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25 th EWGLAM & 10 th SRNWP meetings Lisbon, Portugal, 6-9 October 2003 Joint session with COST-717 WG3. A nudging scheme for the assimilation of rainfall data: application t o the 2001 Algerian Flood. S. Davolio and A. Buzzi ISAC - Institute for Atmospheric Sciences and Climate
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25th EWGLAM & 10th SRNWP meetings Lisbon, Portugal, 6-9 October 2003 Joint session with COST-717 WG3 A nudging scheme for theassimilation 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 s.davolio@isac.cnr.it INSTITUTE OF ATMOSPHERIC SCIENCES AND CLIMATE , ISAC-CNR
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
After comparing RRt and RRf nudging of specific humidity profile THE NUDGING SCHEME • 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
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 availableRRf is compared withRRt • 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.
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. • RRf < RRt q(k) • RRf > RRt q(k) • 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
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
BOLAM MODEL • 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°x0.5° res.
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).
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 Results
C N Results Improved! Rain band slightly shifted eastward but correct in intensity Rain band in phase but intensity too low
nudging forecast forecast nudging Hit Rate False Alarm Results at the end of the nudging stage Hit Rate and False Alarm Rate - 6h precipitation • X axis: • precipitation thresholds (mm/6h) • ( ) = n. points where obs. rain rate > threshold
end of nudging Threshold: 2mm/6h Threshold: 10mm/6h RESULTS after the nudging stage Equitable Threat Score vs simulation time end of nudging
C F N
Impact on cyclone development and evolution F C 12 hours after the end of the assimilation
Impact on cyclone development and evolution N C 12 hours after the end of the assimilation
Sensitivity to rainfall accumulation interval ETS vs simulation time Threshold: 5mm/6h Threshold: 2mm/6h nudging (3h) nudging (1h) forecast nudging (6h) nudging (2h)
Sensitivity to rainfall data errors ETS vs simulation time Threshold: 5mm/6h Threshold: 2mm/6h nudging (half prec) nudging (shift prec) forecast nudging (double prec) nudging
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.
11 Nov 12 UTC 11 Nov 00 UTC Particular ECMWF analysis and BOLAM solutions for the event of Nov. 2001: