What spatial resolution can I expect when using gridded dataset products?
Aug. 31, 2020
Posted by: RDA Team
Note: This page was originally sourced from our Blogger page: http://ncarrda.blogspot.com/2016/07/what-spatial-resolution-can-i-expect.htmlOccasionally, firstname.lastname@example.org fields requests for higher grid resolutions than what we can currently offer. Some users wonder why older data is not available at the higher resolutions in newer data.
The requests have often come from new users of RDA resources--beginning researchers or experienced researchers from communities (e.g. engineering) outside of weather and climate.
In general, data resolution is limited by computational power and the underlying observations.
Globally-gridded analyses and forecasts are limited to the computational power at the time they were created. This table shows typical resolutions in different eras.
|Time Range||Res (Deg)||Res (km)|
Regional models can offer higher resolution than global models, especially if they have higher data density. For instance, ds609.0 NCEP North American Mesoscale (NAM) could offer 12 km resolution in 2012, a time when NCEP's global models only provided 0.5 to 1.0 degree (about 50-100 km) resolution.
Models also may not run on rectilinear grids, which span the globe in even degree increments. Rectilinear grid points are far apart at the equator and close together at the poles. This makes for vastly differently sized tiles, which can slow down models (or create numerical artifacts).
Some models use Gaussian grids that maintain approximately the same horizontal spacing over the globe by reducing the number of grid points approaching the pole.
To learn more about the coupling between the spectral functions and the Gaussian grid points, read this explanation from ECMWF.
The RDA sometimes interpolates spectral and Gaussian models to rectilinear grids to simplify data analysis tasks for our users. Interested users may access the full spectral coefficients of GFS in ds084.6 and reconstruct the data to their own grid.
While one can interpolate data to higher density (smaller spatial separation) grids, it won't increase the resolution of the underlying data, which is limited by the model resolution of the time.
Reanalyses, retrospective analyses performed with higher resolution models grids, can provide higher resolutions than the operational analyses of the era represented in the early part of the reanalysis span.
If your research involves reconstructing what planners could have been able to foresee, then use analysis data for that time. For instance, one RDA user constructed a Palmer drought index for Brazil using historical analysis data. By using only data that was available for that time, decision makers in Brazil can see what information would have been available in past droughts.
If your research involves a long time span and you want the data processed consistently, use reanalysis data.
I alluded earlier to the resolution of the underlying data. Ground-based sensor networks are limited in spatial coverage and change over time. While continental US (CONUS) and Europe may enjoy high spatial density coverage of ground stations, many other areas, such as the oceans and sub-Saharan Africa, do not.
Short-duration field campaigns may offer high spatial and/or temporal resolution, but only for a limited time. We offer a few of those data sets, but their usefulness is limited by their short duration.
For truly global coverage, we turn to satellites. Satellite data is also not spatially uniform due to differences in satellite orbits and sensors.
Different parameters (T, water vapor, cloud tops, albedo) are measured by different sensors at different wavelengths. The horizontal resolution of satellite measurements is related to the wavelength and the size of the telescope lens or mirror.
Characteristics measured in the visible range (albedo) may have higher resolution than characteristics measured in the longer-wavelength microwave range (T and water vapor).
Remote Sensing Systems (REMSS) offers some excellent materials to get-started in understanding satellite data products.
For instance, the resolution of different wavebands on The Special Sensor Microwave Imager (SSM/I) vary so surface temperature and water vapor, even from the same satellite and instrument, will have different resolutions.
Ocean winds, like those obtained from Windsat, are derived from several microwave bands, whose resolutions also vary by wavelength.
Satellite orbits also determine resolution. Geostationary weather satellites fly above the equator and in a much higher orbit (35,800 km above MSL) than polar satellites such as NOAA-NN and DMSP-NN (800 km above MSL). The difference in height also changes the possible resolutions. Look at the difference in total columnar water vapor resolution between TOVS (geostationary) and SSMI (polar) in Stephens et al.
Even with the same orbits and same wavelengths, satellite resolutions have improved (and become closer to the theoretical limits) with improvements in optics and vibration isolation on spacecraft. Satellite data improves over time, but is subject to theoretical limits.
No amount of money or technology can change the theoretical limits. Running models at higher resolutions than the data supports also has limited utility.
One solution for obtaining higher resolutions is to dynamically downscale the data with a physical model such as The Weather Research and Forecasting Model (WRF). Physics-based models use high-resolution terrain and landuse information, couple them with lower-resolution gridded data, and then model the state of the atmosphere with equations describing atmospheric dynamics.
Online and in-person tutorials can help you learn how to use this free community-supported model.