Stuart Fox

The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft together with ground-based multi-frequency radars to provide a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. Ice particle size distributions (PSDs) are retrieved from the ground-based zenith-pointing radars at the time of the radiometric overpasses, and the aircraft in-situ PSDs are used as an independent validation dataset. We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept No, and shape μ of the gamma size distribution) that combines a machine-learning (ML) ensemble with physics-based multi-frequency radar retrievals using 3, 35, and 94 GHz reflectivities. An ensemble of ML models is trained on observations from the Parameterising Ice Clouds using Airborne ObServationS and triple-frequency dOppler radar (PICASSO) campaign, also centred on Chilbolton Observatory. These models predict PSD moments from temperature, pressure, 3 GHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts No and λ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHz reflectivities. Application of the above method to three CCREST-M cases show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHz radar compare favourably with in-situ measurements of IWC, and exponential (μ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean PSDs show generally good agreement with in-situ PSDs as a function of temperature for two of the cases, with IWCs within about 50 % of the in-situ measured IWCs over much of the −50 to −10 °C temperature range. The systematic biases seen in one case are attributed to temporal cloud evolution between radar and in-situ sampling. Independent validation using 200 GHz radar reflectivity profiles shows good agreement between the forward-modelled refllectivities and measurements above about 4.5 km. Below 4.5 km the agreement is more sparse owing to the likely presence of dendritic particles, which depart from the rosette-aggregate scattering assumption.

The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft. The campaign was designed primarily as a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. A key requirement for such closure tests is a near one-to-one relationship between the ice particle size distributions (PSDs) that enter the radiative transfer model and the radiometric measurements. Owing to the FAAM BAe-146 aircraft being unable to perform simultaneously above-cloud radiometric measurements and in-situ sampling within the same volume of cloud, we retrieve PSDs from the ground-based zenith-pointing radars at the time of the radiometric overpasses and then use the aircraft in-situ PSDs as an independent validation dataset. We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept N_o, and shape μ of the gamma size distribution) that combines a machine-learning (ML) ensemble with physics-based multi-frequency radar retrievals using 3, 35, and 94 GHz reflectivities. An ensemble of ML models is trained on observations from the Parameterising Ice Clouds using Airborne ObServationS and triple–frequency dOppler radar (PICASSO) campaign, also centred on Chilbolton Observatory. These models predict PSD moments from temperature, pressure, 3 GHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts N_o and λ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHz reflectivities. In this way, the ML ensemble acts as a compact, data-driven representation of the prior information, this being an alternative approach to the Bayesian optimal-estimation framework. We apply the retrieval to three of the CCREST-M cases with co-incident in-situ aircraft data. We show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHz radar compare favourably with PICASSO derived in-situ measurements of IWC, and exponential (μ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean and median PSDs show generally good agreement with in-situ PSDs as a function of temperature, although systematic biases remain in one case, likely due to temporal cloud evolution between radar and in-situ sampling. The IWCs derived from the retrieved PSDs are generally within about 50 % of the in-situ measured IWCs over much of the –50 to –10^o C temperature range, with near-unity agreement between the estimated and in-situ IWCs for one of the cases. Independent validation using 200 GHz radar reflectivity profiles confirms retrieval consistency where ML predictions are reliable and for a well constrained case, reinforcing the robustness of the retrieval approach and ice crystal scattering model. The retrieved PSDs provide radar-constrained inputs for forthcoming radiative transfer closure studies using collocated mm-wave and sub-mm-wave radiometer observations.

