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Motivation

Exploring Application of Radio Occultation Data in Improving Analyses of T and Q in Radiosonde Sparse Regions Using WRF Ensemble Data Assimilation System H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya NCAR/IMAGe/COSMIC/MMM. Motivation. • Over oceans, radiosondes are sparse.

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Motivation

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  1. Exploring Application of Radio Occultation Data in Improving Analyses of T and Q in Radiosonde Sparse Regions Using WRF Ensemble Data Assimilation System H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya NCAR/IMAGe/COSMIC/MMM

  2. Motivation • Over oceans, radiosondes are sparse. • Current NCEP and ECMWF global analyses of T and Q rely heavily on satellite radiances and winds.

  3. Motivation (cont.) • Significant areas of cloud-cover may exist over oceans, e.g., in case of tropical cyclones and hurricanes. Radiances are not yet routinely used in cloud-covered areas. • In such cases, satellite winds are the major data resource and NCEP and ECMWF analyses of T and Q may have large uncertainty. • Initialization of landing cyclones and hurricane forecasts from such analyses may also have large uncertainty. • Study of the weather and climate over oceans (e.g., ITCZ and MJO) also needs more reliable analyses of T and Q.

  4. Radio Occultation (RO) Refractivity • Have T and Q information with high vertical resolution. • Its observation operators are simple and accurate. • Has good coverage over oceans and not affected by clouds. RO data have potential to improve analyses of T & Q over oceans.

  5. Our Goal Application of RO data to improve:  Analyses of T and Q over oceans  Initialization (large-scale) for cyclone and hurricane forecast  Initialization for tropical cyclone genesis forecast In this preliminary study: We explore impact of current experimental (CHAMP) RO refractivity data on improving analyses of T and Q in the presence of only satellite winds over the Continental US domain.

  6. Experimental Design • EXP 1: Assimilate satellite cloud winds only. (Simulate cloudy situations over oceans)  EXP 2: Assimilate satellite cloud winds + RO refractivity Radiances are not included and this may serve as an upper bound on the impact of RO data. • A total of ~500 RO refractivity profiles during Jan 1-31, 2003 are assimilated. • Impact of RO data on 50km resolution WRF analyses is examined. • Analyses are verified to the ~100 co-located (< 200km and +/- 3 hours) radiosonde profiles, which are withheld from the assimilations.

  7. Why use the WRF Ensemble System? • Advanced (non-local) RO observation operators can be easily implemented (requiring only forward models). This is especially important for the tropics. • Time varying forecast error correlation of T and Q is included in the assimilation of RO data and this may significantly improve retrieval of T and Q from RO refractivity/bending angle.

  8. Radiosondes used for Verification (Jan 1-31, 2003)

  9. Impact of RO Refractivity (T analysis mean & RMS error, entire domain)

  10. Impact of RO Refractivity (Q analysis mean and RMS error, entire domain)

  11. Impact of RO Refractivity (T analysis mean & RMS error, 600 - 800 hPa )

  12. Impact of RO Refractivity (Q analysis mean & RMS error, 600 - 800 hPa)

  13. Conclusion The preliminary results suggest that RO data may have the potential to significantly improve the analyses of T and Q over oceans, especially in cloudy situation. Study to provide high resolution (30km or better) tropical analyses of T and Q using satellite winds and RO refractivity is underway. P.S. WRF ensemble data assimilation system is available to the public on www.image.ucar.edu/DAReS

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