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HOME > JMA's One-month Guidance Tool

JMA's One-month Guidance Tool

Announcement

  • 14 March 2019 - TCC launched JMA's One-month Guidance Tool in March 2019 and has operated it in an "experimental" basis. Please note that although TCC has paid the closest attention to the tool, TCC may change it or suspend its service due mainly to malfunctions without any prior notice. TCC is not responsible for any inconvenience that may be caused by such changes, deletion and suspension of the guidance tool.

Guidance Tool Usage

Advantages of tool utilization


Provision of past observation data


In-browser parameter settings


Figures and data


Sample temperature probability forecast map for each category. Cool-, grey- and warm-colored marks denote below-, near- and above-normal probability, respectively.


Sample temperature probability forecast for three categories. Blue, grey and red bars denote below-, near- and above-normal probability, respectively.


Sample temperature probability forecast for three categories. Blue, grey and red bars denote below-, near- and above-normal probability, respectively. Black and green lines are inter-annual timeseries representations of daily-mean observation and forecast anomaly data, respectively.


Sample reliability diagram. Red lines show reliability and green bars show forecast frequency.


Detailed Options


Basic Concept of Numerical Guidance

Outline of MOS guidance


Objectives of guidance


Data and method


Regression model

  • Multiple linear regression modeling is used for statistical prediction based on past forecasts for the statistical period.
  • In such modeling, the predictand Y is related to the N predictors Xi. The predictand is estimated from a linear combination of predictors.



  • Here, ai represents the regression coefficients, b is the regression constant and ε is the error term.
  • The coefficients ai and the constant b are determined such that the sum of the squares of estimation errors is minimized.
  • The analysis procedure is detailed below.
    • Calculation of the factors ai and b is based on past observation data variables such as temperature and precipitation and on past forecast (i.e., hindcast) elements from the 30-year statistical period (1981 - 2010).
    • Prediction of objective variables from real-time forecast elements multiplied by these factors is conducted using the relevant simultaneous equation.
    • Mapping from the objective variable to three categorized forecasts based on the ranking is conducted.
  • In the guidance tool, the probability density function (PDF) is assumed to have normal distribution.
    Here the mean (xs) is a prediction value from the regression model and the standard deviation (σn) is the error of the model, assumed to be its RMSE based on hindcast data.
  • Threshold values for the three categories are determined from past observation for the period from 1981 to 2010.
  • Probability for each tercile category (below-, near- and above-normal) is calculated with reference to the PDF of guidance and the threshold values for the three categories.
  • The cross-validation technique (Bishop 2006) is not used to create the regression model.

Conceptual diagram for a linear regression model of the predictand y and two predictors (x1 and x2).



Sample of predicted PDF with normal distribution. xs and σn denote the mean forecast and the standard deviation, respectively.



Sample climatorogical and predicted anomalous PDF for guidance forecasting.


Normalization of precipitation data

  • Normal distribution is assumed in the regression model.
  • Temperature distribution is generally approximated as per the normal.
  • Precipitation distribution is generally approximated as per the gamma equivalent rather than the normal.
  • For approximation based on normal distribution, the guidance tool performs normalization of precipitation data to the power of 1/4 by default.


Predictor combination and multi-collinearity issues


Over-fitting issues


Verification for probabilistic forecasts

Reliability diagram

  • Reliability curve (red line): Plots observed frequency (Y-axis) against forecast probability (X-axis). Proximity of the reliability curve to the 45° line (perfect reliability) represents better probabilistic forecast results.
  • Forecast frequency (green bar).

Forecast scores

  • The Brier score (BS) indicates mean squared error of probability forecasts. BS values can be referenced in the downloadable CSV-format file.



    Here N denotes the sample number (i.e., over a period of 30 years), m is the probability category, pim is the forecast probability and oim is the outcome (1 for occurrence, 0 for non-occurrence). BS values range from 0 to 1, with 0 being a perfect forecast.
  • The Brier skill score (BSS) indicates skill relative to a reference forecast (usually climatology).



    Here, BSref indicates the climatological BS. The BSS of a perfect forecast is 1. BSS > 0 indicates an improvement over the climatological forecast, BSS = 0 indicates no higher skill than the climatological forecast, and BSS < 0 indicates lower skill than the climatological forecast.

Sample reliability diagram. Red lines show reliability and green bars show forecast frequency.


Appendix

Accessing the hindcast dataset


Akaike's Information Criterion (AIC)


References



Tokyo Climate Center, Climate Prediction Division, 1-3-4 Otemachi, Chiyoda-ku, Tokyo, Japan.
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