated from altitudes above an altitude with a temperature of −15 °C, and we focused on the ratio of the differential phase (K DP) to radar reflectivity (Z h), which is influenced by both aspect ratio and inverse particle size. We found that K DP/Z h at an altitude with a temperature of −15 °C exhibited a greater magnitude for lower ρ N values. Its correlation coefficient was the best among the polarimetric parameters that we examined. The difference in ice crystal flatness is highlighted rather than the difference in size because aggregation growth has not progressed at this altitude. On the basis of this result, we propose an empirical relationship between K DP/Z h at an altitude with a temperature of −15 °C and ρ N on the ground, thereby facilitating the estimation of snowfall depth by combining the estimated ρ N with the liquid equivalent snowfall rate from, for example, Z h or K DP.
Abstract
The density of newly fallen snow (ρ N) is an important parameter for assessing accumulated snowfall depth. We examined the relationships between polarimetric parameters of X-band radar and the ρ N in dry snow cases with ground temperatures less than 0 °C. Our study was based on observations at Niigata Prefecture, Japan, along the coastal region of the Sea of Japan. This region is subjected primarily to sea-effect snow during the winter monsoon season, and convective clouds and rimed snow are common. We assumed that snow particles that accumulated on the ground originated from altitudes above an altitude with a temperature of −15 °C, and we focused on the ratio of the differential phase (K DP) to radar reflectivity (Z h), which is influenced by both aspect ratio and inverse particle size. We found that K DP/Z h at an altitude with a temperature of −15 °C exhibited a greater magnitude for lower ρ N values. Its correlation coefficient was the best among the polarimetric parameters that we examined. The difference in ice crystal flatness is highlighted rather than the difference in size because aggregation growth has not progressed at this altitude. On the basis of this result, we propose an empirical relationship between K DP/Z h at an altitude with a temperature of −15 °C and ρ N on the ground, thereby facilitating the estimation of snowfall depth by combining the estimated ρ N with the liquid equivalent snowfall rate from, for example, Z h or K DP.
Abstract
We analyze the calibration stability of the 17-yr Precipitation Radar (PR) data onboard the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM version 7 is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM version 8 (or later) takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis.
Abstract
We analyze the calibration stability of the 17-yr Precipitation Radar (PR) data onboard the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM version 7 is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM version 8 (or later) takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis.
Abstract
Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm−1), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm−1) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are difficult to model, like non-Local Thermodynamic Equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In part one of this study, as presented here, the amount of unique information (as determined by Empirical Orthogonal Decomposition (EOD)) made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA’s Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part two of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.
Abstract
Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm−1), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm−1) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are difficult to model, like non-Local Thermodynamic Equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In part one of this study, as presented here, the amount of unique information (as determined by Empirical Orthogonal Decomposition (EOD)) made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA’s Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part two of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.
