use of satellite soil moisture data for nwp at the italian...
TRANSCRIPT
Use of satellite soil moisture data for NWP at the
Italian Air Force Meteorological Centre
Paride Ferrante, Francesca Marcucci, Valerio Cardinali, Lucio Torrisi
[email protected], [email protected], [email protected], [email protected]
COMET - Italian Air Force Operational Centre for Meteorology
KENDA (Kilometre-scale ensemble data assimilation) EnKF DA
LETKF Formulation (Hunt et al,2007) Model and sampling errors are taken into account using:
Analysis
Ensemble Mean
Analysis
Ensemble Perturb. 21
a
1a
~1W
))((~
w
a
bbTa
Pm
xHyRYP
))()((),....,)()((Y
)1(P~
1
b
11a
bb
m
bb
bbT
xHxHxHxH
YRYIm
ab
ab
WX
wX
a
ba
X
xx
6-hourly assimilation cycle
40 ensemble members + deterministic run with 0.09° (~10Km) grid
spacing (COSMO model), 45 vertical levels
(T,u,v,pseudoRH,ps) set of control variables
Observations: RAOB (also 4D), PILOT, SYNOP, SHIP, BUOY, Wind
Profilers, AMDAR-ACAR-AIREP, MSG3-MET8 AMV, MetopA-B scatt.
winds, NOAA/MetopA-B AMSUA/MHS and NPP ATMS radiances+
Land SAF snow mask, IFS SST analysis once a day
= 0.95
σa2 = variance
an. pert.
“Relaxation-to-Prior Spread” Multiplicative Inflaction according to
Whitaker et al (2010)
Additive noise from scaled ECMWF EPS pertubations
Lateral Boundary conditions from the most recent IFS
deterministic run perturbed using ECMWF EPS
Climatological Perturbed SST
Adaptive selection radius using a fixed number of effective
observations (sum of obs weights)
H-SAF ASCAT soil moisture quality control. Data are rejected if: snow: the analysed snow amount is greater than 0.05 kg/m^2 (not active) Sea point (check land sea mask) frost: the 2m Temperature analysis is below 275.15 K (not active) wetlands: the inundation and wetland amount has a value greater than 15% mountains: the topographic complexity has a value greater than 20% ASCAT estimated error: greater than 8% (ECMWF value, UKMO uses 7%)
This check rejects ASCAT data from regions with dense vegetation and sand dunes Soil type =1 or 2 (ice and rock) “processing flag” 0 (quality of retrieval) Ens.mean Observation Increments > 2.5
( estimated from 1 year statistics for each soyl type)
Observation error:e_o= 2 x BUFR estimated error (suggested by P. De Rosnay ECMWF )
SOIL MOISTURE ASSIMILATION: VERIFICATION RESULTS (parallel test suite from 22 jun 2016 to 23 jul 2016)
Verification results with respect SYNOP and TEMP observations
Synop-2m dew point temperature:
A little improvement of rmse and bias is observed
No impact for other variables (not shown)
TEMP wind vector:
A little improvement of rmse is observed
No impact for other variables (not shown)
SOIL MOISTURE ASSIMILATION: PRE-PROCESSING OF ASCAT HSAF SOIL MOISTURE DATA
ASCAT soil moisture Data provided by EUMETSAT
within the H-SAF project, one of the 8 EUMETSAT
SAFs, lead by the Italian Air Force Met Service
frequency: 5.3 GHz (microwave C-band)
VV polarization
Able to provide a triplet of backscattering coefficients for
each swath
25 km resolution
From backscattering coefficient measurements it is possible to retrieve the soil moisture content in the first 2 cm
below the soil by mean of microwave technique thanks to the high sensitivity of microwaves to the water content
in the soil surface layer (for microwave frequencies in the C-band (< 10 GHz) the addition of liquid water to the soil
strongly increases the soil dielectric constant, and so the backscattering coefficients)
H-SAF ASCAT derived Soil Moisture: degree of saturation (%) in the first 2 cm
COSMO TERRA_ML model soil moisture: liquid water content (m H2O) in the various model
layers
To compare observed and model values the model values are transformed (to have quantities independent from the thickness of
the layers) in volumetric water content (m^3/m^3) in the first 2 cm, using CDF matching method (ECMWF approach)
To scale the ASCAT derived soil moisture to the model climatology so that the cumulative distribution
functions (CDF) of satellite and model soil moisture match (performed for each soil type separately).
• 1 year time series of ASCAT and model SM data (january 2015 - january 2016)
local regression analysis
global regression analysis
CDF Matching Method: Rescalation of ASCAT observation values to the model values
LETKF
10 km45 v.l.
b slope, a intercept
TEST 1 :soil moisture observations influence ONLY the low level
atmospheric variables : l_soil_ana = false , horizontal localization (100 km) ,
vertical localization (10 lower levels)
A little improvement is noticed compared to the
reference case (no use of ASCAT soil moisture data)
lowatm (TEST1) full wso (TEST2) noLowatm (TEST3)
Verification results with respect SYNOP observations
TEST 2 : soil moisture observations influence BOTH the low level atm
+ soil variables (100km, 10 lower levels)
TEST 3 : soil moisture observations influence only soil variables
BIAS RMSE
Small improvement in TD and CCT bias for TEST2 Small improvement in TD and DD rmse for TEST2