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Andrea Cipollone, Andrea Storto, and Simona Masina

Abstract

Recent advances in global ocean prediction systems are fostered by the needs of accurate representation of mesoscale processes. The day-by-day realistic representation of its variability is hampered by the scarcity of observations as well as the capability of assimilation systems to correct the ocean states at the same scale. This work extends a 3DVAR system designed for oceanic applications to cope with global eddy-resolving grid and dense observational datasets in a hybridly parallelized environment. The efficiency of the parallelization is assessed in terms of both scalability and accuracy. The scalability is favored by a weak-constrained formulation of the continuity requirement among the artificial boundaries implied by the domain decomposition. The formulation forces possible boundary discontinuities to be less than a prescribed error and minimizes the parallel communication relative to standard methods. In theory, the exact solution is recovered by decreasing the boundary error toward zero. In practice, it is shown that the accuracy increases until a lower bound arises, because of the presence of the mesh and the finite accuracy of the minimizer. A twin experiment has been set up to estimate the benefit of employing an eddy-resolving grid within the assimilation step, as compared with an eddy-permitting one, while keeping the eddy-resolving grid within the forecast step. It is shown that the use of a coarser grid for data assimilation does not allow an optimal exploitation of the present remote sensing observation network. A global decrease of about 15% in the error statistics is found when assimilating dense surface observations, and no significant improvement is seen for sparser observations (in situ profilers).

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Antonio Gómez Roa, Xavier Flores-Vidal, Orlando Avendaño Gastelum, Rogelio Núñez, Andrés Sandoval Rangel, Cesar A. Liera Grijalva, and Juan Ivan Nieto Hipólito

Abstract

In this work we present an unmanned aircraft vehicle (UAV) designed from off-the-shelf components to release ocean minidrifters. Its endurance (~1 h), payload (~5 kg), offshore range (~30 km), capability of operating into wind conditions of ~10 kt (1 kt ≈ 0.51 m s−1), high-precision autopilot (2–3 m), and flying altitude of ~500 m above sea level, along with its relatively low cost [<$5,000 (U.S. dollars)] enables quick and relatively easy oceanographic applications beyond 10 km offshore. We report here the very first successful ocean drifter releases, performed along the Baja California coast, between Tijuana and Rosarito, Mexico, and the technical details of the UAV. About 50 experiments (flights) allowed us to improve the takeoff and landing, the release tunnel for minidrifters, the cruise speed and altitude to release drifters safely, and to implement a parachute that controls the speed of the freefalling minidrifters. Quick release of up to six drifters (armed with real-time data transfer and web display) between 2 and 12 km offshore were performed at ~500 m above sea level, during a single flight in under 15 min, as opposed to classic techniques using boats or ships that, although can transport much more weight, can take several hours, use more human resources, and increase cost. Here we propose a novel open-source technique that can be used as a simplified method for scientific ocean measurements, as a quick-response emergency tool to map spills or for search and rescue.

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Jairo M. Valdivia, Danny E. Scipión, Marco Milla, and Yamina Silva

Abstract

Agriculture is one of the main economic activities in the Peruvian Andes; rainwater alone irrigates more than 80% of the fields used for agriculture purposes. However, the cloud and rain generation mechanisms in the Andes still remain mostly unknown. In early 2014, the Instituto Geofísico del Perú (IGP) decided to intensify studies in the central Andes to better understand cloud microphysics; the Atmospheric Microphysics And Radiation Laboratory officially started operations in 2015 at IGP’s Huancayo Observatory. In this work, a Ka-band cloud profiler [cloud and precipitation profiler (MIRA-35c)], a UHF wind profiler [Clear-Air and Rainfall Estimation (CLAIRE)], and a VHF wind profiler [Boundary Layer and Tropospheric Radar (BLTR)] are used to estimate rainfall rate at different conditions. The height dependence of the drop size diameter versus the terminal velocity, obtained by the radars, in the central Andes (3350 m MSL) was evaluated. The estimates of rainfall rate are validated to ground measurements through a disdrometer [second-generation Particle, Size, and Velocity (PARSIVEL2)] and two rain gauges. The biases in the cumulative rainfall totals for the PARSIVEL2, MIRA-35c, and CLAIRE were 18%, 23%, and −32%, respectively, and their respective absolute biases were 19%, 36%, and 63%. These results suggest that a real-time calibration of the radars, MIRA-35c and CLAIRE, is necessary for better estimation of precipitation at the ground. They also show that the correction of the raindrop terminal fall velocity, obtained by separating the vertical wind velocity (BLTR), used in the estimation the raindrop diameter is not sufficient, especially in convective conditions.

