Post on 24-Jul-2020
VALIDATION OF SATELLITE PRECIPITATION PRODUCTS WITH USE OF HYDROLOGICAL MODELS –
EUMETSAT H-SAF ACTIVITIES
Jerzy NiedbałaInstitute of Meteorology and Water ManagementHydrological Forecasting Office, Kraków
Bożena Łapeta, Piotr StruzikInstitute of Meteorology and Water ManagementSatellite Remote Sensing Centre, Kraków
Whole H-SAF Team contributed
Institute of Meteorology and Water Management, POLAND, Krakow
EUMETSAT H-SAF –
Satellite Application Facility in Support to Operational Hydrology and Water Management
• EUMETSAT H-SAF activities (very shortly).
• H-SAF validation programme
• IMWM Poland –
H-SAF validation studies of precipitation products:
-
Conventional validation,
-
Hydrological impact studies.
• Conclusions
Presentation outline:
Institute of Meteorology and Water Management, Kraków, PolandHydrological Forecasting OfficeSatellite Remote Sensing Centre
EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF)
H-SAF activities officially started (15 Sept.2005) –
development phase 2005-2010,
12 European countries involved.
Poland coordinates Hydrological Validation and implementation cluster.
The main objectives of H-SAF :
a.
to provide new satellite-derived products
from existing and future satellites with sufficient time and space resolution to satisfy the needs of operational hydrology; identified products:
precipitation soil moisture snowb.
to perform independent validation of the usefulness of the new products
for fighting against floods, landslides, avalanches, and evaluating water resources.
EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF)
H-SAF bottom-up aproachRequirements driven by operational hydrology needs.
Creation of operational satellite products for:-Better spatialisation of conventional measurements,-To complement ground observations on the areas with sparse ground networks and/or not covered by radars,- Merging satellite products with other data sources,- Redundancy of information - useful in case of disaster situation (damage of measuring posts or data links)
Final assessment of satellite products to be done by Hydrological Impact Studies.
Demonstration and training on satellite products use, in real operational environment of State Hydrological Services
Impact studies
Hydrological cycle vs. satellite products
Precipitation ProductsProducts from H-SAF (quality figures refer to the Operational Phase (2010-2015)
Product Resolution (Europe) Accuracy Cycle (Europe) Timeliness
PR-OBS-1SSMI/SSMISPrecipitation rate at ground by MW conical scanners
10 km (with CMIS)15 km (with other
GPM)
10-20 % (rate > 10 mm/h), 20-40 % (rate1 to 10 mm/h), 40-80 % (rate < 1 mm/h)
6 h (with CMIS only)
3 h (with full GPM)
15 min
PR-OBS-2(AMSU Data NOAA)Precipitation rate at ground by MW cross-track scanners Two subproducts 2.1, 2.2
10 kmRanging from MW performance to
degradedone to an amount to be assessed
6h 5 min
PR-OBS-3Precipitation rate at ground by GEO/IR supported by LEO/MW
8Km 40-80% (rate > 10 mm/h) 15min 5min
PR-OBS-53, 6, 12 and 24 hcumulated rain
10 km(from merged MW
+ IR)
Depending on integration interval.Tentative: 10 % over 24 h, 30 % over 3 h 3 h 15 min
CMIS = Conical Scanner Microwave Imager - DMSP GPM = Global Precipitation Measurement mission
Soil Moisture Products
Soil moisture inthe surface layer
25 km (from ASCAT)
40 km (from CMIS)
0.05 m3 m-3 (dependingOn vegetation)
36 h (from ASCAT)6 h (from
CMIS)
2 h
Soil moisture inthe roots region
25 km (from ASCAT)
40 km (from CMIS)
To be assessed (model- dependent).
Tentative: 0.05 m3 m-3
36 h (from ASCAT)6 h (from
CMIS)
2 h
From METOP
Snow Products
Snowrecognition
5 km (in MW)2 km (in
VIS/SWIR/TIR)
95 % probability ofcorrect classification 6 h 2 h
Snow effectivecoverage
10 km (in MW)5 km (in
VIS/SWIR/TIR)
15 % (depending onbasin size and complexity) 6 h 2 h
Snow status(wet or dry) 5 km 80 % probability of
correct classification 6 h 2 h
Snow waterEquivalent 10 km To be assessed.
