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Casey R. Densmore, Steven R. Jayne, and Elizabeth R. Sanabia

Abstract

Airborne expendable bathythermographs (AXBTs) are air-launched, single-use temperature–depth probes that telemeter temperature observations as VHF-modulated frequencies. This study describes the AXBT Real-Time Editing System (ARES), which is composed of two components: the ARES Data Acquisition System, which receives telemetered temperature–depth profiles with no external hardware other than a VHF radio receiver, and the ARES Profile Editing System, which quality controls AXBT temperature–depth profiles. The ARES Data Acquisition System performs fast Fourier transforms on windowed segments of the demodulated signal transmitted from the AXBT. For each segment, temperature is determined from peak frequency and depth from elapsed time since profile start. Valid signals are distinguished from noise by comparing peak signal levels and signal-to-noise ratios to predetermined thresholds. When evaluated using 387 profiles, the ARES Data Acquisition System produced temperature–depth profiles nearly identical to those generated using a Sippican MK-21 processor, while reducing the amount of noise from VHF interference included in those profiles. The ARES Profile Editor applies a series of automated checks to identify and correct common profile discrepancies before displaying the profile on an editing interface that provides simple user controls to make additional corrections. When evaluated against 1177 tropical Atlantic and Pacific AXBT profiles, the ARES automated quality control system successfully corrected 87% of the profiles without any required manual intervention. Necessary future work includes improvements to the automated quality control algorithm and algorithm evaluation against a broader dataset of temperature–depth profiles from around the world across all seasons.

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Peter Rogowski, Mark Otero, Joel Hazard, Thomas Muschamp, Scott Katz, and Eric Terrill

Abstract

Accurate surface meteorological (MET) observations reported reliably and in near–real time remain a critical component of on-scene environmental observation systems. Presented is a system developed by Scripps Institution of Oceanography that allows for rapid, global deployment of ground-based weather observations to support both timely decision-making and collection of high-quality weather time series for science or military applications in austere environments. Named the Expeditionary Meteorological (XMET), these weather stations have been deployed in extreme conditions devoid of infrastructure ranging from tropical, polar, maritime, and desert environments where near continuous observations were reported. To date, over 1 million weather observations have been collected during 225 deployments around the world with a data report success rate of 99.5%. XMET had its genesis during Operation Iraqi Freedom (OIF), when the U.S. Marine Corps 3rd Marine Aircraft Wing identified an immediate capability gap in environmental monitoring of their operation area due to high spatiotemporal variability of dust storms in the region. To address the sensing gap, XMET was developed to be a portable, expendable, ruggedized, self-contained, bidirectional, weather observation station that can be quickly deployed anywhere in the world to autonomously sample and report aviation weather observations. This paper provides an overview of the XMETs design, reliability in different environments, and examples of unique meteorological events that highlight both the unit’s reliability and ability to provide quality time series. The overview shows expeditionary MET sensing systems, such as the XMET, are able to provide long-term continuous observational records in remote and austere locations essential for regional spatiotemporal MET characterization.

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Xiangzhou Song

Abstract

Using buoy observations from 2004 to 2010 and newly released atmospheric reanalysis and satellite altimetry-derived geostrophic currents from 1993 to 2017, the quantitative contribution of daily mean surface currents to air–sea turbulent heat flux and wind stress uncertainties in the Gulf Stream (GS) region is investigated based on bulk formulas. At four buoy stations, the daily mean latent (sensible) heat flux difference between the estimates with and without surface currents range from −18 (−4) to 20 (4) W m−2, while the daily mean wind stress difference ranges from −0.04 to 0.02 N m−2. The positive values indicate higher estimates with opposite directions between surface currents and absolute winds. The transition between positive and negative differences is significantly associated with synoptic-scale weather variations. The uncertainties based on buoy observations are approximately 7% and 3% for wind stress and turbulent heat fluxes, respectively. The new reanalysis and satellite geostrophic currents confirm the uncertainties identified by buoy observations with acceptable discrepancies and provide a spatial view of the uncertainty fields. The mean geostrophic currents are aligned with the surface wind along the GS; therefore, the turbulent heat fluxes and wind stress will be “underestimated” with surface currents included. However, on both sides of the GS, the surface flow can be upwind due to possible mechanisms of eddy–mean flow interactions and recirculations, resulting in higher turbulent heat flux estimations. The wind stress and turbulent heat flux uncertainties experience significant seasonal variations and show long-term trends.