The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft. The campaign was designed primarily as a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. A key requirement for such closure tests is a near one-to-one relationship between the ice particle size distributions (PSDs) that enter the radiative transfer model and the radiometric measurements. Owing to the FAAM BAe-146 aircraft being unable to perform simultaneously above-cloud radiometric measurements and in-situ sampling within the same volume of cloud, we retrieve PSDs from the ground-based zenith-pointing radars at the time of the radiometric overpasses and then use the aircraft in-situ PSDs as an independent validation dataset. We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept N_o, and shape μ of the gamma size distribution) that combines a machine-learning (ML) ensemble with physics-based multi-frequency radar retrievals using 3, 35, and 94 GHz reflectivities. An ensemble of ML models is trained on observations from the Parameterising Ice Clouds using Airborne ObServationS and triple–frequency dOppler radar (PICASSO) campaign, also centred on Chilbolton Observatory. These models predict PSD moments from temperature, pressure, 3 GHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts N_o and λ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHz reflectivities. In this way, the ML ensemble acts as a compact, data-driven representation of the prior information, this being an alternative approach to the Bayesian optimal-estimation framework. We apply the retrieval to three of the CCREST-M cases with co-incident in-situ aircraft data. We show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHz radar compare favourably with PICASSO derived in-situ measurements of IWC, and exponential (μ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean and median PSDs show generally good agreement with in-situ PSDs as a function of temperature, although systematic biases remain in one case, likely due to temporal cloud evolution between radar and in-situ sampling. The IWCs derived from the retrieved PSDs are generally within about 50 % of the in-situ measured IWCs over much of the –50 to –10^o C temperature range, with near-unity agreement between the estimated and in-situ IWCs for one of the cases. Independent validation using 200 GHz radar reflectivity profiles confirms retrieval consistency where ML predictions are reliable and for a well constrained case, reinforcing the robustness of the retrieval approach and ice crystal scattering model. The retrieved PSDs provide radar-constrained inputs for forthcoming radiative transfer closure studies using collocated mm-wave and sub-mm-wave radiometer observations.

The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft. The campaign was designed primarily as a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. A key requirement for such closure tests is a near one-to-one relationship between the ice particle size distributions (PSDs) that enter the radiative transfer model and the radiometric measurements. Owing to the FAAM BAe-146 aircraft being unable to perform simultaneously above-cloud radiometric measurements and in-situ sampling within the same volume of cloud, we retrieve PSDs from the ground-based zenith-pointing radars at the time of the radiometric overpasses and then use the aircraft in-situ PSDs as an independent validation dataset. We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept N_o, and shape μ of the gamma size distribution) that combines a machine-learning (ML) ensemble with physics-based multi-frequency radar retrievals using 3, 35, and 94 GHz reflectivities. An ensemble of ML models is trained on observations from the Parameterising Ice Clouds using Airborne ObServationS and triple–frequency dOppler radar (PICASSO) campaign, also centred on Chilbolton Observatory. These models predict PSD moments from temperature, pressure, 3 GHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts N_o and λ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHz reflectivities. In this way, the ML ensemble acts as a compact, data-driven representation of the prior information, this being an alternative approach to the Bayesian optimal-estimation framework. We apply the retrieval to three of the CCREST-M cases with co-incident in-situ aircraft data. We show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHz radar compare favourably with PICASSO derived in-situ measurements of IWC, and exponential (μ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean and median PSDs show generally good agreement with in-situ PSDs as a function of temperature, although systematic biases remain in one case, likely due to temporal cloud evolution between radar and in-situ sampling. The IWCs derived from the retrieved PSDs are generally within about 50 % of the in-situ measured IWCs over much of the –50 to –10^o C temperature range, with near-unity agreement between the estimated and in-situ IWCs for one of the cases. Independent validation using 200 GHz radar reflectivity profiles confirms retrieval consistency where ML predictions are reliable and for a well constrained case, reinforcing the robustness of the retrieval approach and ice crystal scattering model. The retrieved PSDs provide radar-constrained inputs for forthcoming radiative transfer closure studies using collocated mm-wave and sub-mm-wave radiometer observations.

