Pdpta11 G-Ensemble for Meteorological Prediction Enhancement

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July-2011. Presented in PDPTA´11, Las Vegas-Nevada-USA

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  • Pdpta11 G-Ensemble for Meteorological Prediction Enhancement

    1. 1. Genetic Ensemble (G-Ensemble) for Meteorological Prediction Enhancement Hisham Ihshaish, Ana Cortés, and Miquel A. Senar hisham@caos.uab.es, {ana.cortes,miquelangel.senar}@uab.es Computer Architecture &Operating Systems Department Barcelona, Spain The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA´11) 18-21, July, 2011 Las Vegas, Nevada, USA
    2. 2. Presentation: • Introduction. • Meteorological Models. • Problem and Methodology. • Experimentation. • Conclusions and Future Work.
    3. 3. Introduction:
    4. 4. Introduction:• Why to predict? Is it that important?
    5. 5. Introduction:• Why to predict? Is it that important? Meteorological data is important and critical in numerous aspects of our lives:
    6. 6. Introduction:• Why to predict? Is it that important? Meteorological data is important and critical in numerous aspects of our lives: • It helps in people (decision makings) in aspects like: agriculture, transportation, social activities,...etc. • It is needed for the calculation of other critically needed information: air pollution, fire propagation, environmental quality data,...etc. • The short behavior of some natural disasters could be predicted and as a result: it saves our lives.
    7. 7. Meteorological Models 1 2 Domain Predicted Variables Domain Initial Conditions 4 3 NWP Model t=0 h t=12 h t=N h
    8. 8. Meteorological Models 1 2 Domain Predicted Variables Domain Initial Conditions 4 3 NWP Model t=0 h t=12 h t=N h
    9. 9. Meteorological Models 1 2 Domain Predicted Variables Domain Initial Conditions 4 3 NWP Model t=0 h t=12 h t=N h
    10. 10. Meteorological Models * Dt = 90 km 1 2 Level 3 Domain Predicted Variables Domain Initial Conditions 4 3 Level 2 NWP Model t=0 h t=12 h t=N h Level 1
    11. 11. Meteorological Models 1 2 Domain Predicted Variables Domain Initial Conditions 4 3 NWP Model t=0 h t=12 h t=N h
    12. 12. Meteorological Models 1 2 Domain Predicted Variables Domain Initial Conditions 4 3 NWP Model t=0 h t=12 h t=N h
    13. 13. Meteorological Models 1 2 Domain Predicted Variables Domain Initial Conditions 4 3 NWP Model t=0 h t=12 h t=N h
    14. 14. Meteorological ModelsPrediction Quality Problem Prediction Quality is subject of: • The Numerical Weather Prediction (NWP) model itself. (WRF, MM5,...etc) • Runtime injection of real observations to the NWP model. • Input Data (Initial Conditions). • Physical Parametrization (representation of sub-scale meteorological phenomena)
    15. 15. Meteorological ModelsPrediction Quality Problem Prediction Quality is subject of: • The Numerical Weather Prediction (NWP) model itself. (WRF, MM5,...etc) • Runtime injection of real observations to the NWP model. • Input Data (Initial Conditions). • Physical Parametrization (representation of sub-scale meteorological phenomena)
    16. 16. Meteorological ModelsPrediction Quality: Input Data (Initial Conditions)
    17. 17. Meteorological ModelsPrediction Quality: Input Data (Initial Conditions) Global Forecasting 36 KM Low resolution domain
    18. 18. Meteorological ModelsPrediction Quality: Input Data (Initial Conditions) Global Forecasting 4 KM 36 KM Low resolution domain
    19. 19. Meteorological ModelsPrediction Quality: Input Data (Initial Conditions) Global Forecasting 4 KM Nesting 36 KM Low resolution domain
    20. 20. Meteorological ModelsPrediction Quality: Input Data (Initial Conditions) Global Forecasting 4 KM Nesting 36 KM Low resolution domain
    21. 21. Meteorological ModelsPrediction Quality: Input Data (Initial Conditions) Global Forecasting Uncertainty of initial atmosphere state or Imperfectness of initial conditions 4 KM Nesting 36 KM Low resolution domain
    22. 22. Meteorological ModelsPrediction Quality: Physical ParametrizationSub-scale meteorological processes representation
    23. 23. Meteorological Models Prediction Quality: Physical Parametrization Sub-scale meteorological processes representation• Various meteorological phenomena occurs within scales of 4 KM less than (1 KM). (surface flux of energy, cumulus cloud, solar radiation, ..etc )
    24. 24. Meteorological Models Prediction Quality: Physical Parametrization Sub-scale meteorological processes representation• Various meteorological phenomena occurs within scales of 4 KM less than (1 KM). (surface flux of energy, cumulus cloud, solar radiation, ..etc )• To represent this process in domain scale, NWP model is coupled with physical sub-models that includes these processes in domain grid points.• Physical sub-models provide NWP models with a set of variables by calculating high-resolution data that characterize each sub-scale phenomena or process.
