considerations for the blending of multiple precipitation datasets for hydrological applications...
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Considerations for the blending of multiple precipitation datasets for hydrological applications
Luigi RenzulloResearch ScientistCSIRO Land & Water, Canberra, Australia4th IPWG Workshop, Beijing, China 13 October 2008
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Background:Water Information R & D Alliance - WIRADA
• Commonwealth Water Act 2007
• Australian Bureau of Meteorology (BoM)• Mandate: ”Manage Australia’s water resources information …”;• new responsibilities; new BoM Water Division formed.
• Water Information Research and Development Alliance (WIRADA)• An R & D initiative between the BoM and CSIRO; • partnership of $50M over 5 years (started July 2008)
• 10 WIRADA Projects • Research incl. Water Accounting & Assessment; Water Availability Forecasting (Short- &
Mid- to Long-term); Sensor Networks & Water Informatics
• WIRADA Project 10: Precipitation & Actual Evapotranspiration Products• Aim: Blend rainfall radar, rain gauge, satellite-based PPT and QPF’s to service the need of
the hydrological modelling/monitoring and forecasting community• Help BoM Water Division deliver on their mandate by the Federal Govt.
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Precipitation information “wish list”
What is desired?
• Water Forecasting• Obs: ≤ 1 km resolution (may be < 5 km);
≤ 1 hourly rates• (e)QPF’s: ≤ 1 hourly rates; > 48 hrs lead
time
• Water Accounting• Obs: ≤ 5 km resolution; ≤ daily
accumulations; continental coverage• Rainfall intensity distribution: rainfall
duration; area/ fraction of catchment wet
What is available?
• Rain gauges• ~1000 report < 1 hour of event; ~2000
report < 24 hrs; ~7000 report daily accumulation ~ 6 months after end-of-year; sparse coverage
• Rainfall radar• ~1 km reflectivities; ~10 mins; coverage
limited to populous areas
• Satellite-based estimates• rates reported 0.5-3 hourly intervals; ~6-
25 km resolution; latency ~ several hrs; continental coverage
• QPFs (BoM)• ~5 km (regional) to ~38 km (continental)
resolution; lead times 12 hrs (meso-scale) – 72 (continental); ensembles available
“Current & historical gridded rainfall products at a scale & quality useful for hydrological application.”
WIRADA Science Plan, May 2008
4th IPWG W/shop,Beijing, China, 13-17 October 2008
• Blending multiple data sets is the key• Idea is not new – e.g. rainfall radar
• Project aims to :• Develop strategies for blending
multiple PPT data sets to derive gridded precipitation for use in water accounting & assessment, and the short- & long-term water availability forecasting.
• Demonstrate use of PPT distribution info (e.g. intensity, duration) to improve estimation
Issues & Project Aims
• Disparate spatial resolution & temporal frequency between data sets
• Areas of hydrological significance (e.g. headwater catchments) often inadequately represented
• No individual, definitive PPT data set that meets all requirements
LAPS-0.375°
mesoLAPS-0.125°
TRMM
SILO/BILO
mesoLAPS-0.05°
Murray-DarlingBasin boundary
0°
LAPS-0.375°
mesoLAPS-0.125°
TRMM
SILO/BILO
mesoLAPS-0.05°
Murray-DarlingBasin boundary
0°
• Humble first steps• Quantify spatial & temporal
difference between data sets
4th IPWG W/shop,Beijing, China, 13-17 October 2008
(c) Daily rain gauges (5-jan-05)
Interpolated surfaces of daily rain gauge observations - Total rainfall in 24 hrs to 9am (local time)
Gridded precipitation estimates in Australia
(a) BoM AWAP --- BILO
0 mm d-1 25 mm d-1
5 January 2005
(b) QDNR & M --- SILO
TRMM-derivedDaily rainfall
4th IPWG W/shop,Beijing, China, 13-17 October 2008
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10
Rai
nfal
l rat
e (m
m h
r-1)
UTC 6 Nov 2005 7 Nov 2005
2100 0000 0300 0600 0900 1200 1500 1800 2100 0000
EST 7 Nov 2005 8 Nov 2005
0700 1000 1300 1600 1900 2200 0100 0400 0700 1000
7 Nov 2005 – 1200 UTC
69.7 mm of rainin 24 hours to 9am EST on 8 Nov 2005
Deriving daily rainfall totals from TMPA 3B42 (post-real-time) product
TRMM daily rainfall (mm day-1)
Huffman et al (2007), J. Hydrometeor.,5, 38-55
4th IPWG W/shop,Beijing, China, 13-17 October 2008
UTC + 8
UTC + 9.5
UTC + 10Daily TRMM
Rainfall (mm d-1)
>
TRMM daily rainfall (mm day-1)
4th IPWG W/shop,Beijing, China, 13-17 October 2008 500 mm yr-1-500 mm yr-1 0
1500 mm yr-1
Average Annual Rainfall 1998-2007 (mm yr-1)
0 mm yr-1
BILO SILO TRMM
BILO - SILO BILO - TRMM SILO - TRMM
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Closer look:Differences in orographic rainfall: BILO - TRMM
1300
Elevation (m)
BILO – TRMM (mm yr-1)
Snowy Mt – Victorian Alpine Region Darling Escarpment, WA
100
700
-500
500
0
West coast Tasmania
Which one is correct?
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Average differences for each basin
• BILO – SILO • generally between ±20 mm yr-1
• Surfaces – TRMM • generally between ±70 mm yr-1
Rainfall intensity distribution: rainfall duration; area/ fraction of catchment wet
• Overall exception is Tasmania• BILO > SILO ~ 70 mm yr-1
• Surfaces > TRMM ~800 mm yr-1
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)(12)
(13)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)(12)
(13)
Closer look:Average annual rainfall for major Drainage Divisions
BILO - SILO BILO - TRMM SILO - TRMM10 Darling 1.6 -9.1 -10.7
6 Gulf of Carpentaria 1.4 -66.5 -67.91 Indian Ocean 11.1 -9.9 -217 Lake Eyre 14.7 -20.5 -35.2
11 Murray 2.3 6.5 4.29 NE Coast 17.5 22 4.58 SA Gulf 5.8 37.9 32.1
12 SE Coast -0.3 55.6 55.92 SW Coast 0.5 1.4 1
13 Tasmania 70.6 819.2 748.63 Timor Sea -13 -49.4 -36.44 W Plateau (N) 17.5 -14.8 -32.45 W Plateau (S) 10.8 -39.3 -50.1
Max Min
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Trends in average annual rainfall (selected basins)0
200
400
600
1998 2000 2002 2004 2006
PP
T (
mm
yr-1
)
Murray
BILO
SILO
TRMM
050
010
0015
00
1998 2000 2002 2004 2006
PP
T (
mm
yr-1
)
Tasmania
-80
-40
040
80
Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07
TW
S (
mm
mo
nth-1
)CSRGFZJPLmean
-80
-40
040
80
Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07
TW
S (
mm
mo
nth-1
)
CSRGFZJPLmean
Changes in Terrestrial Water Storage from GRACE
Tapley et al (2004), Science.,305, 503-505
Rodell et al. (2006)Hydrogeol. J., 15, 159-166
Swenson et al (2008),Water Resour. Res., 44.
4th IPWG W/shop,Beijing, China, 13-17 October 2008
02
04
06
08
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TRMMSILOBILO
SW Coast
Average monthly rainfall (selected basins)
01
02
03
04
05
06
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TRMMSILOBILO
Murray
05
01
001
502
002
50Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TRMMSILOBILO
05
01
001
50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TRMMSILOBILO
West Plateau (N) Gulf ofCarpentaria
PP
T (
mm
mo
nth
-1)
PP
T (
mm
mo
nth
-1)
PP
T (
mm
mo
nth
-1)
PP
T (
mm
mo
nth
-1)
Max Min
Biggest Difference (mm month-1)BILO-SILO BILO-TRMM SILO-TRMM
Darling 1.4-Apr -5.8 - Dec -4.8-DecGulf of Carpentaria -2.7 - Dec -18.8-Feb -19.1-FebIndian Ocean 4.3 - Feb -5.8-Feb -10.2-FebLake Eyre 2.7 - Jan -7.3-Dec -7.9-DecMurray 0.8 - Jan 6.9-Jun 6.7-JulNE Coast 3.57- Mar 10.6-Jun 7.13-AprSA Gulf 0.9-Jan 10.6-Jun 9.9-JunSE Coast -1.5-Nov 7.9 - May 8.6-AugSW Coast -1.33-Jul -3.5-Mar -4.5-MarTasmania 12.2 - Jun 102.3 - Sep 94 - AugTimor Sea -8.3-Dec -18.8-Feb -17.6-FebW Plateau (N) 3.2-Feb -5.4-Nov -6.9-NovW Plateau (S) 2.8-Dec -4.8-Aug -6.5-Dec
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Gridded precipitation estimates in Australia
NWP comparisons with TRMM data
BoM’s Limited Area Prediction System (LAPS)mm/hr
0
0000 Z0000 Z 0000 Z0100 Z0200 Z0300 Z 0300 Z 0300 Z0400 Z0500 Z0600 Z0600 Z 0600 Z 0700 Z0800 Z0900 Z0900 Z0900 Z 1000 Z1100 Z1200 Z1200 Z1200 Z 1300 Z1400 Z1500 Z1500 Z 1500 Z 1600 Z1700 Z1800 Z1800 Z 1800 Z 1900 Z2000 Z2100 Z 2100 Z2100 Z 2200 Z2300 Z
Data and forecast from0000 – 2300 UTC on 8 June 2007
* Hunter Valley Floods June 2007
TRMM 3B42RT TRMM 3B42 LAPS
4th IPWG W/shop,Beijing, China, 13-17 October 2008
1mm d-1
( a ) LAPS-0.375º ( b ) mesoLAPS-0.05º
( c ) TRMM 3B42 ( d ) SILO
• Hunter Valley Floods, NSW• June 2007• >$500M insurance (2nd largest
deployment in SES history)
• 24 hr accumulations to 9am on 9 June 2007
• Areal means for ROI:(a) 88.3 mm d-1 (b) 85.3 mm d-1
(c) 82.1 mm d-1 (d) SILO = 96.6 mm d-1
Comparison of daily accumulations (single event)
040
8012
0
72 hr 64 hr 48 hr 36 hr 24 hr 24 hr 24 hr 3B42RT 3B42 SILO BILO
Rai
nfal
l (m
m d
-1)
LAPS-0.375°LAPS-0.125°
LAPS-0.375°TRMM
Interpolatedsurfaces
040
8012
0
72 hr 64 hr 48 hr 36 hr 24 hr 24 hr 24 hr 3B42RT 3B42 SILO BILO
Rai
nfal
l (m
m d
-1)
LAPS-0.375°LAPS-0.125°
LAPS-0.375°TRMM
Interpolatedsurfaces
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Monthly accumulations based on daily data
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
mesoLAPS (0.050o)mesoLAPS (0.125o)
BILOSILO3B423B42RT
June
Accu
mul
ated
Rai
nfal
l (m
m m
onth
-1)
June June
LAPS (0.375o)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
0 5 10 15 20 25 30
010
020
030
040
050
060
0
Acc
umul
ated
Rai
nfal
l (m
m)
mesoLAPS (0.050o)mesoLAPS (0.125o)
BILOSILO3B423B42RT
June
Accu
mul
ated
Rai
nfal
l (m
m m
onth
-1)
June June
LAPS (0.375o)
4th IPWG W/shop,Beijing, China, 13-17 October 2008
Final comments & future directions
• Still early days in WIRADA PPT & AET Products Project• Impact of different PPT on Hydrological model = f(scale)
• Impact of difference on estimation (whole range of hydrological applications) needs to be investigated further refine the requirements
• Future research tasks include:• Blending radar rainfall with rain gauge observations
• Assess impact of using BoM’s best-practice corrected radar rainfall data on lumped-catchment stream flow estimation by:
• quantifying rainfall duration (i.e. sub-daily rainfall intensity distribution); and• quantifying rainfall spatial extent over a catchment.
• Disaggregating daily rainfall using rainfall intensity distributions • Examine pluviometer observations (6 minutely observations) to define rainfall intensity distribution (RID)
functions and assess: • various interpolation schemes for estimating RID parameter values at non-pluviometer locations (incl.
locations with only daily rainfall gauges); and• the errors/biases in RID estimates and impact on disaggregation results.
• Blending near real-time satellite-based precipitation rates with real-time rain gauge observations • Calibrate near real-time satellite-based precipitation products using the available real-time rain gauge data to
produce continental-scale near real-time maps of precipitation. • Statistical downscaling of quantitative precipitation forecasts
• Explore statistical approaches for downscaling QPFs in near real-time, exploiting satellite- and real-time gauge observations when/where available for uptake in stream flow forecasting.
• Using satellite-based precipitation observations to aid interpolation of archived daily rain gauge data • Use satellite-based precipitation estimates as a covariate (along with e.g. elevation, distance-from-coast, …) in
the interpolation of rain gauge observations, thus assessing:• the utility of the satellite observations to give useful information between gauge locations; and • the suitability of the 0.5-3 hrly sampling frequency to provide useful information on rainfall duration rates at
0.5–3 hrly intervals to capture spatial information between gauges locations
Thank you
CSIRO Land and WaterDr Luigi J RenzulloResearch Scientist
Phone: +61 2 6246 5758Email: [email protected]
Contact UsPhone: 1300 363 400 or +61 3 9545 2176Email: [email protected] Web: www.csiro.au