Transcript Slide 1

Fine-scale comparisons of satellite
estimates
Chris Kidd
School of Geography, Earth and Environmental Sciences
University of Birmingham
Rationale for finescale comparisons
Daily and monthly estimates hide algorithm problems:
• Rain areas/occurrence
• Rain intensities
- Temporal and spatial smoothing reduces irregularities
Daily products also have sampling issues – which can
cause strobe-like effects with rain movement
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Which UK validation data set?
Gauges
'Ideal' choice – representing 'true' 'at surface' rainfall, but:
• daily coverage good – hourly sparse (even in the UK)
• poor immediacy (~1-2 months delay)
• higher-temporal resolution available, but poor intensity
resolution (tips/min logging = 6 mm/h min rain rate)
Radar
Temporally and spatially superior (down to 5min, 2km), available
within an hour of collection: but,
• ground clutter & bright band (despite corrections applied)
• range dependency (ditto)
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Daily vs hourly gauge data
Daily gauge network
06-06Z
Hourly gauge network
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Radar: advantages/disadvantages
IR:radar matching
Daily total (mm) 14 Sept 2006
Blue = radar rain / IR no-rain
Red = IR rain / radar no-rain
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Time skill scores of rain retrievals
Radar
PMW
IR
Rainfall is temporally very fickle
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Finescale Comparisons
Instantaneous comparisons:
• Results at instantaneous / 5 km resolutions
• AMSR L2 rainfall product (GPROF)
• PCT (thresholds set – Kidd 1998 → dT×0.04+dT2×0.005)
• data remapped and processed on European IPWG
polar-stereographic projection
Future comparisons…
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
SSMI PCT 06-09-02 06:36
SSMI PCT 06-09-02 07:12
SSMI PCT 06-09-02 09:18
AMSR PCT 06-09-02 03:31
AMSR-L2 06-09-02 13:30
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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correlation
Correlations : instantaneous cases
AMSR PCT & GPROF
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PCT
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3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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rain total ratio
Ratio – accumulation : instan. cases
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PCT ratio
L2 ratio
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3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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Area ratio
Ratio – occurrence : instan. cases
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PCT
L2
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Need for case-classification
- rather than the wholesale 'lumping' all data into large
temporal results – need to look at the component
meteorology associated with the estimates:
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Statistics: blame it on the weather!
Movement:
Is the movement perpendicular
or along the rain band?
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Intensity
What is the range of values
within the rain area?
Size/variability
What is the size and variability
of the rain area(s)?
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Statistical success has as much to do with meteorology as the algorithms ability…
So… what now?
i) we must remember that PM instantaneous results are
better than Vis/IR-based techniques – including merged
techniques
ii) high temporal and spatial data can produce very good
statistics – if the data is of good quality
iii) prescribed temporal and spatial sampling is not always
ideal – are these applicable to applications?
• At present, comparisons at fixed regions and time scales
• Need for flexibility – to match user requirements
• Initial step at thinking about user-defined spatial and
temporal time scales
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Current 'interactive' comparison
The User
FTP
User text
Info checks
e-mail;
date range;
time range
Why FTP?
Simple to use and
set up batch jobs…
User data
QC checks
file size;
byte order;
data range
Radar data
generate time slots;
copy radar files;
accumulate data
E-mail User
Why e-mail?
Puts the results on
the User's desktop…
Disk-store
Maybe a Java
version too?
Statistics:
bias, ratio,
RMSE, CC, HSS
etc
Graphics
'Standard' IPWG
EU region
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Conclusions
Finescale – instantaneous / ~5km important: it allows us
• to disentangle algorithm performance
• to assess performance under different conditions
• address issues of rain occurrence and intensities
But, issues over:
• data integrity (data reliability – flagging of bad pixels)
• instrument noise (e.g. AMSR – and RFI)
Need for fine-resolution test cases: particularly with
common input data sets.
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Freezing levels
“Only one thing we do know is that the freezing level is relatively stable” Tom Wilheit
Surface Variability
Effects and contribution of surface variability to precipitation
retrievals.
V19 stddev
V37
V85
4th International GPM Planning Meeting, DC : 15-17 June 2004
Rain/no-rain induced biases
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-1.0
• Differences in rain/no-rain boundaries reveal regional
variations that do not exist in reality
• Further complicated since rain/no-rain boundaries
tend to differ over land/sea areas
Trends in Global Water Cycle Variables, UNESCO, Paris. 3-5 November 2004
Conclusions
PMW estimates are capable of retrieving light rainfall
Statistics often confuse the issue: more light rain tends to
produce poorer statistics
Instrument noise can be problematic (e.g. AMSR)
Surface screening - potential problems with 'false alarms'
over cold/snow surfaces
Lack of 'common' data sets – different algorithms use
different source data – different Q.C.
global
quick-look
images
Raw Data
Algorithms
Products
Remapping
to polar
stereographic
projection
Gauges
Radar
Statistical
analysis &
image
generation
User-defined
periods
(& resolutions)
Daily 00-24Z
results