Scatterometers at KNMI;Towards Increased

Resolution

Ad.Stoffelen@KNMI.nlHans BonekampMarcos Portabella

http://www.knmi.nl/scatterometer

Isabel

Miami Workshop 8-10 Feb ‘05 2

Overview Scatterometer

winds contain mesoscale detail not captured by NWP fields, but also noise

Mesoscale information is useful for nowcasting

MSS: an effective way of controling the noise

Spatial analysis in progress

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Spectral tail

Spectral response is used in engineering for design of noise properties

Ene

rgy

dens

ity

Ideal

Noisy

Truncated

Wave number

Being used now to increase SeaWinds resolution at KNMI

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Bad rainy case

Nadir noisy

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Broad Wind Direction Minima

Pro

bab i

lit y

of

Wind direction ()

Local minima

Local minima do not represent solution P

Solution bands

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A wide range of probable solutions exists in nadir(of 144 solutions per WVC)

Locally, 100-km product is pretty Unique(P threshold is 10-7)

Broad Minima

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Spatial filter: Mass

conservation Continuity

equation

0U = 0

Vertical motions < horizontal motion

Little divergence Mostly rotation

(extratropics)

Meteorological balance (2D-VAR)

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100 kmMultiple Solution

Scheme

1. Full use of solution probability info

2. Meteorological balance in Ambiguity Removal (2D-VAR)

(Portabella&Stoffelen, 2003)

Smooth solution

exists@100

km

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Standard scheme: < 4 solutions

Erratic at low wind speeds

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Multiple Solution Scheme

Smooth representation

Mesoscale detail kept

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ECMWF Position error

ECMWF First Guess ECMWF First Guess

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General MSS performance @100 km

Mean vector RMS difference with ECMWF FGAT (m/s) 

 

Swath region Standard procedure

MSS NCEP

Sweet 2.48 2.23 2.85

Nadir 2.98 2.45 2.96

MSS better than 4-solution standard, in particular at nadir NCEP background for 2DVAR much worse

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NOAA MSS @ 25 km

Improved coldfront

BetterAroundrain

50 kmPlots !

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NOAA MSS @ 25 km

Improved inflow

BetterAroundrain

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MSS @ 25 km

NOAA

Improved inflow

NCEP

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SummarySummary

- The use of more wind retrieval information in MSS allows consistent mesoscale features in the 25-km product

- A balanced spatial filter such as 2D-VAR is effective in removing noise and keeping meteorology, direction or vector uniformity constraints are less effective

- At 100-km the background wind used for ambiguity removal appears irrelevant, but this needs checking at 25 km

- The spectral behaviour of 2D-Var at 25-km needs to be evaluated

- Verification against buoys is underway

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Further ReferencesFurther ReferencesFor scatterometer-related papers, documentation,

and wind products of the SAFs please refer to

http://www.knmi.nl/scatterometer

We look forward to sharing- Our scatterometer processing software- Our ERS and QuikScat products - Our new wind stress products- Our experience

We fund visiting scientists

E-mail: scat@KNMI.nl

Miami Workshop 8-10 Feb ‘05 18

DIRTH (NOAA product)JPL’s Direction Interval Retrieval Threshold Nudging

DIRTH TN removes noise in 25-km product, but at some expenseUnnormalised notion of P (WVC and speed dependence)P segments exclude probable solutions (T=0.8; 0.2 left out)Medium filter ignores P within segmentNo meteorological balance constraints

DIRTH results inVery smooth fields (> 100 km)Loss of meteorological detail

KNMI proposes Multiple Solution Scheme

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Scatterometer Data Processor

Observations Inversion Ambiguity Removal

Wind Field

OUTPUTOcean Surface Radar Backscatter Observations

Inversion Ambiguity Removal

Quality Control

Pre- Process

Wind Field

INPUTOUTPUT

Quality Monitor

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Ambiguity Probability

Quadratic inner loop approximation?

IFS experiments from KNMI + some visits

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http://www.knmi.nl/scatterometer

QuikSCAT

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NWP Impact @ 100 km

Storm near

HIRLAM misses wave;SeaWinds should bebeneficial!

29 10 2002

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Satellite Application Facilities

Scatterometer sea surface wind R&D Quality control, rain and ice screening Spatial averaging (100 km 25 km) Inversion: Computation of wind solutions and associated

probabilities from measurement information Determination of information content; Observation

operatorAmbiguity removal (spatial filter to determine unique field)

Active monitoring and control (of instrument and processing)

Web site (visualisation) and product distribution

Product enhancement Preparation for ASCAT wind production (METOP; 2006)

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Detail in 100-km product

KNMI 100km

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Product Verification with ECMWF Winds

SD KNMI NOAA Speed 1.31 1.64Direction

13.58 14.58

U 1.60 1.96 V 1.58 1.80 Comparison for a set of triple KNMI-NOAA-ECMWF points

KNMI 100-km product better for NWP assimilation than NOAA NOAA wind speed score relatively bad due to

wind direction spatial filter KNMI rejects less high wind points (Portabella &, 2000)