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Dynamical Long-range Forecast

Dynamical Long-range Forecast. OSE Tomoaki Climate Prediction Division Japan Meteorological Agency. １． Introduction. DEFINITIONS OF METEOROLOGICAL FORECASTING RANGES 1. Nowcasting A description of current weather parameters and 0 -2 hours description of forecasted weather parameters

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Dynamical Long-range Forecast

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  1. Dynamical Long-range Forecast OSE Tomoaki Climate Prediction Division Japan Meteorological Agency

  2. １．Introduction

  3. DEFINITIONS OF METEOROLOGICAL FORECASTING RANGES 1. Nowcasting A description of current weather parameters and 0 -2 hours description of forecasted weather parameters 2. Very short-range weather forecasting Up to 12 hours description of weather parameters 3. Short-range weather forecasting Beyond 12 hours and up to 72 hours description of weather parameters 4. Medium-range weather forecasting Beyond 72 hours and up to 240 hours description of weather parameters

  4. These are partly the Work of Climate Prediction Division in JMA 5. Extended-range weather forecasting Beyond 10 days and up to 30 days 6. Long-range forecasting From 30 days up to two years 7. Climate forecasting Beyond two years (Climate variability prediction, Climate prediction)

  5. Difference between Short- and Long-range Forecast • Long-range weather forecast (from 30 days up to two years) describes averaged weather parameters, expressed as a departure (deviation, variation, anomaly) from climate values for that period. • On the other hand, forecast up to 10 days, such as nowcasting, very short-range weather forecast, short-range weather forecast, and medium-range weather forecast, describe weather parameters (not deviation, not averaged). • Note that extended-range weather forecasting ( beyond 10 days and up to 30 days ) describes weather parameters, usuallyaveraged and expressed as a departure from climate values for that period. http://www.wmo.ch/web/www/DPS/GDPS-Supplement5-AppI-4.html

  6. NWP systems for extended forecast and long-range forecast in JMA • One month forecast (T106) of a few days lead • Once a week, Operational since March 1996 • Extension of medium range forecast • Three month forecast (T63) of a few weeks lead • Once a month,Operational since March 2003 • Six month forecast (T63) of a few weeks lead • Twice a year (Feb. and Sep.), • Operational since September 2003 • Extension of three month forecast

  7. 3-month EPS : Started in March 2003. Executed every month. 6-month EPS : Started in September 2003. Executed 5 times a year(February March and April for JJA, September and October for DJF) 120days integration for Three Month Outlook. Extended integration for Warm Season Outlook(JJA). Extended integration for Cold Season Outlook(DJF). Jan Mar May Apr Oct Dec Jan Feb Mar Feb Jun Jul Aug Sep Nov Detailed Operation Chart of 3- and 6-month EPS

  8. Introduction of Dynamical method to Long-range forecast Dynamical Long-range Forecast makes use of numerical weather prediction (NWP) model. • Long-range forecast makes use of a reduced horizontal resolution version of NWP model for short-range and medium range forecast. • The same physical processes such as cumulus parameterization, radiation and cloud, boundary layer, gravity wave drag, and so on are used. • Development of the NWP model is cooperation of Numerical prediction Division, Climate Prediction Division, and Meteorological Research Institute.

  9. Introduction of Dynamical method to Long-range forecast Different Aspects of Long-range Forecast Modelfrom numerical weather prediction (NWP) model. Long-range Forecast ModelShort-range Forecast Model • Equilibrium State (Climate) <--------> Transient State (Weather) • Cumulus and SST anomaly <--------> Cumulus and Disturbances • Ocean and Land Process<--------> Initial Condition in Atmosphere

  10. 2．Predictability

  11. predictability Uncertainty of Forecast • Errors in Initial Condition • Errors in Raw Observational Data • Errors in Objective Analysis Procedure • Sparse Observation over Ocean • Errors in Forecast Model • Limitation in the Spatial Resolution • Errors in Physical Processes

  12. Concept of the Ensemble forecastMulti-Initials within Errors in Observation Truth Ensemble Mean Initial Errors in Observation Spread Most Likely Forecast Probability Forecast

  13. Advantage of Ensemble Prediction System (EPS) • Probability forecast • Intrinsically stochastic behavior of atmosphere can be predicted with ensemble method. • Forecast with physical consistency • NWP model can represent global circulation in a physically consistent way. • Improvement based on advance of technology • Observation, Study on climate system, Model, Computational power, ……….

