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Timothy J. Wagner and Ralph A. Petersen

Abstract

Routine in situ observations of the atmosphere taken in flight by commercial aircraft provide atmospheric profiles with greater temporal density and, in many parts of the country, at more locations than the operational radiosonde network. Thousands of daily temperature and wind observations are provided by largely complementary systems, the Airborne Meteorological Data Relay (AMDAR) and the Tropospheric Airborne Meteorological Data Reporting (TAMDAR). All TAMDAR aircraft also measure relative humidity while a subset of AMDAR aircraft are equipped with the Water Vapor Sensing System (WVSS) measure specific humidity.

One year of AMDAR/WVSS and TAMDAR observations are evaluated against operational National Weather Service (NWS) radiosondes to characterize the performance of these systems in similar environments. For all observed variables, AMDAR reports showed both smaller average differences and less random differences with respect to radiosondes than the corresponding TAMDAR observations. Observed differences were not necessarily consistent with known radiosonde biases. Since the systems measure different humidity variables, moisture is evaluated in both specific and relative humidity using both aircraft and radiosonde temperatures to derive corresponding moisture variables. Derived moisture performance is improved when aircraft-based temperatures are corrected prior to conversion. AMDAR observations also show greater consistency between different aircraft than TAMDAR observations do. The small variability in coincident WVSS humidity observations indicates that they may prove more reliable than humidity observations from NWS radiosondes.

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IA Houghton, PB Smit, D Clark, C Dunning, A Fisher, N Nidzieko, P Chamberlain, and TT Janssen

Abstract

A distributed sensor network of over one hundred free-drifting, real-time marine weather sensors was deployed in the Pacific Ocean beginning in early 2019. The Spotter buoys used in the network represent a next generation ocean weather sensor designed to measure surface waves, wind, currents, and sea surface temperature. Large distributed sensor networks like these provide much needed long-dwell sensing capabilities in open ocean regions. Despite the demand for better weather forecasts and climate data in our oceans, direct in situ measurements of marine surface weather (waves, winds, currents) remain exceedingly sparse in the open oceans. Due to the large expanse of our oceans, distributed paradigms are necessary to create sufficient data density at global scale, similar to advances in sensing on land and in space. Here we discuss initial findings from this long-dwell open ocean distributed sensor network. Through triple-collocation analysis, we determine errors in collocated satellite-derived observations and model estimates. The correlation analysis shows that the Spotter network provides wave height data with lower errors than both satellites and models. The wave spectrum was also further used to infer wind speed. Buoy drift dynamics are similar to established drogued drifters, particularly when accounting for windage. We find a windage correction factor for the Spotter buoy of approximately 1%, which is in agreement with theoretical estimates. Altogether, we present a completely new open ocean weather data set and characterize the data quality against other observations and models to demonstrate the broad value for ocean monitoring and forecasting that can be achieved using large-scale distributed sensor networks in our oceans.

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Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Richard L. Bankert, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, and Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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Nawal Husnoo, Timothy Darlington, Sebastián Torres, and David Warde

Abstract

In this work, we present a new Quantitative-Precipitation-Estimation (QPE) quality-control (QC) algorithm for the UK weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground clutter mitigation (using CLEAN-AP) and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated “truth” with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data.

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Aart Overeem, Hylke de Vries, Hassan Al Sakka, Remko Uijlenhoet, and Hidde Leijnse

Abstract

The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2-D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. In order to improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) Modified Kraemer (MK) method, i.e. Hitschfeld-Bordan where parameters of the power-law Z hk h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ h and attenuation correction is limited to 10 dB; 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase K dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

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James S. Bennett, Frederick R. Stahr, Charles C. Eriksen, Martin C. Renken, Wendy E. Snyder, and Lora J. Van Uffelen

