Berenguer, M., C. Corral, R. Sánchez-Diezma, and D. Sempere-Torres, 2005: Hydrological validation of a radar-based nowcasting technique. J. Hydrometeor. Accepted for publication.

Corral, C., 2004: Desenvolupament d'un model hidrològic per incorporar informació del radar meteorològic. Aplicació operacional a la conca del riu Besòs, Ph.D. Thesis. GRAHI, UPC, 175.

Delrieu, G. and J. D. Creutin, 1995: Simulation of radar mountain returns using a digitized terrain model. J. Atmos. Oceanic Technol., 12, 1038-1049.

Rinehart, R. E. & E. Garvey, 1978: Three-dimensional storm motion detection by conventional weather radar. Nature, 273, 287-289.

Sánchez-Diezma, R., D. Sempere-Torres, G. Delrieu, and I. Zawadzki, 2001: An Improved Methodology for ground clutter substitution based on a pre-classification of precipitation types. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, 271-273.

Seed, A. W., 2003: A dynamic and spatial scaling approach to advection forecasting. J. Appl. Meteor., 42, 381-388.

Acknowledgements: This work has been done in the framework of the EC projects VOLTAIRE (EVK2-CT-2002-00155) and

FLOODSITE (GOCE-CT-2004-505420). Thanks are also to the Spanish Meteorological Institute (INM) for providing radar data.

References

Validation of a radar-based advection algorithmfrom the perspective of flow forecasting

M. Berenguer, C. Corral, D. Sempere-Torres

Grup de Recerca Aplicada en Hidrometeorologia (GRAHI). Universitat Politècnica de Catalunya, Barcelona (Spain).Grup de Recerca Aplicada en Hidrometeorologia

U N I V E R S I T A T P O L I T È C N I C A D E C A T A L U N Y AGrup de Recerca Aplicada en Hidrometeorologia

U N I V E R S I T A T P O L I T È C N I C A D E C A T A L U N Y A

Hydrological validation

Convective case: 15/11/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

15/11/2001

0 1 2 3 40.5

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τ (h)

eff.

15/11/2001

0 1 2 3 40.5

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τ (h)

eff.

Stratiform case: 19/07/2001

0 1 2 3 40.5

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1.0

τ (h)

eff.

19/07/2001

0 1 2 3 40.5

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1.0

τ (h)

eff.

19/07/2001

0 1 2 3 40.5

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1.0

τ (h)

eff.

15/01/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

15/01/2001

0 1 2 3 40.5

0.6

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0.9

1.0

τ (h)

eff.

15/01/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

Lagrangian persistenceNo forecast S-PROG

3

3

Hydrological validation consists on comparing hydrographs forecasted with a certain anticipation τ against a reference hydrograph simulated with the modelusing the complete series of observed radar scans.

Results are presented as the Nash efficiency of forecasted hydrographs as a function of the anticipation with which they are forecasted:

eff τ( ) =1 −Qref ti( ) − Qti −τ ti( )[ ]2

t i =τ

t p

∑

Qref ti( ) − Qref[ ]2

t i =τ

t p

∑

Coupling a radar-based nowcasting technique with a distributed rainfall-runoff model allows us to improve the quality of forecasted hydrographs.

The bigger the basin, the bigger improvement in flow anticipation.

Results obtained with S-PROG are not better than those obtained with Lagrangian persistence.

The improvement is strongly dependent on the nature of the event.

Factors affecting the quality of forecasted discharges

The impact of different factors of the forecasted rainfall fields has also been studied:

Experiment 1: stationarity of motion fieldsLast observed radar field is advected using updated motion fields (estimated from “future” radar fields).

Experiment 2: forecasted mean areal rainfall over the basinThe model is input with uniform fields with the actually observed mean areal rainfall over the basin.

Experiment 3: rainfall distribution over the basinAnalysis of the hydrographs obtained inputting 2 hours of perfect forecasts to the rainfall-runoff model.

The forecasted rainfall mean over the basin is the key factor for improving forecasted discharges.

Rainfall distribution over the basin is especially important in bigger basins.

15/01/2001

0 1 2 3 40.5

0.6

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0.8

0.9

1.0

τ (h)

eff.

15/01/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

15/01/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

19/07/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

19/07/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

19/07/2001

0 1 2 3 40.5

0.6

0.7

0.8

0.9

1.0

τ (h)

eff.

Mean uniform field Perfect forecast

Updated motion fieldNo forecast S-PROG

Stationarity of the motion field used to advect rainfall map is not a limiting factor for flow forecasting.

Introduction

Nowcastng precipitation is a key element in the anticipation of floods in warning systems.The aim of this work is to study the performance of the radar-based extrapolation technique S-PROG (Seed 2003) for rainfall forecasting in a hydrological framework.This validation is carried out from two perspectives:

On the other hand, we have also studied the impact that different factors related to rainfall forecasts have on the quality of forecasted hydrographs.

rain gaugestream level gauge

INM C-band radar

Besòs(1015 km2)

from the perspective of rainfall, comparing forecasted and observed fields. from the perspective of the forecasted hydrographs simulated using the rainfall-runoff model DiCHiTop.

DiCHiTop Corral (2004):

Able to use non-uniform rainfall fields.

The catchment is divided into square cells (2x2 km2).

Loss function (SCS or TOPMODEL) applied to generate the runoff at cell scale

Each cell flow is routedto the outlet according to a unit hydrograph process.

The total discharge at the outlet is calculated as the sum of all routed cell runoffs.

