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Chunying Liu
,
Eric Freeman
,
Elizabeth C. Kent
,
David I. Berry
,
Steven J. Worley
,
Shawn R. Smith
,
Boyin Huang
,
Huai-min Zhang
,
Thomas Cram
,
Zaihua Ji
,
Mathieu Ouellet
,
Isabelle Gaboury
,
Frank Oliva
,
Axel Andersson
,
William E. Angel
,
Angela R. Sallis
, and
Adedoja Adeyeye

Abstract

This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2.

Significance Statement

The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version.

Open access
Katrina S. Virts
and
William J. Koshak

Abstract

Performance assessments of the Geostationary Lightning Mapper (GLM) are conducted via comparisons with independent observations from both satellite-based sensors and ground-based lightning detection (reference) networks. A key limitation of this evaluation is that the performance of the reference networks is both imperfect and imperfectly known, such that the true performance of GLM can only be estimated. Key GLM performance metrics such as detection efficiency (DE) and false alarm rate (FAR) retrieved through comparison with reference networks are affected by those networks’ own DE, FAR, and spatiotemporal accuracy, as well as the flash matching criteria applied in the analysis.

This study presents a Monte Carlo simulation-based inversion technique that is used to quantify how accurately the reference networks can assess GLM performance, as well as suggest the optimal matching criteria for estimating GLM performance. This is accomplished by running simulations that clarify the specific effect of reference network quality (i.e., DE, FAR, spatiotemporal accuracy, and the geographical patterns of these attributes) on the retrieved GLM performance metrics. Baseline reference network statistics are derived from the Earth Networks Global Lightning Network (ENGLN) and the Global Lightning Dataset (GLD360).

Geographic simulations indicate that the retrieved GLM DE is underestimated, with absolute errors ranging from 11% to 32%, while the retrieved GLM FAR is overestimated, with absolute errors of approximately 16-44%. GLM performance is most severely underestimated in the South Pacific. These results help quantify and bound the actual performance of GLM and the attendant uncertainties when comparing GLM to imperfect reference networks.

Restricted access
Alejandro Cáceres-Euse
,
Anne Molcard
,
Natacha Bourg
,
Dylan Dumas
,
Charles-Antoine Guérin
, and
Giovanni Besio

Abstract

To assess the contribution of wind drag and Stokes drift on the near-surface circulation, a methodology to isolate the geostrophic surface current from high-frequency radar data is developed. The methodology performs a joint analysis utilizing wind field and in situ surface currents along with an unsupervised neuronal network. The isolation method seems robust in the light of comparisons with satellite altimeter data, presenting a similar time variability and providing more spatial detail of the currents in the coastal region. Results show that the wind-induced current is around 2.1% the wind speed and deflected from the wind direction in the range [18°, 23°], whereas classical literature suggests higher values. The wave-induced currents can represent more than 13% of the ageostrophic current component as function of the wind speed, suggesting that the Stokes drift needs to be analyzed as an independent term when studying surface sea currents in the coastal zones. The methodology and results presented here could be extended worldwide, as complementary information to improve satellite-derived surface currents in the coastal regions by including the local physical processes recorded by high-frequency radar systems. The assessment of the wave and wind-induced currents have important applications on Lagrangian transport studies.

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Richard M. Schulte
and
Christian D. Kummerow

Abstract

Satellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. GPM estimates of average rain rate tend to be lower than CloudSat estimates, due in part to GPM being less sensitive to shallow and/or light precipitation. Using coincident observations between GPM and CloudSat, we find that the GPM_2BCMB product misses about two-thirds of total accumulated warm rain compared to the CloudSat 2C-RAIN-PROFILE product. This difference becomes much smaller when products are compared at 1000 m above the surface (mitigating surface clutter issues) and when forcing the frequency of rain from CloudSat to match the frequency from GPM (mitigating sensitivity issues). However, even then a gap of about 25% remains. Using an optimal estimation retrieval algorithm on the underlying data, we retrieve a similar result, but find that the remaining difference between the GPM and CloudSat retrieved rain rates can be almost entirely accounted for by inconsistent assumptions about the shape of the drop size distribution (DSD) that are made in the two retrievals. We conclude that DSD assumptions contribute significantly to the relative underestimation of warm rain by GPM compared to CloudSat. Because the choice of DSD model has such a large effect on retrieved rain rates, more work is needed to determine whether the DSD models assumed by either the GPM_2BCMB or 2C-RAIN-PROFILE algorithms are actually appropriate for warm rain.

