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John A. Kluge, Alexander V. Soloviev, Cayla W. Dean, Geoffrey K. Morrison, and Brian K. Haus

Abstract

A magnetic signature is created by secondary magnetic field fluctuations caused by the phenomenon of seawater moving in Earth’s magnetic field. A laboratory experiment was conducted at the Surge Structure Atmosphere Interaction (SUSTAIN) facility to measure the magnetic signature of surface waves using a differential method: a pair of magnetometers, separated horizontally by one-half wavelength, were placed at several locations on the outer tank walls. This technique significantly reduced the extraneous magnetic distortions that were detected simultaneously by both sensors and additionally doubled the magnetic signal of surface waves. Accelerometer measurements and local gradients were used to identify magnetic noise produced from tank vibrations. Wave parameters of 4-m-long waves with a 0.56-Hz frequency and a 0.1-m amplitude were used in this experiment. Freshwater and saltwater experiments were completed to determine the magnetic difference generated by the difference in conductivity. Tests with an empty tank were conducted to identify the noise of the facility. When the magnetic signal was put through spectral analysis, it showed the primary peak at the wave frequency (0.56 Hz) and less pronounced higher-frequency harmonics, which are caused by the nonlinearity of shallow water surface waves. The magnetic noise induced by the wavemaker and related vibrations peaked around 0.3 Hz, which was removed using filtering techniques. These results indicate that the magnetic signature produced by surface waves was an order of magnitude larger than in traditional model predictions. The discrepancy may be due to the magnetic permeability difference between water and air that is not considered in the traditional model.

Open access
Laur Ferris, Donglai Gong, Sophia Merrifield, and Louis St. Laurent

Abstract

Finescale strain parameterization (FSP) of turbulent kinetic energy dissipation rate has become a widely used method for observing ocean mixing, solving a coverage problem where direct turbulence measurements are absent but CTD profiles are available. This method can offer significant value, but there are limitations in its broad application to the global ocean. FSP often fails to produce reliable results in frontal zones where temperature–salinity (T/S) intrusive features contaminate the CTD strain spectrum, as well as where the aspect ratio of the internal wave spectrum is known to vary greatly with depth, as frequently occurs in the Southern Ocean. In this study we use direct turbulence measurements from Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES) and glider microstructure measurements from Autonomous Sampling of Southern Ocean Mixing (AUSSOM) to show that FSP can have large biases (compared to direct turbulence measurement) below the mixed layer when physics associated with T/S fronts are meaningfully present. We propose that the FSP methodology be modified to 1) include a density ratio (Rρ)-based data exclusion rule to avoid contamination by double diffusive instabilities in frontal zones such as the Antarctic Circumpolar Current, the Gulf Stream, and the Kuroshio, and 2) conduct (or leverage available) microstructure measurements of the depth-varying shear-to-strain ratio Rω(z) prior to performing FSP in each dynamically unique region of the global ocean.

Significance Statement

Internal waves travel through the ocean and collide, turbulently mixing the interior ocean and homogenizing its waters. In the absence of actual turbulence measurements, oceanographers count the ripples associated with these internal waves and use them estimate the amount of turbulence that will transpire from their collisions. In this paper we show that the ripples in temperature and salinity that naturally occur at sharp fronts masquerade as internal waves and trick oceanographers into thinking there is up to 100 000 000 times more turbulence than there actually is in these frontal regions.

Open access
Victor Alari, Jan-Victor Björkqvist, Valdur Kaldvee, Kristjan Mölder, Sander Rikka, Anne Kask-Korb, Kaimo Vahter, Siim Pärt, Nikon Vidjajev, and Hannes Tõnisson

Abstract

Wave buoys are a popular choice for measuring sea surface waves, and there is also an increasing interest for wave information from ice-covered water bodies. Such measurements require cost-effective, easily deployable, and robust devices. We have developed LainePoiss (LP)—an ice-resistant and lightweight wave buoy. It calculates the surface elevation by double integrating the data from the inertial sensors of the microelectromechanical system (MEMS), and transmits wave parameters and spectra in real time over cellular or satellite networks. LP was validated through 1) sensor tests, 2) wave tank experiments, 3) a field validation against a Directional Waverider, 4) an intercomparison of several buoys in the field, and 5) field measurements in the Baltic Sea marginal ice zone. These extensive field and laboratory tests confirmed that LP performed well (e.g., the bias of Hm 0 in the field was 0.01 m, with a correlation of 0.99 and a scatter index of 8%; the mean absolute deviation of mean wave direction was 7°). LP was also deployed with an unmanned aerial vehicle and we present our experience of such operations. One issue that requires further development is the presence of low-frequency artifacts caused by the dynamic noise of the gyroscope. For now, a correction method is presented to deal with the noise.