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Chang Cao, Yichen Yang, Yang Lu, Natalie Schultze, Pingyue Gu, Qi Zhou, Jiaping Xu, and Xuhui Lee

Abstract

Heat stress caused by high air temperature and high humidity is a serious health concern for urban residents. Mobile measurement of these two parameters can complement weather station observations because of its ability to capture data at fine spatial scales and in places where people live and work. In this paper, we describe a smart temperature and humidity sensor (Smart-T) for use on bicycles to characterize intracity variations in human thermal conditions. The sensor has several key characteristics of internet of things (IoT) technology, including lightweight, low cost, low power consumption, ability to communicate and geolocate the data (via the cyclist’s smartphone), and the potential to be deployed in large quantities. The sensor has a reproducibility of 0.03°–0.05°C for temperature and of 0.18%–0.33% for relative humidity (one standard deviation of variation among multiple units). The time constant with a complete radiation shelter and moving at a normal cycling speed is 9.7 and 18.5 s for temperature and humidity, respectively, corresponding to a spatial resolution of 40 and 70 m. Measurements were made with the sensor on street transects in Nanjing, China. Results show that increasing vegetation fraction causes reduction in both air temperature and absolute humidity and that increasing impervious surface fraction has the opposite effect.

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Jerald A. Brotzge, J. Wang, C. D. Thorncroft, E. Joseph, N. Bain, N. Bassill, N. Farruggio, J. M. Freedman, K. Hemker Jr., D. Johnston, E. Kane, S. McKim, S. D. Miller, J. R. Minder, P. Naple, S. Perez, James J. Schwab, M. J. Schwab, and J. Sicker

Abstract

The New York State Mesonet (NYSM) is a network of 126 standard environmental monitoring stations deployed statewide with an average spacing of 27 km. The primary goal of the NYSM is to provide high-quality weather data at high spatial and temporal scales to improve atmospheric monitoring and prediction, especially for extreme weather events. As compared with other statewide networks, the NYSM faced considerable deployment obstacles with New York’s complex terrain, forests, and very rural and urban areas; its wide range of weather extremes; and its harsh winter conditions. To overcome these challenges, the NYSM adopted a number of innovations unique among statewide monitoring systems, including 1) strict adherence to international siting standards and metadata documentation; 2) a hardened system design to facilitate continued operations during extreme, high-impact weather; 3) a station design optimized to monitor winter weather conditions; and 4) a camera installed at every site to aid situational awareness. The network was completed in spring of 2018 and provides data and products to a variety of sectors including weather monitoring and forecasting, emergency management, agriculture, transportation, utilities, and education. This paper focuses on the standard network of the NYSM and reviews the network siting, site configuration, sensors, site communications and power, network operations and maintenance, data quality control, and dissemination. A few example analyses are shown that highlight the benefits of the NYSM.

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Mengyan Feng, Weihua Ai, Guanyu Chen, Wen Lu, and Shuo Ma

Abstract

One-dimensional synthetic aperture microwave radiometer (1D-SAMR) can provide remote sensing images at a higher spatial resolution than those from traditional real aperture microwave radiometers. As 1D-SAMR operates at multiple incidence angles, we proposed a multiple linear regression method to retrieve sea surface temperature at an incidence angle between 0° and 65°. Assuming that a 1D-SAMR operates at various frequencies (i.e., 6.9, 10.65, 18.7, 23.8 and 36.5 GHz), a radiation transmission forward model was developed to simulate the brightness temperature measured by the 1D-SAMR. The sensitivity of the five frequencies to sea surface temperature was examined, and we evaluated the reliability of the regression method proposed in this study. Furthermore, 11 schemes with various frequency combinations were applied to retrieve sea surface temperature. The results showed that the five-frequency combination scheme performed better than the other schemes. This study also found that the accuracy of retrieved sea surface temperature is dependent on incidence angles. Finally, we suggested that the incidence angle range of the 1D-SAMR is necessary to be 30°–60° based on the relationship between the accuracy of retrieved sea surface temperature and the incidence angles.

