US 2025/0093549 A1 · METHOD FOR QUANTIFYING STRUCTURAL FEATURE FORECAST ERROR OF METEOROLOGICAL ELEMENT BASED ON GRAPHICAL SIMILARITY

Abstract

A method for quantifying a structural feature forecast error of a meteorological element based on a graphical similarity is based on the concept of graphical similarity to propose a normalized evaluation technique for a forecast error of a scalar meteorological element such as rainfall, radar reflectivity, temperature, visibility, or wind speed. The method can objectively and truly reflect the true capability of forecasting the meteorological element such as precipitation.

A method for quantifying a structural feature forecast error of a meteorological element based on a graphical similarity, comprising the following steps:

S1:
defining a structural feature of a meteorological element field;
defining an overall structural feature d of the meteorological element field as an area S of a spatial range covered by the meteorological element, a total numerical size R of the meteorological element in a target region, and a structure H of the meteorological element;
α
→
=
[
S
,
R
,
H
]
(
1
)
wherein, {right arrow over (α)} denotes the overall structural feature of the meteorological element field;
S2:
calculating a total amount error and a total area error of the meteorological element;
E
R
=
|
RT
(
f
)
(
t
)
-
RT
(
o
)
(
τ
)
|
max
⁢
(
RT
(
f
)
(
t
)
,
RT
(
o
)
(
τ
)
)
(
2
)
E
S
=
|
ST
(
f
)
(
t
)
-
ST
(
o
)
(
τ
)
|
max
⁢
(
ST
(
f
)
(
t
)
,
RT
(
o
)
(
τ
)
)
(
3
)
wherein, RT(f)(t) denotes a total amount of a forecasted meteorological element field at a time t;
RT(0)(τ) denotes a total amount of an observed meteorological element field at a time τ;
ST(f)(t) denotes a total area covered by the forecasted meteorological element field at the time t;
and ST(0)(τ) denotes a total area covered by the observed meteorological element field at the time τ;
S3:
calculating a structural error of the meteorological element field;
firstly, calculating, based on a probability density function, a structural feature p(f)(ζf,t) of the forecasted meteorological element field and a structural feature p(0)(ζ0,τ) of the observed meteorological element field according to Eqs. (4) and (5), respectively;
{
p
(
f
)
(
ξ
f
,
t
)
=
H
(
f
)
(
ξ
f
,
t
)
ξ
f
=
(
R
f
-
R
min
f
)
/
(
R
max
f
-
R
min
f
)
∈
[
0
,
1
]
(
4
)
wherein, H(f)(ζf,t) denotes a probability density function for the forecasted target element field at the time t;
ζf denotes a coefficient of the meteorological element after the forecasted meteorological element field is normalized;
and ζf takes a value of 0-1;
{
p
(
0
)
(
ξ
f
,
t
)
=
H
(
0
)
(
ξ
0
,
t
)
ξ
0
=
(
R
0
-
R
min
0
)
/
(
R
max
0
-
R
min
0
)
∈
[
0
,
1
]
(
5
)
wherein, H(0)(ζ0,t) denotes a probability density function for the observed target element field at the time t;
ζ0 denotes a coefficient of the meteorological element after the observed meteorological element field is normalized;
and ζ0 takes a value of 0-1;
and
secondly, measuring, based on a Kullback-Leibler (KL) divergence algorithm (6), a similarity EH between the structural feature of the forecasted meteorological element field and the structural feature of the observed meteorological element field;
E
H
=
∑
ξ
f
p
(
f
)
(
ξ
f
,
t
)
⁢
log
⁢
p
(
f
)
(
ξ
f
,
t
)
p
(
o
)
(
ξ
o
,
t
)
(
6
)
S4:
calculating a similarity between the target element field and the observed element field;
calculating the similarity E between the forecasted meteorological element field and the observed element field according to Eq. (2);
E
=
E
R
+
E
S
+
E
H
(
7
)
wherein, ER denotes the total amount error of the meteorological element;
ES denotes the total area error of the meteorological element;
and EH denotes the structural error of the meteorological element.

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Method for quantifying structural feature forecast error of meteorological element based on graphical similarity

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Method for quantifying structural feature forecast error of meteorological element based on graphical similarity

Life of this patent

Abstract

Claims3