Significance Statement

Operational wave buoys are large and therefore expensive and inconvenient to deploy. Many commercially available devices cannot measure short waves and are not tested in ice. Our purpose was to develop an affordable wave buoy that is lightweight, ice resistant, capable of measuring short waves, and also has a longer operating life than existing research buoys. The buoy is easily deployed with a small boat or even an industrial drone, thus reducing operating costs. The buoy is accurate, and captures waves that are too short for operational wave buoys. This is relevant for coastal planning in, e.g., archipelagos and narrow fjords. We measured waves in ice in the Baltic Sea, and are planning to extend these measurements to Antarctica.

Open access
Candice Hall, Robert E. Jensen, and David W. Wang

Abstract

The importance of quantifying the accuracy in wave measurements is critical to not only understand the complexities of wind-generated waves, but imperative for the interpretation of implied accuracy of the prediction systems that use these data for verification and validation. As wave measurement systems have unique collection and processing attributes that result in large accuracy ranges, this work quantifies bias that may be introduced into wave models from the newly operational NOAA National Data Buoy Center (NDBC) 2.1-m hull. Data quality consistency between the legacy NDBC 3-m aluminum hulls and the new 2.1-m hull is compared to a relative reference, and provides a standardized methodology and graphical representation template for future intra-measurement evaluations. Statistical analyses and wave spectral comparisons confirm that the wave measurements reported from the NDBC 2.1-m hulls show an increased accuracy from previously collected NDBC 3-m hull wave data for significant wave height and average wave period, while retaining consistent accuracy for directional results, purporting that hull size does not impact NDBC directional data estimates. Spectrally, the NDBC 2.1-m hulls show an improved signal-to-noise ratio, allowing for increase in energy retention in the lower frequency spectral range, with an improved high frequency spectral accuracy above 0.25 Hz within the short seas and wind chop wave component regions. These improvements in both NDBC bulk and spectral data accuracy provide confidence for the wave community’s use of NDBC wave data to drive wave model technologies, improvements and validations.

Open access
Steven R. Jayne, W. Brechner Owens, Pelle E. Robbins, Alexander K. Ekholm, Neil M. Bogue, and Elizabeth R. Sanabia

Abstract

The Air-Launched Autonomous Micro Observer (ALAMO) is a versatile profiling float that can be launched from an aircraft to make temperature and salinity observations of the upper ocean for over a year with high temporal sampling. Similar in dimensions and weight to an airborne expendable bathythermograph (AXBT), but with the same capability as Argo profiling floats, ALAMOs can be deployed from an A-sized (sonobuoy) launch tube, the stern ramp of a cargo plane, or the door of a small aircraft. Unlike an AXBT, however, the ALAMO float directly measures pressure, can incorporate additional sensors, and is capable of performing hundreds of ocean profiles compared to the single temperature profile provided by an AXBT. Upon deployment, the float parachutes to the ocean, releases the air-deployment package, and immediately begins profiling. Ocean profile data along with position and engineering information are transmitted via the Iridium satellite network, automatically processed, and then distributed by the Global Telecommunications System for use by the operational forecasting community. The ALAMO profiling mission can be modified using the two-way Iridium communications to change the profiling frequency and depth. Example observations are included to demonstrate the ALAMO’s utility.

Open access
Manuel Nunez, Neal Cantin, Craig Steinberg, Virginie van Dongen-Vogels, and Scott Bainbridge

Abstract

The study addresses a network of remote weather stations on the Great Barrier Reef (GBR) that house Licor192 quantum sensors measuring photosynthetically active radiation (PAR) above water. There is evidence of significant degradation in the signal from the sensors after a 2-yr deployment. Main sources of uncertainty in the calibration are outlined, which include degradation of the photodiode, soiling of the sensors by dust and salt spray, cosine responses, and sensitivity to air temperature. Raw PAR data are improved using correction factors based on a cloudless PAR model. Uncertainties in cosine responses of the instrument are low but significant errors may occur if the supporting platform is misaligned and not horizontal. A set of recommendations are provided to improve the quality of the PAR data.

Significance Statement

A method is described to correct historical PAR data collected on the Great Barrier Reef, such that these valuable observations may be improved and used effectively.

Open access
Leo O Lai and Jed O. Kaplan

Abstract

Interpolation of interval data where the mean is preserved, e.g., estimating smoothed, pseudodaily meteorological variables based on monthly means, is a common problem in the geosciences. Existing methods for mean-preserving interpolation are computationally intensive and/or do not readily accommodate bounded interpolation, where the interpolated data cannot exceed a threshold value. Here we present a mean-preserving, continuous, easily implementable, and computationally efficient method for interpolating one-dimensional interval data. Our new algorithm provides a straightforward solution to the interpolation problem by utilizing Hermite cubic splines and midinterval control points to interpolate interval data into smaller partitions. We further include adjustment schemes to restrict the interpolated result to user-specified minimum and maximum bounds. Our method is fast, portable, and broadly applicable to a range of geoscientific data, including interpolating unbounded time series such as mean temperature, and bounded data including mean wind speed or cloud-cover fraction.

