1. Institute of Meteorology and Water Management, POLAND, Krakow
EUMETSAT H-SAF – Satellite Application Facility in Support to Operational Hydrology and Water Management
Validation of satellite precipitation products with use of hydrological models – EUMETSAT H-SAF activities
Jerzy Niedbała
Institute of Meteorology and Water Management
Hydrological Forecasting Office, Kraków
Bożena Łapeta, Piotr Struzik
Satellite Remote Sensing Centre, Kraków
Whole H-SAF Team contributed

2. Presentation outline:
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

3. The main objectives of H-SAF :
Institute of Meteorology and Water Management, Kraków, Poland
Hydrological Forecasting Office
Satellite 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 ,
12 European countries involved.
Poland coordinates Hydrological Validation and implementation cluster.
The main objectives of H-SAF :
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 snow
to perform independent validation of the usefulness of the new products for fighting against floods, landslides, avalanches, and evaluating water resources.

4. EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management
(H-SAF)
H-SAF bottom-up aproach
Requirements 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

5. Hydrological cycle vs. satellite products
Impact studies

6. Precipitation Products
Products from H-SAF (quality figures refer to the Operational Phase ( )
Product
Resolution (Europe)
Accuracy
Cycle (Europe)
Timeliness
PR-OBS-1
SSMI/SSMIS
Precipitation rate at ground by MW conical scanners
10 km (with CMIS)
15 km (with other GPM)
10-20 % (rate > 10 mm/h), % (rate
1 to 10 mm/h), % (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 km
Ranging from MW performance to degraded
one to an amount to be assessed 6h
5 min
PR-OBS-3
Precipitation rate at ground by GEO/IR supported by LEO/MW
8Km
40-80% (rate > 10 mm/h)
15min
5min
PR-OBS-5
3, 6, 12 and 24 h
cumulated rain
(from merged MW + IR)
Depending on integration interval.
Tentative: 10 % over 24 h, 30 % over 3 h
3 h
CMIS = Conical Scanner Microwave Imager - DMSP
GPM = Global Precipitation Measurement mission

7. Soil Moisture Products
Soil moisture in
the surface layer
25 km (from ASCAT)
40 km (from CMIS)
0.05 m3 m-3 (depending
On vegetation)
36 h (from ASCAT)
6 h (from CMIS)
2 h
the roots region
To be assessed (model-dependent).
Tentative: 0.05 m3 m-3
From METOP

8. correct classification basin size and complexity)
Snow Products
Snow
recognition
5 km (in MW)
2 km (in VIS/SWIR/TIR)
95 % probability of
correct classification
6 h
2 h
Snow effective
coverage
10 km (in MW)
5 km (in VIS/SWIR/TIR)
15 % (depending on
basin size and complexity)
Snow status
(wet or dry)
5 km
80 % probability of
Snow waterEquivalent
10 km
To be assessed.
Tentative: 20 mm
SWIR = Short Wave Infrared
TIR = Thermal Infrared

9. Logic of the incremental development scheme
End-user
feedback
Augmented
databases
Advanced
algorithms
New
instruments
Initial
Baseline
Current
Cal/val
programme
Version-1
Version-2
Final Version
Prototyping
Operational End-users and Hydrological validation programme
2007
Inter Consortium beta product delivering

10. H-SAF activities on satellite products validation
Cluster 1
Precipitation products
Cluster 2
Soil Moisture products
Cluster 3
Snow products
Finland
Germany
Poland
Turkey
Romania
Belgium
Germany
Hungary
Italy
Poland
Slovakia
Turkey
Classical
Validation.
Comparison
to ground measurements.
Austria
France
ECMWF
Cluster4
Hydrological
Validation
programme
Belgium, France, Germany, Italy, Poland, Slovakia, Turkey
At least 24 catchments, 14 operational models

11. Lightning detection SAFIR
METEOSAT (7,8,9)
NOAA (all)
FengYun 1D
Ready for Metop
60 Synop
152 Climate
978 Raingauges
196 Snow obs. posts
NWP:
LM-COSMO, ALADIN
IMWM
Poland
Fig Composite image from all Polish radars.
8 radars
989 telemetric posts

12. Variety of climatological conditions
Variety of terrain conditions
Variety of land cover
Different hydrological regimes
Catchment size: 242 – km2
902 raingauges, 21 radars

13. 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

14. RG Data
10 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.

15. 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.

