Dmitry Alferov, Elena Astakhova, Gdaly Rivin, Inna Rozinkina Hydrometcenter of Russia 13-th COSMO General Meeting, Rome, 5-9 September 2011

Contents Global medium-range EPS at the Hydrometcenter of Russia COSMO-RU EPS description COSMO-RU EPS products Case study: freezing rain in Moscow COSMO-RU EPS verification Prospects

Basic model (horis. resolution)Spectral T169L31 (~75 km) Levels31 levels; σ=0.01 – Membership 14: 12 perturbed T169L31 1 control T169L31 1 control SLAV-2008 Initial data perturbations Breeding with regional rescaling, 12hr cycling Model uncertainty perturbationsNo Surface boundary perturbationsNo Runs per day (times in UTC)1 (12 UTC) Forecast length and step interval T+0h -- T+240h at 6 hrs Input Hydrometcenter of Russia OA data Global medium-range EPS at the Hydrometcenter of Russia SLAV-2008 model : Semi-Lagrangian vorticity-divergence dynamical core, ALADIN/LACE parameterizations,0.9x0.72 degrees lon/lat (~70 km), 28 levels

Global EPS products Spaghetti (T850, pmsl, H500) Ensemble meteograms (T2m, prec, T850, total clouds, middle clouds, pmsl) Mean+spread (H500, T850) Plumes(T850, T2m, 6h prec) Postage stamps (T2m, prec) Probability maps (24-h prec, 6-h prec, wind 10m, wind 850 hPa, T850 anom, H500 anom, pmsl anom) Thresholds from the Guideline of the Lead center on Verification of Ensemble Prediction Systems

Since October 2010: Verification results for precipitation are published on the Web site of the Lead Centre on Verification of Ensemble Prediction Systems

Contents Global medium-range EPS at the Hydrometcenter of Russia COSMO-RU EPS description COSMO-RU EPS products Case study: freezing rain in Moscow COSMO-RU EPS verification Prospects

COSMO-RU EPS DESCRIPTION: main features 28 members Different variants of model physics, numerical schemes for model dynamics and boundary conditions schemes Ensemble members: COSMO-RU model, grid: 350×310×40, Δx=Δy=14 km Control experiment: COSMO-RU model, Δx=Δy=7 km Forecast length: 78 hours Computer: SGI Altix 4700 Itanium 2, 1.66 GHz, NUMALink, 1664 PEs, Peak 11 Tflops Performance: 256 CPUs, forecast ready in 7 hours 1 run / day at 00 UTC The model integration domain Moscow Offenbach Sochi Rome Omsk Murmansk Athens

COSMO-RU EPS DESCRIPTION: applied perturbations PERTURBATION PARAMETER RANGE DEFAULT VALUE USED VALUES DYNAMICS Numerical schemeLeapfrog Leapfrog, 2 nd order Runge-Kutta, 2 nd order Runge-Kutta TVD schemes Boundary conditions schemeImplicitImplicit, explicit PHYSICS Deep convection parameterization scheme Tiedtke Tiedtke, Kain-Fritsch Length scale of sub-scale surface thermal patterns over land (pat_len) 0–10000 m500 0, 500, (like used in COSMO- SREPS [Marsigli, 2009]) Scaling factor for the thickness of the laminar boundary layer for heat (rlam_heat) 0.1– , 1.0, 10.0 (tested in COSMO-SREPS and CSPERT projects [Marsigli, 2009])

Contents Global medium-range EPS at the Hydrometcenter of Russia COSMO-RU EPS description COSMO-RU EPS products Case study: freezing rain in Moscow COSMO-RU EPS verification Prospects

COSMO-RU EPS PRODUCTS: probability forecasts (6 h accumulated precipitation) Probability of precipitation > 0.25 mm / 6 h Initial time: 00 UTC , forecast valid at 06 UTC Moscow

COSMO-RU EPS PRODUCTS: spaghetti plots (T2m) 25˚C isotherm spaghetti, 31 July hours (blocking case with extreme heat) Thick black line – actual data; Thick red line – nonperturbed control 78-hour forecast; Thick blue line – ensemble mean; Thin coloured lines – ensemble members.

