1 / 16

Diagnostics of the Nature run By ECMWF PeriodMay1, 2005-May31, 2006 Resolution: T511 91 levels

Diagnostics of the Nature run By ECMWF PeriodMay1, 2005-May31, 2006 Resolution: T511 91 levels Model: IFS cy31r1 November 16, 2006. Cyclone tracs in the Nature run Thoman Jung, ECMWF. Annual total cyclone track. June. August. September. July. October. December. November.

analu
Download Presentation

Diagnostics of the Nature run By ECMWF PeriodMay1, 2005-May31, 2006 Resolution: T511 91 levels

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Diagnostics of the Nature run By ECMWF PeriodMay1, 2005-May31, 2006 Resolution: T511 91 levels Model: IFS cy31r1 November 16, 2006

  2. Cyclone tracs in the Nature run Thoman Jung, ECMWF Annual total cyclone track

  3. June August September July

  4. October December November January

  5. February April March May

  6. Comparison between the ECMWF T511 Nature Run against climatology. 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF TechMemo 452 Tompkins et al. (2004) http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/ tm452.pdf Jung et al.  (2005) TechMemo 471 http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/tm471.pdf Yearly, quarterly and monthly mean files are also posted at http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_NR_Diag/ECMWF_T511_diag The description of the data http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/climplot_README.html

  7. Quickscat SFC wind SSMI 10m wind - Quikscat does not provide winds in rainy areas - Shows known bias in the W Pacific. Model winds are too low in deep convective areas.

  8. SSMI JJA DJF SON MAM

  9. Quickscat JJA DJF SON MAM

  10. TOA LWCF Top of the atmosphere long-wave cloud forcing Ice Water TOA LW NR-CERES - Shows lack of deep convection over the tropical land masses. This agrees with evidence from other data sets. Also agrees with earlier comparisons with ERBE data. A long-standing bias that has improved with recent model changes, but require further attention. - No product where continually cloudy. - Proves that the IR biases in net radiation are due to clouds. - the NOAA product is only for thin cirrus clouds, with a narrow optical septh range, which makes the comparison unreliable

  11. **Top of the atmosphere short-wave cloud forcing **Top of the atmosphere short-wave radiation - This is now dramatically improved compared to earlier cycle of the model, and compared to ERA-40 (for example). - Still small problem in the stratocumulus regions close to the coasts, despite of recent improvements.

  12. **Liquid water path Trmm and SSMI - This is now much better in the trade cumulus regions, than in previous model cycles. - Clear underestimation in western Pacific

  13. **Total-column water vapor TRMM and SSMI - Remarkable agreement in the extra-tropics. Underestimation in the ITCZ.

  14. **Total precipitation, against GPCP, SSMI, and XieArkin TRMM, NASDA and RSS - These comparisons confirm the lack of rainfall over the tropical land masses. - We have an overestimation of precip over the high-SST regions in the tropics. - There is a tendency for deep convection to become locked in with the highest SSTs, which in the east Pacific results in a narrow ITCZ - The TRMM NASDA-3b43 algorithm is presumed to be the most accurate of the two TRMM retrieval products.

  15. Sfcflux Anomaly ESKB Top: Latent, Net Bottom: Sensible solar,infrared - Surface fluxes observations are indirect measurements and are less reliable on a month-by-month basis. Ask Anton Beljaars.

  16. TCC ICCP MODIS - This has improved greatly in recent cycles of the model. In particular, the stratocumulus regions have improved. - The apparent underestimation relative to ISCCP over the Sahara is because this product is thought to overestimate cloud cover there. MODIS shows better agreement with the model over the deserts. - The MODIS product over sea ice is unreliable

More Related