Download ((FREE)) Grib Weather Data

GRIB (GRIdded Binary or General Regularly-distributed Information in Binary form[1]) is a concise data format commonly used in meteorology to store historical and forecast weather data. It is standardized by the World Meteorological Organization's Commission for Basic Systems, known under number GRIB FM 92-IX, described in WMO Manual on Codes No.306. Currently there are three versions of GRIB.Version 0 was used to a limited extent by projects such as TOGA, and is no longer in operational use.The first edition (current sub-version is 2) is used operationally worldwide by most meteorological centers, for Numerical Weather Prediction output (NWP). A newer generation has been introduced, known as GRIB second edition, and data is slowly changing over to this format. Some of the second-generation GRIB is used for derived products distributed in the Eumetcast of Meteosat Second Generation. Another example is the NAM (North American Mesoscale) model.

GRIB files are a collection of self-contained records of 2D data, and the individual records stand alone as meaningful data, with no references to other records or to an overall schema. So collections of GRIB records can be appended to each other or the records separated.

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Each GRIB record has two components - the part that describes the record (the header), and the actual binary data itself. The data in GRIB-1 are typically converted to integers using scale and offset, and then bit-packed. GRIB-2 also has the possibility of compression.

There are 2 parts of the GRIB 1 header - one mandatory (Product Definition Section - PDS) and one optional (Grid Description Section - GDS). The PDS describes who created the data (the research / operation center), the involved numerical model / process (can be NWP or GCM), the data that is actually stored (such as wind, temperature, ozone concentration etc.), units of the data (meters, pressure etc.), vertical system of the data (constant height, constant pressure, constant potential temperature), and the time stamp.

If a description of the spatial organization of the data is needed, the GDS must be included as well. This information includes spectral (harmonics of divergence and vorticity) vs gridded data (Gaussian, X-Y grid), horizontal resolution, and the location of the origin.

The GRIB file format is designed for storing and distributing weather data. GRIB files are widely used in meteorological applications (Wikipedia article, including a list of software to read GRIB files).

GRIB stands for "General Regularly distributed Information in Binary form" and is a WMO (World Meteorological Organisation) standard format for archiving and exchanging gridded data. GRIB is a binary format, and the data is packed to increase storage efficiency. GRIB messages are often concatenated together to form a GRIB file. GRIB files usually have the extension .grib, .grb or .gb.

Currently there are two different coding standards: GRIB edition 1 (commonly referred to as GRIB1) and GRIB edition 2 (GRIB2). The major differences are in the structure of the messages; in GRIB2, several variables are defined with more precision (e.g. in GRIB1, latitudes and longitudes are in milli-degrees while in GRIB2, they are in micro-degrees). Also in GRIB2, longitude values must lie between 0 and 360 degrees), the encoding of the parameter is very different, and in GRIB2 the description of the data is template/table based. Note that a GRIB file can contain a mix of GRIB1 and GRIB2 messages.

The ECMWF model (the Integrated Forecasting System, IFS) currently outputs model-level fields in GRIB2 while pressure and surface level outputs are produced in GRIB1. For example,ERA-Interim (a climate reanalysis dataset provided by ECMWF) is produced in the GRIB edition 1 format. The ERA-Interim data is then made available for download in its native GRIB format.

In some cases, data is also available in NetCDF format as the result of the conversion of the GRIB file to NetCDF. Note that due to this conversion, not all the information in the GRIB file will be included in the NetCDF version, and his is particularly true for the GRIB file metadata. As a result, care should be taken when using these files. At this time, the NetCDF format is not formally supported by ECMWF.

Please be aware that when reading GRIB files where the range of valid data values includes '9999' that some software may incorrectly indicate that these data points are missing. This is because 9999 is the default missing value indicator.

Note that the keys dataDate, dataTime, validityDate, validityTime are available in CAMS/C3S forecast data. Analysis data has different keys.dataDate and dataTime indicate the date/time we forecast from. validityDate and validityTime indicate the date/time we forecast for.

