Space Grant Consortium - University of Wisconsin - Green Bay

High Resolution Radiometer (AVHRR) satellites. All data is no more than 36 hours old. The
background field is provided from a climatology-influenced previous analysis and built upon
through a two-dimensional assimilation scheme (Thiebaux et al. 2005). In addition, ―SST
observations are given a smaller (larger) radius of influence in regions of strong (weak) SST
gradients.‖ (LaCasse 2008) The RTG SST analysis at 0.5-degree is the background SST field for
operational and experimental weather prediction models, including the WRF. The WRF is
becoming increasingly useful in mesoscale studies of Great Lakes meteorological phenomena.
However, there are a few reasons why the RTG SST analysis, even at 0.083-degree resolution, is
not suitable for WRF simulations with domains over the Great Lakes. First, there are a limited
number of in situ data points. Relatively few ships report water temperature. Over the winter
time particularly, many of the open water buoys are collected and stored or moved out of the
Great Lakes during the ice season. Moving these buoys takes a reasonable amount of time and it
can be well into the spring before they are replaced. In addition, many of the thermal eddies
within the Great Lakes occur at much smaller scales than the thermal eddies which comprise the
Gulf Stream. Finally, there is significantly strong upwelling within about five miles of shore in
certain synoptic situations. This upwelling typically leads to a strong WST gradient and fog in
the upwelling area. With no buoys in the near-shore areas of the Great Lakes, and fog to cover
the upwelling from satellite detection, this very important bathymetric response is precluded
from the RTG SST analysis. Furthermore, the RTG SST has developed with a global emphasis
and, despite the use of a spatially varying Gaussian error decorrelation length scale (Thiebaux
2003), the enclosed nature of the lakes prevents effective incorporation of all available data.
In contrast, the Great Lakes Environmental Research Laboratory runs a Great Lakes Coastal
Forecasting System (GLCFS) on a five-kilometer spatial resolution grid which produces an
initial period SST product, which heavily incorporates data from the AVHRR. While there are
some slightly notable gradients present in initial GLCFS conditions, spatial variability tests
normalize the product to remove important SST gradients which are useful in NWP. Spatial
variability testing is important to remove anomalous cold points which could often be clouds
obstructing the clear view of the satellite, but the Great Lakes bathymetry allows for
exceptionally tight SST contrasts in near-shore and open waters. The GLCFS also has to contain
low error, since the initial SST product is used to run a Great Lakes Wave Model for producing
National Weather Service marine wind forecasts distributed operationally.
High-Resolution Meteorological Models
As Mass et al. (2002) emphasizes, high-resolution models, those with a spatial resolution below
ten kilometers, may better represent mesoscale features, but they are not captured through
current methods of verification. The result is that the accuracy of the forecast cannot be
improved through higher-resolution model runs, assuming that the approximations utilized from
parameterizations still hold applicability over smaller grid scales when checked against explicit
diagnostics. Mass’ underlying, important claim is a detailed, refined statement and applicable
case study that NWP is an initial-value problem. There can be no more gained from a model
than what is ingested. A model running with a spatial resolution of 20 kilometers cannot benefit
from WST data at 10-kilometer spatial resolution. Likewise, running at 20-kilometer model with
an initial WST dataset with a 40-kilometer spatial resolution can gain no better verification
statistics than a model run at 40 kilometers, the resolution of the datasets that initialized the
model. While the horizontal resolution of NWP models is on the increase, it is critical to
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