2. NWP Models at DGMAN
Study Domain and Configurations
GME (Global Model) analysis as an Input to
HRM/COSMO
Evaluating Forecasted:
Tracks
Intensity
rainfall
Conclusion & future work
3. Operational Forecasting Model
Prognostic variables Diagnostic variables
Surface pressure ps
Temperature T Vertical velocity ω
Water vapour qv Geopotential φ
Cloud water qc Cloud cover clc
Cloud ice qi Diffusion coefficients tkvm/h
Horizontal wind u, v
Several surface/soil parameters
4. Research Evaluation Model
Prognostic variables Diagnostic variables
pressure perturbation p‘
Temperature T Total air density
specific humidity qv precipitation fluxes of rain
Cloud water qc and snow.
Cloud ice qi
Horizontal/virtical
wind u, v,w
5. x: 193
y:114
levels: 40
FCT Hours:78
FCT times: 00,12 UTC
7 – 35.25 N
30 – 78 E
Initial/LBC: GME
19. GME (driving model) miss located Gonu center and underestimated the
associated wind and pressure drop.
Clear dependency between GME forecast quality and HRM/COSMO
forecast quality.
Even though HRM/COSMO are not TC specialized models, both have
given the signal for Gonu.
COSMO_07 were able to forecast Gonu as Severe Cyclonic Storm starting
from 12UTC run of 4th June 2007.
On average COSMO shows 5% improvement of track forecast and 3% on
the wind intensity over HRM.
Both HRM_07 and COSMO_07 have signaled 48h rainfall of more than
900mm starting at June 5th with reasonable spacial and good amount
agreement with the recorded rainfall.
20. Both HRM & COSMO are not specialized TC models, Testing the new “TC
bogussing” scheme developed on the HRM community.
Testing the effect of introducing Data Assimilation system for the HRM
model.
Plan to introduce SREF system, and test the performance.