on methods of precipitation efficiency estimation brian pettegrew dept. of seas seminar – natr 410...

On Methods of Precipitation Efficiency Estimation

Brian Pettegrew

Dept. of SEAS

Seminar – NATR 410

April 5, 2004

Outline

• Introduction• Why use PE?

• Motivation

• Method/Data

• Synoptic Case Study

• Results and Conclusions

Introduction

• What is Precipitation Efficiency?

• mp is moisture released via precipitation in the form of rain

• mi is ingested moisture via a warm, moist updraft

i

p

m

mPE

Introduction

• Why are we concerned about PE?• Flash flood forecasting• More deaths cause annually from flashfloods

than any other weather phenomena

• Which pre-storm environment is more threatening?• PW of 2.0 and PE of 20%• PW of 2.0 and PE of 80%

Introduction

• Instantaneous PE can very greatly

t=0hE~0

t=1hE~max

t=2hE

mimpInstantaneou

s PE

Introduction• Lag in water cycling

• Doswell (1996)…

Introduction

• Although instantaneous PE can vary, PE over the lifetime of an MCS is constant.

- Lifetime PE a posteriori cannot vary and must stay between 0 and 1

Methods

• How is PE calculated?• Current Operational Method

• RH is a mean value taken between 1000-700 mb

• Value derived in inches

RHPWPE

Methods

• Scofield (1987)• NESDIS – National Environmental Satellite,

Data, and Information Services• Geostationary Operational Environmental

Satellite (GOES)• Operational convective precipitation estimation

technique• Developed for estimating convective precipitation

every half hour

Methods

• Moisture Correction Scheme• Take into account for long-living storms with

strong, steady-state updrafts and outflows, very saturated column, and no dry air entrainment

• Estimates can be amplified

• Scheme adjusts estimates for air that is too moist or too dry

Methods

• Operational use• Hydrometeorological applications

• National Weather Service (NWS) and River Forecast Centers (RFC’s)

• Forecasting QPF

Methods

• Noel and Dobur (2003)• PW/RH method useful in identifying axis of

precipitation, but not stand alone indicator.

• Note: This relationship indicates a potential for the environment to produce precipitation, not an actual efficiency

Methods

• Sellers (1965)

• Climatological mean

• Has seasonal and latitudinal variations

WaterlePrecipitab Average

Depthion Precipitat AveragePE

Methods

• Market et al. (2003)• Used Seller’s to predict PE using GOES

sounder derived values in a pre-convective environment

• Known for its spatial and temporal density

• Initial PE values calculated using gauge network rainfall totals and Rapid Update Cycle (RUC) derived PW

Methods

• Correlated bulk environmental parameters from GOES soundings to calculated PE values

• Best correlations were made to CIN, RH, LCL height, and cloud shear

Methods

• Modified version

• PW difference over a period • Accounts for moisture fluxing out of the storm over

that same time• First experiment using this method

totalRainfall

PWPWPE

earlierlater

Methods

• Moisture Budget• Chester and Newton (1969)

0

0

0

0

11 pp

qVdpg

dpt

q

gEP

Methods

• P is total precipitation

• E is evaporation at the SFC

• g is acceleration due to gravity

• dq/dt is change in specific humidity

• Represents advection of moisture

qV

Methods

• Computes horizontal moisture flux and divergence

• True efficiency• Accounts for all moisture in and out of a system

Data

• Rapid Update Cycle (RUC)• RUC-2

• 40-km gridspacing

• Smoothed to 80-km

• Released in 1998 as update to RUC-1

Data

• Data Assimilation• Initialized from ETA derived model fields and

previous hour’s RUC forecast

• 12-hr output every hour

Data

• Horizontal Resolution• “slope envelope” topography

• Terrain calculated with respect to a plane fit to the high-resolution topography in each grid space (Benjamin et al. 2002)

Data• Horizontal Resolution

40-km RUC Terrain *Benjamin et al. 2002

Data

• Vertical Resolution• All RUC models use hybrid isentropic-sigma

coordinates in forecast and analysis (Bleck and Benjamin 1993)

• RUC-2 fit with 40 vertical levels

Data• Vertical Resolution

40km RUC 40 levels

S-N vertical cross-section - Miss – Wisc – Lake Superior - w. Ontario 12h fcsts valid 1200 UTC 2 Apr 2002

*Benjamin et al. 2002

Data

• Vertical Resolution• Higher resolution leads to better handling of:

• Vertical advection processes

• Improved conservation of potential vorticity

• Improved air-mass integrity and frontal structure with observation influence (Benjamin et al. 2002)

Data

• Convective Parameterization• Model generated convection!!!

