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Comprehensive Summer 2019 Forecast for Hong Kong

Detailed analysis of 2018 JJA, ENSO impact, 2019 seasonal forecast, and consensus from major centers. Includes performance evaluation and predictors for rainfall. Annual outlook & recent developments.

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Comprehensive Summer 2019 Forecast for Hong Kong

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  1. Seasonal Forecast for Summer 2019 for Hong Kong Wing-hang, CHAN Hong Kong Observatory

  2. Outline • Review of seasonal forecast for JJA in 2018 • Seasonal forecast for JJA in 2019 • Annual outlook 2019 and recent development

  3. Review of seasonal forecast for JJA in 2018

  4. Review of JJA in 2018 Rainfall anomalies(%) in JJA 2018 HongKong JJA Climate Temperature: 28.3 – 28.6 ° C Rainfall: 1201-1509 mm

  5. Seasonal forecast for JJA in 2019

  6. Seasonal Climate Forecast ENSO consideration (El Niño) : Forecasts from indices Normal to warm Normal to warm Normal to dry Near normal Prognostic charts: Consensus of major centres: Normal to warm Normal to warm Normal to wet Normal to wet

  7. 1. ENSO consideration

  8. ENSO Status

  9. ENSO F/C

  10. ENSO impact on HK climate Large scale: Source: NOAA Climate.gov Local statistics: Impact on seasonal rainfall/temperature distribution (1950-2018) Source: HKO

  11. 1950-2018 Observations The difference of RF in El Nino years and La Nina years are not statistically significant. The difference of Temp. in El Nino years and La Nina years are not statistically significant.

  12. 2. Prognostic charts:

  13. JJA El Niño Years Composite (1981-2018) Z500_std_anom 500wind_anom C A A 1982,1983,1987,1991,1993,1997,2002,2009,2014,2015

  14. JJA El Niño Years Composite (1981-2018) 850hPa wind anom. Rainfall anom. 1982,1983,1987,1991,1993,1997,2002,2009,2014,2015

  15. ECMWF 2019 JJA FC Charts 500hPa GPH std. anom. 500hPa wind anom. C No significant anti-cyclonic flow near HK

  16. ECMWF 2019 JJA FC Charts 925hPa wind + SST std. anom. Rainfall std. anom. moisture supply

  17. 3. Consensus of major centres

  18. JJA Temperature

  19. ECMWF N – Warm NOAA N – Warm JMA N – Warm

  20. UKMO N – Warm CMA/BCC N – Warm APCC N – Warm MJJ

  21. C3S multi-system N – warm WMO LC N – warm MJJ

  22. JJA Rainfall

  23. ECMWF N – wet NOAA Uncertain JMA N – wet (very close to no signal)

  24. UKMO N – wet CMA/BCC N – wet APCC N – wet MJJ

  25. C3S multi-system N - wet WMO LC N - wet MJJ 17

  26. Tercile Probabilities Summary Forecast category of JJA from different centres * N – Warm/Cool/Wet/Dry : Normal to Warm/Cool/Wet/Dry

  27. 4. Consensus categorical forecast by pre-season indices (PSI) and contemporary indices (CTI)

  28. Predictors for JJA RF in last year PSI: • DJF UMI and Ji index • DJF SST index (SSTa-SSTb) CTI: • JJA EC UMI • JJA EC DMI SSTa SSTb Ji: 1000 hPa V over 10-30N, 115-130E UMI: 1000 hPa V over 7.5-20N, 107.5-120E DMI: anomalous SST gradient between (50E-70E and 10S-10N) and (90E-110E and 10S-0N). @Indian Ocean

  29. 30-year running correlation with JJA rainfall PSI :related to winter monsoon CTI :related to summer monsoon and SST@Indian ocean PSI CTI The correlations are decreasing in recent years

  30. New Approach of Predictor Search • Aim • Out-sampling. Training/searching period is out of the verification period. • A more generic and automatic way to find predictors. • Elements considered • U/V/T/Z anomaly at standard levels(from 1000hPa to 50hPa) 1 • T2m/pRate/SST/sea-ice 2 /u10m/v10m/mslp anomaly • Cluster • Construct correlation map (moving window of 40 years) • Find grid points with confidence level >=95%. • Group neighbouring grid points into cluster • Discard clusters smaller than 500*500 sq. km • Principal Components • Consider first half set of the EOFs (lower order) • Adopt PC with confidence level >=95% Note:1. 150hPa, 100hPa and 50hPa are excluded in current EC5 data. 2. sea-ice is excluded in current EC5 data.

