|Provider:||National Weather Service, NOAA (USA)|
|Model scope:||Atlantic, North America, Pacific|
|Update frequency:||for UTC hours 00, 07, 12, 19|
|Resolution:||0.09°, 5.4nm, 10.0km|
|Model duration:||41 forecasts starting at 0 hr, ending at 11 days|
|Parameters:||wind, wind gust, probabilistic wind, probabilistic pressure, swell/wave combined, other|
|GRIB model date:||Thu Apr 2 19:00:00 2020 UTC|
|Download date:||Thu Apr 2 21:04:10 2020 UTC|
|Download delay:||2hr 04min|
Note: the Download delay is the amount of time required for the GRIB model to compute its forecast and then for the LuckGrib cluster to download the data and make it available. The LuckGrib delay is generally less than 10 minutes, the remainder of the delay is the model compute time.
The National Blend of Models (NBM) is an interesting suite of models, well worth considering as you evaluate weather systems.
The following is from an online description of the NBM:
The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. This new way to produce NDFD grids will be helpful providing forecasters with a suite of information to use for their forecasts. The NBM is considered an important part of the efforts to evolve NWS capabilities to achieve a Weather-Ready Nation.
The NBM model is currently at version 3.1, with a v3.2 upgrade expected in Nov 2019.
The quoted text above mentiones that both NWS and non-NWS models are blended into the NBM.
The list of model inputs, for version 3.0 of NBM, is:
The update to NBM, version 3.1, has added the following model inputs:
The v3.2 update to NBM has added the following model inputs:
The word blend in NBM represents the key feature of this model. A wide variety of models are blended into the NBM final result. This blending process has been shown to improve the skill level present in the individual models.
Cliff Mass, an Atmospheric Science professor at the University of Washington, has published a presentation describing the blending process, referred to as MOS. The presentation is an interesting read, if you want to understand some of the techniques that may be present in the NBM blending process.
The blend in the NBM is different from an ensemble model in two major ways.
first, the blending in the NBM does not use simple averaging, and Prof. Mass’s paper talkes about how this may work, in some detail. The blending in NBM is much more sophisticated than a simple average, and it is able to improve the skill of the result as well as retain detail in the data.
secondly, the input models to the blend are from a wide variety of sources, both from NOAA and from outside of NOAA. For example, models from both Canada and the US Navy are included as elements of the blend. Both high resolution regional models and global models are considered. In a way, the NBM is a meta-ensemble, an advanced blending of other ensemble (and non-ensemble) models
There are four NBM models available, with NBM Oceanic having by far the largest domain, covering most of the Pacific and Atlantic oceans, down to 20° south at a 10km resolution.
Recall that most of the global weather models, such as GFS and CMC GDPS offer a 0.25° resolution. 0.25° is roughly 28km, so the Oceanic domain of the NBM is almost three times the resolution of comparable global models.
While NBM Oceanic is not a global model, it covers a very interesting domain at a finer resolution than has been previously available.
The Oceanic data is also interesting in that it presents probabalistic wind information. Rather than simply presenting a single value for its wind parameters at a point, this model presents five wind speeds with associated probabilities. By examining the spread of these wind speeds, you can make determinations on the certainty of the result.
NBM Oceanic is a weather forecast model which is able to express the degree of certainty it has in its results. This is a crucial difference between this model and most other models (ensemble models, such as GEFS, are also able to express uncertainty in their data, through standard deviation.)
A short tutorial is available, describing the NBM Oceanic probabilistic data in some more detail.
This is a new resource for the weather community to consider. Feedack is welcome on how useful you find this model.
In GRIB-2, GRIB data can be defined in a wide variety of different grids. These grids define the projection from points in the file to latitude/longitude points on earth.
The NBM Oceanic model uses the Mercator projection in its grid definition. The use of the Mercator grid for this data set has some interesting properties.
You will recall that the Mercator projection, as used in the majority of map projections, seems to elongate distances at higher latitudes. By using the Mercator projection grid in the GRIB data, the points end up evenly spaced in the final projection, at all latitudes.
To see this, download a file of the entire NBM Oceanic domain and then zoom into the data and scroll north and south. The grid spacing you see will be constant. A consequence of this grid spacing is that the resolution of the model improves as you increase latitude.
The nominal resolution of this model is 10km, this is the resolution published at this site as well as at the NDFD site. However, the actual local resolution around San Francisco is approximately 8km. Seattle has a local resolution of around 7km. Alaska is down to approximately a 5km resolution.
LuckGrib is one of the very few GRIB viewers that will show you the data in its original grid. Most of the GRIB viewers will either not be able to display this data in its native form, or will convert it to a simple latitude/longitude form, which loses this improvement in resolution at higher latitude.
If you are studying details of a weather model and want to have the best tool available for the job, LuckGrib is the obvious choice.
For additional information, see: