Originariamente Scritto da
Alessandro(Foiano)
aggiungo che quelle poche abbiamo visto benissimo che a volte termicamente siano inaffidabili
e gli addetti ai lavori curano altri aspetti primariamente:
dati termici e pluvio sono un aspetto secondario del loro dovere di lavoratori.
Questa è la rete di stazioni pluvio per rinfrescarci la memoria:
Allegato 370470
il fatto di avere una fitta rete di stazioni non è anche indice di buona qualità dei dati,
basta lasciarla al suo destino senza manutenzione, come succede da anni su alcune con i dati termici.
sì certo, per verificare l'omogeneità dei dati,per la correzione eventuale da fare,
fu ben spiegato il metodo gridding da elz in altro thread:
[SIZE=h2] GRIDDING METHOD [/SIZE] [SIZE=h1] The grid has one degree resolution, both in latitude and in longitude, and was realised with an interpolation technique based on a radial weight and an angular term.
The radial term was realised with a gaussian weighting function with the following form:
Immagine
with
Immagine
where i runs along the stations and Immagine
is the distance between the station i and the grid point (x,y). With this choice of the c parameter, we have weights of 0.5 for station distances equal to Immagine
from the grid point we want to calculate.
Immagine
is defined as the mean distance of one grid point from its next one obtained by increasing both longitude and latitude by one grid step (it is a sort of mean length of the grid mesh diagonal).
For a grid resolution of 1 deg (as in this case) the Immagine
parameter is about 130 km.
The angular term accounts for the geographical separation among the sites with available time series. It has the following form:
Immagine
where Immagine
is the angular separation of stations i and l with the vertex of the angle defined at grid point (x,y).
The final weight is the product of the radial and the angular terms.
Each grid point was calculated under one of the following conditions: i) a minimum of two stations at a distance lower than Immagine
, or ii) a minimum of one station at a distance lower than Immagine
. The grid value computation (once the above conditions were satisfied) was then performed by considering all stations within a distance of 2 Immagine
.
In order to avoid biases due to the different lengths of the station records, for temperature we calculated the grid values starting from the anomalies, whereas for precipitation we started from the relative deviations from the means. The conversion of these anomalies (relative deviations) into absolute values requires the knowledge of the monthly normals at the grid point.
Available grid boxes are indicated in the two figures, both for temperature and precipitation, together with the stations involved in the grid computation.
The national mean seires were obtained by averaging all grid boxes over the italian territory and not the station anomalies.
The reason is as follows:
The availability of station data is typically not sufficient to ensure an even distribution of stations throughout a network. But by averaging station anomalies within regions of similar size (grid boxes) and then calculating the average of all the grid box averages, a more representative region-wide anomaly can be calculated.
This makes grid box averaging superior to simply taking the average of all stations in the domain. A network of 1000 stations could theoretically have 700 stations in the northern half of the domain and 300 stations in the southern half. A simple average of the stations could easily create a bias in the domain-wide average to those stations in the north. [/SIZE]
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