    25. 25. Meteorological Models Prediction Quality: Physical Parametrization Sub-scale meteorological processes representation• Various meteorological phenomena occurs within scales of 4 KM less than (1 KM). (surface flux of energy, cumulus cloud, solar radiation, ..etc )• To represent this process in domain scale, NWP model is coupled with physical sub-models that includes these processes in domain grid points.• Physical sub-models provide NWP models with a set of variables by calculating high-resolution data that characterize each sub-scale phenomena or process.
    26. 26. How a NWP model is coupled with physical sub-models?NWP model t=x t=x+1 WRF
    27. 27. How a NWP model is coupled with physical sub-models? NWP modelST, HF t=x t=x+1 ST, HF, SM, DSE Phys. sub.mdel WRF Albedo Soil Moisture Surface emissivity Surface Roughness Veg. Table variables Evaporation ratios Surface absorption . .
    28. 28. How a NWP model is coupled with physical sub-models? NWP modelST, HF t=x t=x+1 t=x+2 ST, HF, SM, DSE Phys. sub.mdel WRF Albedo Soil Moisture Surface emissivity Surface Roughness Veg. Table variables Evaporation ratios Surface absorption . .
    29. 29. How a NWP model is coupled with physical sub-models? NWP modelST, HF t=x t=x+1 t=x+2 ST, HF, SM, DSE Phys. sub.mdel WRF Albedo Soil Moisture Surface emissivity Surface Roughness The values of these parameters fall within ranges, a small change in Veg. Table variables Evaporation ratios their values produce non-negligible differences in prediction results Surface absorption . .
    30. 30. Prediction is not precise....Hurricane Katrina Case: Prediction of 28.8.2005 at 12:00h. to30.8.2005 at 24:00 (36 hours): 400.00 40 Acc . Precipitation mm 325.00 33LHF W/m2 250.00 26 175.00 19 Observed Predicted Observed Predicted 100.00 12 12 21 30 39 48 12 21 30 39 48 H o u r H o u r
    31. 31. NWP EnhancementEnsemble Prediction System (EPS)• The Basic idea of an EPS method is to reflect the possible variations in initial conditions and physical parameters in the final result of the meteorological prediction.• A set of predictions is conducted, each of which considers a different combination of initial conditions or physical parameters.• The average of all predictions´ results is considered as the final result of prediction
    32. 32. Ensemble Prediction System
    33. 33. Ensemble Prediction System NWP Phys. sub.mdel Albedo Soil Moisture CZIL Surface Roughness Veg. Table REFKDT
    34. 34. Ensemble Prediction System NWP Phys. sub.mdel Albedo AlbedoSoil Albedo Soil Moisture AlbedoSoil MoistureAlbedo Moisture CZIL Soil CZIL SurfaceSoil Moisture Surface CZILMoisture Surface CZIL Roughness RoughnessCZIL Roughness Veg. Table Surface Veg. TableSurface Veg. Table REFKDT Roughness REFKDTRoughness Veg. Table REFKDTVeg. Table REFKDTREFKDT
    35. 35. Ensemble Prediction System NWP Phys. sub.mdel Albedo AlbedoSoil Albedo Soil Moisture AlbedoSoil MoistureAlbedo Moisture CZIL Soil CZIL SurfaceSoil Moisture Surface CZILMoisture Surface CZIL Roughness RoughnessCZIL Roughness Veg. Table Surface Veg. TableSurface Veg. Table REFKDT Roughness REFKDTRoughness Veg. Table REFKDTVeg. Table REFKDTREFKDT
    36. 36. Ensemble Prediction System NWP Ensemble Prediction costs (computationally) and Phys. sub.mdel normally not precise! Albedo Albedo Albedo Soil Soil Moisture What do we propose? AlbedoSoil MoistureAlbedo Moisture CZIL Soil CZIL SurfaceSoil Moisture Surface CZILMoisture Surface CZIL Roughness RoughnessCZIL Roughness Veg. Table Surface Veg. TableSurface Veg. Table REFKDT Roughness REFKDTRoughness Veg. Table REFKDTVeg. Table REFKDTREFKDT
    37. 37. Genetic Ensemble MethodTwo-Phase Prediction Scheme
    38. 38. G-Ensemble
