the university of washington pacific northwest mesoscale analysis system brian ancell, cliff mass,...

The University of The University of Washington Pacific Washington Pacific

Northwest Northwest Mesoscale Analysis Mesoscale Analysis

SystemSystem

Brian Ancell, Cliff Mass, Gregory Brian Ancell, Cliff Mass, Gregory J. HakimJ. Hakim

University of WashingtonUniversity of Washington

MotivationMotivation

High-resolution analyses are High-resolution analyses are important for:important for:

• Operational forecasting (fire weather, Operational forecasting (fire weather, air quality..)air quality..)




• Studying the mesoscale effects of Studying the mesoscale effects of climate changeclimate change




• Studying the mesoscale effects of Studying the mesoscale effects of climate changeclimate change

• Alternative energy developmentAlternative energy development


High-resolution analyses are important for:High-resolution analyses are important for:• Operational forecasting (fire weather, air Operational forecasting (fire weather, air

quality..)quality..)• Studying the mesoscale effects of climate Studying the mesoscale effects of climate

changechange• Alternative energy developmentAlternative energy development

Pacific Northwest complex terrain presents a Pacific Northwest complex terrain presents a challenge to creating good analyseschallenge to creating good analyses

• Flow-dependence during data assimilation may Flow-dependence during data assimilation may be vitalbe vital

An Attractive Option: An Attractive Option: EnKFEnKF

An ensemble Kalman filter (EnKF) An ensemble Kalman filter (EnKF) has strong potential for mesoscale has strong potential for mesoscale analysis:analysis:

• Observational information is spread Observational information is spread spatially using flow-dependent spatially using flow-dependent statisticsstatistics

An Attractive Option: An Attractive Option: EnKFEnKFTemperature observation

3DVAR EnKF


An ensemble Kalman filter (EnKF) An ensemble Kalman filter (EnKF) has strong potential for mesoscale has strong potential for mesoscale analysis:analysis:

• Observational information is spread Observational information is spread spatially using flow-dependent spatially using flow-dependent statisticsstatistics

• Analysis and forecast uncertainty is Analysis and forecast uncertainty is easily calculated and is also flow-easily calculated and is also flow-dependentdependent


An ensemble Kalman filter (EnKF) has An ensemble Kalman filter (EnKF) has strong potential for mesoscale analysis:strong potential for mesoscale analysis:

• Observational information is spread Observational information is spread spatially using flow-dependent statisticsspatially using flow-dependent statistics

• Analysis and forecast uncertainty is easily Analysis and forecast uncertainty is easily calculated and is also flow-dependentcalculated and is also flow-dependent

• Computational resources can handle Computational resources can handle EnKF demandEnKF demand

How the EnKF WorksHow the EnKF Works

An analysis is created from:An analysis is created from:

1) An ensemble of short-term forecasts 1) An ensemble of short-term forecasts (Background)(Background)

2) Observations2) Observations

For a single observation:For a single observation:Observation (T1)

Mean Forecast (T2)

Analysis (T3,V3)

Forecast Variance (V2)

Observation Variance (V1)

How the EnKF WorksHow the EnKF Works

An analysis is created from:An analysis is created from:

1) An ensemble of short-term forecasts 1) An ensemble of short-term forecasts (Background)(Background)

2) Observations2) Observations

For a single observation:For a single observation:Observation (T1)

Mean Forecast (T2)

Analysis (T3,V3)

Forecast Variance (V2)

Observation Variance (V1)

Analysis increment then spread spatially using covariance statistics of ensemble

EnKF ConfigurationEnKF Configuration Large, coarse domain EnKF already Large, coarse domain EnKF already

tested (Torn and Hakim 2008)tested (Torn and Hakim 2008)

- EnKF competitive with global - EnKF competitive with global modelsmodels

EnKF ConfigurationEnKF Configuration

D1 (36km)

D2 (12km)

D3 (4km)

EnKF ConfigurationEnKF Configuration WRF model V2.1.2WRF model V2.1.2 38 vertical levels38 vertical levels 80 ensemble members80 ensemble members 6-hour update cycle6-hour update cycle Observations:Observations:

• Surface temperature, wind, altimeterSurface temperature, wind, altimeter• ACARS aircraft winds, temperatureACARS aircraft winds, temperature• Cloud-track windsCloud-track winds• Radiosonde wind, temperature, Radiosonde wind, temperature,

relative humidityrelative humidityHalf of surface obs used for assimilation, other half for verification

36-km vs. 12-km EnKF36-km vs. 12-km EnKF

SLP, 925-mb temperature, surface winds

36-km

12-km

36-km vs. 12-km EnKF36-km vs. 12-km EnKF

36-km

12-km


EnKF 36-km vs. 12-kmEnKF 36-km vs. 12-km

Improvement of 12-km EnKFAnalysi

sForecast

10%

10% 10%

13%

Wind Temperature

Issue #1 – Issue #1 – Representative ErrorRepresentative Error

Model terrain = Actual Model terrain = Actual terrain terrain

at and near observation sitesat and near observation sites

Actual terrain

Model terrain

Surface ObservationsSurface Observations

Model grid points (12-km resolution)





Observation location




High-resolution terrain data (1.33 km resolution)

Observation location


Using representative observations Using representative observations only, we can reduce observation only, we can reduce observation uncertainty:uncertainty:

Observation Standard DeviationsObservation Standard Deviations

Temp: 1.8 K (36-km) 1.0 K Temp: 1.8 K (36-km) 1.0 K (12-km)(12-km)

Wind: 2.5 m/s (36-km) 1.5 m/s Wind: 2.5 m/s (36-km) 1.5 m/s (12-km)(12-km)


Using representative observations only, we Using representative observations only, we can reduce observation uncertainty:can reduce observation uncertainty:

Observation Standard DeviationsObservation Standard Deviations

Temp: 1.8 K (36-km) 1.0 K (12-km)Temp: 1.8 K (36-km) 1.0 K (12-km)

Wind: 2.5 m/s (36-km) 1.5 m/s (12-Wind: 2.5 m/s (36-km) 1.5 m/s (12-km)km)

Drawback: Lose ~75% of available surface Drawback: Lose ~75% of available surface obsobs


Improvement using reduced observation uncertaintyAnalysi

s5% 10%

Wind Temperature

Issue #2 – Lack of Issue #2 – Lack of Background Surface Background Surface

VarianceVariance Too little background variance exists Too little background variance exists

in model surface fieldsin model surface fields

Issue #2 – Lack of Issue #2 – Lack of Background Surface Background Surface

VarianceVariance Too little background variance exists Too little background variance exists

in model surface fieldsin model surface fields

Solution: Inflate surface variance with variance aloft

Issue #3 – Model Surface Issue #3 – Model Surface BiasBias

Significant biases exist in the model Significant biases exist in the model surface wind and temperature fieldssurface wind and temperature fields

Temperature Bias

Light Wind Speed (<3 knots) Bias

Further Improvement After Further Improvement After Variance Inflation, Bias Variance Inflation, Bias

RemovalRemoval

Improvement using inflation and bias removalAnalysi

s9% 3%

Wind Temperature

EnKF 12-km vs. GFS, NAM, EnKF 12-km vs. GFS, NAM, RUCRUC

RMS analysis errors GF

S2.38 m/s

2.28 KNAMRUCEnKF 12-km

Wind Temperature

2.30 m/s2.13 m/s1.85 m/s

2.54 K2.35 K1.67 K

12-km vs 4-km EnKF12-km vs 4-km EnKF


4-km12-km

SummarySummary A multi-scale, nested WRF EnKF (36km, 12km, 4km) is being tested over the A multi-scale, nested WRF EnKF (36km, 12km, 4km) is being tested over the

Pacific Northwest to produce quality analyses and short-term forecastsPacific Northwest to produce quality analyses and short-term forecasts

Three obstacles to accurate surface analyses were discovered and dealt with Three obstacles to accurate surface analyses were discovered and dealt with using the 12-km EnKF:using the 12-km EnKF:

• Poor model terrain height profile (representative check)Poor model terrain height profile (representative check)• Lack of model surface forecast variance (variance inflation from Lack of model surface forecast variance (variance inflation from

aloft)aloft)• Model surface wind and temperature bias (pre-assimilation bias Model surface wind and temperature bias (pre-assimilation bias

removal)removal)

Resulting WRF 12-km EnKF surface analyses were better than the WRF 36-km Resulting WRF 12-km EnKF surface analyses were better than the WRF 36-km EnKF, GFS, NAM, and RUCEnKF, GFS, NAM, and RUC

Future direction:Future direction:• Better bias removal techniquesBetter bias removal techniques• Tuning of data assimilation parametersTuning of data assimilation parameters• Testing of 4-km nested domainTesting of 4-km nested domain• Evaluation of analysis fields aloftEvaluation of analysis fields aloft• Short-range forecast verificationShort-range forecast verification• Comparison with current NWS mesoscale analysis techniques Comparison with current NWS mesoscale analysis techniques

(RTMA, MOA)(RTMA, MOA)

the university of washington pacific northwest mesoscale analysis system brian ancell, cliff mass,...

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