Downscaling probabilistic seasonal climate forecasts for decision support in agriculture: A comparison of parametric and non-parametric approach

Eunjin Han, Amor V.M. Ines

Research output: Contribution to journalArticle

Abstract

Seasonal climate forecasts (SCF) are produced operationally in tercile-probabilities of the most likely categories, e.g., below-, near- and above-normal rainfall. Inherently, these are difficult to translate into information useful for decision support in agriculture. For example, probabilistic SCF must first be downscaled to daily weather realizations to link with process-based crop models, a tedious process, especially for non-technical users. Here, we present two approaches for downscaling probabilistic seasonal climate forecasts - a parametric method, predictWTD, and a non-parametric method, FResampler1, and compare their performance. The predictWTD, which is based on a conditional stochastic weather generator, was found to be not very sensitive to types of rainfall information (amount, frequency or intensity) in constraining or conditioning the stochastic weather generator, but conditioning the stochastic weather generator on both rainfall frequency and rainfall intensity had distorted the distribution of the downscaled seasonal rainfall total. Both predictWTD and FResampler1 are sensitive to the length of climate data, especially for a wet SCF; climate data longer than 30. years was found suitable for reproducing the theoretical distribution of SCF. FResampler1 performed well as predictWTD in downscaling probabilistic SCF, however, it requires the generation of more realizations to ensure stable simulations of the seasonal rainfall total distributions.

Original languageEnglish (US)
JournalClimate Risk Management
DOIs
StateAccepted/In press - 2017

Profile

climate
prognosis
rainfall
weather
stochastics
distribution
downscaling
conditioning
intensity
agriculture
decision
information
method
process
data
probability
generation
category
simulation
user

Keywords

  • Non-parametric downscaling
  • Parametric downscaling
  • Probabilistic seasonal climate forecast
  • Stochastic disaggregation

ASJC Scopus subject areas

  • Global and Planetary Change
  • Geography, Planning and Development
  • Atmospheric Science
  • Management, Monitoring, Policy and Law

Cite this

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abstract = "Seasonal climate forecasts (SCF) are produced operationally in tercile-probabilities of the most likely categories, e.g., below-, near- and above-normal rainfall. Inherently, these are difficult to translate into information useful for decision support in agriculture. For example, probabilistic SCF must first be downscaled to daily weather realizations to link with process-based crop models, a tedious process, especially for non-technical users. Here, we present two approaches for downscaling probabilistic seasonal climate forecasts - a parametric method, predictWTD, and a non-parametric method, FResampler1, and compare their performance. The predictWTD, which is based on a conditional stochastic weather generator, was found to be not very sensitive to types of rainfall information (amount, frequency or intensity) in constraining or conditioning the stochastic weather generator, but conditioning the stochastic weather generator on both rainfall frequency and rainfall intensity had distorted the distribution of the downscaled seasonal rainfall total. Both predictWTD and FResampler1 are sensitive to the length of climate data, especially for a wet SCF; climate data longer than 30. years was found suitable for reproducing the theoretical distribution of SCF. FResampler1 performed well as predictWTD in downscaling probabilistic SCF, however, it requires the generation of more realizations to ensure stable simulations of the seasonal rainfall total distributions.",
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AB - Seasonal climate forecasts (SCF) are produced operationally in tercile-probabilities of the most likely categories, e.g., below-, near- and above-normal rainfall. Inherently, these are difficult to translate into information useful for decision support in agriculture. For example, probabilistic SCF must first be downscaled to daily weather realizations to link with process-based crop models, a tedious process, especially for non-technical users. Here, we present two approaches for downscaling probabilistic seasonal climate forecasts - a parametric method, predictWTD, and a non-parametric method, FResampler1, and compare their performance. The predictWTD, which is based on a conditional stochastic weather generator, was found to be not very sensitive to types of rainfall information (amount, frequency or intensity) in constraining or conditioning the stochastic weather generator, but conditioning the stochastic weather generator on both rainfall frequency and rainfall intensity had distorted the distribution of the downscaled seasonal rainfall total. Both predictWTD and FResampler1 are sensitive to the length of climate data, especially for a wet SCF; climate data longer than 30. years was found suitable for reproducing the theoretical distribution of SCF. FResampler1 performed well as predictWTD in downscaling probabilistic SCF, however, it requires the generation of more realizations to ensure stable simulations of the seasonal rainfall total distributions.

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