Tentative: 20 mm 6 h 2 h
SWIR = Short Wave InfraredTIR = Thermal Infrared
Logic of the incremental development scheme
End-userfeedback
Augmenteddatabases
Advancedalgorithms
Newinstruments
Initialdatabases
Baselinealgorithms
Currentinstruments
Cal/valprogramme
Version-1 Version-2 Final VersionPrototyping
Operational End-users and Hydrological validation programme
2007Inter
Consortium beta product
delivering
H-SAF activities on satellite products validation
Cluster 1
Precipitation products
Cluster 2
Soil Moisture products
Cluster 3
Snow products
ClassicalValidation.Comparisonto ground measurements.
Cluster4Hydrological Validation programme
BelgiumGermanyHungaryItalyPolandSlovakiaTurkey
AustriaFranceECMWF
FinlandGermanyPolandTurkeyRomania
Belgium, France, Germany, Italy, Poland, Slovakia, Turkey
At least 24 catchments, 14 operational models
Fig. 28 -
Composite image from all Polish radars.
8 radars
989 telemetric posts
IMWM
Poland
METEOSAT (7,8,9)
NOAA (all)FengYun 1D
Ready for Metop60 Synop
152 Climate
978 Raingauges
196 Snow obs. posts
Lightning detection
SAFIR
NWP:
LM-COSMO, ALADIN
••Variety of climatological conditionsVariety of climatological conditions••Variety of terrain conditionsVariety of terrain conditions••Variety of land coverVariety of land cover
••Different hydrological regimesDifferent hydrological regimes••Catchment size: 242 Catchment size: 242 ––
102000 km102000 km22
•• 902 raingauges, 21 radars902 raingauges, 21 radars
Classical validation Case studies
Three days: 22 May, 04 June and 05 June 2007, during which convective precipitation occurred, very heavy, in places.
22 May 2007, Zielona Gora, South-Western Poland
NOAA/AVHRR, RGB channels 1, 2 and 4
RG Data10 minute precipitation sums form Polish automatic rain gauges network.
RG data quality control – analysis of the measurements performed by each gauge (the posts are equipped with two gauges) in order to exclude the wrong data.
The time slots closest to satellite overpass were taken into consideration.
Validation method
For each satellite AMSU / NOAA pixels, the automatic rain gauges situated within that pixel were found.
Different pixel’s size was taken into account depending both on the direction (along and across track) and the pixel position in scanning line.
The pixel shape was assumed to be the rectangular.
If more than one rain gauge were found within one satellite pixel, the ground rain rate value was calculated as a mean of all rain gauges measurements.
H01, 2007-05-22 15.20 UTC H02 v.1.1, 2007-06-04 14.37 UTC H02 v.2.1, 2007-06-04 14:37 UTC
H03 v.1.0, 2007-06-04 14.27 UTC H05 v.1.0, 2007-06-04 15.00 UTC
Preliminary results
Three days with typical convective precipitation 05.2007 i 04-05.06.2007.All available precipitation products were analysed:
91%
2%
18,8 %
89,2 %7%
H03 v1.0
S‐NO D‐NO
S‐NO D‐YES
S‐YES i D‐YES
S‐YES i D‐NO
93% 3%
28 %
72 %
4%
H01 v1.0
S‐NO D‐NO
S‐NO D‐YES
S‐YES i D‐YES
S‐YES i D‐NO
90%
5%
37,6 %
62,4 %
5%
H02 v1.1
S‐NO D‐NO
S‐NO D‐YES
S‐YES i D‐YES
S‐YES i D‐NO
24,43 %
0,26 %7,7 %
92,3 %75,31 %
H02 v2.1
S‐NO D‐NO
S‐NO D‐YES
S‐YES i D‐YES
S‐YES i D‐NO
76%
10% 33,9 %
66,1 %
14%
H05 v1.0 ‐ suma 3‐godzinna opadu
S‐NO D‐NO
S‐NO D‐YES
S‐YES i D‐YES
S‐YES i D‐NO
H01 v1.0 H02 v1.1 H02 v2.1 H03 v1.0 H05 v1.0
Hit rate * 0.28 0.26 0.96 0.26 0.33
False-alarm rate
0.69 0.62 0.92 0.89 0.68
Odds ratio 13.37 11.13 7.73 4.92 4.00
Accuracy 0.94 0.92 0.30 0.91 0.81
Frequency Bias
0.89 0.7 12.49 2.4 1.06
* Strongly dependant on the rain/no rain class numbering
Results of the categorical (dichotomous) statistics obtained for H-SAF precipitation products
1
10
100
1000
10000
0 0,25 0,5 1 2 4 8 10 16 32 64
Num
ber o
f cas
es
Rain rate [mm/h]
Rain gauges H02 v21 H02 v11
1
10
100
1000
10000
0 0,25 0,5 1 2 4 8 10 16 32 64
Num
ber o
f cas
es
Rain rate [mm/h]
Rain gauges H01 v10
1
10
100
1000
10000
0 0,25 0,5 1 2 4 8 10 16 32 64
Num
ber o
f cas
es
3 hour accumulated precipitation [mm]
Rain gauges H05 v10
1
10
100
1000
10000
100000
1000000
Num
ber o
f cas
es