The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft. The campaign was designed primarily as a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. A key requirement for such closure tests is a near one-to-one relationship between the ice particle size distributions (PSDs) that enter the radiative transfer model and the radiometric measurements. Owing to the FAAM BAe-146 aircraft being unable to perform simultaneously above-cloud radiometric measurements and in-situ sampling within the same volume of cloud, we retrieve PSDs from the ground-based zenith-pointing radars at the time of the radiometric overpasses and then use the aircraft in-situ PSDs as an independent validation dataset. We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept N_o, and shape μ of the gamma size distribution) that combines a machine-learning (ML) ensemble with physics-based multi-frequency radar retrievals using 3, 35, and 94 GHz reflectivities. An ensemble of ML models is trained on observations from the Parameterising Ice Clouds using Airborne ObServationS and triple–frequency dOppler radar (PICASSO) campaign, also centred on Chilbolton Observatory. These models predict PSD moments from temperature, pressure, 3 GHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts N_o and λ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHz reflectivities. In this way, the ML ensemble acts as a compact, data-driven representation of the prior information, this being an alternative approach to the Bayesian optimal-estimation framework. We apply the retrieval to three of the CCREST-M cases with co-incident in-situ aircraft data. We show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHz radar compare favourably with PICASSO derived in-situ measurements of IWC, and exponential (μ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean and median PSDs show generally good agreement with in-situ PSDs as a function of temperature, although systematic biases remain in one case, likely due to temporal cloud evolution between radar and in-situ sampling. The IWCs derived from the retrieved PSDs are generally within about 50 % of the in-situ measured IWCs over much of the –50 to –10^o C temperature range, with near-unity agreement between the estimated and in-situ IWCs for one of the cases. Independent validation using 200 GHz radar reflectivity profiles confirms retrieval consistency where ML predictions are reliable and for a well constrained case, reinforcing the robustness of the retrieval approach and ice crystal scattering model. The retrieved PSDs provide radar-constrained inputs for forthcoming radiative transfer closure studies using collocated mm-wave and sub-mm-wave radiometer observations.

We present theory and simulations to show that at frequencies of order 200 GHz (G-band) the radar cross section (σr) of ice particles larger than ∼ a quarter wavelength (0.375 mm) is nearly directly proportional to their mass (m). Hence measurements of radar reflectivity (Z) at this frequency are directly proportional to the ice water content (IWC), with no other assumptions about the shape or breadth of the particle size distribution required. For the same reason, vertically pointing Doppler velocities at this frequency provide the mass-weighted mean vertical velocity of the particles, and the product of Z with the mean Doppler velocity (MDV) is proportional to the snowfall rate (S). This presents the opportunity for straightforward and accurate retrievals of ice microphysics. We explore the sensitivity of such retrievals to the scattering model for ice particles. We find that all seven models examined, four with random orientation and three with horizontal orientation, have σr∝m in this regime, but that the coefficient of proportionality varies between models. The dominant factor controlling this coefficient is the mass-size relationship for the ice particles, and specifically the mass of a wavelength-sized ice particle. If this information is known, or can be assumed, then the ice population parameters above can be retrieved with high accuracy. For mass-weighted mean diameters Dm>0.5 mm the variation in the IWC–Z relationship is within ≈ 30 %, and the variation in the S–(Z×MDV) relationship is within ≈ 15 %. The method is applied to retrieve IWC and S during two case studies, with measurements from the GRaCE 200 GHz Doppler radar at Chilbolton Observatory in the UK. In the first of these case studies, retrieved snowfall rates from particles falling aloft in a precipitating ice cloud were compared to gauge data at the surface. In the second case study, retrieved ice water contents from a deep non-precipitating stratiform ice cloud were compared to measurements made using an evaporative water content probe on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 instrumented research aircraft. In both cases a statistical comparison was necessary because of imperfect colocation of the radar measurements and in-situ/gauge sampling. The measurements fall within the distributions of the retrieved water content and snowfall fields, and follow consistent trends with time (Case 1) and height (Case 2), providing evidence that this method produces realistic retrievals. Application of the same technique at even higher radar frequencies would allow clouds with smaller particles (e.g. in high altitude cirrus clouds) to be characterised. Because of the increased gaseous attenuation at such frequencies, the latter may be more practical from airborne or spaceborne platforms.

