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Operational Forecasting and Sensitivity-Based Data Assimilation Tools

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## Operational Forecasting and Sensitivity-Based Data Assimilation Tools

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**Operational Forecasting and Sensitivity-Based Data**Assimilation Tools Dr. Brian Ancell Texas Tech Atmospheric Sciences**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**Operational Forecasting**• The following characteristics can make an operational forecasting system substantially more valuable: - Probabilistic - High-resolution**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**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**• 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**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**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**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**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**EnKF vs. 3DVAR**Temperature observation 3DVAR EnKF Flow-dependence is key!**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.**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)**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**EnKF 36-km vs. 12-km**Wind Temperature Improvement of 12-km EnKF Analysis 10% 13% Forecast 10% 10%**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!**High-Resolution EnKF Issues**• Issue #2 - Too little background variance exists in model surface fields Good observations are neglected!**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**South Plains Multi-scale WRF EnKF**D3 (2km) D2 (12km) D1 (36km)**South Plains WRF EnKF: High-Resolution Effects**Single, diffuse dryline Double, tight dryline 12-km 2-km**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)**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)**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**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**Impact of Hypothetical Observations**J = 24-hr cyclone central pressure L L Pa^2 1st Observation 2nd Observation**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?**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?**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)