Page: ECCODES ERROR : Wrong number of fields ... Try using the -T option Page: ERA5: What is the spatial reference Page: Extract data from GRIB/NetCDF for a specific location and time Page: How to convert GRIB to CSV Page: How to merge multiple GRIB or NetCDF files

I already can extract grib or can download from certain region, I would like that data to be able to see on a spreadsheet for example latitude, longitude, temperature, rainfall, wind etc... then from there I can take it to a database, or sum /avg etc or manipulate data.

Looking for best solution to store and make easily available big amount of weather data for the machine learning specialists team.Initially I'm fetching data from cds.climate.copernicus.eu in netCDF or grib format. There will be some around 10-20Tb in grib or netCDF.

For data this size that you want to sub-select quickly along multiple dimensions I'd lean toward Redshift. You will want to pay attention to how you want to query the data and establish the data model to provide the fastest access for the needed subsets. You may want to get some help setting this up initially as trial-and-error approach will take a while with this data size. Also Redshift isn't cheap at this scale so ask the budget questions too. This can be reduced if the database only needs to be up part of the time.

Files isn't a terrible idea as long as you can partition the data such that only a subset of files need to be accessed for any query. A partitioning strategy based on YEAR, MONTH, LAT-Decade, and LON-Decade might work - you'll need to understand what queries need to be performed and how fast (what's reasonable time?). This approach will be the least cost.

There is also a combo option - Redshift Spectrum. Redshift can utilize on database information AND in S3 stored data in the same queries. Again setting up the Redshift data model and S3 partitioning will be critical but this combo could give you attributes that will be valuable.

For any of these options you will want to convert to a more database friendly format like Parquet (or even CSV). This conversion process along with how to merge new data will need to be understood. There are lots of cloud tools to help with this processing.

Given the size of data you are working with I'll stress again that learning as you go will be time consuming. You will likely want to find experts in the tools you are working with (and at the data sizes you have) to get up quickly.

GRIB files provide a low cost way of getting a great deal of weather forecast information. There is much misunderstanding of GRIBs, some of it fostered by unrealistic accuracy claims by those selling GRIB services and products. I hope that this and the associated pages will help users to know what they are getting, how to assess what is available at no cost and to avoid paying unnecessarily.

The GRIB listing page include Weatheronline, PassageWeather, XCWeather, Windfinder, WindGuru, for examples. These are easy to use, provided you have the bandwidth. The main disadvantages are that data are not saved and the choice of areas, data, etc is limited. Data are not saved.

From whatever source, it is important to remember that GRIB files are computer generated forecast files from a National Weather Service computer. These are sent without review, and are offered on an as-is basis. There is no assurance that the data are available, accurate or correct. Systems providing information and the computer models are automated and subject to a variety of failures and errors. By using the data, users acknowledge and agree to these limitations.

A GRIB file (GRIdded Binary) contains a wind or current forecast for regular points over an area, at a number of future time instances. The information is stored according to the most common grib 1 format or to the newer grib 2 format. A lot of different techniques are used to compress the files to reduce size without loosing resolution or accuracy.

Seapilot automatically detects whether a file contains wind or current data. When the file has been opened in Seapilot you can visually follow the changes of the wind arrows using a time scale corresponding to the period of the forecast. It is also ready to be be used for finding optimal routes.

At this point in our sailing career, Helena and I have accumulated quite a few offshore miles on Petronella, including two week-long passages up the east coast of the US. Anyone who does this much sailing is likely to take a passionate interest in the weather, and I'm no exception. It helps me anticipate how much wind we are going to have (typically, not enough!), where it will be coming from (typically, on the nose, or dead aft!), what's the best course to steer for where we are going (weather routing), and very occasionally, when to get ready for nasty weather.

Many people these days depend on GRIB data as their primary or even sole source of weather information. It's easy to get, easy to interpret, and seemingly precise. For local sailing, I do the same thing. For example, when we are in Maine, I love to download the HRRR model data using my favorite weather app, LuckGrib, because it tells me if there is going to be fog that day, and where it will be. It's not perfect at this, but it's darn good. ff782bc1db