We present theory and simulations to show that at frequencies of order 200 GHz (G-band) the radar cross section (σ_r) of ice particles larger than ∼ a quarter wavelength (0.375 mm) is nearly directly proportional to their mass (m), and hence measurements of radar reflectivity (Z) at this frequency are directly proportional to the ice water content (IWC), with no other assumptions about the shape or breadth of the particle size distribution required. For the same reason, vertically pointing Doppler velocities at this frequency provide the mass-weighted mean vertical velocity of the particles, and the product of Z with the mean Doppler velocity (MDV) is proportional to the snowfall rate (S). This presents the opportunity for straightforward but accurate retrievals of ice microphysics. We explore the sensitivity of such retrievals to the scattering model for ice particles. We find that all seven models examined, four with random orientation and three with horizontal orientation, have σ_r ∝ m in this regime, but that the coefficient of proportionality varies between models. The dominant factor controlling this coefficient is the mass-size relationship for the ice particles, and specifically the mass of a wavelength-sized ice particle. If this information is known, or can be assumed, then the ice population parameters above can be retrieved with high accuracy. For mass-weighted mean diameters D_m > 0.5 mm the variation in the IWC-Z relationship is within ≈ 30 %, and the variation in the S − (Z × MDV) relationship is within ≈ 15 %. The method is applied to retrieve IWC and S during two case studies, with measurements from the GRaCE 200 GHz Doppler radar at Chilbolton Observatory in the UK. In the first of these case studies, retrieved snowfall rates from particles falling aloft in a precipitating ice cloud were compared to gauge data at the surface. In the second case study, retrieved ice water contents from a deep non-precipitating stratiform ice cloud were compared to measurements made using an evaporative water content probe on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 instrumented research aircraft. In both cases a statistical comparison was necessary because of imperfect colocation of the radar measurements and in-situ/gauge sampling. The retrievals fall within the distributions from the retrieved water content and snowfall fields, and follow consistent trends with time (Case 1) and height (Case 2), providing evidence that this method produces realistic retrievals. Application of the same technique at even higher radar frequencies would allow clouds with smaller particles (e.g. in high altitude cirrus clouds) to be characterised. Because of the increased gaseous attenuation at such frequencies, the latter may be more practical from airborne or spaceborne platforms.

We present theory and simulations to show that at frequencies of order 200 GHz (G-band) the radar cross section (σ_r) of ice particles larger than ∼ a quarter wavelength (0.375 mm) is nearly directly proportional to their mass (m), and hence measurements of radar reflectivity (Z) at this frequency are directly proportional to the ice water content (IWC), with no other assumptions about the shape or breadth of the particle size distribution required. For the same reason, vertically pointing Doppler velocities at this frequency provide the mass-weighted mean vertical velocity of the particles, and the product of Z with the mean Doppler velocity (MDV) is proportional to the snowfall rate (S). This presents the opportunity for straightforward but accurate retrievals of ice microphysics. We explore the sensitivity of such retrievals to the scattering model for ice particles. We find that all seven models examined, four with random orientation and three with horizontal orientation, have σ_r ∝ m in this regime, but that the coefficient of proportionality varies between models. The dominant factor controlling this coefficient is the mass-size relationship for the ice particles, and specifically the mass of a wavelength-sized ice particle. If this information is known, or can be assumed, then the ice population parameters above can be retrieved with high accuracy. For mass-weighted mean diameters D_m > 0.5 mm the variation in the IWC-Z relationship is within ≈ 30 %, and the variation in the S − (Z × MDV) relationship is within ≈ 15 %. The method is applied to retrieve IWC and S during two case studies, with measurements from the GRaCE 200 GHz Doppler radar at Chilbolton Observatory in the UK. In the first of these case studies, retrieved snowfall rates from particles falling aloft in a precipitating ice cloud were compared to gauge data at the surface. In the second case study, retrieved ice water contents from a deep non-precipitating stratiform ice cloud were compared to measurements made using an evaporative water content probe on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 instrumented research aircraft. In both cases a statistical comparison was necessary because of imperfect colocation of the radar measurements and in-situ/gauge sampling. The retrievals fall within the distributions from the retrieved water content and snowfall fields, and follow consistent trends with time (Case 1) and height (Case 2), providing evidence that this method produces realistic retrievals. Application of the same technique at even higher radar frequencies would allow clouds with smaller particles (e.g. in high altitude cirrus clouds) to be characterised. Because of the increased gaseous attenuation at such frequencies, the latter may be more practical from airborne or spaceborne platforms.

