Browse

You are looking at 101 - 110 of 5,275 items for :

  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: All Content x
Clear All
Beth Reid
and
Tom Swanson

Abstract

Loon LLC collected 794 000 h of corona current observations between 15 and ∼20 km above sea level with time resolution between 1 and 30 min. We are publicly releasing this dataset to enable the research community’s understanding of electrical activity in the stratosphere. We validate the reliability of these measurements by aligning our flight data with both nearby Geostationary Lightning Mapper (GLM) events and the Convective Diagnostic Oceanic (CDO) indicator. Corona current observations that exceeded the sensor maximum of 10 μA were associated with high GLM optical flux accumulations along the flight trajectory. Using the CDO indicator as a persistence forecast for future electrical activity was effective at predicting corona current events, and so we highly recommend this data source for real-time stratospheric navigation for vehicles sensitive to the harsh electrical environment of the stratosphere.

Significance Statement

Loon LLC operated a fleet of balloons in the stratosphere, between 15 and 20 km above sea level. The balloons were instrumented with a sensor that measured the current flowing through a wire dangling from the flight vehicle. The observed currents were caused by the motion of nearby charged particles that are often associated with thunderstorms and lightning activity. In this paper we show that Loon’s sensor registered current at the same time lightning was recorded near the balloon by other instruments like the Geostationary Lightning Mapper satellite. This is the first dataset of its kind and size, reaching 794 000 flight hours. We are publicly releasing these data in hopes of aiding scientific discovery by researchers and to help future stratospheric vehicle operators better understand and plan for the electrical environment.

Open access
C. O. Collins III
and
R. E. Jensen

Abstract

We identify and characterize an error in the National Data Buoy Center (NDBC) wave records due to the sustained tilt of a buoy under high winds. We use a standard, operational 3-m aluminum discus buoy from NDBC with two wave systems, one gimballed, and the other strapped down but uncorrected. By comparing the two, we find that the most extreme significant wave heights are systematically overestimated. The overestimation is shown to be confined to a region around the peak frequency in the spectra: 0.05–0.15 Hz. Wave direction and directional spread are unaffected. A bias due to tilt error can be observed starting at winds of 10 m s−1 or wave heights of 4 m. The bias increases as a function of wind speed and wave height, i.e., the bias is +10% when winds are 20 m s−1. Very high waves and winds are relatively rare, so while the tilt error does not affect overall statistics and basic analyses it could potentially affect analysis sensitive to the extremes. A correction is derived for significant wave height, which is a quadratic function of wind speed. The correction is shown to reduce wave heights in uncorrected records, but is found inadequate for general use. There is evidence of tilt error at other NDBC stations, but the full extent of prevalence in the record is not known at this time.

Open access
P. Miron
,
F. J. Beron-Vera
, and
M. J. Olascoaga

Abstract

Recently introduced in oceanography to interpret the near-surface circulation, transition path theory (TPT) is a methodology that rigorously characterizes ensembles of trajectory pieces flowing out from a source last and into a target next, i.e., those that most productively contribute to transport. Here we use TPT to frame, in a statistically more robust fashion than earlier analysis, equatorward routes of North Atlantic Deep Water (NADW) in the subpolar North Atlantic. TPT is applied on all available RAFOS and Argo floats in the area by means of a discretization of the Lagrangian dynamics described by their trajectories. By considering floats at different depths, we investigate transition paths of NADW in its upper (UNADW) and lower (LNADW) layers. We find that the majority of UNADW transition paths sourced in the Labrador and southwestern Irminger Seas reach the western side of a target arranged zonally along the southern edge of the subpolar North Atlantic domain visited by the floats. This is accomplished in the form of a well-organized deep boundary current (DBC). LNADW transition paths sourced west of the Reykjanes Ridge reveal a similar pattern, while those sourced east of the ridge are found to hit the western side of the target via a DBC and also several other places along it in a less organized fashion, indicating southward flow along the eastern and western flanks of the Mid-Atlantic Ridge. Naked-eye inspection of trajectories suggest generally more diffused equatorward NADW routes. A source-independent dynamical decomposition of the flow domain into analogous backward-time basins of attraction, beyond the reach of direct inspection of trajectories, reveals a much wider influence of the western side of the target for UNADW than for LNADW. For UNADW, the average expected duration of the pathways from the Labrador and Irminger Seas was found to be of 2–3 years. For LNADW, the duration was found to be influenced by the Reykjanes Ridge, being as long as 8 years from the western side of the ridge and of about 3 years on average from its eastern side.

