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Forecast impact experiments with CHAMP RO measurements

Forecast impact experiments with CHAMP RO measurements. Sean Healy Acknowledgements Jean-Noël Thépaut, Sami Saarinen, Niels Bormann, Lars Isaksen, Adrian Jupp and Christian Marquardt , GRAS-SAF, UCAR, GFZ, EUMETSAT. Outline. GRAS-SAF 1D bending angle observation operator.

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Forecast impact experiments with CHAMP RO measurements

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  1. Forecast impact experiments with CHAMP RO measurements Sean Healy Acknowledgements Jean-Noël Thépaut, Sami Saarinen, Niels Bormann, Lars Isaksen, Adrian Jupp and Christian Marquardt , GRAS-SAF, UCAR, GFZ, EUMETSAT.

  2. Outline • GRAS-SAF 1D bending angle observation operator. • Assumed observation/forward model errors. • A summary of first 4D-Var forecast impact experiments using CHAMP data from 1st August – 29th September, 2003. • Estimate of the Degrees of Freedom for Signal (DFS) of RO in 4D-Var. • First results with 2D operators. • Conclusions and future plans.

  3. 1D bending angle operator Assuming spherical symmetry the bending angle can be written as: Convenient variable We evaluate this integral using the NWP profile information at the location of the “occultation point” defined by UCAR as location where the excess phase exceeds 500m for the first time. I’m using the GRAS-SAF bending angle code developed by myself and Christian Marquardt (To be used in validation of GRAS data at EUMETSAT).

  4. 1D bending angle operator: main steps • Calculate the geopotential height of model levels (Note NWP surface pressure used here) . • Calculate refractivity, N, on (full) model levels. • Convert to geometric heights (and radius) using a transform given by List (Smithsonian Met Tables). • Calculate the refractive index-radius product, x=nr, on full levels. • Evaluate the integral assuming N varies exponentially between the model levels with x.

  5. Calculation of radius of model levels The geopotential heights of model levels are calculated with a standard ECMWF routine, but we need the radius of these levels. The geometric height is given by The radius is then given by Geopotential height Effective radius from List. g at latitude, l Undulation: height of geoid above ellipsoid Radius of curvature

  6. 1D bending angle operator We assume the refractivity varies exponentially between the model levels to obtain and approximate (Error less than 0.1% ~1/(8ka))

  7. 1D Bending angle operator Hence, the bending can be written analytically where “erf” is the Gaussian error function. Total bending angle given by summing up contributions of this form. Bending above the model top is handled quite easily in this approach. The forward model is not computationally expensive! Don’t need to extrapolate above the NWP model top!

  8. Assumed observation/forward model errors The derived bending angles are interpolated to 200 fixed impact heights (h=a-Roc) between the surface and 40km. Separation is ~100m near surface, 300m near 40km. The percentage error in bending angle falls linearly from 10% at h=0 to 1% at h=10km. Above 10km we use MAX(1%, 6mrad). NO vertical correlations included. Map these to refractivity space with an Abel transform, will give refractivity errors comparable to Kuo et al. (2004).

  9. Assimilation Experiments Performed with the ECMWF 4D-Var system at T319 resolution (~62.5 km horizontal grid). Ran my own control at this resolution. All other available data, including AIRS, assimilated (~2.7 million per 12 hours). CYCLE 26R3. CHAMP data from 1st August – 29th September 2003. Experiment 1 used bending angle data processed by UCAR with Full Spectral Inversion (FSI) “raw” bending angle data. Typically 12,500 bending angles assimilated per 12 hours (cf 2.7 million other obs!). “Raw” means no statistical optimization of the bending angles.

  10. Assimilation Experiments (2) Blacklisted all bending angles with impact heights (h=a-ROC) less than 5km. Known biases in the bending angles. Experiment 1: 1st Aug – 31st Aug, 2003 Good improvement in the stratosphere (details later in talk) but a clear degradation of the southern hemisphere 500hPa heights against observations and analyses, which we will deal with first.

  11. RMS error in 500hPa Z in SH

  12. RMS of control-experiment surface pressure analysis differences (Pa)

  13. The SH 500Z problem – Ps increments The largest errors were over Antarctica and appear to be a result of large surface pressure increments. By assuming uncorrelated errors, we are probably overestimating the surface pressure information. Will address this again later in the talk, using more recent experiments.

  14. Experiment 2: 1st Aug. 29th Sept. 2003. Modified the tangent-linear and adjoint routines to stop the hydrostatic surface pressure increments for GPS observations. This was found to reduce the 500hPa height problems.

  15. Neutral results with this measure.

  16. Zonally averaged mean temperature analysis differences

  17. Zonally averaged RMS analysis differences Significant differences in analyses despite low number of GPS RO observations!

  18. Radiosonde comparisons for Antarctica 12h forecasts Red lines: control Black: experiment Structure in the mean fit caused by inconsistencies in the AIRS and AMSU bias corrections schemes (G. Kelly, ECMWF Pers. Comm)

  19. Verification against radiosondes (SH) Red control Blue experiment

  20. Verification against radiosondes(Tr)

  21. Degrees of Freedom for signal (DFS) The DFS is an information content measure that is used widely in satellite meteorology, e.g., in channel selection. It can be written as, Ie, the Trace of the “Averaging-Kernel” Background error covariance Solution error covariance Recently, there has been increasing interest in calculating the DFS of the 4D-Var system as a means of comparing the contribution of different ob-types, but its quite complicated because of the size of the matrices (Fisher, 2003).