    39. 39. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    40. 40. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    41. 41. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble Observation t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    42. 42. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble Observation t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    43. 43. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble GA Parameter adjustment Observation t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    44. 44. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble GA Parameter adjustment Observation t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    45. 45. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble GA Parameter adjustment Observation t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    46. 46. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble Calibrated Ensemble Set GA Parameter G-Ensemble set adjustment Observation t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    47. 47. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble Calibrated Ensemble Set GA Parameter G-Ensemble set adjustment Observation Calibrated Ens. Member Best Genetic Ens. Member (BeGEM) t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    48. 48. G-Ensemble Calibrating Ensemble members to minimize prediction error using GA. Calibration is done in a phase before prediction time.Init. Ensemble Calibrated Ensemble Set GA Parameter G-Ensemble set adjustment Observation Calibrated Ens. Member Best Genetic Ens. Member (BeGEM) t=-6 h t=-3 h t=00 h t=3 h t=6 h t=n hPhys. Parm. Calibration PredictionAlbedoSoil MoistureCZILSurfaceRoughnessVeg. TableREFKDT...
    49. 49. G-Ensemble Calibration ErrorDepending on the error function used, two G-Ensemble strategies are presented: 1) Single-Variable G-Ensemble Fitness = Fitness for wind component = 7.3 m/s Fitness for temperature = 4.2 c or k
    50. 50. G-Ensemble Calibration ErrorDepending on the error function used, two G-Ensemble strategies are presented: 1) Single-Variable G-Ensemble Fitness = Fitness for wind component = 7.3 m/s Fitness for temperature = 4.2 c or k 2) Multi-Variable G-EnsembleWe use a Normalized RMSD in order to be able to enhance predictions of a set of meteorologicalvariables together.The normalized root mean squared deviation or error (NRMSD or NRMSE) is theRMSD divided by the range of observed values:
    51. 51. G-Ensemble Calibration ErrorDepending on the error function used, two G-Ensemble strategies are presented: 1) Single-Variable G-Ensemble Fitness = Fitness for wind component = 7.3 m/s Fitness for temperature = 4.2 c or k 2) Multi-Variable G-EnsembleWe use a Normalized RMSD in order to be able to enhance predictions of a set of meteorologicalvariables together.The normalized root mean squared deviation or error (NRMSD or NRMSE) is theRMSD divided by the range of observed values: Fitness for wind component = 9.1% Fitness for temperature = 3.7%
    52. 52. G-Ensemble Calibration ErrorDepending on the error function used, two G-Ensemble strategies are presented: 1) Single-Variable G-Ensemble Fitness = Fitness for wind component = 7.3 m/s Fitness for temperature = 4.2 c or k 2) Multi-Variable G-EnsembleWe use a Normalized RMSD in order to be able to enhance predictions of a set of meteorologicalvariables together.The normalized root mean squared deviation or error (NRMSD or NRMSE) is theRMSD divided by the range of observed values: Fitness for wind component = 9.1% Fitness for temperature = 3.7% Fitness (of all) = NRMSD(wind)+ NRMSD(T)+ NRMSD(RAINC) = value%
    53. 53. Experimentation:Benchmark Test Case:On August 28, 2005, Hurricane Katrina was in the Gulf of Mexico where it powered up to a Category 5 Gulf of Mexico
    54. 54. Experimentation