Rain rate [mm/h]
RG H03
H01 v1.0[mm/h]
H02 v1.1[mm/h]
H02 v2.1[mm/h]
H03 v1.0[mm/h]
H05 v1.0[mm]
Mean error -2.938 1.10 -0.82 -1.86 0.01
Mean absolute
error
4.14 2.69 1.25 4.35 1.35
RMSE 7.12 4.12 2.93 11.69 9.08
Multiplicative
Bias
0.4 1.43 0.48 0.57 1.00
Standard Deviation of diff.
6.62 4.00 2.82 11.55 9.09
Results of continuous statistics for H-SAF precipitation products obtained with the use of rain gauges data (in [mm/h] for H01-H03 and in [mm] for H05)
-60-40-20
020406080
100120140
0 10 20 30 40 50 60 70 80
Rai
n ra
te d
iff.
Sat-R
G [
mm
/h]
Rain rate form RG [mm/h]
H02 v1.1
August 2006 – August 2007
First conclusions:
• The obtained results showed that both, detection and estimation of intensity of convective rainfall is very difficult task. All rain rate products significantly underestimate high precipitation and overestimate low precipitation.
• For all products, except for H02 v21, the similar values of the hit rate, false alarm rate and accuracy were obtained. However, high value of odds ratio for H01 suggests that this product recognises the precipitation better than other.
• Both, dichotomous and continuous statistics, showed that rain rate products H02 v21 was too ‘rainy’, especially for the low precipitation categories (form 0 to 1 mm/h)
• The quality of H02 products depends on the pixel number: the closer to the ends of AMSU scan line, the lower quality.
• H03 products quality seems to be lower, with reasonably higher RMSE and lower correlation coefficient. This product has been preliminary validated with the use of hydrological model
Preparation of tool verification Preparation of tool verification --
the hydrological modelthe hydrological model
input data for hydrological models from manual and
automatic ground stations and experimental resarch
hydrological model
output from hydrological model
(simulated hydrograph)
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Oś
war
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iw
szys
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patr
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149190100 P00050B 149190100 B00050B
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war
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i-w
szys
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patr
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149190100 P00050B
comparing simulated and observed hydrographs
simulated mode -
calibration and the verification of hydrological model
• average square error
• average square relative error
• maximum relative error
• time relative error
General hydrological validationGeneral hydrological validation
algorithmalgorithm
(1)(1)
Hydrological model in operating modeHydrological model in operating mode
input data for hydrological models from manul and
automatic ground stations and experimental resarch
hydrological model
output from hydrological model
(forecasted hydrograph)
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war
tośc
iw
szys
tkie
patr
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149190100 P00050B 149190100 B00050B
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war
tośc
i-w
szys
tkie
patr
amet
ryse
rii
149190100 P00050B
comparing forecasting and observed
hydrographs in non-
operating time
operating mode -
starting hydrological model in operating mode
• average square error
• average square relative error
• maximum relative error
• time relative error
General hydrological validationGeneral hydrological validation
algorithmalgorithm
(2)(2)input data for hydrological models from radar system (now-casting)
and meteorological model (forecasting)
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Oś
war
tośc
iw
szys
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patr
amet
ryse
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149190100 P00051B 149190100 P00050B 149190100 B00050B
Hydrological model in operating mode using satellite dataHydrological model in operating mode using satellite data
