Parameterizing ice clouds using airborne observations and triple-frequency doppler radar data
Lead Research Organisation:
University of Manchester
Department Name: Earth Atmospheric and Env Sciences
Abstract
Ice clouds have an important role in the atmosphere, influencing radiative transfer and precipitation formation. The global climatic impact of all clouds types is estimated as a cooling effect. This net cooling effect results from the opposing impacts from liquid clouds (which cool by reflecting sunlight back into space) and ice clouds (which warm through a "greenhouse" effect). Unfortunately there is a lack of understanding of many of the physical processes occurring in ice clouds due to the complexity of ice particle processes and interactions between atmospheric motions, water vapour, and aerosol particles. This means ice clouds are a source of significant uncertainty in climate simulations, and can lead to errors in weather forecasts. Establishing which models are the most accurate remains difficult due to the lack of observations of ice cloud properties.
Remote sensing techniques (e.g. radar) can provide observations of ice clouds over large areas on a continuous basis, making them ideal for assessing model skill. However, these techniques do not typically directly measure the atmospherically relevant quantity (e.g. mass of condensed water in a volume of air), and a retrieval must be used to obtain comparable data. These retrievals must invoke several assumptions about the properties of the ice clouds, properties which in reality are highly uncertain. We propose a project to collect a new dataset using in-situ observations from a research aircraft to directly observed ice cloud properties. At the same time, three different radars operating at difference wavelengths will scan the same clouds to allow a variety of radar retrievals to be developed and evaluated. We will obtain data during overpasses from a variety of different satellites which observe ice clouds from space. This work will improve fundamental understanding of ice cloud properties, and lead to improved remote sensing retrievals, both of which will lead to improved model accuracy and reduced uncertainty.
Remote sensing techniques (e.g. radar) can provide observations of ice clouds over large areas on a continuous basis, making them ideal for assessing model skill. However, these techniques do not typically directly measure the atmospherically relevant quantity (e.g. mass of condensed water in a volume of air), and a retrieval must be used to obtain comparable data. These retrievals must invoke several assumptions about the properties of the ice clouds, properties which in reality are highly uncertain. We propose a project to collect a new dataset using in-situ observations from a research aircraft to directly observed ice cloud properties. At the same time, three different radars operating at difference wavelengths will scan the same clouds to allow a variety of radar retrievals to be developed and evaluated. We will obtain data during overpasses from a variety of different satellites which observe ice clouds from space. This work will improve fundamental understanding of ice cloud properties, and lead to improved remote sensing retrievals, both of which will lead to improved model accuracy and reduced uncertainty.
Planned Impact
National weather services (e.g. the Met Office) will benefit from this research, through improved representation of ice processes in the atmosphere, and how this should be represented in models. This benefit will be felt in both numerical weather forecasting and climate prediction, providing further benefits to the government, public and private sector organisations and businesses.
The project will benefit earth observation organisations and space agencies such as NASA, JAXA, ESA and EUMETSAT. This is because our data set and the subsequent analysis will provide crucial information for development of the retrievals for a number of space-based remote sensors including EarthCARE, GPM and ICI. The Met Office will also benefit in this regard through its contract to develop the ISMAR demonstrator - our data and results will help them interpret their data and retrieve accurate ice water paths in ice clouds.
Hydrological agencies will also benefit from the impacts of the proposed research. This includes governmental and international environment agencies with an interest in flood risk and hydrology. Our research will lead to improved forecasts of precipitation from ice clouds, which can impact on surface hydrological processes and forecasting flood risks.
The project will benefit earth observation organisations and space agencies such as NASA, JAXA, ESA and EUMETSAT. This is because our data set and the subsequent analysis will provide crucial information for development of the retrievals for a number of space-based remote sensors including EarthCARE, GPM and ICI. The Met Office will also benefit in this regard through its contract to develop the ISMAR demonstrator - our data and results will help them interpret their data and retrieve accurate ice water paths in ice clouds.
Hydrological agencies will also benefit from the impacts of the proposed research. This includes governmental and international environment agencies with an interest in flood risk and hydrology. Our research will lead to improved forecasts of precipitation from ice clouds, which can impact on surface hydrological processes and forecasting flood risks.
Publications
O'Shea S
(2021)
Characterising optical array particle imaging probes: implications for small-ice-crystal observations
in Atmospheric Measurement Techniques
Courtier B
(2022)
First Observations of G-Band Radar Doppler Spectra
in Geophysical Research Letters
Lukach M
(2021)
Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method
in Atmospheric Measurement Techniques
Kedzuf N
(2021)
Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations
in Atmospheric Measurement Techniques
O'Shea S
(2019)
Revisiting particle sizing using greyscale optical array probes: evaluation using laboratory experiments and synthetic data
in Atmospheric Measurement Techniques
Description | Existing measurement techniques has biases in derived data. We develop new techniques using stereo imaging and grayscale analysis to remove most of the biases. We propose new instruments are developed which utilize the methods we developed |
Exploitation Route | The data collected and are very valuable for continuing to understand the impact of clouds on solar and terrestrial radiation, and measurement of clouds using radars. Currently being used by PhD students. Lessons learned will be used to develop improved research programmes. |
Sectors | Environment |
Description | FAAM midlife upgrade |
Amount | £6,000,000 (GBP) |
Organisation | Natural Environment Research Council |
Sector | Public |
Country | United Kingdom |
Start | 02/2023 |
End | 10/2025 |
Title | Cloud particle stereo imaging/analysis |
Description | New analysis technique has been developed to extract dual view or "stereo" images from certain in-situ cloud particle imaging systems. This is the first successful implementation of this technique, and was used to investigate cloud particle microphysics from the UKRI-NERC FAAM research aircraft. |
Type Of Material | Data analysis technique |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | The new techniques improves data quality by removing imaging artefacts which are a result of diffraction. |
URL | https://amt.copernicus.org/articles/14/1917/2021/ |
Description | Met Office collaboration |
Organisation | Meteorological Office UK |
Department | Scottish Flood Forecasting Service |
Country | United Kingdom |
Sector | Public |
PI Contribution | Expertise, instrumentation and flight hours on the NERC FAAM aircraft. |
Collaborator Contribution | Expertise, instrumentation and flight hours on the NERC FAAM aircraft. |
Impact | One scientific publication currently under review. https://www.atmos-meas-tech-discuss.net/amt-2018-435/ |
Start Year | 2018 |