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Nikolay P. Nezlin, Mathieu Dever, Mark Halverson, Jean-Michel Leconte, Guillaume Maze, Clark Richards, Igor Shkvorets, Rui Zhang, and Greg Johnson

Abstract

This study demonstrates the long-term stability of salinity measurements from Argo floats equipped with inductive conductivity cells, which have extended float lifetimes as compared to electrode-type cells. New Argo float sensor payloads must meet the demands of the Argo governance committees before they are implemented globally. Currently, the use of CTDs with inductive cells designed and manufactured by RBR, Ltd., has been approved as a Global Argo Pilot. One requirement for new sensors is to demonstrate stable measurements over the lifetime of a float. To demonstrate this, data from four Argo floats in the western Pacific Ocean equipped with the RBRargo CTD sensor package are analyzed using the same Owens–Wong–Cabanes (OWC) method and reference datasets as the Argo delayed-mode quality control (DMQC) operators. When run with default settings against the standard DMQC Argo and CTD databases, the OWC analysis reveals no drift in any of the four RBRargo datasets and, in one case, an offset exceeding the Argo target salinity limits. Being a statistical tool, the OWC method cannot strictly determine whether deviations in salinity measurements with respect to a reference hydrographic product (e.g., climatologies) are caused by oceanographic variability or sensor problems. So, this study furthermore investigates anomalous salinity measurements observed when compared with a reference product and demonstrates that anomalous values tend to occur in regions with a high degree of variability and can be better explained by imperfect reference data rather than sensor drift. This study concludes that the RBR inductive cell is a viable option for salinity measurements as part of the Argo program.

Open access
Luca Baldini and William Emery
Open access
Andres Patrignani, Mary Knapp, Christopher Redmond, and Eduardo Santos

Abstract

The Kansas Mesonet is a multipurpose network consisting of 62 automated environmental monitoring stations (as of 2019) covering the state of Kansas. Each station is equipped with research-grade instrumentation and measures precipitation, air temperature, air relative humidity, barometric pressure, wind speed and direction, solar radiation, soil temperature, and soil moisture. Observations are transferred to dedicated computer servers every 5 min via cellular modems. Data are archived and subjected to periodic quality control tests and are disseminated in near–real time through a dedicated web portal. The observations collected by the Kansas Mesonet are widely used for irrigation water management, crop modeling, pest management, wildland fire management, drought monitoring, wind energy production, environmental research, and animal management. This paper provides a technical overview of the Kansas Mesonet and includes a complete description of the instrumentation, siting criteria, instrument verification procedures, and value-added products.

Open access
Andrew Geiss and Joseph C. Hardin

Abstract

Super resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics.

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Natalie Midzak, John E. Yorks, Jianglong Zhang, Bastiaan van Diedenhoven, Sarah Woods, and Matthew McGill

Abstract

Using collocated NASA Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, a new observational-based method was developed which uses a K-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids, and three different type of irregulars. Intercompared with the collocated SPEC, Inc., Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agrees within 5% with the CPI-reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such as CALIPSO and POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).

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Eric Gilleland

Abstract

When making statistical inferences, bootstrap resampling methods are often appealing because of less stringent assumptions about the distribution of the statistic(s) of interest. However, the procedures are not free of assumptions. This paper addresses a specific situation that occurs frequently in atmospheric sciences where the standard bootstrap is not appropriate: comparative forecast verification of continuous variables. In this setting, the question to be answered concerns which of two weather or climate models is better in the sense of some type of average deviation from observations. The series to be compared are generally strongly dependent, which invalidates the most basic bootstrap technique. This paper also introduces new bootstrap code from the R package “distillery” that facilitates easy implementation of appropriate methods for paired-difference-of-means bootstrap procedures for dependent data.

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Eric Gilleland

Abstract

This paper is the sequel to a companion paper on bootstrap resampling that reviews bootstrap methodology for making statistical inferences for atmospheric science applications where the necessary assumptions are often not met for the most commonly used resampling procedures. In particular, this sequel addresses extreme-value analysis applications with discussion on the challenges for finding accurate bootstrap methods in this context. New bootstrap code from the R packages “distillery” and “extRemes” is introduced. It is further found that one approach for accurate confidence intervals in this setting is not well suited to the case when the random sample’s distribution is not stationary.

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Y. Hu and X. Zou

Abstract

Determining tropical cyclone (TC) center positions is of interest to many researchers who conduct TC analysis and forecasts. In this study, we develop and apply a TC centering technique to Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) observations of brightness temperature and report on an improvement of accuracy by adding a TC spectral analysis to the state of the art [Automated Rotational Center Hurricane Eye Retrieval (ARCHER)], especially for ATMS. We show that the ARCHER TC center-fixing algorithm locates TC centers more successfully based on the infrared channel with center frequency at 703.75 cm−1 (channel 89) of the CrIS than the ATMS channel 22 (183.31 ± 1.0 GHz) due to small-scale features in ATMS channel’s brightness temperature field associated with strong convective clouds. We propose to first apply the ARCHER TC center-fixing algorithm to ATMS channel 4 (51.76 GHz) that is less affected by small-scale convective clouds, and then to perform a set of the azimuthal spectral analysis of the ATMS channel-22 observations with tryout centers within a squared box centered at the ATMS channel-4-determined center. The center that gives the largest symmetric component is the final ATMS-determined center. Compared to the National Hurricane Center best track, the root-mean-square center-fixing errors determined from the two ATMS channels (one single CrIS channel) are 29.9 km (35.8 km) and 28.0 km (30.9 km) for 104 tropical storm and 81 hurricane cases, respectively, in the 2019 hurricane season.

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V. Chandrasekar and Mohit Kumar

Abstract

A new interpulse frequency diverse technique is introduced for weather radar second-trip suppression and retrieval. Interpulse coding is widely used for second-trip suppression or cross-polarization isolation. Here, a new interpulse scheme is discussed, taking advantage of frequency diverse waveforms. The simulations and performance tests are evaluated, keeping in mind NASA dual-frequency, dual-polarization, Doppler radar (D3R). A new method is discussed to recover velocity and spectral width despite the incoherence in samples due to the change of frequency from pulse to pulse. This technique can recover the weather radar moments over a much higher dynamic range of the second-trip contamination than the popular interpulse phase codes, for second-trip suppression and retrieval under specific phase noise conditions. And it has a bigger recovery region of second-trip velocity if the region is drawn with increasing spectral width (compared to other interpulse codes).

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Yuhang Zhu, Yineng Li, and Shiqiu Peng

Abstract

The track and accompanying sea wave forecasts of Typhoon Mangkhut (2018) by a real-time regional forecasting system are assessed in this study. The real-time regional forecasting system shows a good track forecast skill with a mean error of 69.9 km for the forecast period of 1–72 h. In particular, it predicted well the landfall location on the coastal island of South China with distance (time) biases of 76.89 km (3 h) averaging over all forecasting made during 1–72 h and only 3.55 km (1 h) for the forecasting initialized 27 h ahead of the landfall. The sea waves induced by Mangkhut (2018) were also predicted well by the wave model of the forecasting system with a mean error of 0.54 m and a mean correlation coefficient up to 0.94 for significant wave height. Results from sensitivity experiments show that the improvement of track forecasting skill for Mangkhut (2018) are mainly attributed to application of a scale-selective data assimilation scheme in the atmosphere model that helps to maintain a more realistic large-scale flow obtained from the GFS forecasts, whereas the air–sea coupling has slightly negative impact on the track forecast skill.

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