Sentences with phrase «for coarse resolution»

Reference solutions from multiple models define a range of uncertainty that is the target for coarser resolution simulations.

Not exact matches

Previous studies tend to underestimate such connections as simulated land - atmosphere interaction is also resolution - dependent, which means that the signals for changes in small - scale land use are likely to be much weaker in a coarse resolution model,» says Minchao Wu.

For much of the global ocean the coarser resolution is okay, but when you are studying a unique location like the Gulf of Maine, with its complex bathymetry of deep basins, channels, and shallow banks combined with its location near the intersection of two major ocean current systems, the output from the coarser models can be misleading.»

In particular, the coarse and heavily pixelated low resolution and low pixels per inch screen made worse with poor anti-aliasing, the very low color depth, the poor color gamut in ambient light, and also the poor viewing angle performance (because watches are not easily positioned for zero degree viewing).

GCMs tend to be too coarse to resolve cyclones, but high - resolution regional models for storm studies exist.

The main adaptation is that climate - model GCMs have a coarser «grid resolution» that allows them to be run for a large number of model - years with the computers available.

Unfortunately, the figure also confirms that the spatial resolution of theoutput from the GCMs used in the Mediterranean study is too coarse for constructing detailed regional scenarios.To develop more detailed regional scenarios, modelers can combine the GCM results with output from statistical models.3 This is done by constructing a statistical model to explain the observed temperature or precipitation at a meteorological station in terms of a range of regionally - averaged climate variables.

Because most AOGCMs have coarse resolution and large - scale systematic errors, and extreme events tend to be short lived and have smaller spatial scales, it is somewhat surprising how well the models simulate the statistics of extreme events in the current climate, including the trends during the 20th century (see Chapter 9 for more detail).

With respect to hurricane intensity, there are observed trends indicating this and model results predicting this, and while there are problems in each (data problems with hurricanes, coarse resolution in global models, etc.), theoretical arguments also make clear that there will be more energy and water vapor available in the atmosphere to cause more intense hurricanes, so a very strong case can be made for this happening.

Yoshiaki Miyamoto, Hirofumi Tomita and their colleagues from the RIKEN Advanced Institute for Computational Science reveal that in order to realistically simulate the critical features of cloud convection, models will ultimately need to be run at a grid resolution no coarser than 2 kilometers.

«Our finding that convective features change drastically at resolutions of 2 kilometers or more opens up new avenues for research into the interactions between convection and global atmospheric circulation that would have been invisible at coarser resolutions.»

It contains a suite of routines for downscaling coarse scale global climate model (GCM) output to a fine spatial resolution.

These approaches have coarse resolution which is less useful for planning on the regional or local level.

Land use and emissions of air pollutants and greenhouse gases are reported mostly at a 0.5 × 0.5 degree spatial resolution, with air pollutants also provided per sector (for well - mixed gases, a coarser resolution is used).

The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant.

The ClimDown R package publishes the routines and techniques of the Pacific Climate Impacts Consortium (PCIC) for downscaling coarse scale Global Climate Models (GCMs) to fine scale spatial resolution.

For this purpose, coarser model resolution is adequate since the advective transports of energy (latent and sensible heat, geopotential energy), which are an order of magnitude larger than the radiative terms, must by definition globally add to zero.

Their relatively good spectral resolution makes infrared sounders very useful for the determination of cloud properties (day and night), and their coarse spatial resolution has less effect on clouds with large spatial extents like cirrus clouds.

Analyses of tide gauge and altimetry data by Vinogradov and Ponte (2011), which indicated the presence of considerably small spatial scale variability in annual mean sea level over many coastal regions, are an important factor for understanding the uncertainties in regional sea - level simulations and projections at sub-decadal time scales in coarse - resolution climate models that are also discussed in Chapter 13.

To estimate the uncertainty range (2σ) for mean tropical SST cooling, we consider the error contributions from (a) large - scale patterns in the ocean data temperature field, which hamper a direct comparison with a coarse - resolution model, and (b) the statistical error for each reconstructed paleo - temperature value.

Coarser resolution results from four of the CMIP3 models were used as the boundary conditions for the NARCCAP regional climate model studies, with each of the regional models doing analyses with boundary conditions from two of the CMIP3 models.

Where modelled consequences clearly don't match observations the differences can be used to explore what's missing or not quite right — perhaps the modelled elevation of land in certain areas is not quite right, causing a difference in the flow of wind currents, or maybe the grid resolution of the model is too coarse for certain features to properly resolve.

Impact studies rarely use GCM outputs directly because GCM biases are too great and because the spatial resolution is generally too coarse to satisfy the data requirements for estimating impacts.

But for the weather there is no level of fine detail that ceases to matter as you zoom out to the coarser resolution of climate.

What Mandelbrot is saying is that for a map the fine detail ceases to matter as you zoom out to view larger areas at coarser resolution.

But the datasets are not flawless (biases can occur, for example, when stations are being relocated, or when instrumentation is exchanged) and their monthly resolution is too coarse for studies of fine - scale climate features such as changes in daily temperature extremes.

For example, highly accurate and high - resolution infrared SST observations are confounded by the presence of clouds, whereas coarser - resolution passive microwave SST observations are able to measure SST through clouds.