maesters-clim 0.0.2

Maesters-of-Clim tempt to help retriving climate data (climate index, reanalysis) from the main-stream climate insitution (like IRI, PSL, NCEI, RDA).

Maesters-of-Clim

Maesters-of-Clim tempt to help retriving climate data (climate index, reanalysis) from the main-stream climate insitution (like IRI, PSL, NCEI, RDA).

The following support

Institution	Source	DataType	DataName
IRI	IRI	forecast	ENSO Probability
IRI	CPC	Forecast	ENSO Probability
PSL/NCEI	PSL/NCEI	history	Nina 34 Anomaly
PSL/NCEI	PSL/NCEI	history	Nina 3 Anomaly
PSL/NCEI	PSL/NCEI	history	Nina 4 Anomaly
PSL	PSL	history	Nina 1 Anomaly
NCEI	NCEI	history	Nina 1.2 Anomaly
NCEI	NCEI	history	Nina 1.2 SST
NCEI	NCEI	history	Nina 3 SST
NCEI	NCEI	history	Nina 3.4 SST
NCEI	NCEI	history	Nina 4 SST
NCEI	NCEI	history	Indian Ocean Dipole
PSL	PSL	history	Southern Oscillation Index
PSL	PSL	history	Oceanic Nino index
PSL	PSL	history	Trans Nino index
PSL	PSL	history	Arctic Oscillation
PSL	PSL	history	Bivariate ENSO from nina3.4 & soi
PSL	PSL	history	Western Pacific Index
PSL	PSL	history	AMO smoothed
PSL/NCEI	PSL/NCEI	history	AMO unsmoothed
PSL/NCEI	PSL/NCEI	history	Pacific Decadal Oscillation

Install

pip install maesters-clim==0.0.2

Usage

from maesters_of_clim import Climate_Maester
from datetime import datetime

# retrive history climate index from nina
c = Climate_Maester(
    indexes=['nina34a', 'pdo', 'soi'],
    source='psl'
)
df = c.history()

# retrive half-year ENSO forecast probability
c = Climate_Maester(
    indexes='enso',
    source='iri'
)
iridf = c.forecast(pred_at=datetime(2022, 10, 1))

c = Climate_Maester(
    indexes='enso',
    source='cpc'
)
cpcdf = c.forecast(pred_at=datetime(2022, 10, 1))

TODO

The following support is on the way. 🚀🚀🚀

1. Data

• Climate index from NCEI
• ERA5 reanalysis from RDA and AWS

2. Basic Computation

• Compiste Analysis