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Hamid Goharnejad
,
Will Perrie
,
Bash Toulany
,
Mike Casey
, and
Minghong Zhang

Abstract

The provision of reliable results from numerical wave models implemented over vast ocean areas can be considered as a time-consuming process. In this regard, the estimation of areas with maximum similarity in wave climate spatial areas and the determination of associated representative point locations for these areas can play an important role in climate research and in engineering applications. To deal with this issue, we apply a state-of-the-art clustering method, Geo-SOM, to determine geographical areas with similar wave regimes, in terms of mean wave direction (MWD), mean wave period (T0), and significant wave height (Hs). Although this method has many strengths, a weakness is related to detection and accounting of the most extreme and rare events. To resolve this deficiency, an initial preprocessing method (called PG-Geo-SOM) is applied. To evaluate the performance of this method, extreme wave parameters, including Hs and T0, are calculated. We simulate the present climate, represented as 1979 to 2017, compared to the future climate, 2060–98, following the Intergovernmental Panel on Climate Change (IPCC) future scenario RCP8.5 in the northwestern Atlantic Ocean. In this approach, the wave parameter data are divided into distinct groups, or clusters, motivated by their geographical positions. For each cluster, the centroid spatial point and the time series of data are extracted, for Hs, MWD, and T0. Extreme values are estimated for 5-, 10-, 25-, 50-, and 100-yr return periods, using Gumbel, exponential, and Weibull stochastic models, for both present and future climates. Results show that for parameter T0, the impact of climate change for the study area is a decreasing trend, while for Hs, in coastal and shelf areas up to about 1000 km from the coastline, increasing trends are estimated, and in open-ocean areas, far from the coast, decreasing trends are obtained.

Full access
Jessica Maier
and
Robert Knuteson

Abstract

Hyperspectral infrared satellite observations from geostationary platforms allow for the retrieval of temperature and water vapor measurements with higher temporal and vertical resolution than was previously available. The Chinese satellite Fengyun-4A (FY-4A) includes the Geostationary Interferometric Infrared Sounder (GIIRS), which has the ability to measure vertical profiles of temperature and water vapor from space at times when ground-based upper-air soundings are not available and can fill an important need in short-range weather prediction. In this study, convective available potential energy (CAPE) and lifted index (LI), which are used for forecasting atmospheric instability, were computed using the SHARPpy algorithm used by the NWS Storm Prediction Center. However, remote infrared and microwave sensing is lacking detailed information in the boundary layer, so the addition of the NOAA Meteorological Assimilation and Data Ingest System (MADIS) surface data may be necessary in order to get accurate temperature and moisture measurement near the surface. This study uses 10–16 May 2019 in the coastal region near Hong Kong for evaluating the use of hourly surface observations combined with satellite soundings from FY-4A GIIRS at 2-h intervals. The GIIRS plus MADIS surface-based CAPE and LI estimates are compared to estimates derived from low-Earth-orbiting (LEO) SNPP and NOAA-20 from NOAA, MetOp from EUMETSAT, NWP reanalysis, and local radiosondes. In the case study, the 2-h sampling interval of the GIIRS geostationary sounder was able to capture the rapid transition (16 h) from a stable to an unstable atmosphere in both CAPE and LI. The use of surface observations with satellite soundings gave mixed results, possibly due to the complex terrain near Hong Kong.

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Brian Emery
,
Anthony Kirincich
, and
Libe Washburn

Abstract

Previous work with simulations of oceanographic high-frequency (HF) radars has identified possible improvements when using maximum likelihood estimation (MLE) for direction of arrival; 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 multiple signal classification method (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.

Significance Statement

We identify and test a method based on the likelihood ratio (LR) for determining the number of signal sources in observations subject to direction finding with maximum likelihood estimation (MLE). Direction-finding methods are used in broad-ranging applications that include radar, sonar, and wireless communication. Previous work suggests accuracy improvements when using MLE, but suitable methods for determining the number of simultaneous signal sources are not well known. Our work shows that the LR, when combined with MLE, performs at least as well as alternative methods when applied to oceanographic high-frequency (HF) radars. In some situations, MLE and LR obtain superior resolution, where resolution is defined as the ability to distinguish closely spaced signal sources.

