Browse

You are looking at 1 - 10 of 5,229 items for :

  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: All Content x
Clear All
Martin Schön, Keri Anne Nicoll, Yann Georg Büchau, Stefan Chindea, Andreas Platis, and Jens Bange

Abstract

Atmospheric electricity measurements made from small unmanned aircraft systems (UAS) are rare but are of increasing interest to the atmospheric science community due to the information that they can provide about aerosol and turbulence characteristics of the atmospheric boundary layer (ABL). Here we present the first analysis of a new data set of space charge and meteorology measurements made from the small, electric, fixed-wing UAS model MASC-3. Two distinct experiments are discussed: (1) Flights past a 99 m metal tower to test the response of the charge sensor to a fixed distortion of the electric field caused by the geometry of the tower. Excellent agreement is found between the charge sensor response from the MASC-3 and modeled electric field around the tower. (2) Vertical profiles up to an altitude of 2500 m to study the evolution of the ABL with the time of day. These flights demonstrated close agreement between the space charge profiles and temperature, relative humidity, and turbulence parameters, as would be expected on a fair-weather day with summertime convection. Maximum values of space charge measured were of order 70 pC m−3, comparable with other measurements in the literature from balloon platforms. These measurements demonstrate the suitability of small UAS for atmospheric electrical measurements, provided that care is taken over the choice of aircraft platform, sensor placement, minimization of electrical interference, and careful choice of the flight path. Such aircraft are typically more cost-effective than manned aircraft and are being increasingly used for atmospheric science purposes.

Restricted access
Coltin Grasmick, Bart Geerts, Jeffrey R. French, Samuel Haimov, and Robert M. Rauber

Abstract

Properties of frozen hydrometeors in clouds remain difficult to sense remotely. Estimates of number concentration, distribution shape, ice particle density, and ice water content are essential for connecting cloud processes to surface precipitation. Progress has been made with dual-frequency radars, but validation has been difficult because of lack of particle imaging and sizing observations collocated with the radar measurements.

Here, data are used from two airborne profiling (up & down) radars, the W-band Wyoming Cloud Radar and the Ka-band Profiling Radar, allowing for Ka-W-band Dual-Wavelength Ratio (DWR) profiles. The aircraft (the University of Wyoming King Air) also carried a suite of in situ cloud and precipitation probes. This arrangement is optimal for relating the “flight-level” DWR (an average from radar gates below and above flight level) to ice particle size distributions measured by in situ optical array probes, as well as bulk properties such as minimum snow particle density and ice water content. This comparison reveals a strong relationship between DWR and the ice particle median-volume diameter. An optimal range of DWR values ensures the highest retrieval confidence, bounded by the radars’ relative calibration and DWR saturation, found here to be about 2.5 to 7.5 dB. The DWR-defined size distribution shape is used with a Mie scattering model and an experimental mass-diameter relationship to test retrievals of ice particle concentration and ice water content. Comparison with flight-level cloudprobe data indicate good performance, allowing microphysical interpretations for the rest of the vertical radar transects.

Restricted access
A. Addison Alford, Michael I. Biggerstaff, Conrad L. Ziegler, David P. Jorgensen, and Gordon D. Carrie

Abstract

Mobile weather radars at high frequencies (C, X, K and W-band) often collect data using staggered Pulse Repetition Time (PRT) or dual Pulse Repetition Frequency (PRF) modes to extend the effective Nyquist velocity and mitigate velocity aliasing while maintaining a useful maximum unambiguous range. These processing modes produce widely dispersed “processor” dealiasing errors in radial velocity estimates. The errors can also occur in clusters in high shear areas. Removing these errors prior to quantitative analysis requires tedious manual editing and often produces “holes” or regions of missing data in high signal-to-noise areas. Here, data from three mobile weather radars were used to show that the staggered PRT errors are related to a summation of the two Nyquist velocities associated with each of the PRTs. Using observations taken during a mature mesoscale convective system, a landfalling tropical cyclone, and a tornadic supercell storm, an algorithm to automatically identify and correct staggered PRT processor errors has been developed and tested. The algorithm creates a smooth profile of Doppler velocities using a Savitzky-Golay filter independently in radius and azimuth and then combined. Errors are easily identified by comparing the velocity at each range gate to its smoothed counterpart and corrected based on specific error characteristics. The method improves past dual PRF correction methods that were less successful at correcting “grouped” errors. Given the success of the technique across low, moderate, and high radial shear regimes, the new method should improve research radar analyses by affording the ability to retain as much data as possible rather than manually or objectively removing erroneous velocities.