input data for hydrological models from manul and
automatic ground stations and experimental resarch
hydrological model
output from hydrological model
(standard forecasted hydrograph and
forecasted hydrograph computed using
satellite data)
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20,00
40,00
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30-08-06 6:00 30-08-06 18:00 31-08-06 6:00 31-08-06 18:00 01-09-06 6:00 01-09-06 18:00
Oś
war
tośc
iw
szys
tkie
patr
amet
ryse
rii
149190100 P00050B 149190100 B00050B
comparing two forecasted
hydrographs (computed on base standard or satellite data) with observed hydrograph in non-
operating time
operating mode -
starting hydrological model in operating mode
• average square error
• average square relative error
• maximum relative error
• time relative error
temperature, rainfall and snow
rainfall and snow
soil moisture
satellite data General hydrological validationGeneral hydrological validation
algorithmalgorithm
(3)(3)
satellite data
input data for hydrological models from radar system (now-
casting) and meteorological model (forecasting)
Hydrological validation planHydrological validation plan
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Oś
war
tośc
iw
szys
tkie
patr
amet
ryse
rii
149190100 P00051B 149190100 P00050B 149190100 B00050B
comparing two forecasting
hydrographs (computed on base of standard or satellite data) with observed hydrograph in non-
operating time
• average square error
• average square relative error
• maximum relative error
• time relative error
statistical analyses
use of the satellite data increases the quality of
hydrological forecasting
we recommend satellite data as an input to
hydrological forecasting model
we recommend standard data as an input to hydrological
forecasting model
YES NO
criteria of choice
General hydrological validationGeneral hydrological validation
algorithmalgorithm
(4)(4)
NEITHER „YES”
NOR „NO”
Further research must be done: when, where and why
use of satellite data gives negative results. Satellite data could be used in case when other data are not available
Feedback to Clusters 1,2,3
Test site SoTest site Sołłaa
3-9-2007 5-9-2007 7-9-2007 9-9-2007 11-9-2007 13-9-2007 15-9-2007 17-9-2007 19-9-2007 21-9-2007 23-9-2007 25-9-2007 27-9-2007 29-9-2007
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
500.0
550.0
600.0
650.0
700.0
[]
10.0
9.5
9.0
8.5
8.0
7.5
7.0
6.5
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
[]Time Series Discharge
• rainfall event: September 2007• calibration:
• based on manual ground stations •based on telemetric stations
• simulation: imput data from telemetric stations were exchanged by satellite data during 5-6.09.2007
Modelling resultsModelling results
manual ground stations telemetric stations
input data from telemetric stations were exchanged by satellite data during 5-6.09.2007
Error estimationError estimation
calibrationbasis on the standard net
calibrationbasis on the telemetric net
simulationbasis on the satellite data
correlation coeficient(model quality(*))
0,969(very good)
0,992(excellent)
0,966(very good)
peak error 0,483 0,081 0,341volume error 0,295 0,159 0,251peak time error
0,125 -0,042 -0,153
root mean square error
0,926 0,983 0,913
water balance 0,0 0,0 12,8
(*) - on basis Sarma P.B.S., Delleur J.W., Rao A.R., 1973, Comparison of rainfall - runoff models for urban areas, Journal of Hydrology 18(3/4), 329-347
(preliminary) CONCLUSION:
exchange of ground observations by satellite data don’t relapse results
of
hydrological
model
Conclusions:
1.
H-SAF is preparing operational structure for hydrology. Without acceptation of products and their quality (at least among EUMETSAT Member and Cooperationg States), this activity will be useless.
2.
First H-SAF products already available (inter SAF distribution).
3.
Preliminary validation is promising –
further studies required.
4.
Hydrological impact studies –
way forward to avoid problem with comparison of completely different data.