P

t

TOPMODELrural cell

OUTLET

CELL

TOTAL

Q cell

Qb + Qse

Q = Σ Q cellcell

RZ Qv

DQes

Qb

NSZ

SZ

P

SCSurban cell

TOPMODEL stores

Routing

Loss function

Nash UH G(n,K)

Time delay (tr)

Nash UH G(n,K)

Time delay (tr)

time

tr

UH

Transference function

rural cellTopmodel

2x2km2 hillslope

river

HYDROLOGICALCELL

urban cellSCS

The rainfall-runoff model

S-PROG (Seed, 2003)

Advection

Field evolutionXk,i,j(t+τ)=φ1,k(t)∑Xk,i,j(t+τ−1)+φ2,k(t)∑Xk,i,j(t+τ−2)

t-2 t-1 t

t+2 t+3t+1

Motion field at t Scale analysis

OBSERVED

FORECASTED

Scale decomposition: Zi,j(t)=Σ σk(t)∑Xk,i,j(t)+µk(t)k=1

n

φ1,k(t), φ2,k(t)ρk,t(1), ρk,t(2)

S-PROG is an extrapolation technique based on Lagrangian persistence with the capability of filtering small scale patterns as they become unpredictable.

Tracking algorithmMotion field is estimated using a TREC technique (Rinehart & Garvey, 1978) .

Scale analysisThe reflectivity field Z(t) is decomposed into n fields, Xk, representing the variability of the field in different ranges of scales. This is done by applying a band-pass filter in the spectral domain. An AR(2) model is fitted to the temporal series of Xk(t)

ForecastForecast is done in the Lagrangian domain according to the AR(2) models fitted to Xk(t). As smallest scales are poorly autocorrelated, they become smoother as the forecasting time increases.

Hydrological validation was carried out in the Besòs catchment (1015 km2) and 2 of its subbasins (Mogent -180 km2- and Ripoll -65 km2-).

Implementation framework

Typical Mediterranean climate.

Reflectivity data were measured with the Corbera de Llobregat C-band radar.

Radar data have been processed according to the following QC scheme:

Barcelona area

INM radar

SAIH rain gauge

stage level sensor

50 km0 10

km

20 km

135

142

139

138

136

143

MOCA

PRS1

Ripoll(65 km2)

Mogent(180 km2)

This study has been carried out in the vicinity of Barcelona (Spain).

This basin is a complex system: upper part is mainly rural and forested and outlet is very densely populated.

Mountain interceptioncorrection

Raw scan

Clutter suppression(mean clutter map)

Secondary lobe and small noise specs suppression

Cluttersubstitution

Corrected scan

Delrieu et al. (1995)

Sánchez-Diezmaet al. (2001)

0 100

2

forecasting time (min)20 30 40 50 60

3

1

RM

SE(m

m h

-1)

0 100

2

RM

SE(m

m h

-1)

forecasting time (min)20 30 40 50 60

3

1

RM

SE(m

m h

-1)

0 100

2

forecasting time (min)20 30 40 50 60

3

1

RM

SE(m

m h

-1)

0 100

2

forecasting time (min)20 30 40 50 60

3

1

Analysis in rainfall terms

Comparison of forecasted against actually measured rainfall fields expressed as the RMSE (mm/h) as a function of the lead time in different/sized domains.

RM

SE(m

m h

-1)

0 100

2

428/09/2000

forecasting time (min)20 30 40 50 60

RM

SE(m

m h

-1)

0 100

2

428/09/2000

forecasting time (min)20 30 40 50 60

RM

SE(m

m h

-1)

0 100

2

428/09/2000

forecasting time (min)20 30 40 50 60

RM

SE(m

m h

-1)

0 100

2

428/09/2000

22/12/2000 22/12/2000 22/12/2000 22/12/2000

forecasting time (min)20 30 40 50 60

256x256 km2 domain

256x256 km2 domain

Lagrangian persistenceEulerian persistence S-PROG

Lagrangian persistence improves the results obtained of Eulerian persistence.

The smoothing capability of S-PROG minimizes the RMSE of forecasted rainfall fields.

Results become noisier in smaller basins

Forecasted hydrograph τ = 3 hours

015

January2001

15

10

5

0 I (mm

/h)

0

50

100

150

200

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ulat

ed fl

ow (m

3 /s)

16 17

Qt3(t3+τ)Qt3(t3+τ)

Qt1(t1+τ)Qt1(t1+τ)

Qt2(t2+τ)Qt2(t2+τ)

Qt3(t3+τ)

t = t3

τ=3hours

15 January2001

0

50

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150

200

Sim

ulat

ed fl

ow (m

3 /s)

17

15

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0Forecast (θ=2hours)Radar

015

January2001

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5

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/h)

0

50

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150

200

Sim

ulat

ed fl

ow (m

3 /s) I (m

m/h)

16 17

τ=3hours

Qt1(t1+τ)

t = t1

Forecast (θ=2hours)Radar

015

January2001

15

10

5

0 I (mm

/h)

0

50

100

150

200

Sim

ulat

ed fl

ow (m

3 /s)

16 17t = t2

τ=3hours

Qt2(t2+τ)

Forecast (θ=2hours)Radar

forecasted hydrograph

reference hydrograph

real-time hydrograph real-time hydrograph real-time hydrograph

Forecasted hydrographs are generated with the flow estimates, Qti(ti+τ) simulated with an anticipation τ at every time step ti during the event, simulating real-time conditions

OBS

ERVA

TIO

NS

LAG

RA

NG

IAN

PER

SIST

ENC

EFO

REC

AST

SS-

PRO

GFO

REC

AST

S