Restricted access
Silvia Innocenti, Pascal Matte, Vincent Fortin, and Natacha Bernier

Abstract

Reconstructing tidal signals is indispensable for verifying altimetry products, forecasting water levels, and evaluating long-term trends. Uncertainties in the estimated tidal parameters must be carefully assessed to adequately select the relevant tidal constituents and evaluate the accuracy of the reconstructed water levels. Customary harmonic analysis uses Ordinary Least Squares (OLS) regressions for their simplicity. However, the OLS may lead to incorrect estimations of the regression coefficient uncertainty due to the neglect of the residual autocorrelation.

This study introduces two residual resamplings (moving-block and semi-parametric bootstraps) for estimating the variability of tidal regression parameters and shows they are powerful methods to assess the effects of regression errors with non-trivial autocorrelation structures. A Monte Carlo experiment compares their performance to four analytical procedures selected from those provided by the RT_Tide, UTide, and NS_Tide packages and the robustfit.m MATLAB function. In the Monte Carlo experiment, an Iteratively Reweighted Least Squares (IRLS) regression is used to estimate the tidal parameters for hourly simulations of one-dimensional water levels. Generally, robustfit.m and the considered RT_Tide method overestimate the tidal amplitude variability, while the selected UTide and NS_Tide approaches underestimate it. After some substantial methodological corrections the selected NS_Tide method shows adequate performance. As a result, estimating the regression variance-covariance with the considered RT_Tide, UTide, and NS_Tide methods may lead to the erroneous selection of constituents and underestimation of water level uncertainty, compromising the validity of their results in some applications.

Restricted access
Ryan C. Scott, Fred G. Rose, Paul W. Stackhouse Jr., Norman G. Loeb, Seiji Kato, David R. Doelling, David A. Rutan, Patrick C. Taylor, and William L. Smith Jr.

Abstract

Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time-space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu-Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ~30-40% (~20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <Ÿ1% (2-3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ~3.5% (~9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.

Restricted access
Baixin Li, Huan Tang, Dongfang Ma, and Jianmin Lin

Abstract

Mesoscale eddies are a mechanism for ocean energy transfer, and identifying them on a global scale provides a means of exploring ocean mass and energy exchange between ocean basins. There are many widely used model-driven methods for detecting mesoscale eddies; however, these methods are not fully robust or generalizable. This study applies a data-driven method and proposes a mesoscale detection network based on the extraction of eddy-related spatiotemporal information from multisource remote sensing data. Focusing on the northwest Pacific, the study first analyzes mesoscale eddy characteristics using a combination of gridded data for the absolute dynamic topography (ADT), sea surface temperature (SST), and absolute geostrophic velocity (UVG). Then, a deep learning network with a dual-attention mechanism and a convolutional long short-term memory module is proposed, which can deeply exploit spatiotemporal feature relevance while encoding and decoding information in the gridded data. Based on the analysis of mesoscale eddy characteristics, ADT and UVG gridded data are selected to be the inputs for the detection network. The experiments show that the accuracy of the proposed network reaches 93.38%, and the weighted mean dice coefficient reaches 0.8918, which is a better score than those achieved by some of the detection networks proposed in previous studies, including U-Net, SymmetricNet, and ResU-Net. Moreover, compared with the model-driven approach used to generate the ground-truth dataset, the network method proposed here demonstrates better performance in detecting mesoscale eddies at smaller scales, partially addressing the problem of ghost eddies.

Restricted access
Charanjit S. Pabla, David B. Wolff, David A. Marks, Stephanie M. Wingo, and Jason L. Pippitt

Abstract

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, Virginia, has been established as a semipermanent supersite for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research-quality precipitation instruments, including NASA’s S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual-Frequency Precipitation Radar (DPR), combined DPR–GPM Microwave Imager (GMI) and GMI level II precipitation retrievals compared to WPRF ground observations from a 6-yr collection of satellite overpasses. Multisensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (Dm), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 min after overpass time but are not consistent between cases. In addition, GPM Core Observatory Dm retrievals are within level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (<1.0 mm h−1). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multisensor DPR/GMI combined algorithms outperform single-sensor DPR algorithm.

Significance Statement

Satellites are beneficial for global precipitation surveillance because extensive ground instruments are lacking, especially over oceans. Ground validation studies are required to calibrate and improve precipitation algorithms from satellite sensors. The primary goal of this study is to quantify the differences between satellite raindrop size and rain-rate retrieval with ground-based observations. Rainfall-rate algorithms require assumptions about the mean raindrop size. Results indicate Global Precipitation Measurement (GPM)/satellite-based mean raindrop size is within acceptable error (±0.5 mm) with respect to ground measurements. In addition, GPM satellite measurements overestimate light rain (<1.0 mm h−1), which is important during the winter months and at high latitudes. Illuminating the challenges of GPM satellite-based precipitation estimation can guide algorithm developers to improve retrievals.

Restricted access
Han Liu, Zezong Chen, Chen Zhao, and Sitao Wu

Abstract

Wavenumber–frequency spectra obtained with coherent microwave radar at upwind-grazing angle consist of energy along the ocean wave dispersion relation and additional features that lie above this relation labeled as “high-order harmonic” and below this relation known as “group line.” Due to these nonlinear features, low-frequency components appear in the radar-estimated wave spectrum and the energy and peak frequency of the dominant wave spectrum decrease, which are responsible for the overestimation of radar-measured wave period. According to the component distribution in the wavenumber–frequency spectrum, a mean wave period inversion method based on a dispersion relation filter for coherent S-band radar is proposed. The method filters out the “group line” and preserves the high-order harmonic to compensate for the energy loss caused by the decrease of peak frequency of the dominant wave spectrum. A two-dimensional inverse Fourier transform is applied to the filtered wavenumber–frequency spectrum. Then the radar-measured velocity sequence is selected to obtain the velocity spectrum via a one-dimension Fourier transform. The wave height spectrum is estimated from the one-dimensional velocity spectrum by the direct transform relationship between the one-dimensional velocity spectrum and the wave height spectrum. Later, mean wave periods can be derived by the first moment of the wave height spectrum. A 13-day dataset collected with a shore-based coherent S-band radar deployed at Zhelang, China, is reanalyzed and used to retrieve mean wave periods. Comparisons between the measurements of radar and wave buoy are conducted. The results indicate that the proposed method improves the wave period measurement for coherent S-band radar.

Significance Statement

This work provides a mean wave period inversion method for coherent S-band radar. The mean wave period is always overestimated due to the “group line” in the wavenumber–frequency spectrum and the energy loss caused by the decrease of peak frequency of the dominant wave spectrum. Therefore, dealing with these estimation errors is important.

Restricted access
Hans van Haren and Fred C. Bosveld

Abstract

Knowledge about the characteristics of the atmospheric boundary layer is vital for the understanding of redistribution of air and suspended contents that are particularly driven by turbulent motions. Despite many modeling studies, detailed observations are still demanded of the development of turbulent exchange under stable and unstable conditions. In this paper, we present an attempt to observationally describe atmospheric internal waves and their associated turbulent eddies in detail, under varying stable conditions. Therefore, we mounted 198 high-resolution temperature (T) sensors with 1-m spacing on a 200-m-long cable. The instrumented cable was attached along the 213-m-tall meteorological mast of Cabauw, Netherlands, during late summer 2017. The mast has standard meteorological equipment at extendable booms at six levels in height. A sonic anemometer is at 60 m above ground. The T sensors have a time constant in air of τa ≈ 3 s and an apparent drift about 0.1°C month−1. Also due to radiation effects, short-term measurement instability is 0.05°C h−1 during nighttime and 0.5°C h−1 during daytime. These T-sensor characteristics hamper quantitative atmospheric turbulence research, due to a relatively narrow inertial subrange of maximum one order of magnitude. Nevertheless, height–time images from two contrasting nights show internal waves up to the buoyancy period of about 300 s, and shear and convective deformation of the stratification over the entire 197-m range of observations, supported by nocturnal marginally stable stratification. Moderate winds lead to 20-m-tall convection across weaker stratification, weak winds to episodic <10-m-tall shear instability across larger stratification.

Restricted access
Adi Kurniawan, Paul H. Taylor, Jana Orszaghova, Hugh Wolgamot, and Jeff Hansen

Abstract

An apparent giant wave event having a maximum trough-to-crest height of 21 m and a maximum zero-upcrossing period of 27 s was recorded by a wave buoy at a nearshore location off the southwestern coast of Australia. It appears as a group of waves that are significantly larger both in height and in period than the waves preceding and following them. This paper reports a multifaceted analysis into the plausibility of the event. We first examine the statistics of the event in relation to the rest of the record, where we look at quantities such as maximum-to-significant wave height ratios, ordered crest–trough statistics, and average wave profiles. We then investigate the kinematics of the buoy, where we look at the relationship between the horizontal and vertical displacements of the buoy, and also attempt to numerically reconstruct the giant event using Boussinesq and nonlinear shallow water equations. Additional analyses are performed on other sea states where at least one of the buoy’s accelerometers reached its maximum limit. Our analysis reveals incompatibilities of the event with known behavior of real waves, leading us to conclude that it was not a real wave event. Wave events similar to the one reported in our study have been reported elsewhere and have sometimes been accepted as real occurrences. Our methods of forensically analyzing the giant wave event should be potentially useful for identifying false rogue wave events in these cases.

Restricted access