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Thomas E. Cropper
,
David I. Berry
,
Richard C. Cornes
, and
Elizabeth C. Kent

Abstract

Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model which quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover and relative wind speed are then used to estimate the heating bias ship-by-ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.

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Samuel Brenner
,
Jim Thomson
,
Luc Rainville
,
Daniel Torres
,
Martin Doble
,
Jeremy Wilkinson
, and
Craig Lee

Abstract

Properties of the surface mixed layer (ML) are critical for understanding and predicting atmosphere–sea ice–ocean interactions in the changing Arctic Ocean. Mooring measurements are typically unable to resolve the ML in the Arctic due to the need for instruments to remain below the surface to avoid contact with sea ice and icebergs. Here, we use measurements from a series of three moorings installed for one year in the Beaufort Sea to demonstrate that upward-looking acoustic Doppler current profilers (ADCPs) installed on subsurface floats can be used to estimate ML properties. A method is developed for combining measured peaks in acoustic backscatter and inertial shear from the ADCPs to estimate the ML depth. Additionally, we use an inverse sound speed model to infer the summer ML temperature based on offsets in ADCP altimeter distance during open-water periods. The ADCP estimates of ML depth and ML temperature compare favorably with measurements made from mooring temperature sensors, satellite SST, and from an autonomous Seaglider. These methods could be applied to other extant mooring records to recover additional information about ML property changes and variability.

Open access
Jacob T. Carlin
,
Edwin L. Dunnavan
,
Alexander V. Ryzhkov
, and
Mariko Oue

Abstract

Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase K dp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar K dp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these K dp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and K dp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed K dp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and K dp within the sublimation zone, which results in a K dp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive K dp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased K dp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP.

Significance Statement

Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars.

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Guangping Liu
,
Yongping Jin
,
Youduo Peng
,
Deshun Liu
, and
Liang Liu

Abstract

A new full-ocean-depth macroorganisms pressure-retaining sampler (FMPS) was designed to collect pressure-retaining macroorganisms samples from the abyssal seafloor. A mathematical model for pressure compensation in the FMPS recovery process was developed. The effects of FMPS structural parameters, pressure compensator structural parameters, and sampling environment on the pressure retention performance of FMPS were analyzed. Using the developed FMPS engineering prototype, FMPS internal pressure test, high-pressure chamber simulation sampling, and pressure-retaining test was carried out. The test results show that the key components of FMPS can carry 115 MPa pressure, FMPS can complete the sampling action in the high-pressure chamber of 115 MPa, the pressure is maintained at 105.5 MPa, and the pressure drop rate (ratio of pressure drop during FMPS recovery to sampling point pressure) is 9.13%; the experimental results are consistent with the theoretical calculation. The test verified the feasibility of FMPS design and the reliability of pressure retention, providing a theoretical basis and technical support for the design and manufacture of full-ocean-depth sampling devices.

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Trevor W Harrison
,
Nate Clemett
,
Brian Polagye
, and
Jim Thomson

Abstract

Tidal currents, particularly in narrow channels, can be challenging to characterize due to high current speeds (> 1 m s−1), strong spatial gradients, and relatively short synoptic windows. To assess tidal currents in Agate Pass, WA, we cross-evaluated data products from an array of acoustically-tracked underwater floats and from acoustic Doppler current profilers (ADCPs) in both station-keeping and drifting modes. While increasingly used in basin-scale science, underwater floats have seen limited use in coastal environments. This study presents the first application of a float array towards small-scale (< 1 km), high resolution (< 5 m) measurements of mean currents in energetic tidal channel and utilizes a new prototype float, the µFloat, designed specifically for sampling in dynamic coastal waters. We show that a float array (20 floats) can provide data with similar quality to ADCPs, with measurements of horizontal velocity differing by less than 10% of nominal velocity, except during periods of low flow (0.1 m s−1). Additionally, floats provided measurements of the three dimensional temperature field, demonstrating their unique ability to simultaneously resolve in situ properties that cannot be remotely observed.

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Cameron Bertossa
,
Tristan L’Ecuyer
,
Aronne Merrelli
,
Xianglei Huang
, and
Xiuhong Chen

Abstract

The Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) will fill a gap in our understanding of polar processes and the polar climate by offering widespread, spectrally-resolved measurements through the Far InfraRed (FIR) with two identical CubeSat spacecraft. While the polar regions are typically difficult for skillful cloud identification due to cold surface temperatures, the reflection by bright surfaces, and frequent temperature inversions, the inclusion of the FIR may offer increased spectral sensitivity, allowing for the detection of even thin ice clouds. This study assesses the potential skill, as well as limitations, of a neural network-based cloud mask using simulated spectra mimicking what the PREFIRE mission will capture. Analysis focuses on the polar regions. Clouds are found to be detected approximately 90% of time using the derived neural network. The NN’s assigned confidence for whether a scene is ‘clear’ or ‘cloudy’ proves to be a skillful way in which quality flags can be attached to predictions. Clouds with higher cloud top heights are typically more easily detected. Low-altitude clouds over polar surfaces, which are the most difficult for the NN to detect, are still detected over 80% of the time. The FIR portion of the spectrum is found to increase the detection of clear scenes and increase mid-to-high altitude cloud detection. Cloud detection skill improves through the use of the overlapping fields of view produced by the PREFIRE instrument’s sampling strategy. Overlapping fields of view increase accuracy relative to the baseline NN while simultaneously predicting on a sub-FOV scale.