  14. 2．One-Month Prediction Since March 1996 - Ensemble Prediction - - Land Surface Assimiliation - - Improved Cumulus Scheme -

  15. Atmosphere Land Ocean Dynamical Seasonal Forecast SystemOne-Month Prediction Analysis Ensemble Prediction System (Atmosphere-Land) • Products • Guidance • Map • Verification Initial Boundary Condition Systematic Error Verification Persistent SST Anomaly Seasonal Forecast Experiments (Hind-cast)

  16. One-Month Ensemble Forecast (An Example） Temperature at 850hPa over Eastern Japan Forecast Observation Observation Feb. 2002 Forecast Observation Apr. 2002 Mar. 2002

  17. 2．One-Month Prediction Since March 1996 - Ensemble Prediction - - Land Surface Assimiliation - - Improved Cumulus Scheme -

  18. Global Snow Depth Analysis System (Kurino and Tokuhiro, 2003) Shortwave Radiation Precipitation Global Atmosphere Assimilation System Atmospheric Forcing (00 06 12 18UTC） Input Input Simple Biosphere Model (T106) Snow-Depth Analysis Snow (00UTC) SYNOP Snow Depth First Guess Land Surface Assimilation （00 06 12 18ＵＴＣ） Snow Depth Ground Surface/Soil Temperature Soil Wetness SSM/I Snow Cover Land Surface Model Initials Snow Depth Soil Wetness Dynamical Seasonal Prediction

  19. Land Assimilation System Snow Depth based on Land Assimilation System on Jan. 31 1989 Soil Wetness based on Land Assimilation System on Jan. 31 1989

  20. Effect of Global Snow Depth Analysis System Improved Skill for One-Month Forecast （Initial Date2001/5/31/12Z）　 Correlation between Forecast and Observation over Eurasia With With Without Without June 1st June 30 June 1st June 28 Temperature at 850 hPa Geopotential Height at 500 hPa

  21. Effect of NewGlobal Snow Depth Analysis System Improved Skill for One-Month Forecast （Initial Date2001/10/31/12Z）　 Correlation between Forecast and Observation over Eurasia New New Old Old Nov 1st Nov 30 Nov 1st Nov 30 Temperature at 850 hPa Geopotential Height at 500 hPa

  22. 2．One-Month Prediction Since March 1996 - Ensemble Prediction - - Land Surface Assimiliation - - Improved Cumulus Scheme -

  23. ・ Entrainment and Detrainment in Downdraft (Nakagawa and Shinpo, 2003) Convective Downdraft TRMM without Entrainment, Detrainment with To moderate downdraft effect

  24. 3．3- and 6- Month Prediction Since March 2003 and September 2003 - Two Tiered Method System - - Skill in Hindcast Experiment -

  25. predictability Two kinds of Atmospheric Predictability < Predictability of 1st kind >Originates from Initial conditionDeterministic forecast fails beyond two weeks due to the growth of errors contained in the initial states.Chaotic behavior of atmosphere comes from its strong non-linearity. < Predictability of 2nd kind > Originates from lower boundary conditionEffective for longer time scale; Month to season

  26. Predictability +Fx +Fy +Fz Predictability of 1st kind Analogy Predictability of 2nd kind The whole structure of Attracter (equilibrium) is modified by External Forcings

  27. predictability Relative importance of Initial Condition and Boundary Condition Initial Condition Boundary Condition Importance Predictability of 2nd kind Predictability of 1st kind 1-Month 3-Month Hour Day Week Month Season Year Average Time Scale Intra-seasonal Oscillation Meso TyphoonTropical disturbances Global Warming ENSO

  28. predictability Lower Boundary Condition of Atmosphere ◎OceanSea Surface Temperature (SST) Sea Ice◎Land Surface Soil Temperature Soil Moisture Snow Cover, Snow Depth Vegetation ( Grass, Tree etc. ) Most IMPORTANT to the atmospheric variability !

  29. 3．3- and 6- Month Prediction Since March 2003 and September 2003 - Two Tiered Method System - - Skill in Hindcast Experiment -

  30. Two Methods for Numerical Seasonal Prediction Present Two-Tiered Way Future One-Tiered Way Atomosphere-Land Coupled Model Atmosphere-Land-Ocean Coupled Model Atmospheric Model Separately predicted SST is prescribed as boundary Ocean model is coupled

  31. predictability Merits and Defects of Two Tactics Tactics 1 CGCM Merit : Ideal if SST prediction is correct. Defect : (1) SST errors cannot be corrected. (2) Needs large computer resources. Tactics 2 Two-tiered method Merit : (1) Predicted SST can be corrected. (2) computer resources can be saved. Defect : Air to sea interactions are neglected for atmospheric prediction.

