Browse

You are looking at 81 - 90 of 5,275 items for :

  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: All Content x
Clear All
Meng-Yuan Chen
,
Ching-Lun Su
,
Yuan-Han Chang
, and
Yen-Hsyang Chu

Abstract

In this study, a data processing method based on the empirical mode decomposition (EMD) of Hilbert–Huang transform (HHT) is developed at Chung-Li VHF radar to identify and remove the aircraft clutter for improving the atmospheric wind measurement. The EMD decomposes the echo signals into the so-called intrinsic mode functions (IMFs) in the time domain, and then the aircraft clutter that is represented by a number of specific IMFs can be identified in the radar returns and separated from the clear-air echoes that are observed concurrently by the VHF radar. The identified clutter is validated by using the aircraft information collected by the Automatic Dependent Surveillance–Broadcast (ADS-B) receiver. It shows that the proposed algorithm can detect the aircraft echoes that are mixed with the clear-air echoes. After implementing the algorithm on the experimental data, the atmospheric horizontal wind velocities are estimated after the aircraft clutter is removed. To evaluate the degree of the improvement of the horizontal wind measurement, a comparison in the horizontal wind velocities between Chung-Li VHF radar and a collocated UHF wind profiler radar is made. The results show that the use of EMD and the proposed data processing method can effectively reduce the uncertainty and substantially improve the precision and reliability of the horizontal wind measurement.

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
Free 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
Sebastian Essink
,
Verena Hormann
,
Luca R. Centurioni
, and
Amala Mahadevan

Abstract

Horizontal kinematic properties, such as vorticity, divergence, and lateral strain rate, are estimated from drifter clusters using three approaches. At submesoscale horizontal length scales O ( 1 10 ) km , kinematic properties become as large as planetary vorticity f, but challenging to observe because they evolve on short time scales O ( hours to days ) . By simulating surface drifters in a model flow field, we quantify the sources of uncertainty in the kinematic property calculations due to the deformation of cluster shape. Uncertainties arise primarily due to (i) violation of the linear estimation methods and (ii) aliasing of unresolved scales. Systematic uncertainties (iii) due to GPS errors, are secondary but can become as large as (i) and (ii) when aspect ratios are small. Ideal cluster parameters (number of drifters, length scale, and aspect ratio) are determined and error functions estimated empirically and theoretically. The most robust method—a two-dimensional, linear least squares fit—is applied to the first few days of a drifter dataset from the Bay of Bengal. Application of the length scale and aspect-ratio criteria minimizes errors (i) and (ii), and reduces the total number of clusters and so computational cost. The drifter-estimated kinematic properties map out a cyclonic mesoscale eddy with a surface, submesoscale fronts at its perimeter. Our analyses suggest methodological guidance for computing the two-dimensional kinematic properties in submesoscale flows, given the recently increasing quantity and quality of drifter observations, while also highlighting challenges and limitations.

Significance Statement

The purpose of this study is to provide insights and guidance for computing horizontal velocity gradients from clusters (i.e., three or more) of Lagrangian surface ocean drifters. The uncertainty in velocity gradient estimates depends strongly on the shape deformation of drifter clusters by the ocean currents. We propose criteria for drifter cluster length scales and aspect ratios to reduce uncertainties and develop ways of estimating the magnitude of the resulting errors. The findings are applied to a real ocean dataset from the Bay of Bengal.

Restricted access
Rachel W. Obbard
,
Alice C. Bradley
, and
Ignatius Rigor

Abstract

This paper describes a remotely monitored buoy that, when deployed in open water prior to freeze up, permits scientists to monitor not only temperature with depth, and hence freeze up and sea ice thickness, but also the progression of sea ice development—e.g., the extent of cover at a given depth as it grows (solid fraction), the brine volume of the ice, and the salinity of the water just below, which is driven by brine expulsion. Microstructure and In situ Salinity and Temperature (MIST) buoys use sensor “ladders” that, in our prototypes, extend to 88 cm below the surface. We collected hourly measurements of surface air temperature and water temperature and electrical impedance every 3 cm to track the seasonal progression of sea ice growth in Elson Lagoon (Utqiaġvik, Alaska) over the 2017/18 ice growth season. The MIST buoy has the potential to collect detailed sea ice microstructural information over time and help scientists monitor all parts of the growth/melt cycle, including not only the freezing process but the effects of meteorological changes, changing snow cover, the interaction of meltwater, and drainage.

Significance Statement

There is a need to better understand how an increasing influx of freshwater, one part of a changing Arctic climate, will affect the development of sea ice. Current instruments can provide information on the growth rate, extent, and thickness of sea ice, but not direct observations of the structure of the ice during freeze up, something that is tied to salinity and local air and water temperature. A first deployment in Elson Lagoon in Utqiaġvik, Alaska, showed promising results; we observed fluctuations in ice temperatures in response to brief warmings in air temperature that resulted in changes in the conductivity, liquid fraction, and brine volume fraction within the ice.