• Grell Scheme• Three phases

• 1) Dynamic Control

• 2) Feedback

• 3) Static Control

Data

• Dynamic Control• Determines modulation of convection by the

environment

• Based on environmental stability

• Observed change of available buoyant energy is known

Data

• Dynamic Control (cont’d)• Purely Predictive

• Amount and size of convective elements

• Convective activity related to total moisture convergence

• Function of time and space, not cloud type (Grell 1993)

Data

• Feedback• Modification of the environment by the

convection

• Distributes total integrated heating and drying in the vertical

• Adjusts the atmosphere to a moist neutral state

• Dependent on temperature and moisture differences between cloud and environment

Data

• Static Control• Determines updraft or downdraft properties

• Feedback dependent on static control• Entrainment, detrainment, downdraft properties, and

microphysics of cloud model

• (Grell 1993)

Data

• RUC data used• Initial and forecast fields from RUC output

obtained every six-hours• Initial times 00, 06, 12, 18

– Forecast hours 03, 06, and 09 used

• All times in Zulu

Data

• Precipitable Water• PW of a column calculated via trapezoidal

integration from model derived variables

• Precipitation• 3-hr precipitation accumulation parameters

from RUC output were used• (all calculations performed via RUC data for

internal consistency)

Data

• GEMPAK (GEneral Meteorological PAcKage)• Scripts constructed to calculate PE

• Mapped out in grid format over desired region

Synoptic Analysis

May 6, 2003

• Huntsville, AL

• Several F1 and F0 Tornado Damage

• Flooding of Tennessee River• Huntsville metropolitan area severe flashfloods

Rainfall totals

•

SFC 00Z

850 mb 00Z

700 mb 00Z

500 mb 00Z

300 mb 00Z

SFC 12Z

850 mb 12Z

700 mb 12Z

500 mb 12Z

300 mb 12Z

Results

• Area MCS average• Precipitation

• Ingested Moisture

• PE• Scofield

• Seller’s

• Modified

• Moisture budget

Results

• A ratio of ingested moisture and total accumulated precipitation taken to have a total PE for the area to correlate too

Correlations

• Point correlation between four methods

Correlations

0900Z

Sellers Scofield Modified

Scofield 0.2257

Modified 0.2196 0.2679

Moisture

Budget

0.7108 0.0851 -0.2434

Correlations

1200 Z

Sellers Scofield Modified

Scofield 0.2568

Modified 0.4807 -0.1629

Moisture Budget

-0.1298 -0.1436 -0.0897

Correlations

1500 Z

Sellers Scofield Modified

Scofield 0.1695

Modified -0.1007 0.3625

Moisture Budget

-0.1063 -0.5535 -0.2439

Correlations

1800 Z

Sellers Scofield Modified

Scofield -0.1699

Modified -0.1689 0.5268

Moisture Budget

0.1534 -0.1141 -0.0159

Correlations

2100 Z

Sellers Scofield Modified

Scofield 0.2520

Modified 0.4504 0.0718

Moisture Budget

0.3986 -0.1485 0.1355

Correlations

• Values correlated over lifetime of storm

Correlations

Sellers Scofield Modified

Scofield -0.3825

Modified -0.3771 0.1102

Best PE 0.2535 -0.8685 0.3817

Conclusions

• Area average of efficiency calculations correlated well for a short-lived MCS

• Operational method showed strong negative correlation.

Future Work

• San Antonio, TX• Long-lived rain event

• Up to 33” of rain fell over a seven-day span

• Similar correlations in progress

Acknowledgements

• COMET/UCAR

• National Weather Service

• Dr. Patrick Market - UMC

• Dr. Neil Fox – UMC

• Chris Schultz – UMC

• Dave Jankowski – UMC

Works Cited

• Benjamin, S.G., J.M. Brown, K.J. Brundage, D. Devenyi, G.A. Grell, D. Kim, B.E. Schwartz, T.G. Smirnova, T.L. Smith, S. Weygandt, and G.S. Manikin, 2002: RUC20 – The 20-km version of the Rapid Update Cycle. NWS Technical Procedures Bulletin No. 490.

• Bleck, R., and S.G. Benjamin, 1993: Regional weather prediction with a model combining terrain-following and isentropic coordinates. Part I: model description. Mon. Wea. Rev.,121, 1770-1785.

• Doswell, C.A., III, H.E. Brooks, and R.A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560-581.

• Grell, G., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764-787.

• Market, P.S., S. Allen, R.A. Scofield, R. Kuligowski, A. Gruber, 2003: Precipitation Efficiency of warm-season midwestern mesoscale convective systems. Wea. Forecasting, 18, 1273-1285.

Works Cited

• Newton, C.W. and E. Palmen, 1969: Atmospheric Circulation Systems: Their Structure and Physical Interpretation. Academic Press, 603 pp.

• Noel, J. and J.C. Dobur, 2003: A pilot study examining model derived precipitation efficiency for use in precipitation forecasting in the eastern united states. Nat. Wea. Dig., 26, 3-8.

• Scofield, R.A., 1987: The NESDIS Operational Convective Precipitation Estimation Technique. Mon. Wea. Rev., 115, 1773-1792.

• Sellers, W.D., 1965: Physical Climatology. The University of Chicago Press, 272 pp.

• Thank you for your time!

• Questions?

• Comments?

• Email: bpp4y8@mizzou.edu