ASSIMILATING DENSE PRESSURE OBSERVATIONS—A PREVIEW OF HOW THIS MAY IMPACT ANALYSIS AND NOWCASTING

Luke Madaus -- Wed., Sept. 21, 2011

Past problems

Weather models still poorly predict the timing and intensity of significant weather events

Images from Phil Regulski

For short-range forecasts, important to capture variability at small scales using very high resolution Eckel and Mass (2005)

Data assimilation can try to introduce small-scale features – if variables assimilated are chosen judiciously

Why pressure?

Less sensitive to representativeness error

Widely available observations Has far-reaching meso- and synoptic-

scale relevance Also can provide information in the

vertical (Dirren et al 2007)

Fundamental question

To investigate: Use a large ensemble capable of resolving

mesoscale features Need observations at a density sufficient to

represent the same scales of variability we are trying to model

To what extent can pressure observations be used to describe phenomena on the mesoscale?

High-resolution

Current Setup

• 4 km grid spacing• 80 member ensemble• Quasi-explicit resolution of:• Some convective

processes• Small-scale boundaries• Some localized orographic effects

• Need observed data to match!

Weisman et al. 2008

Data sources

ASOS -- 103

All potential obs -- 1850

Data sources

TOTAL – 1000-1600 observations hourly across Pac. NW

ASOS – Canada and US (100) Weather Underground (650) AWS Schoolnet (80) CWOP (250) RAWS (5) Oregon RWIS (10) Pendleton WFO Network (15) Land/Sea Synop (30) Other (50)

Oct. 24, 2011 Convergence Zone

An “unforecast” convergence zone forms around 14Z (6AM PDT) and moves south across north Seattle during the morning commute

Oct. 24, 2011 Convergence Zone Started 4km domain at 6Z and assimilated

data through 15Z Control – No assimilation Real-Time EnKF all observation types Pressure-only assimilation every 3 hours

without bias removal Pressure-only assimilation every 3 hours with

bias removal Real-Time EnKF + additional pressure

observations every 3 hours Pressure-only assimilation every 1 hour with

bias removal

Oct. 24, 2011 Convergence Zone

Control Run – No assimilationCurrent EnKF System—3hr cycle

Oct. 24, 2011 Convergence Zone

Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle

Oct. 24, 2011 Convergence Zone

Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle

Oct. 24, 2011 Convergence Zone

Just Pressure Assim.—1hr cycleCurrent EnKF System—3hr cycle

Conclusion

Pressure observations alone seem to be able to capture much of small-scale variability

Pressure observation adjustments affect analysis of dynamic fields (pressure,winds) in a positive way Better precipitation development forecasts

as a result Hourly assimilation looks like it could do

wonderful things…

Future work

Running long-term case (April 10-30, 2011) for more robust statistics Focus on reducing errors in pressure and

wind analyses Subsequently improved wind and

precipitation forecasts. Looking to get more from pressure

observations through assimilating pressure tendency

Acknowledgements

Advisors – Cliff Mass and Greg Hakim Phil Regulski Rahul Mahajan Mark Albright Jeff Anderson and Nancy Collins at NCAR Northwest Modeling Consortium

References

Anderson, J., B. Wyman, S. Zhang, T. Hoar, 2005: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. J. Atmos. Sci., 62, 2925-2938.

Dirren, S., R. Torn, G. Hakim, 2007: A data assimilation case study using a limited-area ensemble filter. Mon. Wea. Rev., 135, 1455-1473.

Eckel, F. A., C. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Weather and Forecasting, 20, 328-350.

Mass, C. and G. Ferber, 1990: Surface pressure perturbations produced by an isolated mesoscale topographic barrier, part 1: general characteristics and dynamics. Mon. Wea. Rev., 118, 2579-2596.

McMurdie, L., C. Mass, 2004: Major numerical forecast failures over the northeast Pacific. Weather and Forecasting, 19, 338-356.

Miller, P. and M. Barth, 2002: RSAS Technical Procedures Bulletin. MSAS/RSAS. Web. Accessed: Sept. 12, 2011.

Weisman, M., C. Davis, W. Wang, K. Manning, J. Klemp, 2008: Experiences with 0-36-h explicit convective forecasts with the WRF-ARW model. Weather and Forecasting, 23, 407-437

Wheatley, D. and D. Stensrud, 2010: The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system. Mon. Wea. Rev., 138, 1673-1694.

Whitaker, J., G. Compo, X. Wei, T. Hamill, 2001: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 1190-1200.

Oct. 24, 2011 Convergence Zone

Pressure tendency

Covariances not as strong—less impact than raw pressure (Wheatley and Stensrud 2009)

Pressure tendency requires continuity of observation

Not currently supported in the DART EnKF assimilation framework

Different Parameterization

WSM-3 microphysics WSM-5 microphysics

How bad is bias?

Before Bias Correction535/1100 error >1.5mb

After Bias Correction50/1100 error > 1.5mb

Pressure tendency

What about pressure tendency as a way to avoid bias?

biasobs ptptp )()(

])([])([)()( biasbiasobsobs pttpptpttptp

)()( ttptpptend

truthtend

obstend pttptptp )()()(

Bias not present in this representation of pressure obs

EnKF assimilation

Pressure (hPa)1010

hPa

1009 hPa

EnsembleObservation

New Estimate