Jonas Bhend, Irina Mahlstein, Christoph Spirig, Jacopo Riboldi, Mark - - PowerPoint PPT Presentation

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Sep 28, 2023 417 likes •666 views

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Seasonal forecasting of climate indices Jonas Bhend, Irina Mahlstein, Christoph Spirig, Jacopo Riboldi, Mark Liniger, Christof Appenzeller

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Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss

Seasonal forecasting of climate indices

Jonas Bhend, Irina Mahlstein, Christoph Spirig, Jacopo Riboldi, Mark Liniger, Christof Appenzeller

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2 Forecasting climate indices, Euporias workshop, Santander

Indices: Example Heating Degree Days (HDD)

HDD: Index to reflect demand of energy to heat a building. Defined as number of degrees that day’s average temperature is below 18°C. Characteristics of Indices:

Nonlinear transfer function of basic climate variable
scalar
Often based on daily data
Aggregated or counts over time
Threshold based, absolute or relative to climatology

DJF, Nov Initialization, ECMWF System4

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3 Forecasting climate indices, Euporias workshop, Santander

Summary

Threshold-based climate indices require some form of daily bias correction of seasonal forecasts. De-biasing is non-trivial due to small sample of re-forecast /

bservation pairs, auto-correlation and bias in higher-order

moments. Smoothing of observed daily climatology improves de-biasing and skill of forecasts.

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4 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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5 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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6 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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7 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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8 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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9 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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10 Forecasting climate indices, Euporias workshop, Santander

Example: Debiasing daily series

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11 Forecasting climate indices, Euporias workshop, Santander

Bias correction of daily data: daily mean temperature

Problem: 30 years of observations not enough to calculate daily climatology Explore smoothing in model world by perfect model approach average of 30 years with 1 member Loess smoothing of 30 years with 1 member average of 30 years with 51 members K

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12 Forecasting climate indices, Euporias workshop, Santander

Bias correction of daily data: daily mean temperature

MAE between full hindcast daily mean and

f hindcast climatology estimated from 1 member through

fit daily data DJF, Nov Initialization, System4

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13 Forecasting climate indices, Euporias workshop, Santander

Bias correction of daily data: daily mean temperature

Mean hindcast spread MAE for daily data

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14 Forecasting climate indices, Euporias workshop, Santander

Bias correction of daily data: 5% percentile of daily temperature

MAE between full hindcast daily mean and

f estimated from 1 member through

fit 5day window

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15 Forecasting climate indices, Euporias workshop, Santander

Bias correction of daily data: 90% percentile of daily precipitation

MAE between full hindcast daily mean and

f estimated from 1 member through

fit 5-day window

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16 Forecasting climate indices, Euporias workshop, Santander

Skill of debiased vs. raw metrics: Heating degree days climatology

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17 Forecasting climate indices, Euporias workshop, Santander

Skill of debiased vs. raw metrics: HDD correlation

HDD correlation (raw) HDD correlation (debiased)

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18 Forecasting climate indices, Euporias workshop, Santander

Skill of debiased vs. raw metrics: HDD continuos ranked probability skill score

HDD crpss (raw) HDD crpss (debiased)

Good Bad

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19 Forecasting climate indices, Euporias workshop, Santander

HDD crpss (raw) HDD crpss (debiased)

Good Bad

Temperature crpss (raw) Temperature crpss (debiased)

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20 Forecasting climate indices, Euporias workshop, Santander

Skill of debiased vs. raw metrics: Frost day climatology

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21 Forecasting climate indices, Euporias workshop, Santander

Skill of debiased vs. raw metrics: Frost days correlation

FD correlation (raw) FD correlation (debiased)

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22 Forecasting climate indices, Euporias workshop, Santander

Skill of debiased vs. raw metrics: FD continuous ranked probability skill score

FD crpss (raw) FD crpss (debiased)

Good Bad

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23 Forecasting climate indices, Euporias workshop, Santander

Summary

Threshold-based climate indices require some form of daily bias correction of seasonal forecasts. Daily de-biasing is non-trivial due to small sample of re- forecast / observation pairs, auto-correlation and biases in higher-order moments (yet to be addressed). Smoothing of observed daily climatology improves de-biasing and skill of forecasts.

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24 Forecasting climate indices, Euporias workshop, Santander

Deliverable WP22

Please send whatever you have as soon as possible to christoph.spirig@meteoswiss.ch

Climatologies of climate indices
Forecasts of climate indices
Skill metrics of climate indices