16. H01, UTC
H02 v.1.1, UTC
H02 v.2.1, :37 UTC
H03 v.1.0, UTC
H05 v.1.0, UTC

17. Preliminary results
Three days with typical convective precipitation i
All available precipitation products were analysed:

18. Results of the categorical (dichotomous) statistics obtained for H-SAF precipitation products
H01 v1.0
H02 v1.1
H02 v2.1
H03 v1.0
H05 v1.0
Hit rate *
0.28
0.26
0.96
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
0.30
0.91
0.81
Frequency Bias
0.7
12.49
2.4
1.06
* Strongly dependant on the rain/no rain class numbering

20. 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)
H01 v1.0
[mm/h]
H02 v1.1
H02 v2.1
H03 v1.0
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

21. August 2006 – August 2007

22. 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

23. General hydrological validation algorithm (1)
Preparation of tool verification - the hydrological model
simulated mode - calibration and the verification of hydrological model
input data for hydrological models from manual and automatic ground stations and experimental resarch
hydrological model
output from hydrological model (simulated hydrograph)
comparing simulated and observed hydrographs
average square error
average square relative error
maximum relative error
time relative error

24. General hydrological validation algorithm (2)
input data for hydrological models from radar system (now-casting) and meteorological model (forecasting)
General hydrological validation algorithm (2)
Hydrological model in operating mode
operating mode - starting hydrological 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)
comparing forecasting and observed hydrographs in non-operating time
average square error
average square relative error
maximum relative error
time relative error

25. General hydrological validation algorithm (3)
input data for hydrological models from radar system (now-casting) and meteorological model (forecasting)
General hydrological validation algorithm (3)
satellite data
Hydrological model in operating mode using satellite data
satellite data
rainfall and snow
operating mode - starting hydrological model in operating mode
output from hydrological model (standard forecasted hydrograph and forecasted hydrograph computed using satellite data)
comparing two forecasted hydrographs (computed on base standard or satellite data) with observed hydrograph in non-operating time
input data for hydrological models from manul and automatic ground stations and experimental resarch
hydrological model
average square error
average square relative error
maximum relative error
time relative error
soil moisture
temperature, rainfall and snow

26. average square relative error maximum relative error
General hydrological validation algorithm (4)
Hydrological validation plan
criteria of choice
use of the satellite data increases the quality of hydrological forecasting
comparing two forecasting hydrographs (computed on base of standard or satellite data) with observed hydrograph in non-operating time
statistical analyses
NEITHER „YES” NOR „NO”
YES NO
average square error
average square relative error
maximum relative error
time relative error
we recommend satellite data as an input to hydrological forecasting model
we recommend standard data as an input to hydrological forecasting model
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

27. Test site Soła 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

28. Modelling results input data from telemetric stations were
exchanged by satellite data during
manual ground stations
telemetric stations

29. Error estimation (preliminary) CONCLUSION:
calibration
basis on the standard net
basis on the telemetric net
simulation
basis on the satellite data
correlation coeficient
(model quality(*))
0,969
(very good)
0,992
(excellent)
0,966
peak error
0,483
0,081
0,341
volume error
0,295
0,159
0,251
peak time error
0,125
-0,042
-0,153
root mean square error
0,926
0,983
0,913
water balance
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),
(preliminary) CONCLUSION:
exchange of ground observations by satellite data don’t relapse results of hydrological model

30. Conclusions:
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.
First H-SAF products already available (inter SAF distribution).
Preliminary validation is promising – further studies required.
Hydrological impact studies – way forward to avoid problem with comparison of completely different data.