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22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Tim Hewison NWP Bias Monitoring Double-Differencing as inter-calibration technique.

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Presentation on theme: "22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Tim Hewison NWP Bias Monitoring Double-Differencing as inter-calibration technique."— Presentation transcript:

1 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Tim Hewison NWP Bias Monitoring Double-Differencing as inter-calibration technique

2 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Background Numerical Weather Prediction (NWP) models Routinely compare Observations with ‘Background’ (short-range forecast model fields) In Observation space – after applying radiative transfer model to model fields extracted to coordinates of observations 1 st step of assimilation cycle To monitor and correct relative biases Observations-Model Potential method to monitor instruments’ relative biases By Double Differencing against NWP model fields Hypothesis: Background error terms tend to cancel out –Bullet

3 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea UK Met Office Datasets UK Met Office process satellite data from : Meteosat/SEVIRI – IR channels only – in clear sky only Metop/IASI – only 35/8741 channels - in clear sky only Aqua/AIRS – only 35/ channels – in clear sky only Metop/HIRS – IR channels only – in clear sky only Envisat/AATSR – IR channels only – in clear sky only NOAA/Metop/AMSUA/AMSUB/MHS – microwave – all sky …and many more! UK Met Office supplied EUMETSAT with Bias Monitoring data –For above instruments –Over 1 or 2 year period

4 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea © Crown copyright Met Office Calculation of radiance biases For channel n Sensor 1: DBT(n) = mean{y n - H n (x i )} i= 1 to k obs For channel m Sensor 2: DBT(m) = mean{y m - H m (x i )} i= 1 to k obs Assume same bias? x i atmospheric state vector for observation i Compare DBT(n) with DBT(m) (double difference)

5 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea © Crown copyright Met Office Binning of data ParameterSEVIRIHIRSIASIAIRS TemporalHourly4 hrs12 hrs Spatial1 in 4All1 in 41 in 9 Latitude30 o bands Longitude30 o bands Channels8 IR19 IRTable 2 Incidence angles10 o bins Surface typesea/land/ice CloudClear/cloudy Day/night Clear/cloudy Day/night Clear/cloudy Day/night Clear/cloudy Day/night Scene radiance10 bins channel specific 10 bins channel specific 10 bins channel specific 10 bins channel specific

6 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea © Crown copyright Met Office SEVIRI vs HIRS/IASI 13.4um bias HIRS SEVIRI IASI

7 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea © Crown copyright Met Office SEVIRI vs HIRS/IASI 13.4um bias IASI-NWP SEVIRI-NWP (SEVIRI-NWP) -(IASI-NWP)

8 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea © Crown copyright Met Office EUMETSAT Inter-Calibration of Met9/SEVIRI-Metop/IASI IR13.4 SEVIRI-IASI

9 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea © Crown copyright Met Office SEVIRI vs HIRS/IASI 13.4um bias (SEVIRI-NWP) -(IASI-NWP) SEVIRI-IASI Same Slopes Different Offsets

10 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea SEVIRI & IASI have Different Background Errors Because different channels see different things, Background error terms don’t fully cancel! B 1 ≠B 2 We want to obtain radiance difference:(O 1 -O 2 )= (L M -L M|R ) From NWP Bias Monitoring statistics:(O-B) 1 = (L M -L M|N ) and (O-B) 2 = (L R’ -L R’|N ) M = Monitored Instrument (SEVIRI), N = NWP Model Fields R = Reference Instrument (IASI), R’ = Subset of Reference Instrument A priori we can use IASI obs to synthesis L M & L R & L R’ and use these to establish relationships: L M|R = ƒ( L R ) & L R’|R = ƒ( L R ) However, we cannot establish L R’|N or L M|N without L R|N

11 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea SEVIRI & HIRS have Different Background Errors Because different channels see different things, Background error terms don’t fully cancel! B 1 ≠B 2 We want to obtain radiance difference:(O 1 -O 2 )= (L M -L M|R ) From NWP Bias Monitoring statistics:(O-B) 1 = (L M -L M|N ) and (O-B) 2 = (L R -L R|N ) M = Monitored Instrument (SEVIRI), N = NWP Model Fields R = Reference Instrument (HIRS) A priori we can use IASI obs to synthesis L M & L R and use these to establish the relationship: L M|R = ƒ( L R ) However, we cannot establish L R|N or L M|N without L R|N

12 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Hybrid Method So: Use Inter-Calibration of Collocated Observations –within mstats bins –|Lat|<30°, |Lon-Lon SS |<30°, | θ |<30°, |Tb-Tb STD |<10K, … To ‘calibrate’ NWP Double-Difference Stats –in the same bins –at same time/dates –to account for different background errors Allows NWP bias statistics to be used to study dependencies –SEVIRI-IASI as (L M -L M|R )= ƒ(λ, φ, θ, T b, …)

13 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Double Difference Analysis Procedure Read selection of IASI data for training dataset, L R Convolve IASI data with SEVIRI SRS, L M|R Extract IASI channels present in mstats data, L R’|R Multiple regression of L M|R and L R’|R to define coefficients w i,j Clear sky only? Select different date – make it a weekend Read mstats data for SEVIRI-NWP, ( L M -L M|N ) i over 4wk period Read mstats data for IASI-NWP, (L R’ -L R’|N ) j over 4wk period Double-Difference (SEVIRI-NWP)-(IASI-NWP): ( L M -L M|N ) i – ∑ j w i,j (L R’ -L R’|N ) j Read all SEVIRI-IASI collocation data: L M, L R over 4wk period Apply a cloud mask and land/sea mask to SEVIRI-IASI data Bin SEVIRI-IASI data into same bins as mstats Compare mean and SD of ( L M -L M|N ) i – ∑ j w i,j (L R’ -L R’|N ) j and L M -L R in bins

14 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Calibrating Model Biases Statistics give similar trends NWP Double Differences Radiance synthesized from IASI observations for: –Clear Sky –Over Sea –At Night –in the same bins: |Lat|<30°, |Lon-Lon SS |<30° Slopes of (L M -L M|N )-(L R’ -L R’|N ) similar to L M|R -L R’|R for –Incidence Angle, θ –Tb (limited range) + Black: double differences (O-B) SEVIRI -(O-B) IASI : (L M -L M|N )-(L R’ -L R’|N ) X Red: direct radiance differences O SEVIRI -O IASI : (L M -L M|R ) For all observed radiances Subtract these for a given bin to estimate magnitude of model contributions First Rough Results

15 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Results To be continued…

16 22 March 2011: GSICS GRWG & GDWG Meeting Daejeon, Korea Interesting Irrelevancies Statistics give similar trends NWP Double Differences Radiance synthesized from IASI observations for: –Clear Sky –Over Sea –At Night –in the same bins: |Lat|<30°, |Lon-Lon SS |<30° Slopes of (L M -L M|N )-(L R’ -L R’|N ) similar to L M|R -L R’|R for –Incidence Angle, θ –Tb (limited range) + Black: radiance difference between SEVIRI channels and IASI subsets, L M|R -L R’|R : X Red: double differences (O-B) SEVIRI -(O-B) IASI : (L M -L M|N )-(L R’ -L R’|N ) But B SEVIRI ≠ B IASI, so calculating O SEVIRI -O IASI doesn't really help!


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