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

3. Operational Forecasting The following characteristics can make an operational forecasting system substantially more valuable: 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: 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 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) 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) 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) 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 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) 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 over other data assimilation systems - Ensemble system -> straightforward forecast probabilities 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) 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 over other data assimilation systems - Ensemble system -> straightforward forecast probabilities probabilities - Sensitivity-based adaptive data assimilation tools to improve forecasts improve forecasts

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

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

12. Operational EnKF: Some Results D1 (36km) D2 (12km) D3 (4km) 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: COMET Project: 1) Evaluate a multi-scale WRF EnKF EnKF 2) Compare operational WRF EnKF surface analyses to current operational NWS analyses to current operational NWS surface analysis techniques (RTMA and surface analysis techniques (RTMA and MOA) MOA)

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

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

16. High-Resolution EnKF Issues Issue #1 - Significant biases exist in the model surface wind and temperature fields Issue #1 - Significant biases exist in the model surface wind and temperature fields Temperature BiasLight 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 Issue #2 - Too little background variance exists in model surface fields Good observations are neglected!

18. EnKF 12-km vs. GFS, NAM, RUC RMS analysis errors GFS2.38 m/s2.28 K NAM RUC EnKF 12-km WindTemperature 2.30 m/s 2.13 m/s 1.85 m/s 2.54 K 2.35 K 1.67 K

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

20. South Plains WRF EnKF: High- Resolution Effects Single, diffuse drylineDouble, tight dryline 12-km2-km

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

23. What is Ensemble Sensitivity? Basic recipe for ensemble sensitivity: Basic recipe for ensemble sensitivity: 1) An ensemble of forecasts (via the EnKF) 2) Response function (J) at some forecast time Ensemble sensitivity is the slope of the linear regression of J onto the initial conditions J ToTo ∂J/∂Xo Slope = ∂J/∂Xo

24. What is Ensemble Sensitivity? Basic recipe for ensemble sensitivity: Basic recipe for ensemble sensitivity: 1) An ensemble of forecasts (via the EnKF) 2) Response function (J) at some forecast time Ensemble sensitivity is the slope of the linear regression of J onto the initial conditions J ToTo ∂J/∂Xo Slope = ∂J/∂Xo Examples of J - Dryline strength, position - Wind power

25. Impact of Hypothetical Observations J = 24-hr cyclone central pressure LL 1st Observation2nd Observation Pa^2

26. EnKF Adaptive Data Assimilation Tools The application of sensitivity-based data assimilation tools can answer these important questions: 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? forecast high-impact weather?

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

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