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Mahdi Razaz, Daniela Di Iorio, Binbin Wang, and Ian MacDonald

Abstract

Two video time-lapse cameras (VTLCs) were deployed by a remotely operated underwater vehicle (ROV) to observe the temporal and spatial variability of a natural hydrocarbon seep at 1180 m depth in the Green Canyon 600 lease block, Gulf of Mexico. The VTLCs were positioned approximately 60 and 90 cm away from the vent, each recording 15 s video bursts at 30 frames per second, illuminated by a 2000 lumen (lm) LED lamp. One camera functioned for 2 weeks; the second camera recorded 568 video bursts at 6 h intervals from 3 September 2017 to 2 February 2018 (153 days). Over the campaign period, seepage from three vents along a 10 cm cluster shifted toward a new fault line with up to nine intermittent individual vents shifting along 20 cm. We developed a semisupervised algorithm using Mathematica and ImageJ routines to resolve the rise velocity and size of individual bubbles. The algorithm was applied to the last 30 frames of each video burst. Bubble characteristics were also analyzed in the videos recorded by the ROV camera. Processing VTLC records yielded a bubble size distribution comparable (5% deviation) to the ROV camera, while the rise velocities were found to be 12% smaller than the ROV data. Hydrocarbon flux estimated from VTLC data was also compared favorably (2% difference) with synoptic physical collections of hydrocarbons into an ROV-held funnel. The long-term measurements indicate that bubble rise velocity was weakly correlated to the discharge rate as well as to the cross-flow velocity.

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Yicun Zhen, Pierre Tandeo, Stéphanie Leroux, Sammy Metref, Thierry Penduff, and Julien Le Sommer

Abstract

Because of the irregular sampling pattern of raw altimeter data, many oceanographic applications rely on information from sea surface height (SSH) products gridded on regular grids where gaps have been filled with interpolation. Today, the operational SSH products are created using the simple, but robust, optimal interpolation (OI) method. If well tuned, the OI becomes computationally cheap and provides accurate results at low resolution. However, OI is not adapted to produce high-resolution and high-frequency maps of SSH. To improve the interpolation of SSH satellite observations, a data-driven approach (i.e., constructing a dynamical forecast model from the data) was recently proposed: analog data assimilation (AnDA). AnDA adaptively chooses analog situations from a catalog of SSH scenes—originating from numerical simulations or a large database of observations—which allow the temporal propagation of physical features at different scales, while each observation is assimilated. In this article, we review the AnDA and OI algorithms and compare their skills in numerical experiments. The experiments are observing system simulation experiments (OSSE) on the Lorenz-63 system and on an SSH reconstruction problem in the Gulf of Mexico. The results show that AnDA, with no necessary tuning, produces comparable reconstructions as does OI with tuned parameters. Moreover, AnDA manages to reconstruct the signals at higher frequencies than OI. Finally, an important additional feature for any interpolation method is to be able to assess the quality of its reconstruction. This study shows that the standard deviation estimated by AnDA is flow dependent, hence more informative on the reconstruction quality, than the one estimated by OI.

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Shashank S. Joshil, Cuong M. Nguyen, V. Chandrasekar, J. Christine Chiu, and Yann Blanchard

Abstract

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s−1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m−2 with the benchmark microwave method.

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Clément Guilloteau and Efi Foufoula-Georgiou

Abstract

The quantitative estimation of precipitation from orbiting passive microwave imagers has been performed for more than 30 years. The development of retrieval methods consists of establishing physical or statistical relationships between the brightness temperatures (TBs) measured at frequencies between 5 and 200 GHz and precipitation. Until now, these relationships have essentially been established at the “pixel” level, associating the average precipitation rate inside a predefined area (the pixel) to the collocated multispectral radiometric measurement. This approach considers each pixel as an independent realization of a process and ignores the fact that precipitation is a dynamic variable with rich multiscale spatial and temporal organization. Here we propose to look beyond the pixel values of the TBs and show that useful information for precipitation retrieval can be derived from the variations of the observed TBs in a spatial neighborhood around the pixel of interest. We also show that considering neighboring information allows us to better handle the complex observation geometry of conical-scanning microwave imagers, involving frequency-dependent beamwidths, overlapping fields of view, and large Earth incidence angles. Using spatial convolution filters, we compute “nonlocal” radiometric parameters sensitive to spatial patterns and scale-dependent structures of the TB fields, which are the “geometric signatures” of specific precipitation structures such as convective cells. We demonstrate that using nonlocal radiometric parameters to enrich the spectral information associated to each pixel allows for reduced retrieval uncertainty (reduction of 6%–11% of the mean absolute retrieval error) in a simple k-nearest neighbors retrieval scheme.

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