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I Am The Wind Sub Download

The quilt pattern is a digital download pdf of the booklet. Front and back cover are on single page each and internal pages 1-10 are supplied in 5 pages with pattern right and left pages together a sheet. Patterns are not for copying or mass distribution, and no online sharing of pattern instructions is allowed. The pattern is for your own personal use to make the Wind Drifter quilt.

I Am The Wind Sub Download

Note for version and later: An issue was discovered in how calm winds are encoded in the ISD after 2013. We describe the cause and impacts in Dunn et al, 2022, ERC. From this version onwards we have adjusted our routines to recover these calm periods. We have also updated the record values used by the World Record Check, and hence there has been an extra increment in the version number (08-Feb-2022, updated 08-Jul-2022)

A station listing with IDs, names and location information is here (also with names).The data are provided as netCDF files, one file per station. Thevariables that are quality controlled are: temperature, dewpointtemperature, sea-level pressure, wind speed and direction, cloud data(total, low, mid and high level). Also included in the files are pastsignificant weather and cloud base but these have not beenquality controlled and we make no guarantees about their quality andcompletness. Also available are the quality control flags and thedata values which have been removed in the quality control process.

Individual stations withinthe ISDwere composited when it was appropriate to do so. Then stations were selected on the basis oftheir length of record and reporting frequency. These 9678 stationswere passed through a suite of automated quality control testsdesigned to remove bad data while keeping the extremes. None of theISD flags were used. The QC tests focussed on the temperature,dewpoint temperature and sea-level pressure variables, although somewere applied to the wind speed and direction and cloud data. Thedata files also contain other variables which were pulled through fromthe raw ISD record, but have had no QC applied. Some final filteringwas performed to select those stations which in our opinion are mostuseful for climate studies. Note: These data have notyet been homogenised and so trend fitting should be undertaken withcaution, however homogeneity information is available here

We have assessed the homogeneity of four of the observedmeteorological variables present in HadISD: temperature, dew pointtemperature, sea-level pressure and wind speed. This has beenperformed on monthly averages of the sub-daily data, and is fullydescribed in Dunn et al, 2014, Climate of the Past, 10, 1501-1522. The homogeneityassessment results for v3.3.0.2022f are available fordownload here.

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The CCMP wind analysis is a near-global, high spatial and temporal resolution gridded dataset of surface wind vectors spanning 1987-present. The input data are a combination of inter-calibrated satellite data from numerous radiometers and scatterometers and in-situ data from moored buoys. An algorithm finds a best-fit solution to all of the available observations, using the ERA-Interim reanalysis winds as a first guess. The temporal record is relatively stable, and the data are recommended for studies of daily to interannual variability. The analysis generally performs poorly in rain and under high wind conditions, and is not well suited for studies of global wind trends. Please see more details in the "Expert Guidance" tab.

Wentz, F.J., J. Scott, R. Hoffman, M. Leidner, R. Atlas, J. Ardizzone, 2015: Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0, [indicate date subset, if used]. Remote Sensing Systems, Santa Rosa, CA. Available online at [Accessed dd mmm yyyy]. *Insert the appropriate information in the brackets.

The Cross-Calibrated Multi-Platform (CCMP) is a gridded wind vector analysis dataset produced by using satellite and buoy wind measurements, and a background model wind field all integrated into 6-hourly global maps at 0.25 deg resolution. The focus of this wind analysis is to provide users with satellite-based consistent and gap-free surface wind over the globe at high spatial and temporal resolution. This dataset starts in 1987, and is well suited for model comparisons.

The original dataset CCMP V1.1 was developed by Robert Atlas and his team. It incorporates microwave satellite observations processed at Remote Sensing Systems, in situ observations from moored buoys, and the ECMWF operational model wind as a background, by using a Variational Analysis Method (VAM). The method is described in detail in Atlas et al, 1996, Hoffman et al, 2003, and Atlas et al, 2011. The original V1.1 CCMP product was processed incrementally, over a number of years, in small batches using the satellite data available at the time of processing, and therefore, assimilated inconsistently-processed satellite winds. Funding for the original CCMP ended in 2012, and the last processed winds refer to December 2011. Motivated by the considerable demand for the continuation of this widely used dataset, in 2015 the processing code was transferred to Remote Sensing Systems with the objective of producing an updated, improved, and consistently reprocessed version of the vector wind analysis, CCMP V2.0 [Scott et al, 2016].

