Journal of Atmospheric and Oceanic Technology
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Published By American Meteorological Society

1520-0426, 0739-0572

Abstract The Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community 20 years of globally observed top of the atmosphere (TOA) fluxes critical for climate and cloud feedback studies. The CERES Flux By Cloud Type (FBCT) product contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and also reveal more insight into the interactions between clouds and climate. The FBCT product provides 1° regional daily and monthly shortwave (SW) and longwave (LW) cloud-type fluxes and cloud properties sorted by 7 pressure layers and 6 optical depth bins. Historically, cloud-type fluxes have been computed using radiative transfer models based on observed cloud properties. Instead of relying on radiative transfer models, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. These biases are mitigated by constraining the cloud-type fluxes within each footprint with the CERES Single Scanner Footprint (SSF) observed flux. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. Several examples of FBCT data are presented to highlight its utility for scientific applications.


Author(s):  
Pierre Durand ◽  
Patrice Medina ◽  
Philippe Pastor ◽  
Michel Gavart ◽  
Sergio Pizziol

Abstract An instrumentation package for wind and turbulence observations in the atmospheric boundary layer on an unmanned aerial vehicle (UAV) called BOREAL has been developed. BOREAL is a fixed wing UAV built by BOREAL company which weighs up to 25kg (5kg of payload) and has a wingspan of 4.2m. With a light payload and optimal weather conditions, it has a flight endurance of nine hours. The instrumental payload was designed in order to measure every parameter required for the computation of the three wind components, at a rate of 100 s−1 which is fast enough to capture turbulence fluctuations: a GPS-IMU platform measures the three components of the groundspeed a well as the attitude angles; the airplane nose has been replaced by a five-hole probe in order to measure the angles of attack and sideslip, according to the so-called radome technique. This probe was calibrated using computational fluid dynamics (CFD) simulations and wind tunnel tests. The remaining instruments are a Pitot tube for static and dynamic pressure measurement, and temperature/humidity sensors in dedicated housings. The optimal airspeed at which the vibrations are significantly reduced to an acceptable level was defined from qualification flights. With appropriate flight patterns, the reliability of the mean wind estimates, through self-consistency and comparison with observations performed at 60m on an instrumented tower could be assessed. Promising first observations of turbulence up to frequencies around 10Hz and corresponding to a spatial resolution to the order of 3m, are hereby presented.


Abstract Using NOAA’s S-band High Power Snow-Level Radar, HPSLR, a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized Gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion, w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter, Dm, from the measured reflectivity, Z, produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, NC. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.


Abstract Observations of thermodynamic and kinematic parameters associated with derivatives of the thermodynamics and wind fields, namely advection, vorticity, divergence, and deformation, can be obtained by applying Green’s Theorem to a network of observing sites. The five nodes that comprise the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) profiling network, spaced 50 -80 km apart, are used to obtain measurements of these parameters over a finite region. To demonstrate the applicability of this technique at this location, it is first applied to gridded model output from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model, using profiles from the locations of ARM network sites, so that values calculated from this method can be directly compared to finite difference calculations. Good agreement is found between both approaches as well as between the model and values calculated from the observations. Uncertainties for the observations are obtained via a Monte Carlo process in which the profiles are randomly perturbed in accordance with their known error characteristics. The existing size of the ARM network is well-suited to capturing these parameters, with strong correlations to model values and smaller uncertainties than a more closely-spaced network, yet it is small enough that it avoids the tendency for advection to go to zero over a large area.


Author(s):  
T. M. Chin ◽  
E. P. Chassignet ◽  
M. Iskandarani ◽  
N. Groves

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse - the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov Random Fields are provided and its relationship to finite difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in-situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4°ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multi-core architectures and uses Climate and Forecasts compliant Network Common Data Format for Input/Output. The package is freely available with an open source license from www.tendral.com/tsis/


Abstract The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning-based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The Random Forest (RF) and Neural Network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively-sensed cloud boundaries from the radar and lidar systems onboard the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.


Abstract The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean-atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then, a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016-2018, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA) and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies.


Abstract The inception of a moored buoy network in the northern Indian Ocean in 1997 paved the way for systematic collection of longterm time series observations of meteorological and oceanographic parameters. This buoy network was revamped in 2011 with OMNI (Ocean Moored buoy Network for north Indian Ocean) buoys fitted with additional sensors to better quantify the air-sea fluxes. An inter-comparison of OMNI buoy measurements with the nearby WHOI mooring during the year 2015 revealed an overestimation of downwelling longwave radiation (LWR↓). Analysis of the OMNI and WHOI radiation sensors at a test station at NIOT during 2019 revealed that the accurate and stable amplification of the thermopile voltage records along with the customized data logger in the WHOI system results in better estimations of LWR↓. The offset in NIOT measured LWR↓ is estimated firstly by segregating the LWR↓ during clear sky conditions identified using the downwelling shortwave radiation measurements from the same test station, and secondly, finding the offset by taking the difference with expected theoretical clear sky LWR↓. The corrected LWR↓ exhibited good agreement with that of collocated WHOI measurements, with a correlation of 0.93. This method is applied to the OMNI field measurements and again compared with the nearby WHOI mooring measurements, exhibiting a better correlation of 0.95. This work has led to the revamping of radiation measurements in OMNI buoys and provides a reliable method to correct past measurements and improve estimation of air-sea fluxes in the Indian Ocean.


Abstract Oceanic density fronts can evolve, be advected, or propagate as gravity currents. Frontal evolution studies require methods to temporally track evolving density fronts. We present an automated method to temporally track these fronts from numerical model solutions. First, at all time steps contiguous density fronts are detected using an edge detection algorithm. A front event, defined as a set of sequential-in-time fronts representing a single time-evolving front, is then identified. At time step i, a front is compared to each front at time step i + 1 to determine if the two fronts are matched. An i front grid point is trackable if the minimum distance to the i + 1 front falls within a range. The i front is forward-matched to the i + 1 front when a sufficient number of grid points are trackable and the front moves onshore. A front event is obtained via forward tracking a front for multiple time steps. Within an event, the times that a grid point can be tracked is its connectivity and a pruning algorithm using a connectivity cutoff is applied to extract only the coherently evolving components. This tracking method is applied to a realistic 3-month San Diego Bight model solution yielding 81 front events with duration ≥ 7 hours, allowing analyses of front event properties including occurrence frequency and propagation velocity. Sensitivity tests for the method’s parameters support that this method can be straightforwardly adapted to track evolving fronts of many types in other regions from both models and observations.


Author(s):  
Anthony Kirincich ◽  
Libe Washburn

Abstract Previous work with simulations of oceanographic HF radars has identified possible improvements when using Maximum Likelihood Estimation (MLE) for directional-of-arrival (DOA), however methods for determining the number of emitters (here defined as spatially distinct patches of the ocean surface) have not realized these improvements. Here we describe and evaluate the use of the Likelihood Ratio (LR) for emitter detection, demonstrating its application to oceanographic HF radar data. The combined detection-estimation methods MLE-LR are compared with MUSIC and MUSIC parameters for SeaSonde HF radars, along with a method developed for 8-channel systems known as MUSIC-Highest. Results show that the use of MLE-LR produces similar accuracy in terms of the RMS difference and correlation coefficients squared, as previous methods. We demonstrate that improved accuracy can be obtained for both methods, at the cost of fewer velocity observations and decreased spatial coverage. For SeaSondes, accuracy improvements are obtained with less commonly used parameter sets. The MLE-LR is shown to be able to resolve simultaneous closely spaced emitters, which has the potential to improve observations obtained by HF radars operating in complex current environments.


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