eWater Technical Report

Australian Government Bureau of Meteorology forecast and real-time observational hydrometeorological data for hydrologic forecasting

V.A. Kuzmin, A.W. Seed and J.P. Walker

Australian Government Bureau of Meteorology forecast and real-time observational

hydrometeorological data for hydrologic forecasting

V.A. Kuzmin1,2, A.W. Seed2 and J.P. Walker1

November 2007

1 Department of Civil and Environmental Engineering, The University of Melbourne

2 Australian Government Bureau of Meteorology These organisations are partners in eWater CRC

eWater Cooperative Research Centre Technical Report

eWater CRC is a cooperative joint venture whose work supports the ecologically and economically sustainable use of Australia’s water and river systems. eWater CRC was established in 2005 as a successor to the CRCs for Freshwater Ecology and Catchment Hydrology, under the Australian Government’s Cooperative Research Centres Programme.

Please cite this report as:

Kuzmin V.A., Seed A.W. and Walker J.P. 2007. Australian Government Bureau of Meteorology forecast and real-time observational hydrometeorological data for hydrologic forecasting. eWater Technical Report. eWater Cooperative Research Centre, Canberra. http://ewatercrc.com.au/reports/Kuzmin_et_al-2007-Hydrologic_Forecasting.pdf

Contact information:

Dr Vadim Kuzmin. Department of Civil and Environmental Engineering, The University of Melbourne, Victoria, Australia. http://www.civenv.unimelb.edu.au/autohome/webpage.php3?login=vkuzmin

Dr Alan Seed. Weather Forecasting Group, Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia. http://www.bom.gov.au/bmrc/wefor/staff/awseed/

Associate Professor Jeffrey Walker. Department of Civil and Environmental Engineering, The University of Melbourne, Victoria, Australia. http://www.civenv.unimelb.edu.au/autohome/webpage.php3?login=jwalker

© eWater Cooperative Research Centre 2007

This report is copyright. It may be reproduced without permission for purposes of research, scientific advancement, academic discussion, record-keeping, free distribution, educational use or other public benefit, provided that any such reproduction acknowledges eWater CRC and the title and authors of the report. All commercial rights are reserved.

Published November 2007

eWater CRC University of Canberra ACT 2601, Australia

Phone (02) 6201 5168 Fax (02) 6201 5038 Email info@ewatercrc.com.au Web www.ewatercrc.com.au

Contents Executive summary.................................................................................................................... 1 1 Introduction ............................................................................................................................ 2 2 Bureau of Meteorology forecast systems............................................................................... 2

2.1 The Bureau of Meteorology NWP models ..................................................................... 2 2.1.1 Global AnalysiS and Prediction (GASP) ............................................................... 2 2.1.2 Limited Area Prediction System (LAPS) ............................................................... 5

2.2 Multi-model forecast systems........................................................................................ 9 2.2.1 Operational Consensus Forecasts (OCF)............................................................. 9 2.2.2 Probability Matched Ensemble (PME) .................................................................. 9

2.3 Short-Term Ensemble Prediction System (STEPS) .................................................... 11 3 Observations........................................................................................................................ 12

3.1 Satellite products ......................................................................................................... 12 3.1.1 Daily solar radiation............................................................................................. 12 3.1.2 NDVI.................................................................................................................... 12

3.2 Radar rainfall estimations and forecasts ..................................................................... 13 3.3 Surface observations................................................................................................... 15

4 Product delivery ................................................................................................................... 16 5 Proposed products............................................................................................................... 18 6 Summary.............................................................................................................................. 20 Acknowledgements.................................................................................................................. 20 References............................................................................................................................... 21 Glossary................................................................................................................................... 22

Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Executive summary This report identifies those outputs of the Australian Government Bureau of Meteorology Numerical Weather Prediction (NWP) models (Table 1) and observational networks (Table 2) that are of relevance to the hydrologic community.

Table 1. Numerical Weather Prediction forecasts that are required for hydrological prediction.

Meteorological fields Current Desired

Lead

tim

e, h

Spa

tial

reso

lutio

n, k

m

Fore

cast

ing

step

, h

Det

erm

inis

tic (D

) or

ens

embl

e (E

)

Sou

rce

Lead

tim

e, h

Spa

tial

reso

lutio

n, k

m

Fore

cast

ing

step

, h

Det

erm

inis

tic (D

) or

ens

embl

e (E

)

72 37.5 1 D LAPS 375Precipitation 72 100 12 E LAPS EPS

144 12.5 1 E

Actual evapotranspiration 72 37.5 1 D LAPS 375 96 12.5 1 E Potential evapotranspiration 72 37.5 1 D LAPS 375 96 12.5 1 E Surface pressure 72 37.5 1 D LAPS 375 96 12.5 1 E Wind speed 72 37.5 1 D LAPS 375 96 12.5 1 E Surface air temperature 72 37.5 1 D LAPS 375 96 12.5 1 E Dew point temperature 72 37.5 1 D LAPS 375 96 12.5 1 E Long-wave downward radiation 72 37.5 1 D LAPS 375 96 12.5 1 E Short-wave downward radiation 72 37.5 1 D LAPS 375 96 12.5 1 E

Table 2. Observational data that are required for hydrological prediction

Current Desired Meteorological fields

Latency Spatial resolution Step Latency Spatial

resolution Step

Precipitation – – – 15 min 1 km 1 hRadar rainfall (hourly accumulation; limited coverage)

