Standard number: QX/T 507-2019
Standard name: Climate prediction verification--El Nino/La Nina
English name: Climate prediction verification--El Nino/La Nina
Standard format: PDF
Release time: 2019-09-30
Implementation time: 2020-01-01
Standard size: 672K
Standard introduction: This standard is drafted in accordance with the rules given in GB/T1.1-2009
This standard is proposed and managed by the National Technical Committee for Climate and Climate Change Standardization (SAC/TC540).
Drafting unit of this standard: National Climate Center.
Main drafters of this standard: Lu Bo, Tian Ben, Wan Jianghua, Ren Hongli. This standard gives the verification method for El Niño/La Niña predictions
This standard is applicable to the business and scientific research of El Niño/La Niña predictions
2 Terms and Definitions
The following terms and definitions apply to this document
Sea surface temperature; SST
The numerical value of the ocean surface water temperature.
Note: The unit is degrees Celsius (
GB/T3366-2017, definition 2.1]
Average climate normals
The multi-year average of meteorological elements, the average of the last three full decades is taken as the climate average
Sea surface temperature anomaly SST anomaly; SSTA
Sea surface temperature anomaly
The difference between the sea surface temperature and the multi-year climate average.
GB/T33666-2017, definition 2.2]
El Nino/La Nina index EI Nino/ I a Nina index
A sea surface temperature monitoring index reflecting the El Nino/La Nina phenomenon.
Note: Usually refers to NINO3.4 index, NNO3 index, NNO4 index, etc. For the definition of each index, please refer to GB/T33666-2017.
This standard provides the verification method for El Nino/La Nina prediction. This standard is applicable to the business and scientific research of El Nino/La Nina prediction.

ICS07.060
Meteorological Industry Standard of the People's Republic of China
QX/T507—2019
Climate prediction verification
El Nino/La Nina
Climate prediction verificationEl Nino/La Nina Nina Industry Standard Information Service Platform
Release on 2019-09-30
China Meteorological Administration
Implemented on 2020-01-01
Industry Standard Information Service Platform
2 Terms and Definitions
3 El Nino/La Nina Forecast Verification
3.1 Historical Forecast Verification
3.2 Real-time Forecast Verification
References
QX/T507—2019
Industry Standard Information Service Platform
Industry Standard Information Service Platform
This standard was drafted in accordance with the rules given in GB/T1.1—2009. This standard was proposed and managed by the National Technical Committee for Climate and Climate Change Standardization (SAC/TC540). The drafting unit of this standard: National Climate Center. The main drafters of this standard: Lu Bo, Tian Ben, Wan Jianghua, Ren Hongli. QX/T507-2019
Industry Standard Information Service Platform
Industry Standard Information Service Platform
1 Scope
Climate Forecast Verification
El Nino/La Nina
This standard gives the verification method for El Nino/La Nina prediction. This standard applies to the business and scientific research of El Nino/La Nina prediction. 2 Terms and Definitions
The following terms and definitions apply to this document. 2.1
Sea surface temperature
sea surface temperature; SST
The numerical value of sea surface temperature.
Note: The unit is Celsius (℃).
[GB/T33666—2017. Definition 2.1]
climate normalsbzxZ.net
climate average
The multi-year average of meteorological elements, taking the average of the last three full decades as the climate average. 2.3
Sea surface temperature anomalySSTanomaly:SSTA
Sea surface temperature anomaly
The difference between the sea surface temperature and the multi-year climate average. [GB/T33666—2017, Definition 2.2]
El Nino/La Nina index Nino/LaNinaindex Sea temperature monitoring index reflecting El Nino/La Nina phenomenon QX/T507—2019
Standard information service platform
Note: Usually refers to NINO3.4 index, NINO3 index NINO number, etc. For definitions of each index, please refer to GB/T33666—20173 El Nino/La Nina prediction test
Historical prediction test
3.1.1 Test variables
The NINO3.4 index should be used to characterize the observed and historically reported El Nino/La Nina states, and a comprehensive test should be conducted on the overall prediction skills of historical report test results or long-term prediction results. 3.1.2 Temporal Anomaly Correlation Coefficient Index
The Temporal Correlation Coefficient (TCC) index Itc is used to test the historical prediction skills of the El Nino/La Nina index. If ITcc is greater than or equal to 0.6, the overall prediction skills are considered to be good. The calculation of IT is shown in formula (1): 1
QX/T507—2019
Where:
The historical temporal anomaly correlation coefficient index of the El Nino/La Nina index forecast one month ahead; The number of lead months for the El Nino/La Nina index forecast; The total number of samples of historical returns;
The corresponding forecast value of G one month ahead; - The value of the first observation sample of the El Nino/La Nina index. Real-time prediction test
Test variable
...(1)
It is advisable to use the NINO3.4 index as the test variable and conduct a real-time prediction test on the skill of the prediction results in the last 12 months based on the recent El Niño/La Niña status.
