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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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Yuxin Zhao, Dequan Yang, Wei Li, Chang Liu, Xiong Deng, Rixu Hao, and Zhongjie He

Abstract

A spatiotemporal empirical orthogonal function (STEOF) forecast method is proposed and used in medium- to long-term sea surface height anomaly (SSHA) forecast. This method embeds temporal information in empirical orthogonal function spatial patterns, effectively capturing the evolving spatial distribution of variables and avoiding the typical rapid accumulation of forecast errors. The forecast experiments are carried out for SSHA in the South China Sea to evaluate the proposed model. Experimental results demonstrate that the STEOF forecast method consistently outperforms the autoregressive integrated moving average (ARIMA), optimal climatic normal (OCN), and persistence prediction. The model accurately forecasts the intensity and location of ocean eddies, indicating its great potential for practical applications in medium- to long-term ocean forecasts.

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Charles N. Helms, Matthew L. Walker McLinden, Gerald M. Heymsfield, and Stephen R. Guimond

Abstract

The present study describes methods to reduce the uncertainty of velocity–azimuth display (VAD) wind and deformation retrievals from downward-pointing, conically scanning, airborne Doppler radars. These retrievals have important applications in data assimilation and real-time data processing. Several error sources for VAD retrievals are considered here, including violations to the underlying wind field assumptions, Doppler velocity noise, data gaps, temporal variability, and the spatial weighting function of the VAD retrieval. Specific to airborne VAD retrievals, we also consider errors produced due to the radar scans occurring while the instrument platform is in motion. While VAD retrievals are typically performed using data from a single antenna revolution, other strategies for selecting data can be used to reduce retrieval errors. Four such data selection strategies for airborne VAD retrievals are evaluated here with respect to their effects on the errors. These methods are evaluated using the second hurricane nature run numerical simulation, analytic wind fields, and observed Doppler radar radial velocities. The proposed methods are shown to reduce the median absolute error of the VAD wind retrievals, especially in the vicinity of deep convection embedded in stratiform precipitation. The median absolute error due to wind field assumption violations for the along-track and for the across-track wind is reduced from 0.36 to 0.08 m s−1 and from 0.35 to 0.24 m s−1, respectively. Although the study focuses on Doppler radars, the results are equally applicable to conically scanning Doppler lidars as well.

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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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