We will add new data at the end here until we figure out which model wins out!
It can happen that crucial decisions depend on details of a forecast. Usually this will be for a longer term forecast, since one or two days out are generally pretty good. We have a specific case at hand now, which is a classic, real-world example. The vessel has two optional routes, on each a crucial turn must be made roughly 96 hr from now, meaning we are in interested in what happens on Friday, May 31.
The two main primary sources we have are the GFS and the new FV3 GFS, which will replace the former on June 12. We see below that these two do not agree at all for Friday.
Again, Friday shown here is 4 days out from these Monday 18z forecasts. The displays are the meteograms from LuckGrib. The standard GFS we have used for years shows roughly 20 kts from the east, or just above east. The in-principle-better model FV3 GFS shows 30 kts from the SE. For the vessel at hand here, and almost all vessels for that matter, this is a huge difference.
These LuckGrib meteograms are plotted with the vessel following a specific course at a specific speed. Thus the output applies to the time and location of the vessel. Below we see these two data images with the vessel in the location shown by the blue line in each.
For a specific comparison at 12z on Friday, the GFS is 18.3 kts at 077 whereas the FV3 GFS is 27.5 kts at 139.
So what can we do faced with this? GFS itself offers no probabilities or other qualifications. We have in our textbook Modern Marine Weather several ways to evaluate a forecast using the 500-mb maps and other means, but this situation is rather different from the examples in the book as this is for the Southern Hemisphere, off the coast of Queensland.
We got the above data from the powerful LuckGrib app, which offers many model options and very neat presentation of the data. Two models offered in LuckGrib that help us look at probabilities are the standard deviation outputs from the Global Ensemble Forecast System (GEFS) model and the probabilistic wind forecasts from the Oceanic National Blend of Models (NBM).
Below are the same meteograms (route and dates) for these two models, followed as above with the actual plot of the wind showing values at 12z on Friday.
And now the specific values at the vessel at 12z Friday.
These data require a bit of explanation. The GEFS ensemble data comes from starting a computation with a base model forecast, called the Control run, which is similar but not identical to the base GFS forecast. Then they repeat this forecast 20 times, each time making some likely variation, such as changing the input data or trying different physics solutions. The idea is that the spread in these results is a measure of the uncertainty in the forecast. We could actually look at each of the 20 runs to see how widely they vary, or as LuckGrib has decided we look at the mean values of all of the runs along with the standard deviation of this set of runs.
The standard deviation (SD) is a measure of the spread in uncertainty assuming the different runs introduce more or less random differences. In the most basic sense, SD is defined in statistical terms with the curve below, from our book Mariners Pressure Atlas, in which we use these ideas for storm forecasting in the tropics based on pressure values.
The basic idea is the larger the SD, the broader the distribution of winds, with 68% of the observed winds expected to be within 1 SD of the mean, as shown below.
which is not the same as 20.5 ± 6.2, which equals 14.3 to 26.7 kts, instead we see 16.2 to 25.5 kts.
This is a subtlety that might not matter too much in many practical situations, but since this output itself is a new concept, it might help to know the source of the apparent discrepancy. The issue is the GEFS model presents the SD as a vector quantity, meaning they do not give it for the wind speed alone, but rather they give separate values for the E-W component and the N-S component of the wind vector. Thus we have a situation like the one shown below.
Here is the latest data from Tuesday morning:
The GEFS give us a spread of 15.0 to 23.5 kts, with directions varying from E to SE. So according to the ensemble analysis, this forecast is still up in the air.
The NBM calls for a mean wind of 21.1 kts from the SE, with 25% chance of being > 23.1 kts and 25% chance of being <17.1 kts. Again, no reassurance from this at all.
That is where we stand. No confidence for crucial decisions on or near Friday.
I will contact our meteorologist friends in Australia to see if they have any insights on what is taking place and what they think might happen. This could all be tied to pressure systems moving across the continent of Australia which they will likely understand.
In the meantime the vessel (Jacob Adoram's Emerson) is going as slow as possible waiting for some guidance in the forecasts.
With the GEFS data we were only looking at the standard deviations, but this model also computes probabilistic winds, but the public presentation does not reach down to Australia.
Following on to see how these forecasts pan out...
======= New data from Wednesday morning =======
Update times from 06z to 18z, as explained below.
These two models are definitely closer now. We will also see below that the ECMWF is right between these two forecasts. We still have the FV3 calling for much stronger wind on Sat than GFS.
We can compare these to the ECMWF, which can be read from windy.com.
This is the summary:
GFS 13.6 @ 105
FV3 17.0 @ 120
EC 16 @ 105
GEFS 18.2 @115 with range 15.3 to 22.2 @ E to SE
NBM 21.6 @ 132 with a 25% chance of < 17.5
So both the probabilistic models (see below) cover the predictions of the 3 direct models. The NBM looks to be the most off, but we have to keep this in perspective. The NBM for US waters updates every hour, but this oceanic version updates on a strange schedule. There is a new run at 00, 05, 07, 12, 17, and 19z. It then has a short latency and is available in about 1 hr after each run. The GFS, for comparison, is run every 6 hr at the synoptic times, but it has a total latency of about 5 hr. GEFS has a latency of about 6 hr and FV3 is about 7 hr. So in principle, the Oceanic NBM could be the newest data... but this remains unclear till we learn more. It is blending models that are much older, so the full implications are not clear yet.
Now that we have this data so conveniently from LuckGrib, the next step will be to learn more about how to use it, which in a sense we are struggling with at the moment.
We will continue to add data when we get it. In the meantime, we look at the overall weather patterns that are leading to this in another post.
====== Thursday morning ==========
For our 12z Friday test case and adding a Sun 12z new test case, we have:
FV3 19.9 @ 093 Sun 12z = 20 @ SE
GFS 16.1 @ 107 Sun 12z = 22 @ SE
EC1 17.5 @ 090 Sun 12z = 14.5 @ SE
EC2 23.5 @ 132 Sun 12z = 19.7 @ 107
We do not have direct access to ECMWF data as this is licensed data (ie the UK sells it), but we have two ways to access it, at least in principle. We have an account with PredictWind, which offers a model called PWE that is based on the ECMWF data and we have the windy.com output, which they claim is the ECMWF. These are shown below.
Again, this is AU time, so 12z on Fri is at 22z, which is 17.5 @ E, with a GFS report of 15.5 @ 110, which is close to what we read directly for the GFS. The EC forecasts 20+ winds all the way through next Tuesday.
This PredictWind version of EC that we access via the nav program Expedition. This gives 23.5 @ 132 for Friday. These two presumed sources of EC wind are so different we have to conclude we do not know what the official EC forecast is. Below are the actual numbers for the PredictWind version:
31-May-19 1200,,132,23.5 (windy.com value 17 E-SE)
02-Jun-19 1200,,108,20.0 (windy.com value = 15 SE)
We get a bit more specification from NBM. A median wind of 19.3 from the SE, but only 10% chance of less than 14.7; 25% chance less than 16.2. So low wind side is unlikely, whereas we still have a high wind side.
At this point, I do not see any justification for gambling on the lighter side of the forecast, and indeed we should likely count on more rather than less.
With all this said, and still more to say, we can pause an look at how these models have done so far in predicting the winds that Jacob is actually reporting and we can measure with ASCAT.
This article will soon be a live link: Compare ASCAT, Observations, and Model Forecasts.