15 min 1 km 1 h 15 min 1 km 1 h

Actual evapotranspiration <1h 60 m, 1 km, 4 km

1 h, 1 d, 15 d

< 1h 12.5 km 1 h

Potential evapotranspiration No regular updating

37.5 km Climatologydata

< 1h 12.5 km 1 h

Surface pressure < 1h 37.5 km 6 h < 1h 12.5 km 1 hWind speed No regular

updating 1° Climatology

data < 1h 12.5 km 1 h

Surface air temperature < 1h 37.5 km 1 h < 1h 12.5 km 1 hDew point temperature – – – < 1h 12.5 km 1 hLong-wave downward radiation – – – < 1h 12.5 km 1 hShort-wave downward radiation Available by

1400 UTC 5 km 1 day < 1h 12.5 km 1 h

NDVI 3 days 2–3 km 30 d < 24 h 1km 7 d

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

1 Introduction This report specifies those outputs from the Australian Government Bureau of Meteorology (hereafter ‘the Bureau’) Numerical Weather Prediction (NWP) systems and observational networks that are of relevance when estimating and forecasting runoff, soil moisture, and evapotranspiration. The fields of precipitation and surface latent heat (evapotranspiration) are required by water balance models and the additional fields of surface pressure, air temperature, wind speed, dew point temperature, and long- and short-wave downward radiation are required by land-surface models that include explicit energy balance calculations. Consequently, these fields have been identified for use in models that forecast irrigation water demand and river runoff. The Bureau generates a wide range of forecast products. These are based on a number of models with a range of resolutions, domains, and forecast lead times, and not all models generate all of the ‘hydrological’ fields identified. This report summarises the range of models that are used by the Bureau, their characteristics in terms of domain, resolution and lead time, and the surface fields that they output (Hollis 2004).

The Bureau also operates an extensive network of automatic weather stations taking (point) surface observations with the data available in near-real-time, with some being also interpolated into gridded products. There are also a number of near-real-time products that are derived from satellite remotely sensed observations and weather radars. This report summarises the main characteristics of the surface observation networks and the products that are derived from these data as well as the products that are based on remote sensing (Bureau of Meteorology 2005, 2006).

2 Bureau of Meteorology forecast systems

2.1 The Bureau of Meteorology NWP models Currently the Bureau uses two major Numerical Weather Prediction (NWP) systems to generate forecasts nested over 5 spatial domains as shown in Figure 1. These are:

The Global AnalysiS and Prediction system (GASP) which provides long-range, low resolution forecasts over the entire globe.

The regional Limited Area Prediction System (LAPS) which provides higher resolution forecasts over shorter lead times for the Australian region. This model is also run over a number of smaller domains and with higher resolutions (Lee 2006).

A summary of the fields that are generated by the NWP systems is given in Tables 3 and 4.

2.1.1 Global AnalysiS and Prediction (GASP) GASP is a global model run by the Bureau of Meteorology in Melbourne that generates forecasts with a spatial resolution of 1° (~100 km) out to 7 days (Figure 2). The model is run at 0 UTC and 12 UTC each day and is used to generate the boundary conditions that are required by the limited-area models. The boundary conditions of the GASP model are perturbed by the global assimilation prognosis Ensemble Prediction System (GASP EPS) and an ensemble of 32 global forecasts with a resolution of 1.5° (~150 km) out to a lead time of 10 days are produced each day (Figure 3). GASP EPS is the only operational system run by the

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Bureau that generates probabilistic forecasts of rainfall and other meteorological fields (MSLP, 850 hPa, temperatures anomalies, surface wind speed, wind speed at 200 and 500 hPa) (Lee 2006).

Figure 1. Domains for the NWP model forecasts that are produced by the Bureau

Figure 2. Example of a GASP forecast of 24 hour rainfall accumulation.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Figure 3. Probability of rainfall accumulation exceeding (a) 5 mm and (b) 20 mm for Australia on day 6 in the forecast period based on GASP EPS. (c) Time series of forecasts with uncertainty for mean sea level pressure, 3-hour precipitation accumulation, wind speed and temperature for Melbourne, based on GASP EPS.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

2.1.2 Limited Area Prediction System (LAPS) LAPS is used to produce timely forecasts with a lead time out to a maximum of 72 hours over several domains and resolutions. LAPS 375 is the basic version of the LAPS model; it generates forecasts over the area that extends from 17° N to 65°S, 65°E to 184°E and surface forecast fields are output at 0.375° (~37.5 km) resolution every 1 hour. This model is used for broad-scale predictions over all of Australia for lead times out to 72 hours.

This system is implemented in a number of configurations. The most important for hydrologic prediction are: (a) the LAPS Ensemble Prediction System (LAPS EPS) which is to be used for producing ensemble forecasts; and (b) the MesoLAPS system that has higher spatial resolution (0.125° or ~12.5 km and 0.05° or ~5 km) and operates over a number of smaller domains.

The LAPS Ensemble Prediction System (LAPS EPS) perturbs the boundary conditions and some of the model physics to produce a 24-member ensemble with a 0.5° (~50 km) resolution out to a lead time of 72 hours; see Figures 4 and 5.

Higher resolution forecasts are generated by a number of MesoLAPS model runs that operate at either 0.125° (~12.5 km) or 0.05° (~5 km) over smaller domains (see Figure 6 for model identification) that are nested within the LAPS PT375 region and for lead times out to 36 hours.

Figure 4. Example LAPS EPS estimates of: (a) precipitation accumulation; and the probability that the 24-hour rainfall will exceed (b) 10 and (c) 20 mm.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Figure 5. (a) Example of LAPS05VicTas model output for Melbourne; the uncertainties are based on historical model performance. (b) Example of LAPS EPS output for Melbourne; the uncertainties are based on the spread in the ensemble of forecasts (here whiskers denote the ensemble minima and maxima, boxes show the 25% and 75% quartiles, points correspond to the median and the red line describes the control forecast obtained from the same model run at the maximum resolution possible).

Figure 6. Domains and names for MesoLAPS forecasts.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Tabl

e 3.

Ove

rvie

w o

f the

Bur

eau

of M

eteo

rolo

gy N

WP

mod

els.

Dom

ain

Latit

ude

Long

itude

M

odel

Sta

rt Fi

nish

S

tart

Fini

sh

Model/ output resolution, °

Forecastupdate, h

Forecast step/ extent, h

Ensemble (E) or deterministic (D)

Ensemble size

Desired for eWater

Com

men

ts

LAP

S 3

75

65.0

00°S

17

.125

°N

65.0

00°E

18

4.62

5°E

0.

375

12

1/72

D

–

Y

This

is th

e ba

sic

oper

atio

nal m

odel

cov

erin

g al

l Aus

tralia

, but

its

spa

tial r

esol

utio

n is

not

like

ly to

be

suffi

cien

t for

the

eWat

er

indu

stry

par

tner

s. T

he m

odel

led

field

s ar

e gi

ven

in T

able

2.

LAP

S 3

75 E

PS

65

.000

°S

17.1

25°N

65

.000

°E

184.

625°

E

0.75

/ 0.

524

24

/ 72

E

24

Y

This

mod

el w

orks

in e

xper

imen

tal m

ode

and

cove

rs a

ll A

ustra

lia, b

ut te

mpo

ral o

utpu

t res

olut

ion

is lo

w. F

or e

Wat

er

goal

s, it

sho

uld

be in

crea

sed.

M

esoL

AP

S 1

25

55.0

00°S

4.

875°

N

95.0

00°E

16

9.87

5°E

0.

125

12

1/48

D

–

Y

This

ver

sion

of t

he L

AP

S m

odel

als

o co

vers

all

Aus

tralia

, with

hi

gher

spa

tial r

esol

utio

n bu

t sho

rter l

ead

time

(2 d

ays)

, and

th

ere

is o

nly

a de

term

inis

tic ru

n.

38.0

00°S

30

.050

°S

147.

000°

E

154.

950°

E

NS

W (c

entre

d on

Syd

ney)

46

.000

°S

34.0

50°S

13

9.00

0°E

15

0.95

0° airotci

V

Ean

dTa

sman

ia

31.0

00°S

22

.050

°S

148.

000°

E

155.

950°

tsaE-htuo

S

EQ

ueen

slan

d 37

.000

°S

28.0

50°S

11

2.00

0°E

11

9.95

0°E

S

outh

-Wes

t Wes

tern

Aus

tralia

Mes

oLA

PS

05

39.0

00°S

31

.050

° S

13

3.00

0°E

14

0.95

0°E

0.05

12

1/

36

D

–

This

ver

sion

of t

he

LAP

S m

odel

has

hi

ghes

t spa

tial

reso

lutio

n bu

t sh

orte

st le

ad ti

me

(1.5

day

s).

Sou

th A

ustra

lia (c

entre

d on

Ade

laid

e)

E

5–9

Th

is fo

reca

st s

yste

m h

as lo

w te

mpo

ral a

nd s

patia

l res

olut

ions

bu

t a lo

ng le

ad ti

me.

It a

llow

s th

e m

ean

and

stan

dard

de

viat

ions

to b

e de

term

ined

toge

ther

with

all

ense

mbl

e m

embe

rs fo

r 24

h pr

ecip

itatio

n fo

reca

sts.

Poo

r Man

’s

Ens

embl

e 65

.000

°S

17.1

25°N

65

.000

°E

184.

625°

E

112

24

/ 15

6

D

5–9

2

dete

rmin

istic

fore

cast

s of

qua

ntita

tive

prec

ipita

tion

amou

nt

and

a sp

aghe

tti p

lot o

btai

ned

from

sev

eral

inte

rnat

iona

l m

odel

s.G

AS

P E

PS

G

loba

l 1.

5 12

3/

24

0E

32

Out

put:

mea

n &

sta

ndar

d de

viat

ion.

Pre

dict

ed fi

elds

: Rai

n,

MS

LP, 8

50 h

Pa,

tem

pera

ture

s an

omal

ies,

sur

face

win

d sp

eed,

w

ind

spee

d at

200

and

500

hP

a, o

btai

ned

from

diff

eren

t run

s of

the

sam

e m

odel

.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Table 4. The Bureau of Meteorology NWP model outputs, their relative quality (1=good to 3=poor), and potential for hydrological applications.

SAM archival system MARS database

Fields Units

LAPS

375

LAPS

375

E

PS

Mes

oLAP

S

125

Mes

oLAP

S

05

LAPS

375

LAPS

375

E

PS

Mes

oLAP

S

125

Mes

oLAP

S

05

Qua

lity

indi

cato

r D

esire

d fo

r hy

drol

ogy

Boundary layer convective energy flux W/m2 1–2 Convective precipitation mm 2–3 Y Cyclone tracks 2 Dew point temperature K 1 Divergence – East-west surface stress 2 Equivalent potential temperature K 1–2 Forest fire danger index 1–2 Geopotential height m 1–2 Grassland fire danger index 1–2 High cloud coverage % 1 Land-sea mask code 1 Long-wave incoming radiation W/m2 1–2 Y Low cloud coverage % 1 Mean sea level pressure Pa 1 Medium cloud coverage % 1 Moisture advection 1–2 Moisture convergence 1–2 Net downward radiation at surface W/m2 1–2 Y Non-convective precipitation mm 2–3 North-South surface stress 2 Outgoing long-wave radiation W/m2 1–2 Potential temperature K 1–2 Precipitation 24 h mm 2–3 Pressure of cloud top Pa 2 Relative humidity % 1–2 Screen height (2 m) dew point K 1–2 Y Screen height (2 m) temperature K 1–2 Y Sensible heat W/m2 1–2 Short-wave incoming radiation W/m2 2 Y Skin temperature K 1–2 Y Soil temperature K 1–2 Soil wetness 1–2 Surface latent heat flux / evapotranspiration W/m2 1–2 Y Surface mixing ratio 1 Surface net radiation W/m2 1–2 Surface pressure Pa 1 Y Surface sensible heat flux W/m2 1–2 Temperature advection 1–2 Temperature at diff. pressure levels / surface K 2 Temperature gradient at 950 hPa K/m 2 Thickness (1000–500 hPa) m 2 Thunderstorms (probability) % 1–2 Total cloud coverage % 1 Total precipitation since last output mm 1–3 U-component ageostrophic wind m/s 2 U-component of wind m/s 2 V-component ageostrophic wind m/s 2 V-component of wind m/s 2 Vertical velocity m/s 2 Wet bulb potential temperature K 1–2 Wind at 10 m (U and V components) m/s 2 Y Wind speed at diff. pressure levels m/s 2

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

2.2 Multi-model forecast systems The Bureau operates two systems that ingest forecasts from a number of NWP systems. These are:

1. Operational Consensus Forecasts (OCF). 2. Probability Matched Ensemble (PME).

The OCF produces forecasts of daily weather elements for specific point locations around Australia, (Woodcock and Engel 2005) while the PME provides an average daily precipitation forecast from an ensemble of Australian and international models (Ebert 2001).

2.2.1 Operational Consensus Forecasts (OCF) OCF compares the forecasts from a number of models with observations over the past 30 days to develop corrections for systematic bias for each model as well as to estimate model accuracy after bias correction for each model and location. The OCF forecast for a particular location is the weighted sum of the bias-corrected forecasts from each model (Engel 2005; Bureau of Meteorology 2006).

OCF produces forecasts of daily maximum, minimum and ground temperatures, rainfall amounts and associated probabilities, sunshine hours and evaporation for more than 600 sites throughout Australia. The available forecast period can be up to 7 days depending on weather elements. Currently the NWP models used in OCF include the local systems: GASP, LAPS PT375, MESO LAPS PT125, MESO LAPS PT050 (SEQLD, SYDNEY and VICTAS domains) and those from the overseas’ centres: ECMWF, NCEP, UKMO and JMA. An example of the model output is found in Table 5.

Table 5. Example of OCF product for Melbourne, Victoria. Lat: –37.8075 Long: 144.9700 Stn No: 086071 WMO No: 94868

Day Max air temp (°C)

Min air temp (°C)

Rain in 24 hours

(mm)

Ratio of models fore-casting rain > 0.2 mm

Actual evaporation

(mm)

Min groundtemp (°C)

Wed 06/12/2006 21 – 0.0 0/11 4.4 – Thu 07/12/2006 24 10 0.0 0/6 5.2 8 Fri 08/12/2006 31 12 0.0 0/3 6.6 10 Sat 09/12/2006 37 16 2.0 1/2 8.4 13 Sun 10/12/2006 28 21 1.7 2/2 6.0 17 Mon 11/12/2006 23 14 0.0 0/1 5.1 15 Tue 12/12/2006 23 12 0.0 0/1 4.7 12 Wed 13/12/2006 26 12 0.0 0/1 – 10 Thu 14/12/2006 32 12 0.0 0/1 – -

2.2.2 Probability Matched Ensemble (PME) NWP models from the Bureau, US National Oceanic and Atmospheric Administration, Met Office (U.K.), Japanese Meteorological Agency, European Centre for Medium Range Weather Forecasting, Meteorological Service of Canada, and Deutscher Wetterdienst are combined into a ‘Probability Matched Ensemble’ using the technique of Ebert (2001). Models that are included in the ensemble have different temporal and spatial resolutions, areas of coverage and run times, so the PME forecasting scheme first re-projects the model output onto a common grid and time step. The number of models that are available for the forecasts reduces from 7 for the first day to 3 after 4 days. The spatial and temporal resolution of the

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Figure 7. Example of PME average 24-hour rainfall forecast for day 1; note that the forecast rainfall is averaged over catchment areas in this display for hydrological applications.

Figure 8. Examples of the chance of 24-h rainfall above certain thresholds calculated by PME.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

PME output is low (0.375° and 24 h), but it produces forecasts that are more accurate than any individual forecast included in the ensemble (Ebert 2001). The only predicted field for this system is rain accumulated in 24 hours (Figures 7 and 8). The PME also provides forecasts of probability of precipitation for thresholds of 1, 5, 10, 15, 25, and 50 mm per day. Currently the Bureau is planning the future extension of the Poor Man’s Ensemble approach to 6 hourly time resolution (and perhaps finer spatial resolution) in the context of the gridded OCF project.

2.3 Short-Term Ensemble Prediction System (STEPS) STEPS is capable of generating ensemble forecasts of precipitation for up to 6 hours of lead time (Bowler et al. 2006). This model has been developed jointly by the Bureau and the UK Met Office. It does this by blending a forecast that is based on radar rainfall observations with the NWP forecasts that are generated by MesoLAPS. At present the system is used to generate ensemble forecasts that are based on a single radar out to two hours lead time. The full version of STEPS is expected to be ready for testing once the software to mosaic radar rainfall estimates made from a network of radars into a single product is completed (expected the end of 2007). STEPS currently generates deterministic forecasts for precipitation accumulation over the next 30, 60, or 90 minutes and estimates the probability that the precipitation accumulation in the next 60 minutes will exceed 1, 2, 5, 10, 20, 50 mm. The forecasts cover the 256 km × 256 km domains centred on the new Doppler weather radars that are being installed at Adelaide, Melbourne, Yarrawonga, Sydney, and Brisbane. The forecasts are updated at 10-minute intervals and have 1 km spatial resolution. Examples of the deterministic 30-minute forecast and the probability that rainfall accumulation will exceed 1 mm for Brisbane are shown in Figure 9. These forecasts will be used to generate short-term hydrological forecasts for important (yet small) urban catchments especially relevant for eWater as major Australian cities – Sydney, Melbourne, Brisbane – are covered.

Figure 9. (a) Deterministic forecast for 30-minute rainfall accumulation over Brisbane. (b) Probability that the rainfall accumulation in the next 60 minutes will exceed 5 mm for

Brisbane.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

3 Observations

3.1 Satellite products

3.1.1 Daily solar radiation The Bureau uses the hourly visible radiation from MTSAT-1R to calculate the downward radiative fluxes at ground level (Gautier et al. 1980; Weymouth and Le Marshall 1994; Weymouth and Le Marshall 2001). These hourly irradiances are integrated to give daily insolation totals. The gridded data covers all of Australia at 5 km resolution and a sample output is shown in Figure 10.

Figure 10. Daily solar exposure over Australia.

3.1.2 NDVI The Bureau compiles a composite of the maximum NDVI value recorded for each pixel over land for the past calendar month. Composites of maximum values over 5 and 9 days have been trialled in the past but not currently available. An example of the monthly composite is found in Figure 11. This field is mapped with 2–3 km spatial resolution over Australia (Tuddenham et al. 1994).

Figure 11. Maximum NDVI for November 2006.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

The NDVI can be calculated from any sensor having the ability to measure light in both red and near infra-red portions of the spectrum. Instruments such as the AVHRR sensor onboard the NOAA series of satellites have no on-board calibration and users of the time series data need to confirm that the issues of (1) sensor degradation; (2) atmospheric correction; and (3) sun-target-sensor geometry have been adequately modelled for their use.

3.2 Radar rainfall estimations and forecasts The coverage and type of radar in the Bureau radar network is shown in Figure 12 and Figure 13. The Bureau is in the final stages of testing Rainfields, the system that uses radar observations to estimate rainfall, on the new Doppler radars in Adelaide and Brisbane, and plan to roll out rainfall products for the other radars in the network that have suitable specifications (Chumchean et al. 2006).

The Rainfields server generates quantitative precipitation estimates for a 256 km x 256 km region that is centred on the radars at Brisbane, Sydney, Melbourne, Adelaide and Yarrawonga. The basic product is a 10-minute rainfall accumulation with 1 km spatial resolution. Hourly accumulations and accumulations for the last 60 minutes, 24 hours, since 9 am local time (updated at 10-minute intervals) are displayed on the Web (http://www.bom.gov.au/weather/radar/) and placed on the FTP server for use by clients. An example of the ‘since 9 am’ product for Brisbane is shown in Figure 14.

Figure 12 Areas covered by weather radar data. Note that Doppler radars have been installed in Adelaide and Brisbane since this diagram was drawn, and new Doppler radars for Sydney

and Melbourne will be operational by June 2008.

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

Figure 13. Details of the type of weather radar in the network. The high resolution Doppler radars have the highest specification and yield the best rainfall estimates. Radars that are

shared with wind finding are not suitable for rainfall estimation.

Figure 14. Example of the ‘since 9 am’ Rainfields product for Brisbane

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Forecast and real-time observational hydrometeorological data for hydrologic forecasting

3.3 Surface observations The Bureau operates a network of daily rain gauges and automatic weather stations (Figure 15) that observe hourly or 1 minute air temperature, dew point temperature, relative humidity, wind speed and direction, atmospheric pressure and precipitation (Plummer et al. 2003). The Bureau also collects data from external agencies and these data are not necessarily available for on-forwarding to other agencies. The only gridded products that are derived from the network of point observations are maps of daily rainfall and maximum and minimum daily temperature. The network of daily rain gauges is used to define a daily rainfall map that covers all of Australia at a 0.25° resolution (Ebert and Weymouth 1999). Since the daily rain gauges are read at 9 AM local time, this product is only available once the data from WA have been collected (approximately at 12:30 EST) (Figure 16).

In addition, upper-layer soil moisture, precipitation, transpiration, soil evaporation, surface runoff, and drainage with continental coverage for 1981 to present, at daily time resolution and 0.05° spatial resolution are expected to be available for general distribution through the Australian Water Availability Project (AWAP) in July 2007.

Figure 15. Meteorological observational network of Australia,

Figure 16. Example of a daily rainfall map from http://www.bom.gov.au/silo/.

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4 Product delivery The Bureau delivers products to external users through an FTP site and the Bureau web site. External users can register and subscribe to receive products on the Registered Users FTP site. There are two main portals on the Bureau web site that give access to forecasts and observations of weather elements that are of special interest to the water industry; the Special Information for Land Owners (SILO) web-site http://www.bom.gov.au/silo/ (Jeffrey 2001) which provides a rich source of meteorological and agricultural data of particular interest to anyone involved in the agricultural arena (by subscription), and the Water And The Land site (WATL) http://www.bom.gov.au/watl/ which is designed as a portal for weather and climate information for primary industry and natural resource managers and is freely available on the Internet. A screen shot of the WATL site can be seen in Figure 17 and the list of products that are available at this site is given in Table 6.

Figure 17. The Bureau’s existing Water and the Land Web site http://www.bom.gov.au/watl/.

Table 6. Products that are available on the WATL Web site

Fields Coverage Spatial resolution

Frequency of regular updating

Rainfall

3 month outlook Australia – Monthly Forecast rainfall: Chance of rainfall (PME)

Australia, states 37.5 km Twice daily

Forecast rainfall: 4 day totals Australia, states, districts

37.5 km Twice daily

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Fields Coverage Spatial resolution

Frequency of regular updating

Latest radar images 128 or 256 km around radars

1 km 10 minutes

Latest rainfall (24 h / since 9 a.m. / last 1h)

Australia, states, some districts

37.5 km Daily / hourly / hourly

Recent rainfall

Australia, states 1° Daily Recent rainfall report Australia, states 1° Weekly Recent drought report Australia, states 1° Monthly Average rainfall Australia, states 1° Climatological data –

no regular updating Rainfall trends Australia, states 1° Climatological data –

no regular updating

Cloud

Latest satellite image Australia – Hourly Latest satellite loop (4 h) Australia – Hourly

Temperature

3 month outlook Australia 1° Monthly Recent temperature (Min, Max, mean, anomalies)

Australia 1° Daily

Average temperature Australia, states 1° Climatological data –no regular updating

Temperature trends Australia 1° Climatological data –no regular updating

Wind

Average wind Australia 1° Climatological data –no regular updating

Pressure

4 day forecast chart Australia 37.5 km Daily between 2 pm and 3 pm EST

Latest chart Australia 37.5 km 6 hours Monthly MSLP anomaly Australia 2.5° Monthly

El Niño & La Niña

ENSO forecasts Australia – Monthly Latest ENSO report Australia – Fortnightly Southern Oscillation Index Australia – Monthly

Humidity

Average humidity Australia 1° Climatological data –no regular updating

Recent Vapour Pressure Australia 0.25° Daily

Evaporation

Average evaporation Australia 1° Climatological data –no regular updating

Average evapotranspiration Australia 1° Climatological data –no regular updating

Sunshine

UV Index forecast Australia 1° Daily Recent Solar Exposure Australia 1° Daily Average sunshine maps Australia 1° Climatological data –

no regular updating

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5 Proposed products The observed and forecast weather elements can be used in daily river operations in a number of ways, depending on the timeliness, resolution, and accuracy of the product and the user needs. Many of the graphical products that are currently on the WATL and SILO web pages can be used immediately for qualitative applications. However, it is difficult to obtain an estimate of the average for a particular river catchment from the image and this can be a limiting factor when attempting to use the data. A system has been developed to calculate the mean areal forecast rainfall over a catchment, and the radar rainfall data can be served as averages over user defined sub-catchments. Web-based navigation and mapping tools that allow the user to navigate and estimate the product values at points or over areas will enhance the value of these products to the water industry.

Using the data in a quantitative way in models that forecast river flow and irrigation demand requires a mechanism to move the geo-referenced data from the Bureau to the application in real-time, and an understanding of the error structure for each product (at the scale of the application) so that the data are used appropriately. Representing the uncertainty (in rainfall forecasts in particular) to the users is a major challenge that needs to be addressed before optimum use can be made of hydrometeorological forecasts in quantitative, real-time, hydrological prediction systems. Tables 7 and 8 list the weather elements that are required for quantitative land surface modelling.

The details of the implementation of the data server are yet to be developed, but it is likely that the products will be served through the Bureau’s FTP server.

Table 7. Forecast meteorological fields of interest to the water industry

Meteorological fields Current Desired

Lead

tim

e, h

Spa

tial

reso

lutio

n, k

m

Fore

cast

ing

step

, h

Det

erm

inis

tic (D

) or

ens

embl

e (E

)

Sou

rce

Lead

tim

e, h

Spa

tial

reso

lutio

n, k

m

Fore

cast

ing

step

, h

Det

erm

inis

tic (D

) or

ens

embl

e (E

)

72 37.5 1 D LAPS 375Precipitation 72 100 12 E LAPS EPS

144 12.5 1 E

Actual evapotranspiration 72 37.5 1 D LAPS 375 96 12.5 1 E Potential evapotranspiration 72 37.5 1 D LAPS 375 96 12.5 1 E Surface pressure 72 37.5 1 D LAPS 375 96 12.5 1 E Wind speed 72 37.5 1 D LAPS 375 96 12.5 1 E Surface air temperature 72 37.5 1 D LAPS 375 96 12.5 1 E Dew point temperature 72 37.5 1 D LAPS 375 96 12.5 1 E Long-wave downward radiation 72 37.5 1 D LAPS 375 96 12.5 1 E Short-wave downward radiation 72 37.5 1 D LAPS 375 96 12.5 1 E

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Table 8. Currently available and desired observational data for hydrological prediction.

Current Desired Meteorological fields

Latency Spatial resolution Step Latency Spatial

resolution Step

Daily precipitation Available 12:30 pm EST

0.05° and 0.25°

24 h – – –

Precipitation since 9 am

Available 12:30 pm EST

37.5 km 24 h – – –

Radar rainfall (hourly accumulation)*

15 min 1 km 1 h 15 min 1 km 1 h

Precipitation – – – 15 min 1 km 1 hActual evapotranspiration <1h 60 m, 1 km,

4 km 1 h, 1 d, 15 d < 1h 12.5 km 1 h

Potential evapotranspiration No regular updating

37.5 km Climatology data

< 1h 12.5 km 1 h

Daily min and max surface air temperature (9 am – 9 pm)

Available 12:30 pm EST

0.05° and 0.25°

24 h – – –

Point data from AWS (surface air temperature, wind, rel. humidity)

< 1h Point data 1h or 1 min –** –** –**

Surface pressure < 1h 0.04° (hourly) and 37.5 km

1 h or 6 h < 1h 4 km 1 h

Wind speed No regular updating

0.04° (hourly) and 1°

Climatology data

< 1h 12.5 km 1 h

Dew point temperature Available 12:30 pm EST

0.05° Twice daily at 9 am, 3 pm

< 1h 12.5 km 1 h

Long–wave downward radiation

– – – < 1h 12.5 km 1 h

Short–wave downward radiation

Available 14:00 UTC

0.05 o Daily < 1h 12.5 km 1 h

Surface air temperature < 1h 37.5 km 1 h < 1h 12.5 km 1 hNDVI (monthly) 3 days 2–3 km 30 d < 24 h 1km 7 d

* Limited coverage. ** To be replaced with interpolated meteorological fields.

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6 Summary Observation and forecast products of the Australian Government Bureau of Meteorology Numerical Weather Prediction system and observational networks have the potential provide strong informational basis for hydrological forecasting. The forecasting system should be able to produce more accurate hydrologic forecasts, improve our knowledge of water quantity and quality, and, hence, optimise water use in Australia. The beginnings of a system to deliver these data and forecasts to external users has been developed through the registered users ftp site and WATL, but more work is required so as to improve access to a greater range of the forecast products and to package the data and forecasts in a way that is convenient for hydrological applications.

Acknowledgements We must acknowledge the help which we have received in the preparation of this summary document. Specifically, information regarding utilisation of the Australian Government Bureau of Meteorology Numerical Weather Prediction system products was provided by Dr Kamal Puri, and friendly advice, numerous ideas, links, and papers on the Bureau forecasts verification were kindly provided by Dr Elizabeth Ebert. We also would like to thank Jim Elliott, Soori Sooriyakumaran, and Anthony Leggett of the Bureau Hydrology Group for their amiable help and invaluable suggestions that allowed us to unite and summarise the information about the Bureau NWP system output, and Dr Tim McVicar of CSIRO Land and Water for providing useful suggestions that essentially improved this report.

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References Bowler N. E., Pierce C., and Seed A. W. 2006. STEPS: a probabilistic forecast scheme which

merges an extrapolation forecast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society 132: 2127–2155.

Bureau of Meteorology. 2005. Annual Report 2004–05. Australian Government Bureau of Meteorology, Melbourne, Australia. http://www.bom.gov.au/inside/eiab/reports/ar04-05/

Bureau of Meteorology. 2006. Annual Report 2005–06. Australian Government Bureau of Meteorology, Melbourne, Australia. http://www.bom.gov.au/inside/eiab/reports/ar05-06/

Chumchean S., Seed A.W. and Sharma A. 2006. An integrated approach to error correction for real-time radar-rainfall estimation. Journal of Atmospheric and Oceanic Technology 23: 67–79.

Ebert E.E. and Weymouth G.T. 1999. Incorporating satellite observations of ‘no rain’ in an Australian daily rainfall analysis. Journal of Applied Meteorology 38: 44–56.

Ebert E.E. 2001. Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Monthly Weather Review 129: 2461–2480.

Engel C. 2005. Hourly Operational Consensus Forecasts (OCF). BMRC Research Report No. 115. Australian Government Bureau of Meteorology, Melbourne, Australia. http://www.bom.gov.au/bmrc/pubs/researchreports/RR115.pdf

Gautier C., Diak G. and Masse S. 1980. A simple model to estimate the incident solar radiation at the surface from GOES satellite data. Journal of Applied Meteorology 19: 1005–1012.

Hollis A.J. (editor). 2004. The past, present and future of numerical modelling. Extended abstracts of presentations at the 16th annual BMRC Modelling Workshop, Melbourne, 6–9 December 2004. BMRC Research Report No. 104. Australian Government Bureau of Meteorology, Melbourne, Australia. http://www.bom.gov.au/bmrc/pubs/researchreports/RR104.pdf

Jeffrey S.J., Carter J.O., Moodie K.B. and Beswick A.R. 2001. Using spatial interpolation to construct comprehensive archive of Australian climate data. Environmental Modelling and Software 16: 309–330.

Lee J. 2006. Numerical weather prediction model performance summary – January to March 2006. Australian Meteorological Magazine 55: 161–163. http://www.bom.gov.au/amm/papers2006.shtml

Plummer N. et al. 2003. Progress of automatic weather stations in meeting the needs of climate. The 3rd International Conference on Experiences with Automatic Weather Stations, Torremolinos, Spain, February 19–21. ftp://ftp.bom.gov.au/anon/home/ncc/data/SRCE/ICEAWS_03_NP.doc

Tuddenham W.G., Le Marshall J.F., Rouse B.J. and Ebert E.E. 1994. The real time generation and processing of NDVI from NOAA-11: a perspective view from the Bureau of Meteorology. Proceedings 7th Australian Remote Sensing Conference, Melbourne, 1–4 March 1994. Remote Sensing & Photogrammetry Association of Australasia. p. 495–502.

Weymouth G. and Le Marshall J.F. 1994. An operational system to estimate insolation over the Australian region. In: Le Marshall J.F. and Jasper J.D. (editors). The Technical Proceedings of the 2nd Pacific Ocean Remote Sensing Conference, Melbourne, Australia. p. 443–449.

Weymouth G. and Le Marshall J.F. 1999. An operational system to estimate global solar exposure over the Australian region from satellite observations – I. Method and the initial climatology. Australian Meteorological Magazine 48: 181–195.

Weymouth G. and Le Marshall J.F. 2001. Estimation of daily surface solar exposure using GMS-5 stretched-VISSR observations: the system and basic results: Australian Meteorological Magazine 50: 263–278.

Woodcock F. and Engel C. 2005. Operational Consensus Forecasts. Weather and Forecasting 20: 101–111.

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Glossary AWAP. Australian Water Availability Project (CSIRO, Bureau of Rural Science, Bureau of

Meteorology).

Control forecast. ‘Best’ forecast produced by using a higher resolution model, or expert analysis, or any other way that exists out of the model and is to be used for a model verification or forecast assessment.

CRC. The Cooperative Research Centres is an Australian Federal Government programme, which was established to bring together researchers and research users. eWater is one such CRC.

Deterministic forecast. Forecast which does not directly reflect the process uncertainty. Normally it looks like a single value that corresponds to the process mean or median.

DWD. Deutscher Wetterdienst.

ECMWF. European Center for Medium-range Weather Forecasts.

Ensemble forecast. Probabilistic forecast based on modeling a number of possible scenarios of the natural process.

ENSO. El Niño-Southern Oscillation, a global coupled ocean-atmosphere phenomenon.

EPS. Ensemble Prediction System is to be used for producing ensemble forecasts, i.e. series of possible scenarios.

Forecast extent. Time interval between producing a forecast and latest predicted value or event.

Forecast update. Time interval between consecutive model runs.

Forecasting step. Time step between consecutive forecasts.

GASP. Global AnalysiS and Prediction system which provides long-range, low resolution forecasts over the entire globe.

GASP EPS. The GASP Ensemble Prediction System an ensemble of 32 global forecasts that are generated using the GASP model which is run at a lower resolution

GTS. Global Telecommunications System.

JMA. In this paper: climate model developed at Japan Meteorological Agency.

LAPS. The regional Limited Area Prediction System (LAPS) provides higher resolution forecasts over shorter lead times for the Australian region.

LAPS EPS. The LAPS Ensemble Prediction System (LAPS EPS) perturbs the boundary conditions and some of the model physics to produce a 25-member ensemble with a 0.5° resolution out to a lead time of 72 hours.

Lead time. The length of time between the issuance of a forecast and the occurrence of the phenomena that were predicted

MARS. Meteorological Archiving and Retrieval System

MDA. Model-data assimilation.

MesoLAPS. Configuration of the LAPS that has higher spatial resolution and operates over a number of smaller domains.

Model output resolution. Grid size of the 2D or 3D hydrologic or meteorological models output.

MSLP. Mean Sea Level Pressure.

MTSAT-1R. A three axis body stabilized spacecraft launched 26/2/2005 and used to calculate the downward radiative fluxes at ground level.

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NCEP. U.S. National Centers for Environmental Prediction

NDVI. Normalised Difference Vegetation Index. NDVI products are produced by the Bureau of Meteorology for the Australian region using measurements from the Advanced Very High Resolution Radiometer (AVHRR).

NDWI. A normalised difference water index for remote sensing of vegetation liquid water from space.

NWP. Numerical Weather Prediction.

OCF. The Operational Consensus Forecast compares the forecasts from a number of models and forecasts of daily weather elements for specific point locations around Australia.

PDF. Probability Distribution Function

PME. Probability Matched Ensemble

Probabilistic forecast. Type of NWP forecast that reflects physical and computational uncertainty of the modeled process. PF can be presented as a PDF or a set of statistics.

RS. Remote Sensing is the measurement or acquisition of information by a recording device that is not in physical contact with the object under study.

SAM. System used by the Bureau for archiving large volumes of data, especially NWP output

SILO. The Special Information for Land Owners (http://www.bom.gov.au/silo/), which provides a rich source of historic and real-time meteorological and agricultural data of particular interest to anyone involved in the agricultural area.

Spatial resolution. Grid size of the NWP gridded products.

STEPS. Short-Term Ensemble Prediction System.

UKMO. U.K. Met Office.

WATL. Water And The Land (http://www.bom.gov.au/watl/), which serves real-time data for the primary industries.