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State of the Italian Climate
This work by CNR-ISAC is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Latest Month Analysis
APRIL 2013
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Temperature Anomaly of The Latest Month
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Precipitation Anomaly of The Latest Month
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Latest Season Analysis
(Winter:DJF; SUMMER:MAM; Summer:JJA; Autumn:SON)
WINTER 2013
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Temperature Anomaly of The Latest Season
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Precipitation Anomaly of The Latest Season
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Latest Year Analysis
Meteorological Year
(Annual values correspond to the period from December to November)
2012
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Temperature Anomaly of The Latest Year
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Precipitation Anomaly of The Latest Year
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Solar Year
(Annual values correspond to the period from January to December)
2012
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Temperature Anomaly of The Latest Year
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Precipitation Anomaly of The Latest Year
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Long-Term Analysis
Italian Mean Temperature Series
(deviation from the 1971-2000 mean)
Year
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Meteorological Year
(December to November)
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Solar Year
(January to December)
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Winter
(DJF)
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Spring
(MAM)
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Summer
(JJA)
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Autumn
(SON)
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MEAN TEMPERATURE
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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Italian Mean Precipitation Series
(percentage deviation from the 1971-2000 mean)
Year
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Meteorological Year
(December to November)
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Solar Year
(January to December)
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Winter
(DJF)
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Spring
(MAM)
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Summer
(JJA)
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Autumn
(SON)
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GRIDDING METHOD
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:
with
where i runs along the stations and 
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  from the grid point
we want to calculate.
 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
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:
where  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 ,
or ii) a minimum of one station at a distance lower than .
The grid value computation (once the above conditions were satisfied)
was then performed by considering all stations within a distance of
2 .
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.
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