development of an active sensor module for the rttov...
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Development of an active sensor module for the RTTOV-SCATT
radiative transfer simulator
Introduction
Active microwave sensors are becoming widely used observations within the Numerical Weather Prediction community, either for validating model forecasts or for assimilation purposes. Just as in the forward simulation of passive microwave observations, it
is necessary to make assumptions on the scattering properties of hydrometeors. With the objective of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions in a widely-used tool in
the NWP community, an active sensor module is currently under development within the RTTOV-SCATT V12 software (Saunders et al., 2018). The first simulations of the GPM/DPR instrument as well as the Cloudsat/CPR instrument with this simulator are
shown, based on the AROME model running operationally at Météo-France over five domains in the Tropics.
Statistical comparisons are made between observations and simulations considering several radiative properties assumptions of the snow. Theses simulations results are used to assess the quality of the cloud forecasts with the modification of the auto-
conversion function for solid hydrometeors within the AROME microphysics.
Fabrice Duruisseau(1), Philippe Chambon(1), Ghislain Faure(1) and Alan Geer(2)
(1) : CNRM UMR-3589, Météo-France & CNRS, Toulouse, France
(2) : ECMWF, Reading, United Kingdom Correspondence to [email protected]
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., ... & Bormann, N. (2018). An
update on the RTTOV fast radiative transfer model (currently at version 12). Geoscientific Model
Development, 11(7), 2717-2737.
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of
hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci.
Data, 10, 1301-1326, https://doi.org/10.5194/essd-10-1301-2018, 2018.
Conclusion and perspectives
Simulating radar reflectivities with the RTTOV-SCATT software
Comparing observed and simulated reflectivities at Ku, Ka and W bands
The authors are grateful to Sara Q. Zhang and Mary Borderies for fruitful
discussions which helped developing this framework. This work is
supported by CNES under the Megha-Tropiques program.
Regional model AROME in Tropics Simulated active microwave sensors for clouds and precipitation
The convective scale AROME model covers five tropical domains (French Polynesia, Caribbean, Guyana, Indian Ocean, New-
Caledonia) in addition to the domain over the Western Europe. Among the configurations listed below, forecasts from the research
version are used in the present study :
Figure1: AROME domains over Western Europe and the 5 tropical
domains covering French Overseas territories
Over Western Europe In the Tropics
AROME in operations AROME in operations Research version
Horizontal
resol./Nb levels
1.3km / 90 levels 2.5 km / 90 levels 2.5 km / 90 levels
Microphysics /
Deep conv.
ICE3 / Explicit ICE3 / Explicit ICE3 / Explicit
Boundary ARPEGE ECMWF ECMWF
DAS 3D-Var 1h cycles Cold starts + IAU 3D-Var 3h cycles
Forecasts +42h +42h +42h
Figure 5: Hurricane IRMA observed by the GOES 13 satellite on September 5, 2017 at
18h UTC (black and white colors) and collocated observations from GPM/DPR Ku band
(13 GHz) radar instrument at 3km height (blue to red colors).
Collocated observations (DPR and CPR) and
AROME over the Carribean’s +3h forecasts are
compared in the observation space with the
modified RTTOV-SCATT simulator over a ~1
month period (Aug 1st, 2017 to August 25, 2017):
➔ Ku band (13.6 GHz) DPR/GPM
➔ Ka band (35.5 GHz) DPR/GPM
➔ W band (94 GHz) CPR/CLOUDSAT
The RTTOV-SCATT software is a widely used tool among NWP centres to
simulate microwave observations and is equipped with all the necessary features
to build up a simple space-radar simulator, benefiting from all the developments
implemented for passive microwave observations like DDA-based Single
Scattering Properties (SSP) (Geer and Baordo, 2014).
In the present study, the following SSP and Particle Size Distributions (PSD) are
used for the simulation of reflectivities with a modified version of the RTTOV-
SCATT V12 software:
◼ Rainfall: SSP => Mie sphere ;
PSD => Marshall-Palmer distribution
◼ Cloud Ice and Cloud Water: SSP => Mie sphere
PSD => Modified Gamma distribution
◼ Snow particle: SSP => the 15 new particle shapes available within RTTOV
12 represented on Figure 4 (Eriksson et al., 2018)
PSD => Field et al. 2007 in tropical regime.
The simulated reflectivities shown in this study take into account the attenuation
both by hydrometeors and gases.
Figure 6: The same atmospheric scene as observed in Figure 5 but simulated with the AROME model. The
Ku (13 GHz) of GPM/DPR reflectivities are simulated using the IconCloudIce as SSP for snow particles and
the IR simulations are computed using RTTOV-CLOUD with the Baran parametrization for ice clouds.
Figure 4: 15 particles from
Eriksson et al. (2018) available
in RTTOV-SCATT V12
Figure 7: Normalized histograms of observed (left column), simulated (middle column) attenuated reflectivities. The normalization is
performed for each elevation separately by the maximum of occurrence at each elevation. Number of samples contained in the histograms
(right column) as function the altitude. The comparison covers a time period of 25 days in August 2017 over the Antilles AROME domain.
Simulations with attenuation are performed considering the IconCloudIce as snow particle for the 3 frequencies: Ku-13 GHz (first row),
Ku-35 GHz (second row) and W-94 GHz (third row). Black solid line on histograms represent the simulations mean for Ku and Ka band
and the simulations mode for the W band. The same information is shown for observation with dotted lines.
Figure 8: Same as Figure 7 but for atmospheric columns with no detected signal
above the freezing level.
Figure 9: Same as Figure 7 but for atmospheric columns with a detected signal
above the freezing level.
Comparisons of the distributions of observed
and reflectivities simulated with attenuation are
shown in Figure 7.
Then, the atmospheric profiles have been split
into two sub-categories by categorizing the
meteorological scenes which do or do not
present a reflectivity above the sensitivity of
each radar above 4.5km of altitude (~ freezing
level):
➢ Figure 8: samples with no signal above the
freezing level
➢ Figure 9: samples with signal above
freezing level
𝑁𝑜𝑟𝑚.𝐻𝑖𝑠𝑡. 𝐸𝑟𝑟𝑜𝑟 =1
𝑁𝑙𝑒𝑣𝑒𝑙 . 𝑁𝑏𝑖𝑛
𝑙
𝑁𝑙𝑒𝑣𝑒𝑙
𝑟
𝑁𝑏𝑖𝑛
𝑛𝑙,𝑟𝐴𝑅𝑂𝑀𝐸 − 𝑛𝑙,𝑟
𝑂𝐵𝑆 2
The previous comparisons as well as comparisons with IR data using
GOES 13 observations (not shown) indicate that AROME forecasts are
characterized by a lack of cold cloudiness (IR comparisons) as well as
a lack of precipitation (radar comparisons). A sensitivity experiment of
these results to a modification of the auto-conversion function of the
AROME microphysics scheme was conducted and assessed using
space radar observations. The original function as well as the modified
ones are shown on Figure 10.
The simulations shown previously are compared to new AROME
forecasts and new simulations with the modified function, and for the
15 particle shapes shown above. Two metrics are defined, to compare
both the normalized histograms and the occurrence of reflectivities.
They are defined as follows:
Figure2: Illustration of Dual-frequency
Precipitation Radar (© NASA)
Figure3: Illustration of the Cloud
Profiling Radar (© NASA).
GPM Dual-frequency Precipitation Radar Cloudsat Cloud Profiling Radar
On board the Cloudsat satellite
94 GHz frequency
Nadir-viewing
Horizontal resolution:
1.2 km cross-track
3.5 km along-track
On board the Global Precipitation
Measurement Core satellite.
KuPR KaPR
Freq.
(GHz)
13.6 35.5
Hori. resol.
(km)
5 5
Swath width
(km)
245 120
- To evaluate occurrence error of the reflectivities, the following
computation is done, where ml is the number of samples at level l :
- To evaluate the differences between normalized histograms, the
following metric is defined, where nl,r is the normalized value of the
histograms at level l and in reflectivity bin r :
Figure10: Auto-conversion function from Ice to Snow as
function of temperature as used in operations for AROME
and modified for the sensitivity experiment
Symbols used in Figure11
and Figure12
corresponding to the 15
different particle shapes
used.
