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Generates weather

Project description

weathergen generates time-varying weather parameters.

Single-level parameters

Parameter

Tag

Description

Units

Total column water vapor

total_water_vapor

The total amount of water vapor

mm

Total cloud cover

total_cloud_cover

The proportion of sky covered by clouds

[none]

Precipitation rate

total_precipitation

The rate of precipitation

mm/hr

Multi-level parameters

Parameter

Tag

Description

Units

Air temperature

air_temp

The temperature of the air

K

Air pressure

pressure

The air pressure

hPa

Usage

Install the package using pip:

pip install weathergen

In Python, import the package and pass a site and an array of times to the `generate` function. For example, to simulate weather for Princeton, New Jersey between June 1st and September 1st at a resolution of a minute we would write

import numpy as np
from datetime import datetime
import weathergen

t0 = datetime(2022,6,1).timestamp()
t1 = datetime(2022,9,1).timestamp()

gen_times = np.arange(t0, t1, 60)

weather = weathergen.generate(site='princeton', time=gen_times)

Note that the supplied year is arbitrary; the underlying model considers only annual and diurnal climatological variations. The supported sites are listed below, and are also stored in weathergen.sites. Specified times should be supplied in Unix time.

The weather parameters are contained in the attributes of the weather object, e.g. weather.air_temp or weather.pressure. The values are typically two-dimensional with shape (n_times, n_heights), where weather.time and weather.height describe the time and height of each dimension, in Unix time and meters above sea level. Single-level parameters are described by a single number for each time and do not have a layer dimension.

Methodology

See paper.

Sites

Supported sites are shown below. Sites are chosen for the presence of astronomical observatories, or because I think that they’re climatologically interesting.

sites

tag

description

country

notes

latitude (°N)

longitude (°E)

altitude (masl)

tag

description

country

notes

latitude (°N)

longitude (°E)

altitude (masl)

arecibo

Arecibo, Puerto Rico

usa

Arecibo Observatory

18.344

-66.753

498

armazones

Cerro Armazones

chile

ELT, VLT

-24.59

-70.192

3046

barrow

Barrow, Alaska

usa

GML

71.323

-156.611

11

basrah

Basrah

iraq

Highest extreme temperatures

30.526

47.776

5

bure

Plateau de Bure

france

NOEMA

44.634

5.908

2552

cambridge

Cambridge, Massachusetts

usa

Harvard University, MIT

42.374

-71.111

8

chajnantor

Cerro Chajnantor

chile

ACT, ALMA, APEX, ASTE, FYST, SO, TAO

-22.985

-67.741

5040

danakil

Danakil Desert

ethiopia

Highest average temperatures

13.392

40.821

-125

effelsberg

Effelsberg

germany

ERT

50.524

6.883

319

falklands

Falkland Islands

uk

Mild Southern Ocean climate

-51.892

-59.221

31

graham

Mount Graham, Arizona

usa

LBT, VATT

32.702

-109.89

3178

granada

Pico Veleta, Granada

spain

IRAM

37.066

-3.393

2850

green_bank

Green Bank, West Virginia

usa

GBT

38.43

-79.84

807

honolulu

Honolulu, Hawaii

usa

The nicest weather in the world

21.382

-157.993

8

kerguelen

Kerguelen Islands

france

Extreme Southern Ocean climate

-49.349

70.219

10

london

London

uk

The worst weather in the world

51.477

0.0

12

lucknow

Lucknow

india

Highest extreme PWV

26.85

80.95

121

malta

Malta

malta

Mediterranean climate

35.881

14.449

90

mauna_kea

Mauna Kea, Hawaii

usa

Mauna Kea Observatory

19.823

-155.475

4205

mcmurdo

McMurdo Bay, Antarctica

antarctica

McMurdo Station

-77.846

166.668

10

murchison

Murchison, Western Australia

australia

MRO, SKA

-26.703

116.671

395

narrabri

Narrabri, New South Wales

australia

ATCA

-30.313

149.55

237

ngari

Ngari, Tibet

china

AliCPT

32.33

80.03

5176

nobeyama

Nobeyama Observatory, Nagano

japan

45m, NMA

35.942

138.476

1350

north_cape

Northern Cape

south africa

HERA, MeerKAT, SKA

-30.721

21.411

1075

owens

Owens Valley, California

usa

OVRO

37.232

-118.295

1222

pachon

Cerro Pachón, Chile

chile

LSST

-30.245

-70.749

2663

princeton

Princeton, New Jersey

usa

Princeton University

40.344

-74.661

58

puna

Puna de Atacama

argentina

LLAMA

-24.192

-66.475

4820

quibdo

Quibdó, Colombia

colombia

Highest average PWV

5.692

-76.658

43

samoa

American Samoa

usa

GML

-14.247

-170.564

42

singapore

Singapore

singapore

Very consistent climate

1.354

103.812

15

socorro

Socorro, New Mexico

usa

VLA

34.1

-107.6

2120

south_pole

South Pole

antarctica

BICEP2, GML, Keck, SPT

-90.0

0.0

2835

summit

Summit Camp, Greenland

denmark

GML, Summit Station

72.579

-38.46

3126

teide

Mount Teide, Tenerife

spain

Teide Observatory

28.3

-16.51

2390

washington

Mount Washington, New Hampshire

usa

Very erratic weather

44.271

-71.303

1917

yakutsk

Yakutsk, Siberia

russia

Lowest extreme temperatures

62.03

129.73

95

Project details


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