Restricted access
Katie Kirk
,
Gregory Dusek
,
Philippe Tissot
, and
William Sweet

Abstract

The demand for nearshore wave observations is increasing due to spatial gaps and the importance of observations for accurate models and better understanding of inundation processes. Here, we show how water level (WL) standard deviation (sigma, σ) measurements at three acoustic NOAA tide gauges that utilize an Aquatrak sensor [Duck, North Carolina, Bob Hall Pier (BHP) in Corpus Christi, Texas, and Lake Worth, Florida] can be used as a proxy for significant wave height (H m0). Sigma-derived H m0 is calibrated to best fit nearby wave observations and error is quantified through RMSE, normalized RMSE (NRMSE), bias, and a scatter index. At Duck and Lake Worth, a quadratic fit of sigma to nearby wave observations results in a R 2 of 0.97 and 0.83, RMSE of 0.11 and 0.11 m, and NRMSE of 0.09 and 0.22, respectively. A linear fit between BHP sigma and H m0 is best, resulting in R 2 0.62, RMSE of 0.22, and NRMSE of 0.26. Regression fits deviate across NOAA stations and from the classic relationship of H m0 = 4σ, indicating H m0 cannot be accurately estimated with this approach at these Aquatrak sites. The dynamic water level (DWL = still WL ± 2σ) is calculated over the historic time series showing climatological and seasonal trends in the stations’ daily maximums. The historical DWL and sigma wave proxy could be calculated for many NOAA tide gauges dating back to 1996. These historical wave observations can be used to fill observational spatial gaps, validate models, and improve understanding of wave climates.

Significance Statement

There is a large spatial gap in nearshore real-time observational wave data that can provide critical information to researchers and resource managers regarding inundation and erosion, help validate coastal hydrodynamic models, and provide the maritime community with products that help ensure navigational safety. This study utilizes existing infrastructure to help fill the demand for nearshore wave observations by deriving a proxy for wave height at three sites. This work shows spatial variability in the regression fits across the sites, which should be explored at more stations in future work. Multidecadal length time series were also used at the sites to investigate climatological and seasonal trends that provide insight into wave climates and wave driven processes important for coastal flooding.

Restricted access
Rong Tang
,
Qian Li
, and
Shaoen Tang

Abstract

The image-based visibility detection methods have been one of the active research issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyze the effectiveness of various visual features in visibility detection from three aspects, namely, visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene, including three traditional visual features such as local binary patterns (LBP), histograms of oriented gradients (HOG), and contrast as well as three deep learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then the support vector regression (SVR) models, which are used to map visual features to visibility, are also trained, respectively based on the region of interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection.

Significance Statement

The visual feature extraction is a basic step for image-based visibility detection and significantly affects the detection performance. In this paper, we compare six candidate visual features, including traditional and deep learned features, from visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene. Then the SVR models are also trained to construct the mapping relations between different kinds of features and the visibility of each scene. The experiment results show that the deep learned features exhibit better performance in both feature analysis and model training, especially NIMA achieves the best fitting performance with fewer feature dimensions.

