Introduction and usage recommendations

In this section a number of examples for data download and visualization/analysis are presented within the R environment, through the loadECOMS function. Currently, there are four different seasonal to annual hindcasts and one observational gridded dataset available at ECOMS-UDG. All of them are available through the common interface loadECOMS, and therefore the argument values may vary slightly. For instance, arguments members and leadMonth do not apply in the case of the observational gridded dataset WFDEI, and hence ignored. Similarly, the output data structure may vary consequently, and forecast data types include the initialization dates and the names of the chosen members, while this information is not included for other types of gridded data.

The examples given have been kept deliberately simple in order to preserve a moderate output size (<150 Mb) and reasonable execution times (<10 minutes), although larger (and hence more time-consuming) requests can be done. The limitations in data loading depend essentially on two factors:

  1. Object size: Requesting too large objects may deplete the available memory. Currently R runs on 32- and 64-bit operating systems, and most 64-bit OSes (including Linux, Solaris, Windows and OS X) can run either 32- or 64-bit builds of R. The memory limits depends mainly on the build, but for a 32-bit build of R on Windows they also depend on the underlying OS version. For more details, type in the R console help("Memory-limits")
  2. Loading time: The time spent in a request does not depend exclusively on the size of the object to be loaded, but also largely depends on the characteristics of the internet connection and the ECOMS-UDG traffic load at the moment of accessing the data. Thus, if the data request takes too long, we strongly advice to simplify the requested dataset and try to divide the job into smaller queries. Also note that the first request after logging-in into ECOMS runs slower. This is due to some information that is stored in the cache memory after the first data request, notably improving the performance subsequently.

In the particular case of global domain selections (lonLim and latLim arguments set to NULL) for forecast data, it is recommended that only single-member, single-year selections are performed, due to the large size of this type of requests. Note that this is just an approximate recommendation. Object sizes also depend on the spatial resolution (CFS has approximately 1º horizontal res., while System4 is 0.75º and WFDEI 0.5º). Similarly, while GCM data will normally return data for the whole Earth (including oceans) for most variables, many observational datasets (like WFDEI) provide only data for land areas. In addition, It is always advisable to temporally aggregate to the maximum level possible. To this aim, it is possible to aggregate monthly using the argument aggr.m to specify a monthly aggregation function (see EXAMPLE 3), which dramatically reduces the size of the data, allowing for large global domain data requests. Type help("loadECOMS") for details on time aggregation options.

Basic loading and other data manipulation examples

  • EXAMPLE 2: Calculating multi-member bias: A worked example on how to compute the bias of several members of a forecast (CFS) against an observed reference (WFDEI), and on how to specify monthly aggregations of the original data.

More examples

A companion vignette to the ECOMS-UDG paper in Climate Services provides several worked examples:

Last modified 4 years ago Last modified on Aug 4, 2017 10:49:13 AM