Advanced methods for assimilating satellite data in numerical weather prediction
Lead Research Organisation:
University of Reading
Department Name: Meteorology
Abstract
Data assimilation, the process of initializing a computer model forecast using the latest observational data, has proven fundamental to the accuracy of modern day weather forecasting. Every day of the order of 107 observations are assimilated at the Met Office. These observations come from a myriad of different instruments onboard various platforms; including weather balloons, aircrafts and ships. However, observations from instruments onboard satellites have been shown to have the greatest impact on producing accurate forecasts. This is due to their extensive coverage, high sampling resolution and the information they provide about key model variables: temperature, humidity and winds. The problem is that satellite data often exhibit systematic errors, for example due to poor calibration, adverse environmental effects, or errors in the radiative transfer equations that relate the observations to the model variables. Systematic errors in the data violate the theory that is central to data assimilation and so for satellite data to be useful they must first be bias corrected. The methods currently in use for performing the bias correction rely on the assumption that the computer model assimilating the observational data itself is unbiased. Unfortunately this is rarely true and is becoming a limiting factor in the use of satellite data. This project will develop new mathematical techniques for performing bias correction that are able to distinguish and correct for biases in both the observations and model.
People |
ORCID iD |
Alison Fowler (Primary Supervisor) | |
Devon Francis (Student) |
Publications
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
NE/S007261/1 | 30/09/2019 | 29/09/2028 | |||
2285064 | Studentship | NE/S007261/1 | 30/09/2019 | 29/09/2023 | Devon Francis |
Description | In weather prediction, we combine a previous forecast with observations to make the best 'guess' of what is happening right now. This is known as data assimilation. However, there are biases within both the previous forecasts (biases in mathematical models) as well as in observations, especially satellites. Most operational centres correct for observational biases, but use unbiased observations to correct for model biases within the observation bias correction. We have found that the location of the unbiased observations is important in determining whether an unbiased observation can reduce the effect of model bias within the observation bias correction. |
Exploitation Route | If there is an area with a sparse number of unbiased observations, then operational centres can be aware of where in the system there are more likely to be model biases and so be more wary of the data that comes from those locations, as well as looking into developing more unbiased observations for those locations in the future. |
Sectors | Aerospace Defence and Marine Environment |
Description | Met Office CASE funding |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Have published a paper together in QJRMS. |
Collaborator Contribution | Funding to visit the Met Office. Supervisor support with PhD work and submitted paper. |
Impact | Paper submitted to QJRMS. |
Start Year | 2020 |
Description | The Brilliant Club Scholars Programme |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | Designed a KS4 course for year 9/10 students about my PhD. Delivered course over approximately 6 weeks to the students. The Brilliant Club is a charity designed to engage more students (especially those less likely to go to University) with Universities. |
Year(s) Of Engagement Activity | 2022,2023 |