|Provider:||National Weather Service, NOAA (USA)|
|Update frequency:||every hour|
|Model duration:||49 forecasts starting at 0 hr, ending at 10 days|
|Parameters:||wind, wind gust, rain|
|GRIB model date:||Fri Jan 24 10:00:00 2020 UTC|
|Download date:||Fri Jan 24 11:05:02 2020 UTC|
|Download delay:||1hr 05min|
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 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
Note that there are two NBM models which cover Hawaii. This one, provides data at a resolution of 2.5km. The other model is NBM Oceanic, which has a resolution of 10km.
NBM Oceanic contains probabilistic data, NBM Hawaii data is not probabilistic. This will be something to consider when you choose among these two models.
The ability of the Oceanic domain data to express degrees of certainty and uncertainty in its data is unique, and potentially very useful.
A short tutorial is available, describing the NBM Oceanic probabilistic data in some more detail.
For additional information, see: