Quality indicators in an
operational precipitation
product
IPWG meeting 4
Beijing, 13-17 October 2008
Presented by:
Thomas Heinemann
Meteorological Operations Division
EUMETSAT
[email protected]
Slide: 1
Overview
1. News from METOP/HRPT
2. The Multi Sensor precipitation Estimate (MPE), a real-time
precipitation algorithm
3. Why shall we provide quality information
4. The MPE quality indicators (QI)
5. How useful are the MPE QIs
6. Outlook
Slide: 2
News from METOP-A HRPT
• METOP-A was launched on 19 October 2006
• LRPT direct data transmission was not activated
• HRPT direct data transmission service failed soon after
activation
• Root cause was heavy ion radiation causing the failure of a
component of the AHRPT Solid State Power Amplifier (SSPA)
• To minimise the risk of failure to the HRPT-B unit a "partial"
HRPT service in those areas where the risk of damage from
heavy ions is reduced, has been implemented.
• For southbound passes over Europe and the North Atlantic,
HRPT side B will be activated starting around 60°N.
• First activation was on 29 September 2008 (2 month trial)
Slide: 3
Slide: 4
MPE: a real-time precipitation algorithm
•
•
Combines passive microwave from polar orbiting satellites with IR data
from geo-stationary satellites.
Algorithm is based on the classical blending approach.
•
Instantaneous rain rate data are produced every 15/30min in original Geosatellite pixel resolution (MET-7 INDOEX, MET-8 RSS, MET-9 0°) in the
operational environment of the MSG groundsegment.
•
Processing is done in near-real time mode with a time delay of < 10
minutes between image acquisition and data dissemination.
•
Data are provided on the internet and via EUMETCAST in GRIB-2 data
format and in addition visualised on the EUMETSAT web-page.
Slide: 5
MPE: a real-time precipitation algorithm
Slide: 6
Who are the (designated) users of real-time
precipitation algorithms ?
NRT or RT precipitation data are
essential for:
Photos: WFP
• Short term weather forecasts and nowcasting
• Operational short term hydrological and acricultural applications
In large areas of the world methods based on ground
measurements or polar orbiting satellite products cannot fulfil
the NRT requirements and a dense radar network is not
available ( Africa, Asia !!!)
Slide: 7
Why (still) a blending algorithm ?
EUMETSAT ‘s and its users requirements for the rain-rate algorithm are:
1. To provide a real-time product in high temporal and spatial resolution.
2. To use a scientifically mature algorithm which has been proven to work
operationally.
•
•
Most other algorithm types cannot be used in real-time.
Other real-time algorithm’s are either very similar to the used one or still
in development phase.
But tests with other algorithms were done:
Hydro-estimator implemented for South Africa, CMORPH version tested, cooperation with H-SAF and NOAA …
Slide: 8
MPE and Hydro-Estimator in South Africa
MPE results (left) and Hydroestimator results (right) of the
instantaneous rain rate (mm/hour) based on the 10:00 UTC
MSG image of 6 November 2007.
Slide: 9
MPE validation by the European PEHRPP site
Slide: 10
MPE validation by the European PEHRPP site
0.800
0.700
0.600
MPE
0.500
CMORPH
HYDROEST
0.400
NOGAPS
PMIR
0.300
3B42RT
0.200
0.100
0.000
20080101
20080131
20080301
20080331
Courtesy: Chris Kidd
Slide: 11
20080430
20080530
20080629
20080729
20080828
Why (quantitative) quality indicators?
• Users trust data only if they have a clear vision how accurate
they are.
• Most algorithms perform in some conditions better than in
others (especially combined algorithms).
• Algorithm developers have more a-priori information available
and know their algorithm better than the users.
• Many algorithms depend on the results of previous data
analysis (eg. cloud mask). The quality of the previous steps
affects the quality of the final product.
• All this information should be provided to the users.
• Different applications need different QI’s!
Slide: 12
Continuous re-adjustment of LUTs as source for
MPE quality indicators
Blending principle:
Co–located microwave rain-rates and IR brightness temperature
for a specific region and time-span are used to derive a
monotonic relation between IR BT and rain rate.
Slide: 13
Definition of MPE QIs
QI1 := Correlation coefficient between MPE rain-rates for the
co-located IR data and the microwave data rain-rates
QI2 := Standard deviation between MPE rain-rates for the
co-located IR data and the microwave data rain-rates
Slide: 14
MPE Correlation QI
Slide: 15
MPE standard deviation QI
Slide: 16
Test strategy for QIs
Purpose : Test if MPE rain-rates in areas with high QI are really
better.
Method: Compare MPE rain rates ffrom the real-time algorithm
with microwave rain-rates.
Precondition: None of the microwave rain rates used for the
comparison are included in the co-locations.
Limitation: Not a real validation of rain-rates but of the
matching-algorithm.
Slide: 17
Correlation QI for 0.25° cell size
Slide: 18
Correlation QI for 5° cell size
Slide: 19
Histogram of QI1, January
Slide: 20
Histogram of QI1, July
Slide: 21
Summary
• EUMETSAT committed to continue the operational service for a
disk-wide real-time rain-rate product
• The current algorithm should be updated to a mature, stateof-the-art algorithm which fulfils the requirements.
• The EUMETSAT Hydrology SAF is developing additional
algorithms for various applications
• Effective and adapted Quality Indicators are essential for the
optimal application of precipitation products, especially in
models.
• The MPE QIs based on the co-location statistics are useful
indicators to identify the areas where the MPE algorithm
should not be used.
Slide: 22