Simulating measurements from the ISMAR radiometer using a new light scattering approximation

Email: karina.mccusker@pgr.reading.ac.uk

It is widely known that clouds pose a lot of difficulties for both weather and climate modelling, particularly when ice is present. The ice water content (IWC) of a cloud is defined as the mass of ice per unit volume of air. The integral of this quantity over a column is referred to as the ice water path (IWP) and is considered one of the essential climate variables by the World Meteorological Organisation. Currently there are large inconsistencies in the IWP retrieved from different satellites, and there is also a large spread in the amount produced by different climate models (Eliasson et al., 2011).
A major part of the problem is the lack of reliable global measurements of cloud ice. For this reason, the Ice Cloud Imager (ICI) will be launched in 2022. ICI will be the first instrument in space specifically designed to measure cloud ice, with channels ranging from 183 to 664 GHz. It is expected that the combination of frequencies available will allow for more accurate estimations of IWP and particle size. A radiometer called ISMAR has been developed by the UK Met Office and ESA as an airborne demonstrator for ICI, flying on the FAAM BAe-146 research aircraft shown in Fig. 1.

Figure 1: The Facility for Airborne Atmospheric Measurements (FAAM) aircraft which carries the ISMAR radiometer.

As radiation passes through cloud, it is scattered in all directions. Remote sensing instruments measure the scattered field in some way; either by detecting some of the scattered waves, or by detecting how much radiation has been removed from the incident field as a result of scattering. The retrieval of cloud ice properties therefore relies on accurate scattering models. A variety of numerical methods currently exist to simulate scattering by ice particles with complex geometries. In a very broad sense, these can be divided into 2 categories –
1: Methods that are accurate but computationally expensive
2: Methods that are computationally efficient but inaccurate

My PhD has involved developing a new approximation for aggregates which falls somewhere in between the two extremes. The method is called the Independent Monomer Approximation (IMA). So far, tests have shown that it performs well for small particle sizes, with particularly impressive results for aggregates of dendritic monomers.

Radiometers such as ICI and ISMAR convert measured radiation into brightness temperatures (Tb), i.e. the temperature of a theoretical blackbody that would emit an equivalent amount of radiation. Lower values of Tb correspond to more ice in the clouds, as a greater amount of radiation from the lower atmosphere is scattered on its way to the instrument’s detector (i.e. a brightness temperature “depression” is observed over thick ice cloud). Generally, the interpretation of measurements from remote-sensing instruments requires many assumptions to be made about the shapes and distributions of particles within the cloud. However, by comparing Tb at orthogonal horizontal (H) and vertical (V) polarisations, we can gain some information about the size, shape, and orientation of ice particles within the cloud. If large V-H polarimetric differences are measured, it is indicative of horizontally oriented particles, whereas random orientation produces less of a difference in signal. According to Gong and Wu (2017), neglecting the polarimetric signal could result in errors of up to 30% in IWP retrievals. Examples of Tb depressions and the corresponding V-H polarimetric differences can be seen in Fig. 2. In the work shown here, we explore this particular case further.

Figure 2: (a) ISMAR measured brightness temperatures, showing a depression (decrease in Tb) caused by thick cloud; (b) Polarimetric V-H brightness temperature difference, with significant values reaching almost 10 K.

Using the ISMAR instrument, we can test scattering models that could be used within retrieval algorithms for ICI. We want to find out whether the IMA method is capable of reproducing realistic brightness temperature depressions, and whether it captures the polarimetric signal. To do this, we look at a case study that was part of the NAWDEX (North Atlantic Waveguide and Downstream Impact Experiment) campaign of flying. The observations from the ISMAR radiometer were collected on 14 October 2016 off the North-West Coast of Scotland, over a frontal ice cloud. Three different aircraft took measurements from above the cloud during this case, which means that we have coincident data from ISMAR and two different radar frequencies of 35 GHz and 95 GHz. This particular case saw large V-H polarimetric differences reaching almost 10 K, as seen in Fig. 2(b). We will look at the applicability of the IMA method to simulating the polarisation signal measured from ISMAR, using the Atmospheric Radiative Transfer Simulator (ARTS).

