Reanalyses use in operational weather forecasting

Transcription

1 Reanalyses use in operational weather forecasting Roberto Buizza ECMWF, Shinfield Park, RG2 9AX, Reading, UK 1

2 2017: the ECMWF IFS includes many components Model components Initial conditions Forecasts Model components atmosphere land waves EDA-25 4DV 18km - TCo639L137 HRES FC 9km TCo1279L137 (d0-10) atmosphere land waves HRES 4DV 9km TCo1279L137 sea-ice currents ORAS5-5 3DV-FGAT 0.25 degrees z75 ORCA ENS-51 FC 18km TCo639L91 (d0-15) 36km TCo319L91 (d15-46) atmosphere land waves SEAS4-51 FC 36km TCo319L91 (m0-7) sea-ice currents Atmosphere grids: T CO (cubic octahedral Gaussian reduced grid) or T L (Gaussian linear grid) Ocean grid: ORCA (tri-polar grid) 2

3 The ECMWF reanalyses continue to provide valuable data ECMWF ERA-Interim & ERA-Interim/Land 1979-to-date ERA-CLIM2 CERA-20C CERA-SAT (2000-to-date) ECMWF C3S ORAS5 (Ocean and sea-ice) 1979-to date ERA5 (atm and land) to-date C3S in ~ 2020 ERA6 (1979-to date) coupled? Reanalysis Period Atmosphere Ensemble Ocean/seaice component ERAI 1979-to-date TL255L60 (80 km) CERA20C TL159L91 (120 km) 10 NEMO/LIM2: 1.0 degree, z42 CERASAT (then 2000-to-date) TL319L91 (65 km) 10 NEMO/LIM2: 0.25 degrees, z75 ORAS to-date N/A (driven by ERA-I) 5 NEMO/LIM2: 0.25 degrees, z75 ERA to-date (then back to 1957) TL639L91 (35 km) 10 low-res -- 3

4 ECMWF main products are based on 51 ensembles Temperature Temperature fc j fc 0 PDF(t) reality PDF(0) Forecast time 4

5 The ensemble reforecasts start from reanalyses Medium-range/monthly ensemble (ENS): Forecasts: 51 members run every day to 15 days (twice a week up to 46d). Reforecasts: forecasts run twice a week for the past 20y to estimate the M-climate; they start from ERA-I and ORAS5 reanalyses Seasonal ensemble (S5): Forecasts: 51 members run once a month. Reforecasts: 15 forecasts are run once a month for the past 30y; they start from the ERA-I and ORAS5 reanalyses 20y November 51 Tco639 L62 The re-fc initial conditions come from ERA-Interim 5

6 Reanalyses are needed for ensemble operational forecasts Some of the ensemble products (e.g. EFI) are expressed in terms of anomalies or probability of extremes: these products are generated by comparing ensemble forecasts and reforecasts 1. Reanalyses are required to initialize the ensemble re-forecasts required to generate these (and other) operational products 2. Reanalyses are required to produce a robust estimate of the reliability and skill of our ensembles 6

7 The Extreme Forecast Index is computed using fcs and re-fcs By comparing the model climate CDF MCLI and the forecast CDFs (coloured lines), we can define the Extreme Forecast Index (EFI) and predict extremes as seen by the model. The EFI is the average difference between the CDF MCLI and the fc CDF: M CLI FC A EFI = 1 2 p CDF( p) π 0 p(1 p) dp M CLI FC B

8 Ex 1: Italy, TP24 obs to (Nov 2016) 24 Nov Nov 06z 335 mm/d 8

9 Ex 1: EFI forecasts provide valuable guidance The ENS-EFI t+7d forecast identified Liguria and Piemonte as areas with a high probability of extreme (i.e. far from the climate distribution) precipitation. The ENS-EFI +3d forecast confirmed that an extreme event could affect the area. ENS +7d fc from 18 Nov 2016 (00UTC) EFI (shading) and SOT for TP24 on 25 Nov ENS +3d fc from 22 Nov 2016 (00UTC) EFI (shading) and SOT for TP24 on 25 Nov 9

10 Ex 1: EFI forecasts provide valuable guidance By comparing the model climate CDF MCLI and the forecast CDFs (coloured lines), we can define the Extreme Forecast Index (EFI) and predict extremes as seen by the model. In this case, PR(10WG>25m/s)~30%, with EFI values in excess of 80% indicating that the forecast predicts an anomalous situation. CDF MCLI Fc CDFs +132h +24h 10

11 Ex 2: re-forecasts are used to generate weekly anomaly fcs This figure shows the ENS weekly forecasts of the cold anomaly that affected Central Europe between 31 July and 6 August The anomalies are computed by comparing ENS fcs and refcs. ENS gave some good signal up to 4 weeks before the event. +4 week forecast anomaly

12 Ex 3: reforecasts are used to evaluate trends in performance OPE ERA5 ERA-I NH Extratropics 12

13 Ex 4: reforecasts are used to estimate fc skill for rare events Re-forecasts can be used to estimate the skill of the ECMWF ensembles considering a large, statistically significant data set. This figure shows the skill of the ECMWF Seasonal Ensemble (S4) forecasts starting on 1 Nov for DJF in predicting the North Atlantic Oscillation (NAO). T639 ac = 0.51 (From Franco Molteni) 13

14 Ex 5: reforecasts can be used to investigate predictability Forecasts of circulation patterns associated with T-anomalies over Europe are being diagnosed using leading Euro-Atl EOFs: ±NAO and Scandinavian Blocking/anti-blocking. The EOFs are used to investigate tropical/extra-tropical teleconnections: results indicate an a- symmetric MJO impact on the NAO: fcs initiated during an MJO event show higher skill in predicting -NAO up to day 18 (right), but the sensitivity is small and not-significant for the +NAO. Active MJO +NAO Active MJO -NAO (From Laura Ferranti) 14

15 Conclusions At ECMWF, reanalyses are used to generate operational products and to assess predictability: 1. Some of the operational ensemble products need reanalyses to be generated, since they are generated by comparing ensemble forecasts and reforecasts, which are initialized using reanalyses 2. Reanalyses are also required to produce a robust estimate of the reliability and skill of our ensembles 15