- uncertainty and spatio-temporal trends Juha Aalto, Pentti Pirinen, - - PowerPoint PPT Presentation

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- uncertainty and spatio-temporal trends Juha Aalto, Pentti Pirinen, - - PowerPoint PPT Presentation

Nov 13, 2022 181 likes •416 views

Producing a long-term gridded data set in Finland - uncertainty and spatio-temporal trends Juha Aalto, Pentti Pirinen, Kirsti Jylh 10th EUMETNET Data Management Workshop, St. Gallen, Switzerland, 30.10.2015 1. Introduction and aims PLUMES

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Producing a long-term gridded data set in Finland

  • uncertainty and spatio-temporal trends

Juha Aalto, Pentti Pirinen, Kirsti Jylhä 10th EUMETNET Data Management Workshop,

  • St. Gallen, Switzerland, 30.10.2015
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  • 1. Introduction and aims
  • PLUMES consortium
  • Task: create a high-quality daily gridded climate data set of

the key variables across 1961-2010 (”FMI_ClimGrid_1.0”)

  • Focus on interpolation uncertainty
  • Use gridded data to investigate temporal trends in climate
  • Compare the results with existing data (E-OBS)

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 2

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  • 1. Introduction – gridded data
  • Spatially continous data based on a set of observations
  • Most often based on a statistical model
  • Important applications: climate change studies, forest

management, agriculture, biosphere modelling, permafrost ….

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 3

Observations Spatial grid Gridded data

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  • 2. Data – observations
  • Seven climate variables:
  • mean temperature (Tday)
  • maximum temperature (Tmax)
  • minimum temperature (Tmin)
  • precipitation sum (Prec)
  • mean relative humidity (RH)
  • air pressure (P)
  • snow depth (Sn)

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 4

  • Data sources:
  • FMI database
  • ECA&D pan-European database
  • Sweden, Norway, Russian and

Estonia

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  • 2. Data – observations

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 5

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  • 2. Data – quality control
  • National operational QC
  • ”Non-blended” ECA&D series
  • Misscodings, duplicates
  • Local outlier detection protocol:
  • 1. compare each value to local average

and stdev (station in turn excluded)

  • 2. Compare the local stdev to long-term

monthly stdev (1961-2010)

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 6

150km

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  • 2. Data – grid specifications
  • Spatial resolution = 10 km x 10 km
  • Euref-FIN TM35 (epsg: 3067)
  • 5224 points (3364 inside, 1860
  • utside Finland)

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 7

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  • 2. Methods – kriging interpolation
  • Kriging interpolates the value at given point using a weighted

average of the know values inside a neighborhood

  • Weights are assigned by (decreasing) function of the distance,

based on the spatial covariance structure

  • Variogram is used to quantify the spatial dependency in the data

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 8

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  • 2. Data – background data

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 9

  • Used as covariates in the

interpolation model (i.e. trend model)

  • Latitude and longitude
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  • 2. Methods – details
  • Separate trend model was estimated for each day
  • ”Semi” climatological variogram models:
  • Range = monthly means of daily ranges (1961-2010)
  • Separate sill for each day
  • Nugget = 30 % of the measurement precision (e.g. 0.03 for Temp)
  • Exponential variogrammodels
  • Global kriging

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 10

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  • 2. Methods – interpolating

precipitation and snow

  • High and potentially discrete variation vs. sparse
  • bservation network
  • Satellite and radar data might improve
  • Solution: interpolate the probability of precipitation / snow

depth and combine with interpolated amounts

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 11

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  • 2. Methods - evaluation
  • 20 independent evaluation

stations

  • Compare the observed and

interpolated values

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 12

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  • 3. Results – interpolation accuracy

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 13

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  • 3. Results – seasonal variation in

accuracy

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 14

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  • 3. Results – interpolation accuracy

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 15

  • Some meteorological conditions are more challenging

to interpolate than others…

Temperature inversion?

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  • 3. Results - uncertainty

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 16

  • 50 random

permutation / day

  • > 50 different

interpolations

  • Daily uncertainty

estimate for each variable Sources of uncertainty:

  • Observations

(measurements, network, inhomogeneities …)

  • Background variables

(georeferencing, averaging…)

  • Interpolation method
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  • 3. Results – a comparison with E-OBS

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 17

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  • 4. Trends in past climate

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 18

  • Daily grid averages
  • > seasonal / annual aggregates
  • Most uncertain areas excluded

from the trend analysis

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  • 4. Trends in past climate

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 19

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  • 5. Conclusions
  • Long-term gridded dataset were succesfully produced
  • Daily permutation-based uncertainty estimates
  • Clear, but locally varying signal of past climate change
  • Wind and solar radiation in the future
  • The dataset will be made freely available with regular

updates

  • Manuscript in progress…

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 20

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Computing environment

  • R in linux server (FMI supercomputer ”Voima”)
  • Required R-packages: gstat, sp, rgdal, raster, maptools,

PresenceAbsence, Roracle

  • Total time of calculations ~ 6 days / per variable

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 21

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www.fmi.fi

10/30/2015 Juha Aalto | juha.aalto@fmi.fi 22

More information:

juha.aalto@fmi.fi pentti.pirinen@fmi.fi