MSC Ensemble Prediction System an update Richard Verret

MSC Ensemble Prediction System – an update Richard Verret, Normand Gagnon, Stéphane Beauregard, Jacques Hodgson, Benoit Archambault Canadian Meteorological Center Meteorological Service of Canada

Outline • Current and proposed EPS set-up at CMC. • Basic products. • NAEFS products. • Conclusions. Page 2

Canadian EPS - current set-up 96 perturbed analyses based on Ensemble Kalman filters Mean One 16 -day control integration with SEF model Selection of 16 initial conditions T 149 L 28 Spread inflation Eight 16 -day integrations with SEF model resolution ~150 km Eight 16 -day integrations with GEM model 1. 2° L 28 Integration done twice a day (00 and 12 UTC) Page 3 Products

Canadian EPS - proposed set-up 96 perturbed analyses based on Ensemble Kalman filters Mean One 16 -day control integration with GEM model Selection of 20 initial conditions resolution ~100 km Spread inflation Twenty 16 -day integrations with GEM model 0. 9° L 28 Integration done twice a day (00 and 12 UTC) Page 4 Products

May 12 2006 00 UTC 120 -h Valid May 17 2006 00 UTC Page 9

Probability of 24 -h precipitation amounts calibrated May 7 2006 00 UTC – 96 - to 120 -h Probability of 24 -h precipitation amounts Valid 00 UTC May 11 2006 – 00 UTC May 12 2006 Page 10

Bayesian Model Averaging Prepared at 00 UTC May 19 2006 Page 11

Page 12

NAEFS set-up • CMC: NCEP: – 16 members + 1 control – 14 members + 1 control – Multi-model: – Single model: • 8 GEM – 1. 2° L 28 • GFS – T 126 L 28 • 8 SEF – T 149 L 27 • – Perturbed Kalman filter data assimilation cycles. – Ensemble Transform breeding method. – Integration done two times a day (00 and 12 UTC) out to 16 days. – Integration done four times a day (00, 06, 12, 18 UTC) out to 16 days. To come: • To come: – 20 GEM members – 20 GFS members – 0. 9° L 28 – T 190 L 28 Page 15

EPS-grams CMC NCEP Page 16 NAEFS

Mean and standard deviation CMC NCEP Available for: • 24 -h precipitation amounts • 10 m wind speed • 2 m temperature • Mean sea level pressure • 500 h. Pa geopotential heights • 1000 -500 h. Pa thickness • 200 h. Pa wind speed May 10 2006 00 UTC 24 - to 48 -h 24 -h precipitation amounts Valid May 12 2006 00 UTC NAEFS Page 17

Probability of exceedance over 24 -h periods at least once during the specified forecast range CMC Available for: • Precipitation amounts: • < 0. 2 mm • > 5 mm • > 10 mm • > 25 mm • 10 m wind speed: • > 15 km/h • > 50 km/h • > 90 km/h • Minimum temperature: • < 0°C • < -15°C • < -30°C • Maximum temperature: • > 0°C • > 15°C • > 30°C • Wind chill: • < -15°C • < -30°C • < -40°C • < -55°C NCEP non calibrated NAEFS Page 18 May 18 2006 00 UTC 0 - to 120 -h Probability 24 -h precipitation amounts exceeding 10 mm at least once Valid 00 UTC May 18 2006 – 00 UTC May 23 2006

Probability of total precipitation amounts during the specified forecast period CMC NCEP Available for: • Precipitation amounts: • < 0. 2 mm • > 5 mm • > 10 mm • > 25 mm • > 50 mm non calibrated NAEFS Page 19 May 10 2006 00 UTC 0 - to 336 -h Probability precipitation amounts exceeding 50 mm Valid 00 UTC May 10 2006 – 00 UTC May 24 2006

Bias correction algorithm For multi-model ensemble, the bias correction is applied on each member separately May 14 2006 00 UTC – 120 -h 500 h. Pa GZ CMC ensemble average (contour) / Average bias correction (color) Valid 00 UTC May 19 2006 Page 20

Week 2 product • Methodology: – Get temperatures from members debiased toward CDAS reanalyses. – Average four instantaneous temperatures (00, 06, 12 and 18 UTC) each day for each member. – Average daily temperatures over seven days (day 8 to 14) for each member. – Get percentile from reanalysis weekly climatology for each member. – Count members in each tercile to get probabilities of « above » and « below » . – « normal » = 1 – ( « above » + « below » ). – Calibration of forecasts. Page 21

Page 22

Page 24

Where will we be Tomorrow? • Increased number of members and resolution of models: – 20 members, – ~100 km. • Stochastic physics. • Extension of public forecasts: – Days 6 -7 using EPS outputs. – Usage of EPS outputs for days 3 -7. • Wider usage of NAEFS Grand Ensemble: – – – Improved bias correction scheme (1 st moment). Correction of 2 nd moment. Member weighting scheme. Forecast products in terms of climatological anomalies. More end products. Page 31

Where will we be after Tomorrow ? • Regional EPS : – – • 20 members GEM-LAM. ~25 km resolution. Model perturbations (CAPE). Initial condition perturbations. Products for decision making processes: – EPS provides the best source of probabilistic information for decision making. – Reliability of probabilistic forecasts is required. – Extreme forecast index. • Products statistical post-processing: – Verification – Calibration Page 32