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Discussion contributed on 03/12/2003 by René GOMMES.

Last modified December 03, 2003 16:33

Dear colleagues, On 13 Nov. 2003 the FAO agrometeorology Group sent requests to the FAO/WMO AGROMET-L and to CLIMLIST ask for help to locate average monthly station (point) Penman-Monteith data to be included in the new version of LocClim

Dear colleagues,
On 13 Nov. 2003 the FAO agrometeorology Group sent requests to the FAO/WMO AGROMET-L and to CLIMLIST ask for help to locate average monthly station (point) Penman-Monteith data to be included in the new version of LocClim http://www.fao.org/sd/2002/EN1203a_en.htm. All replies we received except one offered gridded data. Each of them got an answer repeating what we said in the announcement, i.e. "we specifically exclude any data from global or local grids (Interpolated data and area-wide ones). We need real station data, with geographic coordinates and altitude". Another example: earlier this year, I requested a friend in an African Meteorological Service to provide me actual rainfall data for some stations. He sent me data extracted from a popular 10-daily African rainfall grid produced in the US based on GTS data. This is not the place to discuss the complex differences between point data and pixel values, but I find it amazing that even national agrometeorological services use "highly imaginative" data rather than their own station information. I think this raises a very serious issue: many people no longer work with real data. Instead, they use a number of gridded average and "real-time" datasets around, often interpolated with unspecified methods and very large cell sizes. Maybe part of the problem is that meteorological services want money for their data, which leads many users, even at the national level (e.g. agronomic research) to resort to easily accessible grids, avoiding costs and administrative work. 10 years ago, the operational agrometeorologist had a real technical problem because there were no good tools available to spatially interpolate data. This issue was addressed from many sides; today gridding is no longer a serious problem in operational agrometeorology for most variables. Unfortunately, the result is that the perceived need for good data has decreased, as everything is somehow available as grids. We have replaced a real problem associated with the lack on ground data with a data processing problem, which is much easier to solve. We have somehow traded data for colour pictures. There is just no way to make proper weather impact assessments (and this is actually what agrometeorology is about) without adequate data. We have to do something about it.
Any suggestions?
Best regards,
René GOMMES Senior agrometeorologist
Environment and natural resources service FAO of the UN, Rome.

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