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Monika Feldmann, Curtis N. James, Marco Boscacci, Daniel Leuenberger, Marco Gabella, Urs Germann, Daniel Wolfensberger, and Alexis Berne

Abstract

Region-based Recursive Doppler Dealiasing (R2D2) is a novel dealiasing algorithm to unfold Doppler velocity fields obtained by operational radar measurements. It specializes in resolving issues when the magnitude of the gate-to-gate velocity shear approaches or exceeds the Nyquist velocity. This occurs either in highly sheared situations, or when the Nyquist velocity is low. Highly sheared situations, such as convergence lines or mesocyclones, are of particular interest for nowcasting and warnings. R2D2 masks high-shear areas and adds a spatial buffer around them. The areas between the buffers are then identified as continuous regions that lie within the same Nyquist interval. Each region subsequently is assigned its most likely Nyquist interval by applying vertical and temporal continuity constraints, as well as supplemental wind information from an operational mesoscale model. The shear zones are then resolved using 2D continuity in azimuth and range. This 4D procedure is repeated until no further improvement can be achieved. Each iteration with fewer folds identifies fewer but larger continuous regions and less shear zones until an optimum is reached. Residual errors, often related to shear greater than the Nyquist velocity, are contained to small areas within the buffer zones. This approach maximizes operational performance in high-shear situations and restricts errors to minimal areas to mitigate error propagation.

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Sebastián M. Torres and David Schvartzman

Abstract

We propose a simulation framework that can be used to design and evaluate the performance of adaptive scanning algorithms on different phased-array weather radar designs. The simulator is proposed as tool to 1) compare the performance of different adaptive scanning algorithms on the same weather event, 2) evaluate the performance of a given adaptive scanning algorithm on several weather events, and 3) evaluate the performance of a given adaptive scanning algorithm on a given weather event using different radar designs. We illustrate the capabilities of the proposed framework to design and evaluate the performance of adaptive algorithms aimed at reducing the update time using adaptive scanning. The example concept of operations is based on a fast low-fidelity surveillance scan and a high-fidelity adaptive scan. The flexibility of the proposed simulation framework is tested using two phased-array-radar designs and three complementary adaptive scanning algorithms: focused observations, beam clustering, and dwell tailoring. Based on a significant weather event observed by an operational NEXRAD radar, our experimental results consist of radar data that were simulated as if the same event had been observed by arbitrary combinations of radar systems and adaptive scanning configurations. Results show that simulated fields of radar data capture the main data-quality impacts from the use of adaptive scanning and can be used to obtain quantitative metrics and for qualitative comparison and evaluation by forecasters. That is, the proposed simulator could provide an effective interface with meteorologists and could support the development of concepts of operations that are based on adaptive scanning to meet the evolutionary observational needs of the U.S. National Weather Service.

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Brian H. Kahn, Brian J. Drouin, and Tristan S. L’Ecuyer

Abstract

The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will, for the first time, systematically document the far-infrared (15–54 µm) spectral region from space. The environmental sampling characteristics of the PREFIRE CubeSats, defined in terms of surface temperature (Tsfc) and column water vapor (CWV) are evaluated for a range of possible orbit scenarios for both clear-sky and all-sky conditions over a variety of surface types (land, ocean, sea ice, snow, glacier ice) at both poles. Using NASA Aqua’s Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) retrievals to define the climatological ranges of Tsfc and CWV, the fraction of environmental regimes observed by distinct PREFIRE configurations are evaluated. The sampling rates within any single year for two-orbit CubeSat launches spanning both polar regions are ~75% for clear-sky and ~85% for all-sky compared to the AIRS/AMSU climatology. Decreasing mission duration from 12 to 3 months decreases sampling much more (10%–20%) than decreasing the swath width from 15 to 8 footprints (6%–9%). For a single CubeSat launch, a 98° orbital inclination provides slightly better sampling than either 93° or 103°. For a two-orbit CubeSat launch, a combination of 93° + 98° is somewhat preferable to 103° + 98°. Finally, a 50% data loss rate simulated by dropping out every other orbit leads to only a modest 7%–8% reduction in sampling from full data coverage. This statistical analysis demonstrates that low-cost platforms could offer similar coverage as present-day flagship missions for sampling wide-ranging Tsfc and CWV states over polar regions.

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Min Wang, Shudao Zhou, Zhanhua Liu, and Yangchun Zhang

Abstract

The reflection of colors and surfaces of common targets lead to errors in the measurement of visibility by the image method. This study aims to investigate the problem of inaccurate visibility detection. Through analysis of the error of visibility measurement caused by the reflection of the blackboard surface of an artificial target, the design method of improving the structure of the target board is proposed, so as to improve the accuracy of atmospheric visibility measurement by the image method. The experimental results show that the new target board designed by this method can greatly improve the measurement accuracy of the intrinsic apparent brightness ratio, which can increase 18.4% in the fairing environment and closer to −1 in the side light environment. Therefore, when the side light is selected for the image method visibility measurement, more accurate visibility results can be obtained.

