Validation and Sensitivities of Validation and Sensitivities of Dynamic Precipitation Dynamic Precipitation

Simulation for Winter Events Simulation for Winter Events over the Folsom Lake over the Folsom Lake Watershed: 1964–99Watershed: 1964–99

Jianzhong Wang and KonstantiJianzhong Wang and Konstantine P. Georgakakone P. Georgakakoss

Monthly Weather Review: Vol. 133, No. 1, pp. 3–19.

1964–99 (62 winter enents)1964–99 (62 winter enents) CNRFC 1964 - 1999CNRFC 1964 - 1999 SCPP 1980 – 1986SCPP 1980 – 1986 MM5 achieves a good percentage MM5 achieves a good percentage bias score of 103%bias score of 103% spatial grid resolution spatial grid resolution higher than 9 km is necessaryhigher than 9 km is necessary the model performs better for the model performs better for heavyheavy than for than for light and moderatelight and moderate precipitati precipitati

onon

IntroductionIntroduction

the measurement and prediction of its the measurement and prediction of its spatiotemporal distributionspatiotemporal distribution are necessary prerequisites are necessary prerequisites

the the scarcity scarcity of the precipitation sensors of the precipitation sensors it it difficult to delineatedifficult to delineate finer-scale characteristics of precipitation d finer-scale characteristics of precipitation d

istribution istribution National Research Council 1988 National Research Council 1988 , , 1991 1991 , , 1995 1995 ; ; SharrattSharratt et al. 2001 et al. 2001 ; ;

GeorgakakosGeorgakakos 2003 2003 ; Smith 1979 ; Pandey et al. 2000 ; Hevesi et al. 1992 ; Tsin; Smith 1979 ; Pandey et al. 2000 ; Hevesi et al. 1992 ; Tsin

tikidis et al. 2002tikidis et al. 2002 Use Use CNRFCCNRFC SCPPSCPP data validate the precipitation simulations of MM5 data validate the precipitation simulations of MM5 the statistically significant sensitivities of the numerical simulation to the statistically significant sensitivities of the numerical simulation to mm

odel initial and boundary forcingodel initial and boundary forcing, and , and microphysicalmicrophysical parameterization parameterization

studies reportedstudies reported

Colle and Mass (2000) Colle and Mass (2000) Colle et al. (2000) Colle et al. (2000) Vellore et al. (2002) Vellore et al. (2002)

goalgoal

to study how well to study how well dynamical precipitation simulationdynamical precipitation simulation reproduces observ reproduces observed features of precipitation for the Folsom basin on a subbasin scale foed features of precipitation for the Folsom basin on a subbasin scale fo

r r lightlight, , moderatemoderate, and , and heavyheavy precipitation precipitation to study to study the factors that affect simulation accuracythe factors that affect simulation accuracy for precipitation on for precipitation on

hydrologic basin scaleshydrologic basin scales shortcomings of implicit and explicit shortcomings of implicit and explicit cloud and precipitation parametericloud and precipitation parameteri

zation schemeszation schemes are also responsible for low simulation skill are also responsible for low simulation skill

Precipitation events and dataPrecipitation events and data

1958 NCEP reanalysis 1958 NCEP reanalysis II 1999 1999 1964 NCEP reanalysis 1964 NCEP reanalysis IIII 1999 1999 I I 1979 1979 IIII 1999* 1999* 1995 19991995 1999 Eta 40km 3-hEta 40km 3-h 1986 SCPP 19861986 SCPP 1986

1968 CNRFC 19991968 CNRFC 1999

the spatial averaging the spatial averaging

×××

×

× ××

× ×× ×

×

×

R

Why grid boxWhy grid box

the model-simulated precipitation itself is a simulation of the model-simulated precipitation itself is a simulation of mean areal prmean areal precipitationecipitation over the grid area and over the grid area and not a single point valuenot a single point value

the MAP, estimated from either the MAP, estimated from either model grid pointsmodel grid points or or single-point obsersingle-point obser

vationsvations, has , has less varianceless variance and allows better comparisons and allows better comparisons single-point precipitation measurement is quite often single-point precipitation measurement is quite often not representativenot representative

of of the volume of precipitation fallingthe volume of precipitation falling over a given catchment area over a given catchment area

CNRFC operational stations

During SCPP period (denser pbserving network)

0

5

10

15

20

25

30

35

40

45

-135 -130 -125 -120 -115 -110 -105 -100 -95 -90

34

35

36

37

38

39

40

41

42

-127 -126 -125 -124 -123 -122 -121 -120 -119 -118 -117

One way nesting

Resolution is 81,27,9 km;91x91x23;Goddard graupel ,KF

47 cases overestimate

N

nn

N

nnp

XPB

1

1

E

E

N

N

EN

nVar

nVar

PPt

N

XP

R Nnnn

,1

2)(

Concluding remarksConcluding remarks

The model simulates The model simulates best heavy-precipitationbest heavy-precipitation events and overestimate events and overestimate

s MAP consistently for s MAP consistently for light- and moderate-precipitationlight- and moderate-precipitation events. events. spatial model resolution spatial model resolution down to 3 kmdown to 3 km and a and a denser observationdenser observation netwo netwo

rk may be necessaryrk may be necessary the precipitation the precipitation overestimationoverestimation by the model in the upslope of the Sier by the model in the upslope of the Sier

ra Nevada is ra Nevada is reduced reduced significantly from 26% to 3%(CNRFCsignificantly from 26% to 3%(CNRFCSCPSCP

P)----P)----more accuratemore accurate estimation of the true MAP estimation of the true MAP MM5 to MM5 to underestimateunderestimate (overestimate) the actual MAP for most of the (overestimate) the actual MAP for most of the hh

eavyeavy (light to moderate) (light to moderate) winterwinter precipitation events precipitation events

the the 40-km Eta40-km Eta analysis data has a statistically analysis data has a statistically significant advantagesignificant advantage ov ov

er the er the 2.5° NCEP reanalysis2.5° NCEP reanalysis data at the data at the 80%80% confidence level confidence level The The average event-total basin MAPaverage event-total basin MAP model is also found model is also found sensitivesensitive to th to th

e MM5 e MM5 cold microphysicscold microphysics schemesschemes used, but this sensitivity was used, but this sensitivity was lessless t than that due to han that due to changeschanges in model in model initial and boundary fields initial and boundary fields

MM5 provides MM5 provides reasonablereasonable average average event-total MAPevent-total MAP simulations for the simulations for the 4280-km4280-km22 Folsom basin, especially for Folsom basin, especially for heavy-precipitationheavy-precipitation events and events and with operational with operational Eta analysisEta analysis providing providing initialinitial and and boundaryboundary fields fields

lend credence to analyses of lend credence to analyses of mesoscalemesoscale atmospheric circu atmospheric circulation, lation, hydrologichydrologic cycle effects, and cycle effects, and microphysicalmicrophysical effects th effects that are at are based onbased on MM5 dynamical simulations of MM5 dynamical simulations of heavy heavy upsloupslo

pe precipitation events in the regionpe precipitation events in the region

biasbias obsobs

forecastforecast

rainrain No rainNo rain

rainrain AA BB

No rainNo rain CC DD

O

F

CA

BAbias