  22. DFS(2) But we have found (Rodgers, 2000) an alternative approach because Hence, we can estimate the DFS from the time series “Jb” cost value at the analysis. Further, we can estimate the DFS of the RO observations by looking at the differences of the Jb in the control and experiments.

  23. DFS(3) The DFS of the control is The DFS of the GPS experiment is Hence, the DFS associated with the GPS measurements We have increased the total number of observations by ~0.5% but Increased the DFS by ~4%.

  24. Hence, the DFS per RO profile is ~34 (2700/80) and The DFS per bending angle is ~0.216 (2700/12500). Put these numbers in context, Fisher (2003) estimates the DFS of 40 AMSUA radiances from 5 profiles as in a 2 degree by 2 degree box as The DFS of AIRS is This is ~10% of the total (typically, 1 million radiances).

  25. f r Current work: 2D bending angle operators. Approach 1 (Eyre, 1994) : We are currently testing this 2D observation operator in the 4D-Var system. This has required substantial re-coding of the 4D-Var to enable the use of 2D operators (Mats Hamrud). The 2D operator solves the bending angle integral, but uses the radial gradient for the rays position within the 2D plane. Now depends on q! Ds, the change in path-length, can be written in terms of Dq.

  26. Approach 2: 4th order Runge-Kutta solver Rodgers Page 149 But we can simplify the 3rd term We have coded the TL and adjoints of both approaches and introduced them into the 4D-Var system!

  27. Both approaches • All the rays start from the same “occultation point” – as defined in • the UCAR file. A plane 2D plane is constructed using the azimuthal • angle defined in the UCAR file. The 2D plane contains 31 profiles • separated by 40km. Central profile = occultation point. • calculate the bending from the tangent point along the path towards the LEO • Calculate the bending from the tangent point along the path towards the GPS. • Add these bending angles together to get the total bending. • The height of the tangent point (starting point of the 2D ray path) • is estimated from the “derived impact parameter” – i.e., the value • given in the data file. It is derived assuming spherical symmetry!

  28. Some relative CPU times • Have estimated some approximate CPU times for the 1d and 2d operators using the F90 cpu_time command. The runs were performed on a standard linux pc. • The timings are for the bending angle calculation routine, after the refractivity field has be derived. Evaluate 160 bending angle values. ~180 times more expensive than the 1D, but this value is not prohibitive.

  29. First 2D operator (Eyre) impact experiments • Ran from 1st July – 31st August, 2004, using CYCLE 29R1 of the ECMWF model. • Ran a control, 1d operator and 2d operator experiments. • The same observation errors were used in the 1d and 2d experiments. • Used all the bending angle measurements, ie. No longer blacklisting below 4km. • Switched the surface pressure increments back on (These appeared to cause the problems in the original experiments).

  30. Surface Pressure increment problem Note, in the original experiments the largest differences exceeded 2hPa.

  31. Surface pressure increment problem • The 1D operator has not been modified/improved since the initial experiments. • Has the UCAR processing improved - any ideas? • However, CYCLE 29R1 has been modified significantly since CYCLE 26R3. In particular, a new bias correction scheme for surface pressure observations has been introduced and the synop. and drifting buoy observation errors have been significantly reduced. • As a result, the surface pressure over Antarctica is far more constrained than in the original experiments and therefore the GPS RO increments are smaller.

  32. O-B and O-A bending angle distributions for impact heights between 4.5km-5.8km. Red is the 1D operator, black 2D. The distributions are very similar. Could argue that the NH and SH O-B distributions are ~4-5% narrower with the 2D operator, but the difference is relatively small.

  33. Forecast Scores (Z500) Red = 2d Blue = 1d Differences not statistically significant.

  34. RMS Z500 error at day 5 (Against Analysis) Red = 2D

  35. RMS Z500 errors at day-5 (Against radiosonde) Red = 2D

  36. Summary • Implemented a GRAS-SAF 1d bending angle observation operator in the ECMWF 4D-Var system. • In the first forecast impact experiments we had problems with the surface pressure increments. They were switched off. • We have found that relatively few CHAMP observations lead to quite significant temperature analysis differences in the stratosphere. • The CHAMP measurements improve the forecast fit to radiosondes in the SH (300-50 hPa) and 100 hPa in the tropics. • Large DFS information content value associated with GPS observations.

  37. Summary (2) • We have developed two versions of a 2D bending angle operator. Although they are more expensive than 1D, the cost is not prohibitive. • We do not have the same difficulty with surface pressure increments when using CYCLE 29R1. Developments in the bias correction of surface pressure obs. and re-tuning of their errors suggest the background is now more constrained. • Some improvement (~ 5%) in RMS O-B bending angle fit near the surface. • 2D versus 1D operator forecast impact unclear at the moment.

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