fine-scale comparisons of satellite estimates chris kidd school of geography, earth and...
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![Page 1: Fine-scale comparisons of satellite estimates Chris Kidd School of Geography, Earth and Environmental Sciences University of Birmingham](https://reader036.vdocuments.us/reader036/viewer/2022062409/56649ed15503460f94be0d54/html5/thumbnails/1.jpg)
Fine-scale comparisons of satellite estimates
Chris Kidd
School of Geography, Earth and Environmental Sciences
University of Birmingham
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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
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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
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Daily vs hourly gauge dataDaily gauge network
06-06Z Hourly gauge network
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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Radar: advantages/disadvantages
Blue = radar rain / IR no-rainRed = IR rain / radar no-rainDaily total (mm) 14 Sept 2006
IR:radar matching
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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Time skill scores of rain retrievals
Radar
PMW
IR
Rainfall is temporally very fickle
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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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
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SSMI PCT 06-09-02 06:36
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SSMI PCT 06-09-02 07:12
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SSMI PCT 06-09-02 09:18
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AMSR PCT 06-09-02 03:31
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AMSR-L2 06-09-02 13:30
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Correlations : instantaneous cases
AMSR PCT & GPROF
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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Ratio – accumulation : instan. cases
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PCT ratio
L2 ratio
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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Ratio – occurrence : instan. cases
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3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
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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?
IntensityWhat is the range of values within the rain area?
Size/variabilityWhat is the size and variability of the rain area(s)?
Statistical success has as much to do with meteorology as the algorithms ability…
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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
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Current 'interactive' comparison
User dataUser text
Radar datagenerate time slots;copy radar files;accumulate data Graphics
'Standard' IPWG EU region
Statistics:bias, ratio, RMSE, CC, HSS etc
Disk-store
E-mail User
QC checksfile size;byte order;data range
Info checkse-mail;date range;time range
The User
FTP
Why FTP?Simple to use and set up batch jobs…
Why e-mail?Puts the results on the User's desktop…
3rd IPWG workshop, Melbourne, Australia. 23-28 October 2006
Maybe a Javaversion too?
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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
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Freezing levels“Only one thing we do know is that the freezing level is relatively stable” Tom Wilheit
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Effects and contribution of surface variability to precipitation retrievals.
V19 stddevV37 stddevV85 stddev
Surface Variability
4th International GPM Planning Meeting, DC : 15-17 June 2004
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-0.5
Rain/no-rain induced biases
-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
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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.
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Products
Raw Data Algorithms
Radar
Gauges
Remapping to polar
stereographicprojection
globalquick-look
images
Statisticalanalysis &
imagegeneration
Daily 00-24Zresults
User-definedperiods
(& resolutions)