A New FSU Winds Climatology

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435

324

Release 2.5 of the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is a major update (covering 1662-2007) of the world's most extensive surface marine meteorological data collection. Building on extensive national and international partnerships, many new and improved contributing datasets have been processed into a uniform format and combined with the previous Release 2.4. The new data range from early non-instrumental ship observations to measurements initiated in the twentieth century from buoys and other automated platform types. Improvements to existing data include replacing preliminary Global Telecommunication System (GTS) receipts with more reliable, delayed mode reports for post-1997 data, and in the processing and quality control (QC) of humidity observations. Over the entire period of record, spatial and temporal coverage has been enriched and data and metadata quality has been improved. Along with the observations, now updated monthly in near real time, Release 2.5 includes quality-controlled monthly summary products for 2°latitude × 2°longitude (since 1800) and 1°× 1°boxes (since 1960), together with multiple options for access to the data and products. The measured and estimated data in Release 2.5 are subject to many technical changes, multiple archive sources, and historical events throughout the more than three-century record. Some of these data characteristics are highlighted, including known unresolved errors and inhomogeneities, which may impact climate and other research applications. Anticipated future directions for ICOADS aim to continue adding scientific value to the observations, products, and metadata, as well as strengthen the cooperative enterprise through expanded linkages to international initiatives and organisations.

Section: Community Data Products Derived From Icoads Observationsmentioning

confidence: 99%

ICOADS Release 2.5: extensions and enhancements to the surface marine meteorological archive

Woodruff

Worley

Lubker

et al. 2011

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435

324

“…A successive correction method is used to develop the monthly NOC flux fields (Josey et al, 1998). The flux algorithm used for the FSU3 is based on the method of Bourassa et al (2005). The FSU3 fields are the result of a variational method (direct minimisation) with separate objectively determined weights for ship, mooring, and drifter data (Pegion et al, 2000;Bourassa et al, 2005).…”

Section: In Situmentioning

confidence: 99%

“…The flux algorithm used for the FSU3 is based on the method of Bourassa et al (2005). The FSU3 fields are the result of a variational method (direct minimisation) with separate objectively determined weights for ship, mooring, and drifter data (Pegion et al, 2000;Bourassa et al, 2005). Additional differences include smoothing of fluxes (both products), smoothing of the forcing variables (FSU3), and the calculation of monthly fluxes from either the average of individual flux calculations (NOC) or the monthly averaged input data (FSU3).…”

Section: In Situmentioning

confidence: 99%

“…Satellite products are more likely to have similar characteristics over time; however, statistics do change with changes in the satellite observing system. For applications that require long-term monthly surface fluxes (and related variables), research quality climatologies based on in situ observations are available (Da Silva et al, 1994;Josey et al, 1998;Bourassa et al, 2005). In situ products are limited by spatial and temporal sampling issues associated with irregular ship and buoy observations and by inherent observational biases (Gulev et al, 2007a(Gulev et al, , 2007bRisien and Chelton, 2008;Thomas et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of nine monthly air–sea flux products

Smith

Hughes

Bourassa

2010

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A comparison is conducted between nine monthly turbulent air-sea flux products. The analysis includes in situ-based (Florida State University fluxes, FSU3 and National Oceanography Centre, NOC), satellite-based (Hamburg Ocean-Atmosphere Parameters from Satellite data, HOAPS2 and the French Research Institute for Exploitation of the Sea, IFREMER), hybrid (Goddard Satellite-based Surface Turbulent Fluxes, GSSTF2 and objectively analysed fluxes, OAFLUX), and reanalysis (National Centers for Environmental Prediction, NCEPR2, Japanese 25-year reanalysis, JRA, and European Centre for Medium Range Weather Forecasts reanalysis, ERA-40) products. Objectives include documenting the varying analysis methodologies and quantifying the differences and similarities between the nine products. Recommendations are made for developers of future flux products and to guide users to select products most suitable for their application.The comparison examines turbulent fluxes of heat and momentum along with the forcing variables (air temperature, wind speed, humidity, and ocean skin temperature) that are necessary to estimate turbulent fluxes. The wide range of turbulent flux parameterisations, sampling patterns, and averaging techniques within the products are described, including some of the difficulties product differences pose when trying to compare or apply the individual products. Global comparisons of monthly means tend to reveal similar spatial patterns in latent heat flux (LHF) and sensible heat flux (SHF) for the nine products; however, the magnitudes and patterns of variability (expressed as maps of standard deviations) are widely different. Basin scale and regional analysis further reveals large differences in the products (in some cases the interquartile ranges (IQRs) do not overlap for different products), but also reveals potential sources of the differences. For example, some of the variations in LHF can be explained by large differences in the distribution of specific humidity between the products. As a final analysis, we examine how each product represents the variations in turbulent fluxes in the equatorial Pacific (EP) Ocean. This analysis provides an example of how the choice of a flux product, and understanding the strengths and weaknesses of that product, can alter research findings.

“…Other climate applications using ship data (e.g. Bourassa et al, 2005;Uppala et al, 2005) also assume a fixed anemometer height of 20 or 25 m. However, this would allow a spurious increasing trend in height-adjusted winds to remain, as average anemometer heights have continued to increase, to over 30 m by 2002 (Kent et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Trends in ship wind speeds adjusted for observation method and height

Thomas

Kent

Swail

et al. 2007

Globally averaged wind speeds from archived ship reports show an increasing trend since the 1940s, which previous studies attributed to the gradual shift from visually estimated to anemometer-measured winds, and an increase in mean anemometer height. To test this hypothesis, adjustments to account for these changes were applied to individual ship wind observations in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). Annual means calculated from adjusted reports were compared to reanalysis near-surface winds in consistently well-sampled regions for the period 1958-2002.The adjustments to individual ship wind reports for known biases improve the homogeneity over the entire period and remove trends, prior to the early 1980s. However, for the 21-year period 1982-2002, trends remain in the annual mean spatially averaged adjusted winds, of 0.4 ms −1 decade −1 (6% decade −1 ) for estimated speeds, and of 0.2 ms −1 decade −1 (3% decade −1 ) for measured speeds. In contrast, co-located reanalysis near-surface wind speeds show little change, 1% decade −1 or less. Anemometers were installed on increasing numbers of ships during the period of study, even as many of these ships continued to report estimated winds. Part of the trend in estimated wind speeds is probably spurious, most likely a result of observers being increasingly influenced by readings from anemometers located well above 10-m. This influence is indicated by the gradual reduction in the tendency to underestimate nighttime marine winds. Results confirm earlier findings that reanalysis near-surface winds were biased low. The paper summarizes methods that could be employed to account for remaining inhomogeneities and thereby improve the quality of the marine wind climate record.