Full access
Kuo-Nung Wang
,
Chi O. Ao
,
Ramon Padullés
,
Francis J. Turk
,
Manuel de la Torre Juárez
, and
Estel Cardellach

Abstract

Following the successful launch of the Spanish PAZ mission the proof of concept experiment “Radio Occultation and Heavy Precipitation with PAZ” (ROHP-PAZ) started operating in May 2018. The ROHP-PAZ observations demonstrated that precise measurements of the phase shift between horizontal and vertical polarizations from Global Navigation Satellite System (GNSS) L-band signals are sensitive to oriented hydrometeors along the ray paths. While this differential phase shift measurement as a function of time has proven very useful, the regular radio occultation (RO) intermediate products from different polarized channels, such as bending angle and phase retrievals on the domain of impact parameter, have never been exploited. In this research, we studied the characteristics of polarimetric phase and bending angle difference retrieved by the radio-holographic (RH) method to mitigate atmospheric multipath effect and to explore their use in data assimilation. To validate RH approach in polarimetric retrievals, we performed end-to-end simulations where the hydrometeors are modeled by the effective refractivity with different horizontal extents. The simulation results demonstrate that the strong precipitation (>15 mm h−1) with 40-km horizontal extent can be detected with the retrieved bending angle shift. The calibration process on the impact parameter domain has also been developed to extract the differential phase and bending angle shift from the actual polarimetric RO data. Statistics from the PAZ data shows that the mean retrieved RH polarimetric phase shift with various horizontal extent is approximately proportional to the tangent point location rain rate at a ratio of 0.02 rad (mm h−1)−1.

Open access
Bofu Zheng
,
Andrew J. Lucas
,
Robert Pinkel
, and
Arnaud Le Boyer

Abstract

The Wirewalker (WW) ocean-wave-powered vertical profiling system allows the collection of high-resolution oceanographic data due to its rapid profiling, hydrodynamically quiet operation, and long endurance. We have assessed the potential for measuring fine-scale ocean velocities from the Wirewalker platform using commercially available acoustic velocimeters. Although the vertical profiling speed is relatively steady, platform motion affects the velocity measurements and requires correction. We present an algorithm to correct our velocity estimates using platform motion calculated from the inertial sensors—accelerometer, gyroscope, and magnetometer—on a Nortek Signature1000 acoustic Doppler current profiler (ADCP). This correction, carried out ping by ping, was effective in removing the vehicle motion from the measured velocities. The motion-corrected velocities contain contributions from surface wave orbital velocities, especially near the surface, and the background currents. To proceed, we use an averaging approach that leverages both the vertical platform profiling of the system and the ∼15–20 m vertical profiling range resolution of the down-looking ADCP to separate the surface wave orbital velocities and the background flow. The former can provide information on the wave conditions. From the latter, we are able to estimate fine-scale velocity and shear with spectral wavenumber rolloff at vertical scales around 3 m, a vertical resolution several times finer than that possible from modern shipboard or fixed ADCPs with similar profiling range, and similar to recent glider measurements. When combined with a continuous time series of buoy drift calculated from the onboard GPS, a highly resolved total velocity field is obtained, with a unique combination of space and time resolution.

Open access
K. Jossia Joseph
,
Amit Tandon
,
R. Venkatesan
,
J. Thomas Farrar
, and
Robert A. Weller

Abstract

The inception of a moored buoy network in the northern Indian Ocean in 1997 paved the way for systematic collection of long-term time series observations of meteorological and oceanographic parameters. This buoy network was revamped in 2011 with Ocean Moored buoy Network for north Indian Ocean (OMNI) buoys fitted with additional sensors to better quantify the air–sea fluxes. An intercomparison of OMNI buoy measurements with the nearby Woods Hole Oceanographic Institution (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 National Institute of Ocean Technology (NIOT) during 2019 revealed that the accurate and stable amplification of the thermopile voltage records along with the customized datalogger in the WHOI system results in better estimations of LWR↓. The offset in NIOT measured LWR↓ is estimated first by segregating the LWR↓ during clear-sky conditions identified using the downwelling shortwave radiation measurements from the same test station, and second, 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.