For this study, we need to construct a model of the atmosphere to be used in the radiative transfer simulations. The nice thing about this case is that the FAAM aircraft also flew through the cloud, meaning we have measurements from both in-situ and remote-sensing instruments. Consequently, we can design our model cloud using realistic assumptions. We try to match the atmospheric state at the time of the in-situ observations by deriving mass-size relationships specific to this case, and generating particles to follow the derived relationship for each layer. The particles were generated using the aggregation model of Westbrook (2004).

Due to the depth of the cloud, it would not be possible to obtain an adequate representation of the atmospheric conditions using a single averaged layer. Hence, we modelled our atmosphere based on the aircraft profiles, using 7 different layers of ice with depths of approximately 1 km each. These layers are located between altitudes of 2 km and 9 km. Below 2 km, the Marshall-Palmer drop size distribution was used to represent rain, with an estimated rain rate of 1-2mm/hr taken from the Met Office radar. The general structure of our model atmosphere can be seen in Fig. 3, along with some of the particles used in each layer. Note that this is a crude representation and the figure shows only a few examples; in the simulations we use between 46 and 62 different aggregate realisations in each layer.

Figure 3: Examples of particles used in our model atmosphere. We represent the ice cloud using 3 layers of columnar aggregates and 4 layers of dendritic aggregates, and include a distribution of rain beneath the cloud.

To test our model atmosphere, we simulated the radar reflectivities at 35 GHz and 95 GHz using the particle models generated for this case. This allowed us to refine our model until sufficient accuracy was achieved. Then we used the IMA method to calculate the scattering quantities required by the ARTS radiative transfer model. These were implemented into ARTS in order to simulate the ISMAR polarisation observations.
Fig. 4 shows the simulated brightness temperatures using different layers of our modelled atmosphere, i.e. starting with the clear-sky case and gradually increasing the cloud amount. The simulations using the IMA scattering method in the ARTS model were compared to the measurements from ISMAR shown in Fig. 2. Looking at the solid lines in Fig. 4, it can be seen that the aggregates of columns and dendrites simulate the brightness temperature depression well, but do not reproduce the V-H polarization signal. Thus we decided to include some horizontally aligned single dendrites which were not included in our original atmospheric model. The reason we chose these particles is that they tend to have a greater polarization signal compared to aggregates, and there was evidence in the cloud particle imagery that they were present in the cloud during the time of interest. We placed these particles at the cloud base, without changing the ice water content of the model. The results from that experiment are shown by the diagonal crosses in Fig. 4. It is clear that adding single dendrites allow us to simulate a considerably larger polarimetric signal, closely matching the ISMAR measurements. Using only aggregates of columns and dendrites gives a V-H polarimetric difference of 1.8K, whereas the inclusion of dendritic particles increases this value to 8.4K.

Figure 4: Simulated brightness temperatures using different layers of our model atmosphere. Along the x-axis we start with the clear-sky case, followed by the addition of rain. Then we add one layer of cloud at a time, starting from the top layer of columnar aggregates.

To conclude, we have used our new light scattering approximation (IMA) along with the ARTS radiative transfer model to simulate brightness temperature measurements from the ISMAR radiometer. Although the measured brightness temperature depressions can generally be reproduced using the IMA scattering method, the polarisation difference is very sensitive to the assumed particle shape for a given ice water path. Therefore, to obtain good retrievals from ICI, it is important to represent the cloud as accurately as possible. Utilising the polarisation information available from the instrument could provide a way to infer realistic particle shapes, thereby reducing the need to make unrealistic assumptions.

References

Eliasson, S., S. A. Buehler, M. Milz, P. Eriksson, and V. O. John, 2011: Assessing observed and modelled spatial distributions of ice water path using satellite data. Atmos. Chem. Phys., 11, 375-391.

Gong, J., and D. L. Wu, 2017: Microphysical properties of frozen particles inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) polarimetric measurements. Atmos. Chem. Phys., 17, 2741-2757.

Westbrook, C. D., R. C. Ball, P. R. Field, and A. J. Heymsfield, 2004: A theory of growth by differential sedimentation with application to snowflake formation. Phys. Rev. E, 70, 021403.

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