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Changyong Cao, Wenhui Wang, Erin Lynch, Yan Bai, Shu-peng Ho, and Bin Zhang

Abstract

Global Navigation Satellite System (GNSS) radio occultation (RO) is a remote sensing technique that uses International System of Units (SI) traceable GNSS signals for atmospheric limb soundings. The retrieved atmospheric temperature profile is believed to be more accurate and stable than those from other remote sensing techniques, although rigorous comparison between independent measurements is difficult because of time and space differences between individual RO events. Typical RO comparisons are based on global statistics with relaxed matchup criteria (within 3 h and 250 km) that are less than optimal given the dynamic nature and spatial nonuniformity of the atmosphere. This study presents a novel method that allows for direct comparison of bending angles when simultaneous RO measurements occur near the simultaneous nadir overpasses (SNO) of two low-Earth-orbit satellites receiving the same GNSS signal passing through approximately the same atmosphere, within minutes in time and less than 125 km in distance. Using this method, we found very good agreement between Formosa Satellite 7 (FORMOSAT-7)/second Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) satellite measurements and those from MetOp-A/B/C, COSMIC-1, Korea Multi-Purpose Satellite 5 (KOMPSAT-5), and Paz, although systematic biases are also found in some of the intercomparisons. Instrument and processing algorithm performances at different altitudes are also characterized. It is expected that this method can be used for the validation of GNSS RO measurements for most missions and would be a new addition to the tools for intersatellite calibration. This is especially important given the large number of RO measurements made available both publicly and commercially, and the expansion of receiver capabilities to all GNSS systems.

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Marlos Goes, Gustavo Goni, Shenfu Dong, Timothy Boyer, and Molly Baringer

Abstract

This work assesses the value of expendable bathythermograph (XBT) and Argo profiling float observations to monitor the Atlantic Ocean boundary current systems (BCS), meridional overturning circulation (MOC), and meridional heat transport (MHT). Data from six XBT transects and available Argo floats in the Atlantic Ocean for the period from 2000 to 2018 are used to estimate the structure and variability of the BCS, MOC, and MHT, taking into account different temporal and spatial mapping strategies. The comparison of Argo data density along these six XBT transects shows that Argo observations outnumber XBT observations only above mapping scales of 30 days and 3° boxes. The comparison of Argo and XBT data for the Brazil Current and Gulf Stream shows that Argo cannot reproduce the structure and variability of these currents, as it lacks sufficient resolution to resolve the gradients across these narrow jets. For the MHT and MOC, Argo estimates are similar to those produced by XBTs at a coarse mapping resolution of 5° and 30 days. However, at such a coarse resolution the root-mean-square errors calculated for both XBT and Argo estimates relative to a high-resolution baseline are higher than 3 Sv (1 Sv ≡ 106 m3 s−1) and 0.25 PW for the MOC and MHT, respectively, accounting for about 25%–30% of their mean values due to the smoothing of eddy variability along the transects. A key result of this study is that using Argo and XBT data jointly, rather than separately, improves the estimates of MHT, MOC, and BCS.

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Scott D. Landolt, Andrew Gaydos, Daniel Porter, Stephanie DiVito, Darcy Jacobson, Andrew J. Schwartz, Gregory Thompson, and Joshua Lave

Abstract

In its current form, the Automated Surface Observing System (ASOS) provides automated precipitation type reports of rain, snow, and freezing rain. Unknown precipitation can also be reported when the system recognizes precipitation is occurring but cannot classify it. A new method has been developed that can reprocess the raw ASOS 1-min-observation (OMO) data to infer the presence of freezing drizzle. This freezing drizzle derivation algorithm (FDDA) was designed to identify past freezing drizzle events that could be used for aviation product development and evaluation (e.g., Doppler radar hydrometeor classification algorithms, and improved numerical modeling methods) and impact studies that utilize archived datasets [e.g., National Transportation Safety Board (NTSB) investigations of transportation accidents in which freezing drizzle may have played a role]. Ten years of archived OMO data (2005–14) from all ASOS sites across the conterminous United States were reprocessed using the FDDA. Aviation routine weather reports (METARs) from human-augmented ASOS observations were used to evaluate and quantify the FDDA’s ability to infer freezing drizzle conditions. Advantages and drawbacks to the method are discussed. This method is not intended to be used as a real-time situational awareness tool for detecting freezing drizzle conditions at the ASOS but rather to determine periods for which freezing drizzle may have impacted transportation, with an emphasis on aviation, and to highlight the need for improved observations from the ASOS.

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