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 philippe.chambon@meteo.fr

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

_____ Simulations

- - - 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