1. An intraseasonal moisture nudging experiment in a tropical channel version of the WRF model: The model biases and the moisture nudging scale dependencies

2. The MJO Case of Study LON TIME mm/day TRMM m/s U850 Anom. ERA-Interim

3. TRMM PrecipitationNOAA OLR

4. TRMM Daily Precipitation Anomalies (Filled Contours) and NOAA Daily Outgoing Longwave Radiation Anomalies (Line Contours: Blue negative anomalies, :Red positive anomalies).

5. Model Description WRF v3.2 1º x 1º horizontal resolution. 28 vertical levels. Tropical channel domain: Periodic Boundary conditions in the east-west direction. Boundary Conditions form ERA-Interim data

6. Model Physics: Microphysics: WSM3-class simple ice scheme Longwave Radiation: rrtm scheme Shortwave Radiation: Dudhia scheme Land Surface Model: Noah Land Surface Model Boundary Layer: Mellor-Yamada-Janjic scheme

7. The Dry Bias in WRF

8. The dry bias WRF RH – ERAI RH P (hPa) Days P (hPa) Lat Lon P (hPa)

9. Approach to the problem: Humidity Nudging Four-Dimensional Data Assimilation (FDDA) or nudging is the process where the model is set to converge at a desired rate to the analysis or observations. The process adds an extra tendency term to the model equations proportional to the difference between the model simulation and the analysis value at every grid point, forcing the simulation closer to the analysis value.

10. Humidity Nudging From Skamarock et al. 2008. Model forcing termsNudging Tendency term : nudging factor, : four dimensional weight function, :analysis field value

11. WRF RH – ERAI RH P (hPa) Days P (hPa) Lat Lon P (hPa) Reduction of the dry bias

13. Grid Nudging

14. Variations of Nudging Vertical Weight Function

15. Z P Above PBL Vertical FDDA Weight Function (Default) Weight Function

16. Z P Above PBL Vertical FDDA Weight Function Weight Function High Mid Low Mid High

17. Z P Fixed Vertical FDDA Weight Function Weight Function High Mid Low Mid High

18. Grid Nudging: Vertical Weight Function

19. Spectral Nudging of Humidity Spatially filter the data (minimum x,y wavelength) Analysis data (ERA-Interim) Nudging Tendency

20. Spectral Nudging: Remove long wavelengths (small wave numbers)

21. Spectral Nudging: Remove short wavelengths (high wave numbers)

22. Spectral Nudging: Remove specific wavelength (specific wave numbers)

23. Removing the short wavelengths (high wave numbers) improves the control simulations. Mean and Long wavelengths are important in order to improve the MJO simulation. The model “needs” to resolve the moisture large scales-structures well enough in order to obtain a MJO-like event.

33. TIME LON

34. “MJO LINE”

35. Humidity Tendency due to nudging Humidity Tendency due to cumulus scheme MJO-like precipitation simulation Humidity Tendency due to cumulus scheme - CONTROL P (hPa) g/Kg day -1

36. Heating Tendency - Control Heating Tendency – MJO-like precipitation simulation P (hPa) K/day

37. How much nudging is too much nudging? What if G a =1 ?

38. 6 year WRF Simulation (Same Configuration)

39. a)b) c)d) e)f) MAYOCT NOVAPR Observations

40. a)b) c)d) e)f) MAYOCT NOVAPR WRF

41. a)b) c)d) ERAI, TRMMWRF

43. Conclusions Spectral and grid Nudging of water vapor mixing ratio reduces the model dry-bias and allows the model to produce an improved MJO-like precipitation pattern and wind signal. The moisture at mid levels of the troposphere is crucial in order to reproduce the convective signal associated with the MJO. Without nudging, the cumulus schemes remain relative inactive i.e. lack of precipitation during the MJO event. This translates to a weak heating profile. When the MJO precipitation pattern improves, the heating profile resembles the results of other studies more closely. The prediction of the first MJO event improves when nudging is preformed, while the initiation of the second event is not for some cases. This suggests that improving the humidity field is one component of the problem, and we need to investigate further on this matter.