We present theory and simulations to show that at frequencies of order 200 GHz (G-band) the radar cross section (σ_r) of ice particles larger than ∼ a quarter wavelength (0.375 mm) is nearly directly proportional to their mass (m), and hence measurements of radar reflectivity (Z) at this frequency are directly proportional to the ice water content (IWC), with no other assumptions about the shape or breadth of the particle size distribution required. For the same reason, vertically pointing Doppler velocities at this frequency provide the mass-weighted mean vertical velocity of the particles, and the product of Z with the mean Doppler velocity (MDV) is proportional to the snowfall rate (S). This presents the opportunity for straightforward but accurate retrievals of ice microphysics. We explore the sensitivity of such retrievals to the scattering model for ice particles. We find that all seven models examined, four with random orientation and three with horizontal orientation, have σ_r ∝ m in this regime, but that the coefficient of proportionality varies between models. The dominant factor controlling this coefficient is the mass-size relationship for the ice particles, and specifically the mass of a wavelength-sized ice particle. If this information is known, or can be assumed, then the ice population parameters above can be retrieved with high accuracy. For mass-weighted mean diameters D_m > 0.5 mm the variation in the IWC-Z relationship is within ≈ 30 %, and the variation in the S − (Z × MDV) relationship is within ≈ 15 %. The method is applied to retrieve IWC and S during two case studies, with measurements from the GRaCE 200 GHz Doppler radar at Chilbolton Observatory in the UK. In the first of these case studies, retrieved snowfall rates from particles falling aloft in a precipitating ice cloud were compared to gauge data at the surface. In the second case study, retrieved ice water contents from a deep non-precipitating stratiform ice cloud were compared to measurements made using an evaporative water content probe on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 instrumented research aircraft. In both cases a statistical comparison was necessary because of imperfect colocation of the radar measurements and in-situ/gauge sampling. The retrievals fall within the distributions from the retrieved water content and snowfall fields, and follow consistent trends with time (Case 1) and height (Case 2), providing evidence that this method produces realistic retrievals. Application of the same technique at even higher radar frequencies would allow clouds with smaller particles (e.g. in high altitude cirrus clouds) to be characterised. Because of the increased gaseous attenuation at such frequencies, the latter may be more practical from airborne or spaceborne platforms.

We present theory and simulations to show that at frequencies of order 200 GHz (G-band) the radar cross section (σ_r) of ice particles larger than ∼ a quarter wavelength (0.375 mm) is nearly directly proportional to their mass (m), and hence measurements of radar reflectivity (Z) at this frequency are directly proportional to the ice water content (IWC), with no other assumptions about the shape or breadth of the particle size distribution required. For the same reason, vertically pointing Doppler velocities at this frequency provide the mass-weighted mean vertical velocity of the particles, and the product of Z with the mean Doppler velocity (MDV) is proportional to the snowfall rate (S). This presents the opportunity for straightforward but accurate retrievals of ice microphysics. We explore the sensitivity of such retrievals to the scattering model for ice particles. We find that all seven models examined, four with random orientation and three with horizontal orientation, have σ_r ∝ m in this regime, but that the coefficient of proportionality varies between models. The dominant factor controlling this coefficient is the mass-size relationship for the ice particles, and specifically the mass of a wavelength-sized ice particle. If this information is known, or can be assumed, then the ice population parameters above can be retrieved with high accuracy. For mass-weighted mean diameters D_m > 0.5 mm the variation in the IWC-Z relationship is within ≈ 30 %, and the variation in the S − (Z × MDV) relationship is within ≈ 15 %. The method is applied to retrieve IWC and S during two case studies, with measurements from the GRaCE 200 GHz Doppler radar at Chilbolton Observatory in the UK. In the first of these case studies, retrieved snowfall rates from particles falling aloft in a precipitating ice cloud were compared to gauge data at the surface. In the second case study, retrieved ice water contents from a deep non-precipitating stratiform ice cloud were compared to measurements made using an evaporative water content probe on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 instrumented research aircraft. In both cases a statistical comparison was necessary because of imperfect colocation of the radar measurements and in-situ/gauge sampling. The retrievals fall within the distributions from the retrieved water content and snowfall fields, and follow consistent trends with time (Case 1) and height (Case 2), providing evidence that this method produces realistic retrievals. Application of the same technique at even higher radar frequencies would allow clouds with smaller particles (e.g. in high altitude cirrus clouds) to be characterised. Because of the increased gaseous attenuation at such frequencies, the latter may be more practical from airborne or spaceborne platforms.