Abstract

Seagliders® are buoyancy-driven autonomous underwater vehicles whose sub-surface position estimates are typically derived from velocities inferred using a flight model. We present a method for computing velocities and positions during the different phases typically encountered during a dive-climb profile based on a buoyancy-driven flight model. We compare these predictions to observations gathered from a Seaglider deployment on the acoustic tracking range in Dabob Bay (200 m depth, mean vehicle speeds ~30 cm s-1), permitting us to bound the position accuracy estimates and understand sources of various errors. We improve position accuracy estimates during long vehicle accelerations by numerically integrating the flight-model's fundamental momentum-balance equations. Overall, based on an automated estimation of flight-model parameters, we confirm previous work that predicted vehicle velocities in the dominant dive and climb phases are accurate to < 1 cm s-1, which bounds the accumulated position error in time. However, in this energetic tidal basin, position error also accumulates due to unresolved depth-dependent flow superimposed upon an inferred depth-averaged current.

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Pichaya Lertvilai, Paul L.D. Roberts, and Jules S. Jaffe

Abstract

The development of a low-cost Video Velocimeter (VIV) to estimate underwater bulk flow velocity is described. The instrument utilizes a simplified particle image correlation technique to reconstruct an average flow velocity vector from video recordings of ambient particles. The VIV uses a single camera with a set of mirrors that splits the view into two stereoscopic views, allowing estimation of the flow velocity vector. The VIV was validated in a controlled flume using ambient seawater, and subsequently field tested together with an acoustic Doppler velocimeter with both mounted close to the coastal seafloor. When used in non-turbulent flow, the instrument can estimate mean flow velocity parallel to the front face of the instrument with root-mean-squared errors of the main flow within 10% of the ±20 cm/s measurement range when compared to an ADV. The predominant feature of the VIV is that it is a cost-effective method to estimate flow velocity in complex benthic habitats where velocity parallel to the sea floor is of interest.

Open access
Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

We present a novel approach for the automated quality control (QC) of precipitation for a sparse station observation network within the complex terrain of British Columbia, Canada. Our QC approach uses Convolutional Neural Networks (CNNs) to classify bad observation values, incorporating a multi-classifier ensemble to achieve better QC performance. We train CNNs using human QC’d labels from 2016 to 2017 with gridded precipitation and elevation analyses as inputs. Based on the classification evaluation metrics, our QC approach shows reliable and robust performance across different geographical environments (e.g., coastal and inland mountains), with 0.927 Area Under Curve (AUC) and type I/type II error lower than 15%. Based on the saliency-map-based interpretation studies, we explain the success of CNN-based QC by showing that it can capture the precipitation patterns around, and upstream of the station locations. This automated QC approach is an option for eliminating bad observations for various applications, including the pre-processing of training datasets for machine learning. It can be used in conjunction with human QC to improve upon what could be accomplished with either method alone.

Open access
Ganesh Gopalakrishnan, Bruce D. Cornuelle, Matthew R. Mazloff, Peter F. Worcester, and Matthew A. Dzieciuch

Abstract

A strongly nonlinear eddy field is present in and around the Subtropical Countercurrent in the Northern Philippine Sea (NPS). A regional implementation of the Massachusetts Institute of Technology general circulation model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system is found to be able to produce a series of two-month-long dynamically-consistent optimized state estimates between April 2010 and April 2011 for the eddy-rich NPS region. The assimilation provides a stringent dynamical test of the model, showing that a free run of the model forced using adjusted controls remains consistent with the observations for two months. The 4DVAR iterative optimization reduced the total cost function for the observations and controls by 40–50% from the reference solution, initialized using the Hybrid Coordinate Ocean Model 1/12° global daily analysis, achieving residuals approximately equal to the assumed uncertainties for the assimilated observations. The state estimates are assessed by comparing with assimilated and withheld observations and also by comparing one-month model forecasts with future data. The state estimates and forecasts were more skillful than model persistence and the reference solutions. Finally, the continuous state estimates were used to detect and track the eddies, analyze their structure, and quantify their vertically-integrated meridional heat and salt transports.

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Giuseppe Zibordi, Brent N. Holben, Marco Talone, Davide D’Alimonte, Ilya Slutsker, David M. Giles, and Mikhail G. Sorokin

Abstract

The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

Open access