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Diurnal Variability Analysis for GHRSST products Chris Merchant and DVWG.

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Presentation on theme: "Diurnal Variability Analysis for GHRSST products Chris Merchant and DVWG."— Presentation transcript:

1 Diurnal Variability Analysis for GHRSST products Chris Merchant and DVWG

2 Mean ocean diurnal-warming cycles

3 Importance of diurnal cycle in SST Diurnal cycle in air-sea interaction Affects the mean air-sea interaction Source of variability in satellite skin SSTs “Error” in satellite SSTs as estimates of SST depth –Deleterious to SST analyses –Complicates inter-sensor comparison, validation –Affects climate analysis of SST Need a DSST & eDSST estimate in L2P

4 Diurnal Variability Working Group

5

6 DV analysis: Spatially complete blend of diurnal warming estimated from temporal evolution of observed SST from model-based knowledge appropriately combined to give estimated subdaily time evolution of skin/subskin/buoy-depth SST

7 Experimental DVAs Data driven (SST differences, day-night) Analyses differences (skin-foundation) Blending of NWP-driven model and data

8 Based on SEVIRI SSTs + data driven analysis Cloudy areas set to zero diurnal warming 1 May 2006

9 Empirical model (Mark Filipiak poster in GHRSST10) Driven by NWP fluxes and winds (ECMWF)

10 Blending experiment Blend obs and model at 1400h LT (only) Blending done by adjusting the NWP fields to bring model DSST towards observed where available Then use model to propagate observations in time – validate

11 Blending Maximum wind since t 0, w max Integrated surface heating since t 0,, Qint DSST contours at 14h 1 K 2 K Example: Observed DSST = 2 K +/- 0.5 K Model DSST = 1 K Estimated errors in Qint and w max

12 Propagating in space and time At 1400h, the NWP increments are propagated into unobserved areas using assumed length scales For hours before and after 1400h, the NWP is also incremented to be consistent

13 Estimates of DSST field before and after observation time

14 Statistical improvement cf. SEVIRI data-driven analysis. Obs. from 14h only are used in blending SD(M-O) / KCorrelation HourModelBlendedModelBlended 110.450.320.450.62 120.380.260.510.74 130.400.260.520.84 140.410.170.610.94 150.400.250.620.87 160.370.260.620.83 170.340.250.630.81

15 Summary and feedback DVWG pursuing experiments in DVA Aim: “DVACs” providing NRT time- resolved (~hourly) analysis/forecast of diurnal warming for L2P providers Comments on this aim?


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