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Brian J. Fitzgerald
,
J. Broccolo
, and
K. Garrett

Abstract

The Mount Washington Observatory Regional Mesonet (MWRM) is a network of 18 remote meteorological monitoring stations (as of 2022), including the Auto Road Vertical Profile (ARVP), located across the White Mountains of Northern New Hampshire. Each station measures temperature and relative humidity, with additional variables at many locations. All stations need to withstand the frequent combination of intense cold, high precipitation amounts, icing, and hurricane-force winds in a mountain environment. Due to these challenges, the MWRM employs rugged instrumentation, an innovative radio-communications relay approach, and carefully selected sites that balance ideal measuring environments with station survivability. Data collected from the MWRM are used operationally by forecasters (including Mount Washington Observatory and National Weather Service staff) to validate model guidance, by alpine and climate scientists, by recreationalists accessing conditions in the backcountry, by groups operating on the mountain (Cog Railway, toll Auto Road) and by search and rescue organizations. This paper provides a detailed description of the network, with emphasis on how the challenging climate and terrain of this mountain region impacts sensor selection, site maintenance and overall operation.

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Shakeel Asharaf
,
Derek J. Posselt
,
Faozi Said
, and
Christopher S. Ruf

Abstract

Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.

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Bernadette M. Sloyan
,
Christopher C. Chapman
,
Rebecca Cowley
, and
Anastase A. Charantonis

Abstract

In situ observations are vital to improving our understanding of the variability and dynamics of the ocean. A critical component of the ocean circulation are the strong, narrow and highly variable western boundary currents. Ocean moorings that extend from the sea floor to the surface remain the most effective and efficient method to fully observe these currents. For various reasons mooring instruments may not provide continuous records. Here we assess the application of the Iterative Completion Self Organising Maps (ITCOMPSOM) machine learning technique to fill observational data gaps in a 7.5 year time-series of the East Australian Current. The method was validated by withholding parts of fully known profiles, and reconstructing them. For 20% random withholding of known velocity data validation statistics of the u- and v-velocity components are R 2 coefficients of 0.70 and 0.88 and, root mean square errors of 0.038 m s−1 and 0.064 m s−1, respectively. Withholding 100 days of known velocity profiles over a depth range between 60 m to 700 m has mean profile residual differences between true and predicted u- and v-velocity of 0.009 m s−1 and 0.02 m s−1, respectively. The ITCOMPSOM also reproduces the known velocity variability. For 20% withholding of temperature and salinity data root mean square error of 0.04 and 0.38°C, respectively, are obtained. The ITCOMPSOM validation statistics are significantly better than those obtained when standard data filling methods are used. We suggest that machine learning techniques can be an appropriate method to fill missing data and enable production of observational-derived data products.

Open access
Paul Chamberlain
,
Bruce Cornuelle
,
Lynne D. Talley
,
Kevin Speer
,
Cathrine Hancock
, and
Stephen Riser

Abstract

Acoustically tracked subsurface floats provide insights into ocean complexity and were first deployed over 60 years ago. A standard tracking method uses a least squares algorithm to estimate float trajectories based on acoustic ranging from moored sound sources. However, infrequent or imperfect data challenge such estimates, and least squares algorithms are vulnerable to non-Gaussian errors. Acoustic tracking is currently the only feasible strategy for recovering float positions in the sea ice region, a focus of this study. Acoustic records recovered from underice floats frequently lack continuous sound source coverage. This is because environmental factors such as surface sound channels and rough sea ice attenuate acoustic signals, while operational considerations make polar sound sources expensive and difficult to deploy. Here we present a Kalman smoother approach that, by including some estimates of float behavior, extends tracking to situations with more challenging datasets. The Kalman smoother constructs dynamically constrained, error-minimized float tracks and variance ellipses using all possible position data. This algorithm outperforms the least squares approach and a Kalman filter in numerical experiments. The Kalman smoother is applied to previously tracked floats from the southeast Pacific (DIMES experiment), and the results are compared with existing trajectories constructed using the least squares algorithm. The Kalman smoother is also used to reconstruct the trajectories of a set of previously untracked, acoustically enabled Argo floats in the Weddell Sea.

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