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
NAM
RUC
EnKF 12-km
2.38 m/s
2.30 m/s
2.13 m/s
1.85 m/s
2.28 K
2.54 K
2.35 K
1.67 K
South Plains Multi-scale WRF EnKF
D3 (2km)
D2 (12km)
D1 (36km)
South Plains WRF EnKF: HighResolution Effects
Single, diffuse dryline
12-km
Double, tight
dryline
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)
2) Response function (J) at some forecast time
1)
Ensemble sensitivity is the slope of the linear regression of
J
onto the initial conditionsSlope = ∂J/∂Xo
J
To
What is Ensemble Sensitivity?

Basic recipe for ensemble sensitivity:
An ensemble of forecasts (via the EnKF)
2) Response function (J) at some forecast time
1)
Ensemble sensitivity is the slope of the linear regression of
J
onto the initial conditionsSlope = ∂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)