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Operational Forecasting and Sensitivity-Based Data Assimilation Tools

Operational Forecasting and Sensitivity-Based Data Assimilation Tools

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Operational Forecasting and Sensitivity-Based Data Assimilation Tools

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  1. Operational Forecasting and Sensitivity-Based Data Assimilation Tools Dr. Brian Ancell Texas Tech Atmospheric Sciences

  2. Operational Forecasting • Operational Forecasts can be valuable to a wide range of applications including: - National Weather Service (NWS) day-to-day operations - Transportation - Air quality, forest fire prediction - Wind power

  3. Operational Forecasting • The following characteristics can make an operational forecasting system substantially more valuable: - Probabilistic - High-resolution

  4. Operational Forecasting • The following characteristics can make an operational forecasting system substantially more valuable: - Probabilistic - High-resolution • The development of a high-resolution, probabilistic real-time modeling system is a major component of my research

  5. High-Resolution, Probabilistic Forecasting • High-resolution, probabilistic forecasting can be achieved with a Weather Research and Forecasting (WRF) model ensemble Kalman filter (EnKF)

  6. High-Resolution, Probabilistic Forecasting • High-resolution, probabilistic forecasting can be achieved with a Weather Research and Forecasting (WRF) model ensemble Kalman filter (EnKF) Characteristics of a WRF EnKF -Self-contained data assimilation/forecasting system

  7. High-Resolution, Probabilistic Forecasting • High-resolution, probabilistic forecasting can be achieved with a Weather Research and Forecasting (WRF) model ensemble Kalman filter (EnKF) Characteristics of a WRF EnKF - Self-contained data assimilation/forecasting system -Flow-dependent data assimilation gives an advantage over other data assimilation systems

  8. High-Resolution, Probabilistic Forecasting • High-resolution, probabilistic forecasting can be achieved with a Weather Research and Forecasting (WRF) model ensemble Kalman filter (EnKF) Characteristics of a WRF EnKF - Self-contained data assimilation/forecasting system - Flow-dependent data assimilation gives an advantage over other data assimilation systems -Ensemble system -> straightforward forecast probabilities

  9. High-Resolution, Probabilistic Forecasting • High-resolution, probabilistic forecasting can be achieved with a Weather Research and Forecasting (WRF) model ensemble Kalman filter (EnKF) Characteristics of a WRF EnKF - Self-contained data assimilation/forecasting system - Flow-dependent data assimilation gives an advantage over other data assimilation systems - Ensemble system -> straightforward forecast probabilities -Sensitivity-based adaptive data assimilation tools to improve forecasts

  10. How the EnKF Works • EnKF mean update equation: Xa = Xb + K * (Y – H(Xb)) Xa = The analysis vector Xb = The forecast (background) vector Y = The observation vector H = Interpolates model to observation site K = The Kalman gain matrix K = B*HT * (H*B*HT + R)-1 B = Forecast error covariance matrix

  11. EnKF vs. 3DVAR Temperature observation 3DVAR EnKF Flow-dependence is key!

  12. Operational EnKF: Some Results D3 (4km) D2 (12km) D1 (36km) 48-hr mean forecast of sea-level pressure, 925-mb temperature, and surface winds from the operational University of Washington WRF EnKF.

  13. Operational EnKF: Some Results • COMET Project: 1) Evaluate a multi-scale WRF EnKF 2) Compare operational WRF EnKF surface analyses to current operational NWS surface analysis techniques (RTMA and MOA)

  14. Operational EnKF Configuration • 80 ensemble members • 6-hour update cycle • Assimilated observations: - Cloud-track winds - ACARS aircraft temperature, winds - Radiosonde temperature, winds, RH - Surface temperature, winds, altimeter • Half of the observations used for assimilation, half are used for independent verification

  15. EnKF 36-km vs. 12-km Wind Temperature Improvement of 12-km EnKF Analysis 10% 13% Forecast 10% 10%

  16. High-Resolution EnKF Issues • Issue #1 - Significant biases exist in the model surface wind and temperature fields Temperature Bias Light Wind Speed (<3 knots) Bias Biases moved around domain during assimilation!

  17. High-Resolution EnKF Issues • Issue #2 - Too little background variance exists in model surface fields Good observations are neglected!

  18. EnKF 12-km vs. GFS, NAM, RUC Wind Temperature RMS analysis errors GFS 2.38 m/s 2.28 K NAM 2.30 m/s 2.54 K RUC 2.13 m/s 2.35 K EnKF 12-km 1.85 m/s 1.67 K

  19. South Plains Multi-scale WRF EnKF D3 (2km) D2 (12km) D1 (36km)

  20. South Plains WRF EnKF: High-Resolution Effects Single, diffuse dryline Double, tight dryline 12-km 2-km

  21. Adaptive Data Assimilation Tools with an Operational WRF EnKF • Ensemble sensitivity analysis allows the development of data assimilation tools that: 1) Estimate the relative impacts of each assimilated observation (observation impact)

  22. Adaptive Data Assimilation Tools with an Operational WRF EnKF • Ensemble sensitivity analysis allows the development of data assimilation tools that: 1) Estimate the relative impacts of each assimilated observation (observation impact) 2) Estimate the impact of additional, hypothetical observations (observation targeting)

  23. What is Ensemble Sensitivity? • Basic recipe for ensemble sensitivity: • An ensemble of forecasts (via the EnKF) • Response function (J) at some forecast time Ensemble sensitivity is the slope of the linear regression of J onto the initial conditions Slope = ∂J/∂Xo J To

  24. What is Ensemble Sensitivity? • Basic recipe for ensemble sensitivity: • An ensemble of forecasts (via the EnKF) • Response function (J) at some forecast time Ensemble sensitivity is the slope of the linear regression of J onto the initial conditions Slope = ∂J/∂Xo • Examples of J • Dryline strength, position • Wind power J To

  25. Impact of Hypothetical Observations J = 24-hr cyclone central pressure L L Pa^2 1st Observation 2nd Observation

  26. EnKF Adaptive Data Assimilation Tools • The application of sensitivity-based data assimilation tools can answer these important questions: 1) Where should we take observations to best forecast high-impact weather?

  27. EnKF Adaptive Data Assimilation Tools • The application of sensitivity-based data assimilation tools can answer these important questions: 1) Where should we take observations to best forecast high-impact weather? 2) Are the most effective observations adaptive or routine?

  28. EnKF Adaptive Data Assimilation Tools • The application of sensitivity-based data assimilation tools can answer these important questions: 1) Where should we take observations to best forecast high-impact weather? 2) Are the most effective observations adaptive or routine? Current Work - Severe convection, winter weather, flooding (NOAA CSTAR, in review) - Short-term wind forecasting (DOE)