3.2.2 Relative Prediction Error Index
The relative prediction error (RPE) index Ipe is used to perform real-time prediction test of El Nino/La Nina index prediction. The calculation is shown in formula (2): (Y.-G,)-/m
Wherein:
relative prediction error index of El Nino/La Nina index prediction in advance [months]; number of lead months for El Nino/La Nina index prediction: number of samples for real-time prediction test; G, corresponding forecast value one month in advance; value of the first observed sample of El Nino/La Nina index; (2)
root mean square value of the observed recent El Nino/La Nina index, and it is necessary to avoid the situation where the value is too small, and the calculation is shown in formula (3): 05
(S<0. 5°C)
Word service
·（3）
≥0.5℃)
3.2.3 Relative prediction score index
The relative prediction error index IRpe is used to calculate the relative prediction score (RelativePredictionScore; RPS) index Irps before the real-time prediction of the El Niño/La Niña index, see formula (4): Ikps.
Where:
(50×(2-IRPE.)
(IRPE.>2)
(0≤IRPE≤ 2)
Relative prediction score index of El Nino/La Nina index prediction one month in advance; number of lead months for El Nino/La Nina index prediction; relative prediction error index of El Nino/La Nina index prediction one month in advance, calculation method is shown in formula (2). · (4)
3.2.4 Real-time prediction test judgment regulations
QX/T507—2019
Relative prediction score index IRrs varies between 0 and 100 points. The larger the score, the better the real-time prediction skill of the recent El Nino/La Nina index. The higher the IRPs, the more likely it is to have prediction skills. Industry Standard Information Service Platform
QX/T507-2019
References
[1]GB/T33666—2017 El Niño/La Niña Event Discrimination Method[2]Ren Hongli, Liu Ying, Zuo Jinqing, et al. The new generation ENSO prediction system of the National Climate Center and its prediction of the 2014/2016 super El Niño event[J. Meteorology, 2016, 42(5): 521-531[3]Barnston AG,Tippett MK,L'Heureux ML,et al. Skill of real-time seasonal ENSOmodelpredictions during 2002-1l: Is our capability increasing? [JJ.Bulletin of the American MeteorologicalSociety.2012.93:631-651
4]Ren HL,Jin FF,Song LC, et al.Prediction of primary climate variability modes at the BeijingClimateCenter J.Journal ofMeteorologicalResearch, 2017,31(1):204-223[5]Jin EK,Kinter JL,Wang Bet al.Current status of ENSO prediction skill in coupled oceanatmospheremodels[JJ.ClimateDynamics2008.31(6):647-664[6J Luo JJ,Masson S,Behera SK,et al. Extended ENSO predictions using a fully coupled o-cean-atmospheremodel[Jl.JournalofClimate,2008,21(1):84-93[7J Latif M,Barnett TP,Cane MA, et al.A review of ENSO prediction studiesJ]. ClimateDynamics.1994,9(4):167-179
Industry Standard Information Service Platform
Industry Standard Information Service Platform
People's Republic of China
Industry Standard
Meteorological Industry Standard
Climate Forecast Verification El Niño/La Niña
QX/T507—2019
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