Significance Statement

Downwelling longwave radiation (LWR↓) is an important climate variable for calculating air–sea heat exchange and quantifying Earth’s energy budget. An intercomparison of LWR↓ measurements between ocean observing platforms in the north Indian Ocean revealed a systematic offset in National Institute of Ocean Technology (NIOT) Ocean Moored buoy Network for north Indian Ocean (OMNI) buoys. The observed offset limited our capability to accurately estimate air–sea fluxes in the Indian Ocean. The sensor measurements were compared with a standard reference system, which revealed problems in thermopile amplifier as the root cause of the offset. This work led to the development of a reliable method to correct the offset in LWR↓ and revamping of radiation measurements in NIOT-OMNI buoys. The correction is being applied to the past measurements from 12 OMNI buoys over 8 years to improve the estimation of air–sea fluxes in the Indian Ocean.

Open access
Free access
Amato Evan
,
Blake Walkowiak
, and
Robert Frouin

Abstract

Here we present retrievals of aerosol optical depth τ from an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals of τ and more than 95% of retrievals when τ > 0.1 are rejected, resulting in a factor-of-2 reduction in dust-storm averaged τ. We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability in τ during the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites that are located close to dust source regions may require ancillary measurements to aid in the identification of dust.

Significance Statement

In this study we demonstrate that, during dust storms, measurements made with a sun photometer at an AERONET site in the western Sonoran Desert are frequently classified as cloud contaminated by the network’s processing algorithm. We identify the various algorithmic tests that result in the misclassification and discuss the physical reasons why dust typically fails those tests. We then present a method to restore these data that utilizes measurements from a collocated ceilometer. This work highlights the challenges, and one solution, to operating an AERONET site in a region that is close to the sources of airborne dust.

Full access
Min Deng
,
Jeffrey French
,
Bart Geerts
,
Samuel Haimov
,
Larry Oolman
,
Dave Plummer
, and
Zhien Wang

Abstract

As part of the analysis following the Seeded and Natural Orographic Wintertime Storms (SNOWIE) project, the ice water content (IWC) in ice and mixed-phase clouds is retrieved from airborne Wyoming Cloud Radar (WCR) measurements aboard the University of Wyoming King Air (UWKA), which has a suite of integrated in situ IWC, optical array probes, and remote sensing measurements, and it provides a unique dataset for this algorithm development and evaluation. A sensitivity study with different idealized ice particle habits shows that the retrieved IWC with aggregate ice particle habit agrees the best with the in situ measurement, especially in ice or ice-dominated mixed-phase clouds with a correlation coefficient (rr) of 0.91 and a bias of close to 0. For mixed-phase clouds with ice fraction ratio less than 0.8, the variances of IWC estimates increase (rr = 0.76) and the retrieved mean IWC is larger than in situ IWC by a factor of 2. This is found to be related to the uncertainty of in situ measurements, the large cloud inhomogeneity, and the retrieval assumption uncertainty. The simulated reflectivity Ze and IWC relationships assuming three idealized ice particle habits and measured particle size distributions show that hexagonal columns with the same Ze have a lower IWC than aggregates, whose Ze–IWC relation is more consistent with the observed WCR Ze and in situ IWC relation in those clouds. The 2D stereo probe (2DS) images also indicate that ice particle habit transition occurs in orographic mixed-phase clouds; hence, the retrieved IWC assuming modified gamma particle size distribution (PSD) of aggregate particles tends to have a greater bias in this kind of clouds.

Open access
Iwan Holleman
,
Asko Huuskonen
, and
Brandon Taylor

Abstract

Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks.

Open access