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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. [ABSTRACT FROM AUTHOR]

Numerical models are used widely in the oceanic and atmospheric sciences to estimate and forecast conditions in the marine environment. Herein the application of in situ observations collected by automated instrumentation on ships at sampling rates ≤5 min is demonstrated as a means to evaluate numerical model analyses. Specific case studies use near-surface ocean observations collected by a merchant vessel, an ocean racing yacht, and select research vessels to evaluate various ocean analyses from the Hybrid Coordinate Ocean Model (HYCOM). Although some specific differences are identified between the observations and numerical model analyses, the purpose of these comparisons is to demonstrate the value of high-sampling-rate in situ observations collected on ships for numerical model evaluation. [ABSTRACT FROM AUTHOR]

Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m−2 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m−2 in western boundary current regions during winter and can exceed 40 W m−2 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial. [ABSTRACT FROM AUTHOR]

Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications. Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship's heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations. Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 608) for ship speeds less than 2.0 m s 21 . The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (6108) depends upon the ship, winds, and currents in the vessel's region of operation. [ABSTRACT FROM AUTHOR]