CR2025_03 Improving precipitation forecasts using airborne and EarthCARE satellite observations

Lead Supervisor: Thorwald Stein, Department of Meteorology, University of Reading

Email: t.h.m.stein@reading.ac.uk

Co-supervisors: Shannon Mason, European Centre for Medium-range Weather Forecasts and National Centre for Earth Observation; Chris Westbrook, Department of Meteorology, University of Reading

Global precipitation estimates are essential for day-to-day forecasting, flood and drought monitoring, and constraining climate models. For most of the planet, ground observations are sparse and precipitation is estimated from satellite measurements. Among these satellite measurements, spaceborne radar instruments provide the most direct estimate of precipitation, as radar reflectivity is a combined estimate of the mass-squared of ice or liquid particles (i.e., hydrometeors). The first spaceborne cloud radar, CloudSat, which operated 2006-2023, provided unique insight and constraints on global precipitation (Stephens et al. 2010) and the amount of ice in clouds (Stein et al., 2011).

Crucially for global weather and climate modelling, CloudSat provided evidence that the majority of the precipitation that reaches the ground is either snow or has come from snow that has melted into rain (Field and Heymsfield, 2015). Numerical weather prediction models rely on mathematical representations of the characteristics of rain and snow, and the physical processes that control them. These microphysical representations are often highly simplified—based on field campaigns carried out over limited areas, often using fixed global values, and only considering number concentrations. At ECMWF, scientists are implementing more sophisticated models in the forecast model that consider how both the size and number of raindrops and ice particles vary due to microphysical processes. To verify and constrain these models, more detailed information about rain and snow is needed than CloudSat could provide, such as the fall speeds of the hydrometeors and the amount of liquid present.

In May 2024, the EarthCARE satellite (Illingworth et al. 2015, Wehr et al, 2023) successfully launched. Among its instruments, the first Doppler cloud radar in space provides unique high-resolution measurements of the vertical motion of raindrops and ice particles across the globe multiple times per day (see Figure 1). ECMWF scientists are central to the development of algorithms for estimating the properties of clouds and precipitation from EarthCARE observations (ACM-CAP, Mason et al. 2023), but these algorithms require validation against in-cloud aircraft measurements. Globally, several campaigns are ongoing with aircraft flying underneath the satellite track, including with the UK FAAM research aircraft, involving Reading scientists.

In this project you will first use the FAAM measurements from around the UK to validate the EarthCARE retrievals. The probes on FAAM inform us of the particle size distribution and the amount of liquid present in the cloud, as well as the air motion, allowing us to estimate the uncertainty in the retrieval due to different physical processes such as riming and turbulence. Then, considering the full EarthCARE data set, you will consider how the fall speed of hydrometeors varies globally, and what information this give us about microphysical properties of clouds in different regimes and seasons. Working in placements at ECMWF with the scientists who are developing the improved model physics, you will have the opportunity to evaluate the sensitivity of weather forecasts to changes in rain and ice fall speed assumptions and characteristics, and may ultimately leverage your physical insights to inform improvements to the ECMWF weather forecast model. Example objectives are, therefore:

• To identify and characterise the uncertainty in the EarthCARE retrievals of snow and rain properties in different weather conditions
• To estimate how much snow and rain properties vary globally, particularly their fall speeds, and the implications for global precipitation estimates and modelling
• To test the sensitivity of weather forecasts to changes in rain and ice fall speed assumptions.

But, the data sets you will work with are huge and complex, allowing the research to go into unexpected and exciting questions on clouds and precipitation properties and processes.

Training opportunities:

The project involves several placements to ECMWF (at its Reading site, about 2 miles from campus) for a total period of up to 3 months. These provide the student with invaluable experience working in an operational research environment, building both team-working skills in the Physical Processes group, and the technical skills of modifying and running a large and complex model on a supercomputer and analysing the large datasets produced.

Student profile:

This project would be suitable for candidates with a degree in physics, mathematics or a closely related environmental or physical science. An affinity to working with large and complex data sets is essential (though training will be provided).

Co-Sponsorship details

The project includes a placement opportunity at the European Centre for Medium-range Weather Forecasts.

References:

• Field, P. R., and A. J. Heymsfield (2015), Importance of snow to global precipitation, Geophys. Res. Lett., 42, 9512–9520, doi:10.1002/2015GL065497.
• Illingworth, A. J., and Coauthors, 2015: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation. Bull. Amer. Meteor. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1.
• Mason, S. L., Hogan, R. J., Bozzo, A., and Pounder, N. L., 2023: A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product, Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023.
• Stein, T. H. M., Delanoë, J. and Hogan, R.J. 2011: A Comparison among Four Different Retrieval Methods for Ice-Cloud Properties Using Data from CloudSat, CALIPSO, and MODIS. J. Appl. Meteor. Climatol., 50, 1952–1969, https://doi.org/10.1175/2011JAMC2646.1.
• Stephens, G. L., L’Ecuyer, T., Forbes, R., Gettelmen, A., Golaz, J.-C., Bodas-Salcedo, A., Suzuki, K., Gabriel, P., and Haynes, J. 2010: Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.
• Wehr, T., Kubota, T., Tzeremes, G., Wallace, K., Nakatsuka, H., Ohno, Y., Koopman, R., Rusli, S., Kikuchi, M., Eisinger, M., Tanaka, T., Taga, M., Deghaye, P., Tomita, E., and Bernaerts, D., 2023: The EarthCARE mission – science and system overview, Atmos. Meas. Tech., 16, 3581–3608, https://doi.org/10.5194/amt-16-3581-2023.