beginners guide to weather and climate data

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  • A Beginners guide to weather and climate data

    Bristol Data Scientists Meetup | 24 January 2017

    Margriet Groenendijk | Developer Advocate | IBM Watson Data Platform

    @MargrietGr

    https://medium.com/ibm-watson-data-lab

  • https://github.com/MargrietGroenendijk/Bristol

    https://github.com/MargrietGroenendijk/Bristol

  • https://github.com/MargrietGroenendijk/Bristol

    https://github.com/MargrietGroenendijk/Bristol

  • https://github.com/MargrietGroenendijk/Bristol

    https://github.com/MargrietGroenendijk/Bristol

  • Observations + ModelsForecast =

  • Temperature

    Humidity

    Windspeed and direction

    Air pressure

    Rainfall

    Radiation

    http://www.metoffice.gov.uk/public/weather/climate-network/#?tab=climateNetwork

  • Historic weather

    Mean daily maximum temperature

    Mean daily minimum temperature

    Days of air frost

    Total rainfall

    Total sunshine duration

    http://www.metoffice.gov.uk/datapoint/

  • https://business.weather.com/products/the-weather-company-data-packages

    Note: data not cleaned

    Historic weather

    Example: London City airport 1997-2017

  • https://climexp.knmi.nl

  • http://www.cru.uea.ac.uk/data/

  • https://developer.ibm.com/clouddataservices/2016/04/18/predict-temperatures-using-dashdb-python-and-r/

  • pseudo-code test before using!

  • Models

  • 3D grid

    Atmosphere

    Energy

    Water

    Differential equations

    Boundary conditions

    Land

    Ocean

    Lots of parameters

    http://www.climateprediction.net/

  • Important to separate the different PFTs and large cover fraction, because these are all influenced20the f0 parameters and by the evaporation from the soil.21

    Where can the differences between models be related to?22

    What are the uncertainties?23

    Any constraints of the models with observations?24

    2 Theory25

    At the leaf-scale CO2 (A) and water vapour (E) fluxes across stomata are given by (Katul et al., 2009):26

    A = gs(ca ci) (1)27

    E = 1.6gs(ei ea) (2)

    where gs is the stomatal conductance of CO2, ca and ci the ambient and intercellular CO2 concentra-28tions, ea and ei the ambient and intercellular water vapour concentration, and 1.6 the relative diffu-29sivity of water with respect to carbon. The ratio of these fluxes is defined as the leaf-scale water use30efficient (WUEleaf ):31

    WUEleaf =A

    E(3)

    Additionally, WUEleaf can be derived from the gradients of the CO2 concentration and water vapour32between the ambient air and air within the leaf. When combining Eqs. 1 to 3 it follows that:33

    WUEleaf =ca ci

    1.6(es ea)(4)

    where ca and ci are the ambient and internal partial pressures of CO2 [Pa] and es and ea the sat-34urated and ambient water vapour pressure [Pa] The assumption is used that the intercellular water35vapour concentration is saturated and therefore ei in Eq. 1 is replaced with es. [See Katul papers for36conversions between units and why vpd is a good estimate in this equation]37

    To simulate the ecosystem-scale water and carbon fluxes the model developed for the leaf-scale is38used (e.g., Farquhar et al., 1980). This implies that in theory WUE at the ecosystem-scale can be39estimated with both eqs. 3 and 4 and that they should be equal:40

    WUEe =GPP

    ET(5)

    2GPP ET

  • http://www.earth-syst-dynam.net/7/525/2016/esd-7-525-2016.pdf

  • 0 50 100 1500

    100

    200

    300

    400

    500

    600

    vcm,25 (mol m2 s1)

    j m,2

    5 (

    mol

    m2

    s1 )

    1 2 3 4 5 6 7

    0 50 100 1500

    0.2

    0.4

    0.6

    0.8

    1

    vcm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 50 100 1500

    200

    400

    600

    800

    1000

    vcm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 200 400 6000

    0.2

    0.4

    0.6

    0.8

    1

    jm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 200 400 6000

    200

    400

    600

    800

    1000

    jm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 0.5 10

    200

    400

    600

    800

    1000

    (mol mol1)

    (m

    ol m

    ol1

    )

    k-means clustering

    0 50 100 1500

    100

    200

    300

    400

    500

    600

    vcm,25 (mol m2 s1)

    j m,2

    5 (

    mol

    m2

    s1 )

    1 2 3 4 5 6 7

    0 50 100 1500

    0.2

    0.4

    0.6

    0.8

    1

    vcm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 50 100 1500

    200

    400

    600

    800

    1000

    vcm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 200 400 6000

    0.2

    0.4

    0.6

    0.8

    1

    jm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )

    0 200 400 6000

    200

    400

    600

    800

    1000

    jm,25 (mol m2 s1)

    (m

    ol m

    ol1

    )0 0.5 1

    0

    200

    400

    600

    800

    1000

    (mol mol1)

    (mol

    mol1

    )

  • 1 2 3 4 5 6 70

    0.05

    0.1

    0.15

    0.2

    0.25

    0.3

    0.35

    Group

    Rela

    tive

    vege

    tatio

    n di

    strib

    utio

    n (

    )

    CROSAVDBFEBFENFGRAMFO

    1 2 3 4 5 6 70

    0.05

    0.1

    0.15

    0.2

    0.25

    0.3

    0.35

    GroupRe

    lativ

    e cl

    imat

    e di

    strib

    utio

    n (

    )

    ARBOTMTETCTR

    k-means clusters vs. vegetation and climate classifications

  • 0.25

    0.15

    0.20

    A CB

    http://www.earth-syst-dynam.net/7/525/2016/esd-7-525-2016.pdf

  • Climate models

    =

    Algorithms + Observations + Parameters

    =

    Machine Learning

  • invehiclehaildamageclaimseveryyear

    increaseintemperaturemeans$24Mmorein

    electricityspendingperday

    drop insalesforareaswithmorethana10%drop

    intemperature

    Insurance

    Energy & Utility

    Retail

    Some applications

  • Next find the data!

    Open Data

  • Open data in Bristol

    https://opendata.bristol.gov.uk

    Air quality River water quality

    Water levels Flood alerts

    M32 traffic flow Road works

    Crime stats Census data

    Energy consumption by ward Hospital admissions

    NHS

  • Weather + Driving Difficulty Index API

    +

    M32 traffic flow + Road works

    +

    air quality observations

    =

    Predict air quality

  • Weather and Climate Data

    Historical time series

    Historical maps

    Climate model data

    Real-time APIs

    Forecast APIs

  • Thanks!https://github.com/MargrietGroenendijk/Bristol

    http://www.slideshare.net/MargrietGroenendijk/presentations

    @MargrietGr

    https://medium.com/ibm-watson-data-lab