Man-sze, CHEUNG Hong Kong Observatory Seasonal Forecast for Summer 2017 for Hong Kong and the Latest Development in Extended Range Forecast Man-sze, CHEUNG Hong Kong Observatory

Outline Review of seasonal forecast for JJA in 2016 Annual number of tropical cyclones entering the 500 km range of Hong Kong Extended range forecast products for the general public

Review of JJA in 2016 Temperature Normal to above normal Above Normal Forecast Actual Temperature Normal to above normal Above Normal (29.2 °C) Rainfall Near Normal Normal (1056 mm) TC(N500) 2-3 3 TC in 500 km Mirinae (Jul) Nida (Jul) Dianmu (Aug)

Seasonal Forecast for Hong Kong JJA 2017

ENSO Status The central and eastern equatorial Pacific is expected to continue to warm through spring and summer 2017 with increasing chance of El Niño development. 3.1.3SST Weekly / Monthly maps , http://bla19/~rcmop/d3portal/enso/enso_status_fc.htm#enso_status http://bla19/~rcm/noaasst/noaasst.pl

ENSO Status Seasons (2017) AMJ MJJ JJA JAS ASO SON OND http://iri.columbia.edu/our-expertise/climate/forecasts/enso/current/ http://www.pmel.noaa.gov/tao/jsdisplay/ Assorted plot Seasons (2017) AMJ MJJ JJA JAS ASO SON OND Avg. of all models 0.4 0.6 0.7 0.8 0.9 0.5

Tercile category statistics in El Niño's and ENSO neutral year Most likely categories: Temperature in JJA Normal to above normal (global warming) Rainfall in JJA Normal to below normal (El Niño years) Uncertain (ENSO neutral years)

Tercile Probabilities Summary Forecast category of JJA 2017 from different centres Centres Temperature Rainfall CMA/BCC N to AN N to BN ECMWF(MJJ) JMA No signal UKMO NOAA/CPC Normal APCC (JAS) WMO LC (MJJ) Normal to Cool/Warm/Dry/Wet = N - * N – Warm/Cool/Wet/Dry : Normal to Warm/Cool/Wet/Dry

Consensus categorical forecast by pre-season indices

Selected predictors Ji: 1000 hPa V over 10-30N, 115-130E DJF UMI DJF Ji index DJF SST index (SSTa-SSTb) SSTa SSTb Ji: 1000 hPa V over 10-30N, 115-130E UMI: 1000 hPa V over 7.5-20N, 107.5-120E

30-year running correlation with JJA rainfall

Performance of pre-season indices JJA rainfall Random forecast Persistent NA Persistent NB Consensus of pre-season predictors 1981-2016 ~25 26 30 2001-2016 ~11 12 13 14 * 2017 JJA Rainfall : Normal to Above Normal * Miss a dry summer (2002) and a wet summer (2008)

Seasonal Climate Forecast ENSO Consideration: JJA FC Cat Temp. N to AN Rainfall N to BN Normal to warm Normal to dry Consensus of major centres: JJA FC Cat Temp. N to AN Rainfall N to BN Normal to warm Normal to dry Forecasts from pre-season indices JJA FC Cat Rainfall N to AN Normal to wet June - August 2017 Temperature: Normal to above normal Rainfall: Near normal

New method to forecast annual N500

Predictors Nino 3.4 sst 500 hPa zonal wind (12.5-25.0 N, 112.5-140.0 E)

1961-1990 obs and reanalysis

Workflow Training data: 1981/1982 – 1996 + Apply Poisson regression, qq mapping, std anomaly mapping to generate quantitative forecast Get average from the three forecasts Construct a forecast range covering 4 consecutive integers (e.g. mean = 4.x, f/c range = “3-6”, nearest 4 consecutive integers) 2 3 4 5 6 7 Training data: 1981/1982 – 1996 + Verification data: 1997-2016 Reference forecast: mode of bin (width=4) in prevailing climatology

Climatology of N500

Available Model Data ECMWF: NCEP: Forecast up to Sep (initialized on 1 Mar) 1981-2016 NCEP: Forecast up to Nov (initialized in late Feb) 1982-2016

Verification results (1997-2016) Predictor No. of correct forecasts RMSE Climate 12 0.49 NCEP U500 + EC N34 (qq,sam,poi) 15 0.47 N500 Forecast for 2017 : 5.0 (4-7)

Annual outlook issued in March

Seamless forecast – are we ready? Climate projection Seasonal forecast 2-hour RF nowcast 9-day forecast

Extended outlook forecast Tmin temperature forecast in October – March Day 10 – 28 Calibration using qq mapping and standardized anomaly mapping Verification period; 2013/14 -2015/16 for day 10-14; 2011/12 – 2015/16 for day 15-28 Calibrated forecasts are skillful We are developing graphical products for general public

Thank You And we welcome: To share our experience; To cooperate in meteorological technologies and equipment; and To share our data