  31. Clustering grid points of high correlation(ECMWF VS HK JJA rainfall ) • Clustersmap (combined all pressure levels) • ECMWF JJA model FC data VS HK JJA rainfall 500hPa V wind -ve correlation (S’lies-> less rainfall) STR extend to west -> Less rainfall

  32. Consensus Forecast of PSI and CTI Normal to warm Near normal rainfall

  33. Performance of PSI and CTI (% of correct forecast) Forecast for 2019 JJA

  34. Seasonal Climate Forecast ENSO consideration (El Niño) : Forecasts from indices Normal to warm Normal to warm Normal to dry Near normal Prognostic charts: Consensus of major centres: Normal to warm Normal to warm Normal to wet Normal to wet June - August 2019 (preliminary FC) Temperature: Normal to above normal Rainfall: Normal to above normal

  35. Annual outlook 2019and recent development

  36. Annual outlook issued in March 2019

  37. The impact of long-range temperature forecasts on electricity load forecasting长期温度预报对预测用电量的影响陈永铿1 李国梁2 庄家乐2 唐恒伟1 李细明11香港天文台2香港电灯有限公司Hong Kong Observatory Hongkong Electric Company,Limited

  38. Thank you

  39. Appendix

  40. Verification of long-range temperature forecasts 长期温度预报的验证 Correlation coefficients between electricity load per person and monthly mean temperature 全港人均用电量与月平均温度相关系数 Long-range temperature forecasts is relatively skillful in warm month(Jun-Sep). While skill over Mar, Apr and Dec is not good enough. 长期温度预报在温暖月份(6至9月)有一定技巧，个别的月份例如3 、4和12月的表现则不太理想 It shows positive correlation over warm months. 同时，用电量在温暖月份与月平均温度较有正相关

  41. GRCM charts MJJ-RF MJJ-TT GRCM 20190316 - 0321 ENS mean (6 members)HC base time: 1982-2009, 0318 & 0323 (2 members)

  42. Reference forecast for skill evaluation • Climate JFK • Jan, Feb temperature known • Mar-Dec climate Target depends on Jan & Feb obs and records at the time Normal distribution of Mar-Dec temp • Performance metric – BSS • BSS > 0 : better than ref. f/c (max BSS at 1) • BSS < 0 : worse than ref. f/c

  43. Deterministic event forecast Forecast occurrence of event if the probability exceeds a certain threshold

  44. Clustering grid points of high correlation(reanalysis data VS HK JJA rainfall ) • Clustersmap (combined all pressure levels) • JRA55 DJF+MA reanalysis data VS HK JJA rainfall

  45. Clustering grid points of high correlation(reanalysis data VS HK JJA rainfall ) • Clustersmap (combined all pressure levels) • JRA55 JJA reanalysis data VS HK JJA rainfall

  46. JJA Dry Years Composite (1981-2018) Z500_std_anom 500wind_anom C A A C 1981,1983,1984,1989,1990,1991,1992,1993,1996,2002,2004,2011,2012,2015

  47. JJA Dry Years Composite (1981-2018) 850hPa wind anom. NE’lies anomaly 1981,1983,1984,1989,1990,1991,1992,1993,1996,2002,2004,2011,2012,2015

  48. JJA Wet Years Composite (1981-2018) Z500_std_anom 500wind_anom A A C 1994,1995,1997,2001,2005,2008,2017

  49. JJA Wet Years Composite (1981-2018) 850hPa wind anom. SE’lies anomaly 1994,1995,1997,2001,2005,2008,2017

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