Restricted access
Li Zhao
,
Tao Xie
,
William Perrie
,
Ming Ma
,
Jingsong Yang
,
Chengzu Bai
, and
Rick Danielson

Abstract

Sea surface temperature (SST) fronts are important for fisheries and marine ecology, as well as upper-ocean dynamics, weather forecasting, and climate monitoring. In this paper, we propose a new approach to detect SST fronts from RADARSAT-2 ScanSAR images, based on the correlation of SAR-derived wind speeds using the gray level cooccurrence matrix (GLCM) approach. Due to the large differences between the correlation of wind speeds for SST fronts compared to other areas, SST fronts can be detected by the threshold method. To eliminate small-scale features (or noise), the 30 km scale is used as the length threshold for the detection of the SST fronts. The proposed method is effective when wind speeds are between 3 and 13 m s−1. The overall accuracy of our method is about 93.6%, which is sufficient for operational applications.

Restricted access
Erica L. McGrath-Spangler
,
Will McCarty
,
N. C. Privé
,
Isaac Moradi
,
Bryan M. Karpowicz
, and
Joel McCorkel

Abstract

An observing system simulation experiment (OSSE) was performed to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary orbit on numerical weather prediction, with a focus on the proposed sounder on board the Geostationary Extended Observations (GeoXO) program’s central satellite. Infrared sounders on a geostationary platform would fill several gaps left by IR sounders on polar-orbiting satellites, and the increased temporal resolution would allow the observation of weather phenomena evolution. The framework for this OSSE was the Global Modeling and Assimilation Office (GMAO) OSSE system, which includes a full suite of meteorological observations. The experiment additionally assimilated four identical IR sounders from geostationary orbit to create a “ring” of vertical profiling observations. Based on the experimentation, assimilation of the IR sounders provided a beneficial impact on the analyzed mass and wind fields, particularly in the tropics, and produced an error reduction in the initial 24–48 h of the subsequent forecasts. Specific attention was paid to the impact of the GeoXO Sounder (GXS) over the contiguous United States (CONUS) as this is a region that is well-observed and as such difficult to improve. The forecast sensitivity to observation impact (FSOI) metric, computed across all four synoptic times over the CONUS, reveals that the GXS had the largest impact on the 24-h forecast error of the assimilated hyperspectral infrared satellite radiances as measured using a moist energy error norm. Based on this analysis, the proposed GXS has the potential to improve numerical weather prediction globally and over the CONUS.

Significance Statement

The purpose of this study is to understand the impact of the proposed geostationary hyperspectral infrared sounder as part of the Geostationary Extended Observations (GeoXO) program on numerical weather prediction. The evaluation was done using a simulated environment, and showed a beneficial impact on the tropical mass and wind fields and an error reduction in the initial 24–48 h forecasts. Over the contiguous United States, the GeoXO Sounder (GXS) performed well and had the largest impact of the assimilated infrared satellite radiances on the 24 h forecast as measured by a moist energy error norm. Based on the results of this study, the proposed GXS has the potential to improve numerical weather prediction.