    55. 55. ExperimentationTemperature 2m: T2 Sea Surface Temperature: TSKLatent Heat Flux: LHF Wind component: velocity in east-west direction V10Wind component: velocity in north-south Accumulated Precipitation:RAINC direction U10
    56. 56. ExperimentationTemperature 2m: T2 Sea Surface Temperature: TSKLatent Heat Flux: LHF Wind component: velocity in east-west direction V10Wind component: velocity in north-south Accumulated Precipitation:RAINC direction U101. Prediction is held 28.8.2005 at 12:00h. to 30.8.2005 at 24:00 (36 hours)2. Initializing Ensemble combinations of different input data variables randomly.3. Applying GA to calibrate input variables of LSM sub-model to generateGenetic Ensembles
    57. 57. Experimentation30m. Single-Variable G-Ensemble (Ensemble vs G-Ensemble): LHF Ens. size:30 Crossover:0.7 Mutation: 0.2 Iteration: 20 Fitness: RMSD
    58. 58. Experimentation Ens. size:3030m. Single-Variable G-Ensemble (Ensemble vs G-Ensemble): RAINC Crossover:0.7 Mutation: 0.2 Iteration: 20 Fitness: RMSD
    59. 59. Experimentation 40m. Multi-Variable G-Ensemble (Ensemble vs G-Ensemble): Ens. size:40 Crossover:0.7 Mutation: 0.2 Iteration: 20 Fitness: NRMSD
    60. 60. Experimentation 40m. G-Ensemble (Multi-Variable G-Ensemble Error vs Single Variable Error):
    61. 61. Experimentation 40m. G-Ensemble (Multi-Variable G-Ensemble Error vs Single Variable Error):
    62. 62. Experimentation 40m. G-Ensemble (Multi-Variable G-Ensemble Error vs Single Variable Error):
    63. 63. Experimentation 40m. G-Ensemble (Multi-Variable G-Ensemble Error vs Single Variable Error):
    64. 64. Experimentation Cost Vs. Error Reduction Ensemble(size, iteration) Ex. TimeEnsemble (40, 0) 1120 m.Best-G.Ensemble (40,5) 369 m.Best-G.Ensemble (40,10) 709 m.Best-G.Ensemble (40,15) 1024 m.Best-G.Ensemble(40,20) 1549 m.Best G.Ensemble(20, 20) 709 m.
    65. 65. Experimentation Cost Vs. Error Reduction Ensemble(size, iteration) Ex. Time Ensemble (40, 0) 1120 m. Best-G.Ensemble (40,5) 369 m. Best-G.Ensemble (40,10) 709 m. Best-G.Ensemble (40,15) 1024 m. Best-G.Ensemble(40,20) 1549 m. Best G.Ensemble(20, 20) 709 m.
    66. 66. Experimentation Cost Vs. Error Reduction Ensemble(size, iteration) Ex. Time Ensemble (40, 0) 1120 m. Best-G.Ensemble (40,5) 369 m. Best-G.Ensemble (40,10) 709 m. Best-G.Ensemble (40,15) 1024 m. Best-G.Ensemble(40,20) 1549 m. Best G.Ensemble(20, 20) 709 m.
    67. 67. Conclusions and Future Work • We proposed a methodology to enhance methodological prediction process, using Genetic Algorithm functions and techniques. • Our method depends on the adjustment of input data set that causes less possible error compared with observations. • We did also experiments on more cases and predictions are enhanced using our method. and we will make more experiments to cover real prediction cases. • In scenarios with a limited number of computing resources, in which EPS could not be used due to its time constraints, G-Ensemble stands to be a good alternative choice. • Thanks to the enhancement in prediction accuracy, more sophisticated schemes might be developed in the near future by injecting observed meteorological variables at run-time.
    68. 68. Genetic Ensemble (G-Ensemble) for Meteorological Prediction Enhancement Hisham Ihshaish, Ana Cortés, and Miquel A. Senar hisham@caos.uab.es, {ana.cortes,miquelangel.senar}@uab.es Thanks! Questions?
    69. 69. Meteorological Models
    70. 70. Meteorological Models • The aim of a meteorological model is to predict the atmospheric state in the future:
    71. 71. Meteorological Models Now 00:00 Mid Day Midnight Tom.Mid Day t=0 h t=12 h t=24 h t=36 h Initial state Predicted state Predicted state Predicted state 1. surface properties: elevation, land use, vegetation index, temperature of sea surface temperature, ... 2. meteorological variables: temperature, humidity, pressure and wind

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