Figure 11: Scatter diagrams of the Occur.Error vs
Norm.Hist.Error for the default AROME simulations, and
computed for the 15 particle shapes, for the three frequencies
Figure12: Same as Figure 11 but assessing AROME
forecasts with the modified auto-conversion function
Radar reflectivities from the DPR/GPM and the CPR/Cloudsat instruments have been simulated with a modified version of RTTOV-
SCATT V12. Results show a quite good agreement with the observations but several discrepancies remain. A lack of precipitating
clouds was revealed in the AROME model in the Tropics and is consistent with other ongoing studies on accumulated rainfall.
Modifying the auto-conversion function within the micro-physics scheme seems to slightly improve it.
Several perspectives to this work can be drawn:
➢ Continue to improve the microphysics scheme of the AROME model for Tropical regions
➢ Refining the radar simulator to make it available for the community in a future version of the RTTOV package.
Less profiles with simulated
reflectivites >16dBz for DPR
and >-24dBz for CPR
simulations below the
freezing level.
=> Lack of precipitating
clouds in AROME ? Too
strong evaporation ?
Underestimation of rain
leading to simulated
reflectivities < 16dBZ ?
For atmospheric profiles with no signal detected above the freezing level, the
histogram correlations are higher than in Figure 7, except for the 94 GHz for
which highlighting the mode of the distribution is no longer adapted. One can
also point out a longer tail of the simulated reflectivity distributions than in the
observed ones.
For atmospheric profiles with a signal detected above the freezing level,
corresponding to clouds with a higher peaking vertical extension, W band
simulations are characterized by better correlations than with all the samples, but
weaker ones are obtained for Ku and Ka simulations. A maximum of high
reflectivity (~ 40 dBz) can also be seen in the simulations and likely correspond
to too strong extreme instantaneous rainfall in the AROME simulations.
➢ IconCloudIce among best particles across the three frequencies
➢ Improvements of the Occur.Error for Ku and Ka bands (sensitive to rain) but no obvious signal for the second metric
➢ Improvements of the Norm.Hist.Error for W band, indicating a potential better vertical structures of clouds, but no
improvements of the Occur.Error => need statistical testing on these metric to distinguish the robust signals
These preliminary results indicate that the new radar simulator built in RTTOV-SCATT can be used as a tool for assessing
changes to model physics. The results indicate that the tested auto-conversion function could be of interest for AROME but
requires more extensive testing for gaining in understanding of its impacts over longer periods of simulations as well as a
proper statistical testing of the differences between experiments.
Occurrence error
improvement
Occurrence error
improvement
Normalized
histogram error
improvement
Observations Simulations Sample size Observations Simulations Sample size
Observations Simulations Sample size
W(9
4G
Hz)
Ka
(35
.5G
Hz)
Ku
(13
.6G
Hz)
W(9
4G
Hz)
Ka
(35
.5G
Hz)
Ku
(13
.6G
Hz)
W(9
4G
Hz)
Ka
(35
.5G
Hz)
Ku
(13
.6G
Hz)
W(9
4G
Hz)
Ka
(35
.5G
Hz)
Ku
(13
.6G
Hz)
W(9
4G
Hz)
Ka
(35
.5G
Hz)
Ku
(13
.6G
Hz)
AROME-default AROME-modified
Norm. Hist. Error
Norm. Hist. Error
Norm. Hist. Error
Norm. Hist. Error
Norm. Hist. Error
Norm. Hist. Error
Occ
ur.
Err
or
Occ
ur.
Err
or
Occ
ur.
Err
or
Occ
ur.
Err
or
Occ
ur.
Err
or
Occ
ur.
Err
or
High correlation of
normalized histograms are
good indicators of the
realism of simulated
reflectivities
_____ Simulations
- - - Observations
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- - - Observations
_____ Simulations
- - - Observations
_____ Simulations
- - - Observations
_____ Simulations
- - - Observations
_____ Simulations
- - - Observations
_____ Simulations
- - - Observations
_____ Simulations
- - - Observations
_____ Simulations
- - - Observations
𝑂𝑐𝑐𝑢𝑟. 𝐸𝑟𝑟𝑜𝑟 =1
𝑁𝑙𝑒𝑣𝑒𝑙
𝑙
𝑁𝑙𝑒𝑣𝑒𝑙
𝑚𝑙𝐴𝑅𝑂𝑀𝐸 − 𝑚𝑙
𝑂𝐵𝑆 2
Modifying the auto-conversion function for improving cloud forecasts