Restricted 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 σ e 0 ) 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 σ e 0 was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld–Bordan (HB) method suggest that σ e 0 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 σ e 0 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
Brett T. Hoover
,
Jason A. Otkin
,
Eugene M. Petrescu
, and
Emily Niebuhr

Abstract

A method is presented to generate quantitative precipitation estimates over Alaska using kriging to merge sparse, unevenly distributed rain gauge observations with quantitative precipitation forecasts from a three-member ensemble of high-resolution numerical weather prediction models. The estimated error variance of the analysis is computed by starting with the estimated error variance from kriging and then refining the variance in k-fold cross validation by an empirically derived inflation factor. The method combines dynamical model forecast information with observational data to deliver a best linear unbiased estimate of precipitation, along with an analysis uncertainty estimate, that provides a much-needed precipitation analysis in a region where sparse in situ observations, poor coverage by remote sensing platforms, and complex terrain introduce large uncertainties that need to be quantified. For 6-hourly accumulation estimates produced four times daily from 1 August 2019 to 31 July 2020, three analysis configurations are tested to measure the value added by including model forecast data and how those data are best utilized in the analysis. Several directions for further improvement and validation of the analysis product are provided.

Restricted access
Fang-Fang Li
,
Hui-Min Zuo
,
Ying-Hui Jia
,
Qi Wang
, and
Jun Qiu

Abstract

All-sky images derived from ground-based imaging equipment have become an important means of recognizing and quantifying cloud information. Accurate cloud detection is a prerequisite for obtaining important cloud information from an all-sky image. Existing cloud segmentation algorithms can achieve high accuracy. However, for different scenes, such as completely cloudy with obscured sun and partly cloudy with unobscured sun, the use of specific algorithms can further improve segmentation. In this study, a hybrid cloud detection algorithm based on intelligent scene recognition (HCD-ISR) is proposed. It uses suitable cloud segmentation algorithms for images in different scenes recognized by ISR, so as to utilize the various algorithms to their full potential. First, we developed an ISR method to automatically classify the all-sky images into three scenes. In scene A, the sky is completely clear; in scene B, the sky is partly cloudy with unobscured sun; and in scene C, the sun is completely obscured by clouds. The experimental results show that the ISR method can correctly identify 93% of the images. The most suitable cloud detection algorithm was selected for each scene based on the relevant features of the images in that scene. A fixed thresholding (FT) method was used for the images in scene C. For the most complicated scene, that is, scene B, the clear-sky background difference (CSBD) method was used to identify cloud pixels based on a clear-sky library (CSL). The images in the CSL were automatically filtered by ISR. Compared to FT, adaptive thresholding (AT), and CSBD methods, the proposed HCD-ISR method has the highest accuracy (95.62%). The quantitative evaluation and visualization results show that the proposed HCD-ISR algorithm makes full use of the advantages of different cloud detection methods, and is more flexible and robust.

Restricted access
David M. Loveless
,
Timothy J. Wagner
,
Robert O. Knuteson
,
David D. Turner
, and
Steven A. Ackerman

Abstract

Profiles of atmospheric temperature and water vapor from remotely sensed platforms provide critical observations within the temporal and spatial gaps of the radiosonde network. The 2017 National Academies of Science Decadal Survey highlighted that observations of the planetary boundary layer (PBL) from the current space-based observing system are not of the necessary accuracy or resolution for monitoring and predicting high-impact weather phenomena. One possible solution to improving observations of the PBL is supplementing the existing space-based observing system with a network of ground-based profilers. A synthetic information content study is developed utilizing profiles from the Atmospheric Radiation Measurement (ARM) program sites at the Southern Great Plains (SGP), east North Atlantic (ENA), and North Slope of Alaska (NSA) to assess the benefits, in terms of degrees of freedom (DOF), vertical resolution, and uncertainties, of a synergy between the ground-based Atmospheric Emitted Radiance Interferometer (AERI) with space-based hyperspectral infrared (IR) sounders. A combination of AERI with any of the three polar-orbiting IR sounders: the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), or the Infrared Atmospheric Sounding Interferometer (IASI), results in a DOF increase of 30%–40% in the surface-to-700-hPa layer compared to the space-based instrument alone. Introducing AERI measurements to the observing system also results in significant improvements to vertical resolution and uncertainties in the bottom 1000 m of the atmosphere compared to CrIS measurements alone. A synergy of CrIS and AERI exceeds the 1-km-vertical-resolution goal set by the Decadal Survey in the lowest 1000 m.

Restricted access
Free access