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Remotely I-climate-datasets
Sensed I-climate-datasets
information I-climate-datasets
using I-climate-datasets
Artificial I-climate-datasets
Neural I-climate-datasets
Networks I-climate-datasets
( O
PERSIANN B-climate-datasets
) O
. O
Results O
show O
GSMAP B-climate-datasets
to O
have O
over O
all O
lower O
bias O
and O
CMORPH B-climate-datasets
with O
lowest O
Mean O
Absolute O
Error O
( O
MAE O
) O
and O
Root O
Mean O
Square O
Error O
( O
RMSE O
) O
. O
In O
addition O
, O
a O
dichotomous O
rainfall B-climate-nature
test O
reveals O
GSMAP B-climate-datasets
and O
CMORPH B-climate-datasets
have O
low O
Proportion O
Correct O
( O
PC O
) O
for O
convective B-climate-nature
and O
stratiform B-climate-nature
rainclouds B-climate-nature
, O
respectively O
. O
TRMM B-climate-datasets
consistently O
showed O
high O
PC O
for O
almost O
all O
raincloud B-climate-nature
types O
. O
-DOCSTART- -X- O O https://semanticscholar.org/paper/08b5cb16730b30ef0398e3f5302653db6d3afe62
COMPARING O
CITIES O
OF O
THE O
WORLD O
ACCORDING O
TO O
THEIR O
FOOD B-climate-assets
SECURITY I-climate-assets
RISKS O
AND O
OPPORTUNITIES O
. O
Due O
to O
the O