1. 25th EWGLAM & 10th SRNWP meetings
Lisbon, Portugal, 6-9 October 2003
Joint session with COST-717 WG3
A nudging scheme for the assimilation of rainfall data: application to the 2001 Algerian Flood
S. Davolio and A. Buzzi
ISAC - Institute for Atmospheric Sciences and Climate
CNR - National Research Council
INSTITUTE OF ATMOSPHERIC SCIENCES AND CLIMATE , ISAC-CNR

2. Summary Description of the assimilation scheme
Idealized experiments (OSSE, Lagged Forecast)
Case study: Algeria flood 2001
Results of the simulations and scores
Sensitivity tests
Conclusions
INSTITUTE OF ATMOSPHERIC SCIENCES AND CLIMATE , ISAC-CNR

3. THE NUDGING SCHEME
After comparing RRt and RRf nudging of specific humidity profile
k = model -level (for each grid point)
q(k) = specific humidity before nudging
q*(k) = saturation humidity profile (from model)
= typical relaxation time scale
= over/under saturation coefficient
(k) = vertical modulation profile O(1)
If RRf < RRt
q(k) is forced gradually toward a (slightly) super-saturation profile
If RRf > RRt
q(k) is forced toward an under-saturation profile
INSTITUTE OF ATMOSPHERIC SCIENCES AND CLIMATE , ISAC-CNR

4. Remarks (1) What are RRt and RRf ?
Observed rainfall is accumulated over 1-3 hours interval.
RRt : mean constant rain rate within the accumulation interval.
RRf : forecast rain rate up to the current time step, updated every
 20min (once the model precipitation is available – convective
step).
Once available RRf is compared with RRt
Therefore, the scheme does not instantaneously adjust the rain rate at each time step, but rather adjusts the rain accumulated up until the current time step, seeking to recover the observed precipitation at the end of the accumulation interval.

5. Remarks (2)
The forcing is a function of the precipitation type (as estimated by the model)
Stratiform precipitation:
s(k) is such that q is changed only in the middle-lower troposphere where large scale
condensation takes place.
RRf < RRt q(k)
RRf > RRt q(k)
Convective precipitation:
s(k) is such that q is changed only in the boundary layer.
If RRf = 0 and RRt > 0 both types of precipitation are provisionally considered, unless the surrounding grid points are exclusively experiencing one type of rainfall.
As for the convective (and all physical) tendency, the nudging adjustment is distributed over all time steps in the interval between two times at which rain rates are compared.
INSTITUTE OF ATMOSPHERIC SCIENCES AND CLIMATE , ISAC-CNR

6. Nudging vertical profiles
In the presence of both types of precipitation, the profile for large scale precipitation is slightly modified in the lower part in order to have: conv (k) + ls (k)  1

7. BOLAM MODEL u, v, , q, ps dependent variables.
Limited area, hydrostatic, PE model, -coordinate.
u, v, , q, ps dependent variables.
Horizontal resolution  16 km – Vertical resolution: 38 levels (highest resol. in the PBL).
Lat-Lon rotated grid, horizontal discretization  Arakawa C-grid.
Stratiform precipitation described by means of 5 prognostic variables (cloud ice, cloud water, rain, snow, graupel). Simplified approach (Schultz, 1995).
Deep convection  Kain-Fritsch convective scheme.
Initial and boundary conditions: ECMWF analyses 0.5° x 0.5° res.

8. An upper level deep cold trough extending from NE to SW evolving the day after in an upper level low, associated to a very intense cyclone at the surface. Important contribution of PV advection for this rapidly deepening storm over the sea

9. Idealized Experiments
METHOD: Lagged Forecast scheme
Two different simulations from initial condition 12 hours apart:
“Control Run”: represents the reference state and provides the target rain rate.
“Forecast Run”: represents the real forecast to be improved.
Nudging procedure applied for 12 hours to a simulation starting from the same initial condition of the Forecast Run (Nudging Run).

10. Results C F
Different rainfall patterns over the coast and south of the Balearic Islands
Rain band missing in the forecast run
Rain band and area of light rainfall around Sardinia

11. Results N C
Improved!
Rain band slightly shifted eastward but correct in intensity
Rain band in phase but intensity too low

12. Results at the end of the nudging stage
Hit Rate and False Alarm Rate - 6h precipitation
forecast
nudging
Hit Rate
False Alarm
X axis:
precipitation thresholds (mm/6h)
( ) = n. points where obs. rain rate > threshold

13. RESULTS after the nudging stage
Equitable Threat Score vs simulation time
Threshold: 2mm/6h
Threshold: 10mm/6h
end of nudging
end of nudging

14. Cross section

15. C F N

16. C F Impact on cyclone development and evolution
12 hours after the end of the assimilation

17. C N Impact on cyclone development and evolution
12 hours after the end of the assimilation

19. Sensitivity to rainfall accumulation interval
ETS vs simulation time
Threshold: 2mm/6h
Threshold: 5mm/6h
nudging (1h)
nudging (3h)
forecast
nudging (2h)
nudging (6h)

20. Sensitivity to rainfall data errors
ETS vs simulation time
Threshold: 2mm/6h
Threshold: 5mm/6h
nudging (shift prec)
nudging (half prec)
forecast
nudging
nudging (double prec)

21. Conclusions:
The proposed nudging technique allows the assimilation of precipitation also when the rain is not purely convective, an advantage in midlatitudes with respect to reverse scheme.
Encouraging results from the experiments: the scheme seems able both to reduce and increase the precipitation patterns.
Improvements in precipitation forecasts are associated to a better reproduction of vertical motion in the rainy area.
The rainfall forecast improvements is observed during the assimilation phase and persists for several hours of free forecast (18-24 hours).
Improvements on the dynamics: the modification of the 3-dimensional humidity field (and consequently of the latent heat and temperature profiles through the model precipitation scheme) due to the nudging has a positive impact on the development and evolution of the cyclone.
Assimilation of real data seems feasible, even if it is necessary to account for the statistical weight of the background field (model) and observation.

22. Particular ECMWF analysis and BOLAM solutions for the event of Nov
00 UTC
11 Nov
12 UTC