Restricted access
Alain Zuber
,
Wolfgang Stremme
,
Michel Grutter
,
David K. Adams
,
Thomas Blumenstock
,
Frank Hase
,
Claudia Rivera
,
Noemie Taquet
,
Alejandro Bezanilla
, and
Eugenia González de Castillo

Abstract

Total column H2O is measured by two remote sensing techniques at the Altzomoni Atmospheric Observatory (19°12′N, 98°65′W, 4000 m above sea level), a high-altitude, tropical background site in central Mexico. A ground-based solar absorption FTIR spectrometer that is part of the Network for Detection of Atmospheric Composition Change (NDACC) is used to retrieve water vapor in three spectral regions (6074–6471, 2925–2941, and 1110–1253 cm−1) and is compared to data obtained from a global positioning system (GPS) receiver that is part of the TLALOCNet GPS-meteorological network. Strong correlations are obtained between the coincident hourly means from the three FTIR products and small relative bias and correction factors could be determined for each when compared to the more consistent GPS data. Retrievals from the 2925–2941 cm−1 spectral region have the highest correlation with GPS [coefficient of determination (R 2) = 0.998, standard deviation (STD) = 0.18 cm (78.39%), mean difference = 0.04 cm (8.33%)], although the other products are also highly correlated [R 2 ≥ 0.99, STD ≤ 0.20 cm (<90%), mean difference ≤ 0.1 cm (<24%)]. Clear-sky dry bias (CSDB) values are reduced to <10% (<0.20 cm) when coincident hourly means are used in the comparison. The use of GPS and FTIR water vapor products simultaneously leads to a more complete and better description of the diurnal and seasonal cycles of water vapor. We describe the water vapor climatology with both complementary datasets, nevertheless, pointing out the importance of considering the clear-sky dry bias arising from the large diurnal and seasonal variability of water vapor at this high-altitude tropical site.

Restricted access
Igor R. Ivić

Abstract

Simulated weather time series are often used in engineering and research practice to assess radar systems behavior and/or to evaluate the performance of novel techniques. There are two main approaches to simulating weather time series. One is based on summing individual returns from a large number of distributed weather particles to create a cumulative return. The other is aimed at creating simulated random signals based on the predetermined values of radar observables and is of interest herein. So far, several methods to simulate weather time series, using the latter approach, have been proposed. All of these methods are based on applying the inverse discrete Fourier transform to the spectral model with added random fluctuations. To meet the desired simulation accuracy, such an approach typically requires generating the number of samples that is larger than the base sample number due to the discrete Fourier transform properties. In that regard, a novel method to determine simulation length is proposed. It is based on a detailed theoretical development that demonstrates the exact source of errors incurred by this approach. Furthermore, a simple method for time series simulation that is based on the autocorrelation matrix exists. This method neither involves manipulations in the spectral domain nor requires generating the number of samples larger than the base sample number. Herein, this method is suggested for weather time series simulation and its accuracy and efficiency are analyzed and compared to the spectral-based approach.

Significance Statement

All research articles published so far on the topic of weather time series simulation propose the use of inverse discrete Fourier transform (IDFT) when based on the desired Doppler moment values. Herein, a detailed theoretical development that demonstrates the exact source of errors incurred by this approach is presented. Also, a novel method to determine the simulation length that is based on the theoretical error computation is proposed. As an alternative, a computationally efficient general method (not using IDFT) previously developed for the simulation of sequences with desired properties is suggested for weather time series simulation. It is demonstrated that the latter method produces accurate results within overall shorter computational times. Moreover, it is shown that the use of graphics processing unit (GPU), ubiquitous in modern computers, significantly reduces computational times compared to the sole use of central processing unit (CPU) for all simulation-related calculations.

Restricted access
Noureddine Semane
,
Richard Anthes
,
Jeremiah Sjoberg
,
Sean Healy
, and
Benjamin Ruston

Abstract

We compare two seemingly different methods of estimating random error statistics (uncertainties) of observations, the three-cornered hat (3CH) method and Desroziers method, and show several examples of estimated uncertainties of COSMIC-2 (C2) radio occultation (RO) observations. The two methods yield similar results, attesting to the validity of both. The small differences provide insight into the sensitivity of the methods to the assumptions and computational details. These estimates of RO error statistics differ considerably from several RO error models used by operational weather forecast centers, suggesting that the impact of RO observations on forecasts can be improved by adjusting the RO error models to agree more closely with the RO error statistics. Both methods show RO uncertainty estimates that vary with latitude. In the troposphere, uncertainties are higher in the tropics than in the subtropics and middle latitudes. In the upper stratosphere–lower mesosphere, we find the reverse, with tropical uncertainties slightly less than in the subtropics and higher latitudes. The uncertainty estimates from the two techniques also show similar variations between a 31-day period during Northern Hemisphere tropical cyclone season (16 August–15 September 2020) and a month near the vernal equinox (April 2021). Finally, we find a relationship between the vertical variation of the C2 estimated uncertainties and atmospheric variability, as measured by the standard deviation of the C2 sample. The convergence of the error estimates and the standard deviations above 40 km indicates a lessening impact of assimilating RO above this level.

Significance Statement

Uncertainties of observations are of general interest and their knowledge is important for assimilation in numerical weather prediction models. This paper compares two methods of estimating these uncertainties and shows that they give nearly identical results under certain conditions. The estimation of the COSMIC-2 bending angle uncertainties and how they compare to the assumed bending angle error models in several operational weather centers suggests that there is an opportunity for obtaining improved impact of RO observations in numerical model forecasts. Finally, the relationship between the COSMIC-2 bending angle errors and atmospheric variability provides insight into the sources of RO observational uncertainties.

Open access