The datasets used as input in the CCMP V2.0 are listed in Table 1. The satellite data include active instruments (Scatterometers, QuikSCAT and ASCAT) and passive microwave sensors (Radiometers, SSM/I, SSMIS, TMI, GMI, AMSR2, AMSRE, WindSat). The radiometers and scatterometer data have all been intercalibrated to a high level of consistency, using the Remote Sensing Systems Radiative Transfer Model (RTM) V7 or higher as intercalibration target [Ricciardulli et al, 2012; Wentz, 2013; Wentz, 2015; Ricciardulli and Wentz, 2015; Ricciardulli, 2016; Wentz, 2016;]. In situ data include quality-controlled global moored buoys from different sources. The CCMP methodology finds a solution that best fits all the observations using the model-based background wind as a first-guess. The original CCMP used ECMWF 1 deg winds. The new CCMP V2.0 uses the 0.25 deg, 6-hourly Era-Interim as background.

A measure of the uncertainties associated with the CCMP V2.0 Wind vector analysis has been established by comparing it with each satellite data source individually. At the hourly and 0.25 deg scale, the CCMP is practically unbiased versus each satellite datasets for winds lower than 15 m/s. At higher wind speeds, CCMP V2.0 data are lower than satellite observations by about 3-4%. At those wind speeds, the ERA-Interim used as background field is known to underestimate winds compared to satellites. Since the VAM assimilation method integrates both the satellite and background field, at high winds the wind analysis will often result in a wind field lower than the satellite-only estimates. In this first reprocessed version V2.0, no correction has been applied to any of the datasets, including the Era-Interim. In a future CCMP reprocessing, an adjustment will be applied to the Era-Interim background field at high winds in order to make them consistent with satellite data. The standard deviation between CCMP and satellite winds is on average of the order of 0.5-0.7 m/s, and higher for higher winds (1.5 m/s for 25 m/s wind speeds).

At monthly-averaged global scales, the uncertainties are very small, of the order of 0.1 m/s, and the timeseries is very stable. Figure 3 displays the timeseries of the differences between CCMP V2.0 and ASCAT data, for each 0.25 deg gridpoint interpolated at the time of the ASCAT observation, and then averaged over a month in the latitude range between 55NS (red line). The timeseries of the differences is very stable, with values within 0.05 m/s. For comparison, the figure also shows the timeseries of differences between ASCAT and QuikSCAT (blue), WindSat (green) and the NCEP GDAS winds (thin black line). All the satellite data are consistent with each other within 0.1 m/s, and stable over time. The comparison between ASCAT and NCEP GDAS highlights a spurious jump at the end of 2009 due to the change in assimilated observations in the NCEP at the end of the QuikSCAT mission. The aim of this figure is to illustrate that CCMP is well-suited for studies of temporal variability at interannual time scales, while other model analysis datasets, like NCEP GDAS, suffer from spurious trends and should be used with caution for climate variability studies.

- Future reprocessing: Version V2.0 methodology was kept very similar to V1.1. A new version is planned for 2017, including some adjustments of the model background winds at high wind speed to make them more consistent with satellite data.

- Global Trends: At this time, it is not advised to use CCMP V2.0 for analysis of global wind trends, as the actual trend signal is extremely small. This dataset is not well suited for such studies because the ERA-interim background wind field does include some small spurious biases that occur when the new observing systems are added.

- Observations in rain: Satellite observations are not incorporated in the presence of rain, because rain often contaminates the satellite wind measurements. Under rainy conditions, the CCMP wind analysis is therefore mostly driven by the ERA-Interim background field and any available in situ observations.

French, German, Japanese and Spanish translations of the SASB Standards are available. To download translations of the Standards, please select your industry(ies) and fill out the form. 350c69d7ab


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