Significance Statement

Interpolation is often utilized to mathematically estimate smaller time step values when such data are not readily available, for example, the estimation of daily temperature when only monthly temperature values are available. We propose a novel interpolation method based on linking segments of flexible continuous curves that ensures the average of interpolated result will be the same as the original value, which is important for minimizing interpolation errors. We find that our new method takes significantly less computational time when compared with other existing methods, while retaining a similar degree of precision. Furthermore, we outline an additional procedure for users to specify the minimum and maximum bounds of interpolated results if applicable.

Open access
Free access
Taylor A. Gowan, John D. Horel, Alexander A. Jacques, and Adair Kovac

Abstract

Numerical weather prediction centers rely on the Gridded Binary Second Edition (GRIB2) file format to efficiently compress and disseminate model output as two-dimensional grids. User processing time and storage requirements are high if many GRIB2 files with size O(100 MB, where B = bytes) need to be accessed routinely. We illustrate one approach to overcome such bottlenecks by reformatting GRIB2 model output from the High-Resolution Rapid Refresh (HRRR) model of the National Centers for Environmental Prediction to a cloud-optimized storage type, Zarr. Archives of the original HRRR GRIB2 files and the resulting Zarr stores on Amazon Web Services (AWS) Simple Storage Service (S3) are available publicly through the Amazon Sustainability Data Initiative. Every hour, the HRRR model produces 18- or 48-hourly GRIB2 surface forecast files of size O(100 MB). To simplify access to the grids in the surface files, we reorganize the HRRR model output for each variable and vertical level into Zarr stores of size O(1 MB), with chunks O(10 kB) containing all forecast lead times for 150 × 150 gridpoint subdomains. Open-source libraries provide efficient access to the compressed Zarr stores using cloud or local computing resources. The HRRR-Zarr approach is illustrated for common applications of sensible weather parameters, including real-time alerts for high-impact situations and retrospective access to output from hundreds to thousands of model runs. For example, time series of surface pressure forecast grids can be accessed using AWS cloud computing resources approximately 40 times as fast from the HRRR-Zarr store as from the HRRR-GRIB2 archive.

Significance Statement

The rapid evolution of computing power and data storage have enabled numerical weather prediction forecasts to be generated faster and with more detail than ever before. The increased temporal and spatial resolution of forecast model output can force end users with finite memory and storage capabilities to make pragmatic decisions about which data to retrieve, archive, and process for their applications. We illustrate an approach to alleviate this access bottleneck for common weather analysis and forecasting applications by using the Amazon Web Services (AWS) Simple Storage Service (S3) to store output from the High-Resolution Rapid Refresh (HRRR) model in Zarr format. Zarr is a relatively new data storage format that is flexible, compressible, and designed to be accessed with open-source software either using cloud or local computing resources. The HRRR-Zarr dataset is publicly available as part of the AWS Sustainability Data Initiative.

Open access
Shinta Seto, Toshio Iguchi, and Robert Meneghini

Abstract

Spaceborne precipitation radars, including the Tropical Rainfall Measuring Mission’s Precipitation Radar (PR) and the Global Precipitation Measurement Mission’s Dual-frequency Precipitation Radar (DPR), measure not only precipitation echoes but surface echoes as well, the latter of which are used to estimate the path integrated attenuation (PIA) in the surface reference technique (SRT). In our previous study based on analyzing PR measurements, we found that attenuation-free surface backscattering cross sections (denoted by σe0) over land increased in the presence of precipitation. This behavior, called the soil moisture effect, causes an underestimate of the PIA by the SRT as the method does not explicitly consider this effect. In this study, measurements made by Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR), which comprise the DPR, were analyzed to examine whether KuPR and KaPR exhibit similar dependencies on the soil moisture as does the PR. For both KuPR and KaPR, an increase in σe0 was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld-Bordan (HB) method suggest that, σe0 increases with the surface precipitation rate for light precipitation events. Meanwhile, for heavy precipitation, owing to the degradation of the HB method, it is difficult to estimate σe0 quantitatively. Thus, a correction method for PIA that considers the soil moisture effect was developed and implemented into the DPR standard algorithm. With this correction, the surface precipitation rate estimates increased by approximately 18% for KuPR and 15% for the normal scan of KaPR over land.

Open access