COSMO-RU EPS PRODUCTS: spaghetti plots (T2m) 25˚C isotherm spaghetti, 31 July hours (blocking case with extreme heat) Thick black line – actual data; Thick red line – nonperturbed control 78-hour forecast; Thick blue line – ensemble mean; Thin coloured lines – ensemble members. Large instability in the Urals region Ensemble mean forecast shows much better skill (and some members too)

Contents Global medium-range EPS at the Hydrometcenter of Russia COSMO-RU EPS description COSMO-RU EPS products Case study: freezing rain in Moscow COSMO-RU EPS verification Prospects

CASE STUDY: freezing rain in Moscow Intense freezing rain in Moscow region on December 2010 caused damage of trees, electric lines and constructions due to large mass of glazing ice formed during this time. Transport problems and closing of airports followed.

CASE STUDY: freezing rain in Moscow Wind Temp, C PMSL, hPa mm/3h Prec mm/h Clouds Conv clouds, m

CASE STUDY: freezing rain in Moscow Wind Temp, C PMSL, hPa mm/3h Prec mm/h Clouds Conv clouds, m Combined rain and snow

CASE STUDY: freezing rain in Moscow Wind Temp, C PMSL, hPa mm/3h Prec mm/h Clouds Conv clouds, m Temperature inversion near 0˚C

CASE STUDY: freezing rain in Moscow COSMO-RU 18-hour control forecast (grid size 7 km) PMSL, clouds, precipitation PMSL, T2m, H500 Left scale – clouds; Right scale – precipitation; Isolines – PMSL; Initial time: 12 UTC ; Forecast valid at 06 UTC Color scale – T2m; Brown isolines – H500; White isolines – PMSL; Initial time: 12 UTC ; Forecast valid at 06 UTC

CASE STUDY: freezing rain in Moscow Temperature, cross-section along the latitude of Moscow (55.786˚ N) Analysis for COSMO-RU/14 model Initial time: 12 UTC Forecast valid at 06 UTC Longitude Pressure, hPa -4˚C 0˚C Longitude of Moscow (37.557˚ E) 66-hour COSMO-RU ensemble forecast 0˚C isotherm ‘spaghetti’ plot

CASE STUDY: freezing rain in Moscow Temperature, cross-section along the latitude of Moscow (55.786˚ N) Analysis for COSMO-RU/14 model Initial time: 12 UTC Forecast valid at 06 UTC Longitude Pressure, hPa -4˚C 0˚C Longitude of Moscow (37.557˚ E) 66-hour COSMO-RU ensemble forecast 0˚C isotherm ‘spaghetti’ plot Temperature inversion near 0˚C successfully forecasted with high probability Ensemble members with pat_len = m gave the worst forecasts at all lead times

Contents Global medium-range EPS at the Hydrometcenter of Russia COSMO-RU EPS description COSMO-RU EPS products Case study: freezing rain in Moscow COSMO-RU EPS verification Prospects

COSMO-RU EPS VERIFICATION Reliability diagrams T2m > 30˚C July-August 2010 T2m < -20˚C December 2010 – January 2011 Reliability diagrams show the quality of probability forecast. These diagrams show very good forecasts, almost perfect in summer 2010 and a bit worse in winter Forecast probability Observed relative frequency No resolution (climatology)

Contents Global medium-range EPS at the Hydrometcenter of Russia COSMO-RU EPS description COSMO-RU EPS products Case study: freezing rain in Moscow COSMO-RU EPS verification Prospects

Regular ensemble runs A comprehensive study of the effect of different perturbations, better choice of parameter values Reduction of the number of ensemble members for higher performance Analysis of EPS skill in probabilistic forecasting of various weather elements (wind, H500, etc.) High-resolution (2.2 km) ensemble for Sochi region

Thank you!