Restricted access
Viktor Gouretski
,
Lijing Cheng
, and
Tim Boyer

Abstract

Nansen bottle casts served as the main oceanographic instrumentation type for more than a century since the establishing of the technique in the late 1890s. Between the end of the 1960s and the end of the 1990s Nansen cast technique has been gradually replaced by electronic sensor profilers (CTD). Both instrumentation types are considered as the most accurate among other oceanographic instruments and are often used as the unbiased reference. We conducted a comprehensive investigation of the consistency of the temperature data from Nansen casts and CTD profilers analyzing the quasi-collocated bottle and CTD data between the 1960s and the 1990s when both instrumentation types overlap. We found that Nansen casts tend to overestimate the sample depth with reversing mercury-in-glass thermometer temperatures being on average slightly lower compared to CTD data. Respectively, depth and temperature corrections are provided. Further, we estimated the ocean heat content changes between 1955 and 1990 using (along with all other instrumentation types) corrected and uncorrected Nansen cast data. These calculations show that for the upper 2 km layer the global average warming trend for this time period increases from 0.20 ± 0.05 W m−2 for the uncorrected data to 0.28 ± 0.06 W m−2 for the corrected data at the 90% confidence level. Finally, we suggest that the Nansen bottle cast profiles be put into a separate instrumentation group within the World Ocean Database.

Restricted access
Zijin Zhang
,
Xiaolong Dong
, and
Di Zhu

Abstract

The O2-band channel configuration of existing microwave radiometers is not optimal for surface pressure retrieval, which limits the surface pressure retrieval accuracy. In this study, we present the results of theoretically what might be the optimal microwave channels for surface pressure retrieval. An improved iterative selection method is used to select the channels that contain the highest cumulative content of surface pressure information. The selected optimal channel set comprises 16 channels, among which 10 channels are centered at the 50–60 GHz oxygen absorption band and 6 channels are centered around the 118.75 GHz oxygen absorption line. Two representative spaceborne microwave radiometers are used for comparisons, the Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite and the Microwave Humidity and Temperature Sounder (MWHTS) on board the Chinese Fengyun-3C (FY-3C) satellite. The results of information content analysis show that the optimal channel set contains more surface pressure information than that of the combination of SNPP/ATMS and FY-3C/MWHTS (SNPP/ATMS+FY-3C/MWHTS) channels. A representative dataset from the ERA5 data is input into the plane-parallel Microwave Radiative Transfer model to obtain the simulated brightness temperature observations of the selected optimal channels and the SNPP/ATMS+FY-3C/MWHTS channels. Using the simulated observations, retrieval experiments are performed. Experimental results show that retrieval accuracies of the optimal channel set are 1.09 and 1.64 hPa for clear-sky and cloudy conditions, respectively. The retrieval accuracies are 0.60 and 0.65 hPa better than that of the SNPP/ATMS+FY-3C/MWHTS channels for clear-sky and cloudy conditions, respectively.

Restricted access
Steven M. Martinaitis
,
Scott Lincoln
,
David Schlotzhauer
,
Stephen B. Cocks
, and
Jian Zhang

Abstract

There are multiple reasons as to why a precipitation gauge would report erroneous observations. Systematic errors relating to the measuring apparatus or resulting from observational limitations due to environmental factors (e.g., wind-induced undercatch or wetting losses) can be quantified and potentially corrected within a gauge dataset. Other challenges can arise from instrumentation malfunctions, such as clogging, poor siting, and software issues. Instrumentation malfunctions are challenging to quantify as most gauge quality control (QC) schemes focus on the current observation and not on whether the gauge has an inherent issue that would likely require maintenance of the gauge. This study focuses on the development of a temporal QC scheme to identify the likelihood of an instrumentation malfunction through the examination of hourly gauge observations and associated QC designations. The analyzed gauge performance resulted in a temporal QC classification using one of three categories: GOOD, SUSP, and BAD. The temporal QC scheme also accounts for and provides an additional designation when a significant percentage of gauge observations and associated hourly QC were influenced by meteorological factors (e.g., the inability to properly measure winter precipitation). Findings showed a consistent percentage of gauges that were classified as BAD through the running 7-day (2.9%) and 30-day (4.4%) analyses. Verification of select gauges demonstrated how the temporal QC algorithm captured different forms of instrumental-based systematic errors that influenced gauge observations. Results from this study can benefit the identification of degraded performance at gauge sites prior to scheduled routine maintenance.

Restricted access
Imke Durre
,
Anthony Arguez
,
Carl J. Schreck III
,
Michael F. Squires
, and
Russell S. Vose

Abstract

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales.

Significance Statement

Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.

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