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Analog Forecast Models for Space Weather Predictions

This presentation discusses the use of analog forecast models for space weather predictions, including the development of an analog forecast tool using AMIE archives and its modern applications. Conclusions and the importance of analog forecasting techniques are also discussed.

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Analog Forecast Models for Space Weather Predictions

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  1. Analog Forecast Models for Space Weather Predictions April 25th, 2019 Eric A. Kihn NOAA/NCEI

  2. Outline of the Presentation • Some Space Weather Background • The SWA • Assumptions for our project • An Analog Forecast Tool Using AMIE Archives • Modern Applications • Conclusions

  3. STP Modeling Domains Image provided by: Tamas I. Gombosi The SWR created a uniformly distributed, integrated, record of the near-earth (magnetopause tail-ward) STP environment. Part of the effort wasto chose a best of breed model including data assimilation representing each region of the space.

  4. The Space Weather Analysis A project that: • Generated a complete 15 year space weather representation using physically consistent data-driven space weather models. • Created an enhanced look at the space environment on consistent grids, time resolution, coordinate systems and containing key fields • Allow modelers to easily incorporate the impact of the near-Earth space climate in environmentally sensitive models. • Generated a first space weather climatology. July 15th, 2000

  5. SWA Methodology Observed Data Representative Environment The SWA effort integrated and evaluated existing domain specific models to create the best representation of the environment possible. The above flow diagram represents data flow for those models currently incorporated.

  6. SWR Data Products :Satellite Drag 1 hr resolution for the May 1998 storm

  7. Bz + By + Spatial Results (95-97 Avg.) Full 11-Year Average

  8. Data Quality Control Sample station data used in the SWR effort. A long term re-analysis requires careful quality control of a huge volume of data. A single instance of bad data can have ripple effects throughout the entire model run. In particular spikes, baseline shifts, and dropouts are all prominent in the data stream. In a typical small scale study it would be possible for a researcher to hand screen the data, but here the volume requires the development of “intelligent” computer techniques, based on fuzzy-logic, neural computing and other mathematical functions.

  9. A History of Terrestrial Weather Forecasting A brief weather forecast time Summary of model types • 1882 – First synoptic charts drawn for the North Atlantic • 1922 – First experiments in numerical weather forecast (Lewis Richardson) • 1939 – Radiosondes introduced • 1946 – IMO ground station network established • 1962 – First electronic computer used for numerical forecast • 1969 – “Atmospheric predictability as revealed by naturally occurring analogs”, (Lorenz) • 1975 – First GOES satellite launched • 1978 – MM model developed (Penn State) • 1989 – “An operational multi-field analog / anti-analog prediction system for United States seasonal temperatures” – (Barnston, and Livezey) • 1991 – First model used jointly for climate and short-term forecast • 2005 – Ensemble forecast models used operationally (regional, 3-15 day forecast) • Persistence • Trends method • Climatology • Analog method • Numerical Weather Prediction Time Increasing Data

  10. Where we are in STP • Empirical Forecast Models are available: (Ahn [83,98], Fuller-Rowell and Evans [87], Heppner-Maynard, Weimer [96,01,05], IZMEM ) • Data assimilation techniques have been developed: (KRM, AMIE, GAIM) • Numerical Prediction Methods are being developed: (BATS-R-US , Lyon-Fedder-Mobarry, Ogino, etc..) • Analog forecasting techniques are missing! Why should we use analog forecast techniques? • To follow the evolution of terrestrial weather forecasting • We will continue to face a data sparse environment for the foreseeable future • Analog techniques maximize use of our limited input information. • Current MHD codes are to computationally expensive to be used operationally • Effectively one-dimensional input (SW) • A highly forced system - Complete reconfiguration on the scale of our forecast lead (30 min) • We have available a very long-term collection of potential analogs • The technique standardizes what we do now

  11. Analog Forecast Background A graphical illustration of an analog forecast

  12. Analog Forecast Mathematics Fundamental Assumption: If the distance between input vectors in some metric space will also be small. Then:

  13. Analog Forecast Mathematics Cont. Finally the model is tunable via weighting factors: Here the constants Wj are used to weight the different time delays, and Gi are used to weight input parameters. What about stability, chaos? In its general form, the K-nearest neighbors analog prediction involves averaging the outputs from the K-nearest neighbors in the input parameters space of historical observations, possibly with weights proportional to the distance from each neighbor to the input vector which output is to be predicted. The size of the “packet” for the nearest neighbors provides a measure of stability/predictability. An example Lorenz attractor

  14. Analog Forecast Metrics Since we know the “real” value of potential in this case taken as the full data AMIE runs We can develop appropriate metrics to measure performance. For example, if we are interested in how well the model predicts gross scale values (i.e., cross polar cap potential) from the potential pattern we can use: For the location and size of the negative and positive cells, we use

  15. Model Specifics • 15 year database for analogs • 6 time delays • 5 minute time steps • 4 solar wind parameters (V, Bz, By, Kp Est) • Euclidian distance for input parameters • Single analog • Deep Feedforward Network

  16. Index Results • Plot of the analog forecast model vs. AMIE full data run for 5 days • Note no particular bias - agreement generally within 10-15 %

  17. The very large event on May 4th has an inadequate analogs • The bottom plot shows all the various forecast types for the period • Note the performance of Weimer and the analog forecast are similar • Weimer does capture the large event but inadequately

  18. Spatial Results An example of empirical under extreme conditions. An example of Weimer and Analog performing similarly.

  19. Spatial Summary Cont. Summary for May 1998

  20. Conclusions • The solar wind drivers couple most strongly to the HLI on the order of 10 – 30 minutes. • Analog forecast techniques show promise for ionospheric forecasting. • Potential to include seasonal and solar cycle elements Analog Future Enhancements • K-nearest neighbor predictions and packeting • Ground station data • Time and parameter tuning (recursive neural network)

  21. GOES-16 SUVI - Solar Ultraviolet Imager and Thematic Map Algorithm SUVI 195 Å Band Ideal for warm coronal plasma for global structure, active regions, coronal holes SUVI Thematic Map Automatic Identification/Tracking 06 September 2017 10 September 2017 06 September 2017 X9.3 from AR12673 (Refs: Seaton and Darnel 2018, Redmon et al. 2018) Credit: Hughes et al. (2019). GOES-16 data shown in this presentation were created in a preliminary, non-operational environment.

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