  32. July 2003 NINO3 SST Prediction Skill Old Model ： 116 cases Feb.1989 - Nov. 2000 New Model： 88 cases Jan. 1989 - Jan. 2000 Persistency Forecast

  33. predictability Best SST Forecast 1 month forecast Spring MAM Case Smallest RMSE 2-month forecast RED : Coupled Model YELLOW : Persistence GREEN : Climate 4-month forecast 5-month forecast

  34. Atmosphere Land Ocean Dynamical Seasonal Forecast System1- and 3-Month Prediction Analysis Ensemble Prediction System (Atmosphere-Land) • Products • Guidance • Map • Verification Initial Boundary Condition Systematic Error Verification Persistent SST Anomaly Seasonal Forecast Experiments (Hind-cast)

  35. Atmosphere Land Ocean Dynamical Seasonal Forecast System6-Month Prediction Analysis Ensemble Prediction System (Atmosphere-Land) • Products • Guidance • Map • Verification Initial Boundary Condition Systematic Error Verification Statistical Process for Global SST Seasonal Forecast Experiments (Hind-cast) El Niño SST Forecast El Niño Forecast model (Atmosphere-Ocean Coupled Model) Initial SST

  36. SST anomalies are persisted 3-month EPS Predicted Persisted 6-month EPS Mixed Detailed Description of Sea Surface Temperature Persisted Mixed Predicted Lead time (month) 1st 2nd 3rd 4th 5th 6th

  37. Specification of the global spectral model for extended- and long- range forecast at JMA

  38. 3．3- and 6- Month Prediction Since March 2003 and September 2003 - Two Tiered Method System - - Skill in Hindcast Experiment -

  39. Introduction of Dynamical method to Long-range forecast Hindcast • “Hindcast” is a set of systematic forecast experiments for past cases. • Hindcast is performed to estimate skill of the model. • JMA is now performing hindcast in preparation for operational dynamical long-range forecast:Initial time is the last day of every month from Jan. 1984 to Dec. 2001 (18 years) with 5 members each (12*18*5=1080 cases).

  40. Especially, the deviation of SST in the tropics such as ENSO → Deviation of convective activity of large scale → Deviation of divergence of large scale → Deviation of tropical circulation direct and indirect influence on the circulation in the mid- and high- latitudes Model performance What can a model predict ? Circulation in Tropics and mid- and high-latitudes Where does the signal of long-range forecast come from ? Response of atmosphere to the slowly varying boundary conditions

  41. Model performance Linear response on heating in the tropics : Matsuno-Gill response Symmetric heating on the equator heating u,v,w Rossby wave u, v, p in the low level Kelvin Wave North-south mean Walker Circulation * tropical circulation is depicted approximately. Gill.A.E.(1980) QJRMS,447-462

  42. forecast analysis Model performance An example of forecast in the tropics 1988 Sept. 30 initial, 90-day mean (21-110days) In a case of La Nina Red:convergenceBlue:divergence Red:clockwiseBlue:counter-clockwise (in Northern Hemisphere) Red:clockwiseBlue:counter-clockwise (in Northern Hemisphere) anomaly of velocity potential 200hPa anomaly of stream function 200hPa anomaly of stream function 850hPa

  43. Model performance An example of Tele-connection : PNA El Niño in the Eastern Tropical Pacific -----> Convective area shifts towards mid- and eastern Pacific -----> High is formed -----> Strengthened Aleutian Low -----> High over Rocky Mts. -----> Low in the eastern USA

  44. Summer 1998 Day: 1-90 Initial:5.31 850hPa stream function (anal) Precipitation (anal) 850hPa stream function (fcst) Precipitation (fcst)

  45. 4．Dissemination of Forecast Products On Tokyo Climate Center (TCC) / JMA

  46. Dynamical Forecast and Verification

  47. 1-Month Forecast 3-Month Forecast 6-Month Forecast

  48. Usage of GPV Effective Usage of Global GPV • From global GPV to regional/local temperature/precipitation forecasts - Downscaling by Regional model - Downscaling by Statistical method - Statistical guidance to forecasters • Needs for capacity building

  49. Usage of GPV Guidance : Statistical downscaling Forecast or probability of temperature, precipitation etc. at desired forecast area Ensemble prediction Linear regression Results of hindcast or Objective Analysis Linear regression Observed temperature, precipitation etc. at desired forecast area

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