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Abstracts with display material | PSE

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PSE4 | Harry Otten Prize for Innovation in Meteorology: Finalists' Session

17:00–17:20
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EMS2021-510
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solicited
Kirien Whan and Kate Saunders

Extreme wind gusts have severe socio-economic impacts, so any source of extra information on this variable is invaluable for mitigating associated damages and
protecting vulnerable communities. Unfortunately, networks of ocial measurement stations are limited in their ability to observe wind gusts. Official stations
are separated by vast distances, so extreme wind gusts often go unobserved due to the highly localised nature of these events. A wealth of additional observa-
tions is available from personal weather stations (PWSs) and could be used in combination with official observations to observe extreme gust events. However,
concerns about underlying data quality have to date prevented the usage of gust data from PWSs.

Research for other meteorological variables has demonstrated that with appropriate quality control PWSs can contribute high-quality observations that complement ocial measurements. It is well known that PWSs can provide useful and reliable temperature and precipitation observations. For crowd-sourced wind variables, the situation is more dicult. Crowd-sourced wind observations have di erent sources of error that pose signi cant challenges to quality control. For example, instrumentation is non-standard which results in di erent sensor sensitivities, and non-standard station placements introduce severe spatial in-consistencies and result in censoring of low wind speeds. Chen et al. (2021) recently developed a  exible approach to quality control and bias adjustment (QC/BA) that addresses this for wind speeds. They incorporate QC steps for official stations and develop new QC/BA steps to address the novel challenges posed by crowd-sourced data. Chen et al. (2021) showed after QC/BA, the wind speed climatology of a network of PWSs matched well with the climatology of ocial stations, and the wind speed variability between PWSs was similar to that of official  tations. Additionally, subsequent analysis has shown that the quality controlled and bias adjusted data from PWSs is able to detect small scale extreme wind speeds  ssociated with thunderstorms, that were not observed at official stations. No attempt has yet been made to quality control crowd-sourced observations of wind gusts  espite how impractical it is to obtain widespread observations of wind gusts using standard techniques.

In this project we will develop the necessary methods and software for the QC/BA of wind gusts. As part of this, we will develop inter-variable consistency checks between crowd-sourced wind speeds, wind gusts and wind directions. We will also produce an open-source, high-quality wind gust data set from PWSs that can be used to improve forecasts, warnings, and veri cation of extreme gusts.

References

Chen, J., Saunders, K. & Whan, K. (2021), `Quality control and bias correction of citizen science wind observations', Quarterly Journal of the Royal Meteo-
rological Society (under review) .

How to cite: Whan, K. and Saunders, K.: Second Wind: Extending the official wind gust records with citizen science observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-510, https://doi.org/10.5194/ems2021-510, 2021.

PSE8 | Highlight Talks

16:45–17:15
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EMS2021-308
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solicited
Leonhard Scheck, Stefan Geiss, Liselotte Bach, Alberto de Lozar, and Martin Weissmann

Satellite provide high-resolution information on the state of the atmosphere and thus represent observations are well-suited for data assimilation and model evaluation. So far mainly the thermal infrared channels have been utilized for these purposes. However, there is a rising interest to use also the channels in the solar part of the spectrum, which contain additional, complementary information. Visible channels can provide better information on the water and ice content of clouds than thermal infrared channels, have no problems to detect low clouds and are sensitive to cloud microphysics and the cloud top structure. Moreover, visible reflectances are strongly correlated with the solar irradiation at the surface and thus their assimilation has a clear potential to improve also radiation forecasts.

So far visible satellite images have not been assimilated directly for operational purposes, as multiple scattering dominates in the visible spectral range and makes radiative transfer (RT) computations with standard methods complex and slow. Only recently, we developed a sufficiently fast and accurate forward operator that relies on a compressed reflectance look-up table (LUT) computed with slow standard RT methods. Here we report on using feed-forward neural networks as an alternative to the look-up table and demonstrate that it is possible to achieve higher speed and accuracy. Moreover, both the amount of training data and the memory required by the operator can be reduced by three orders of magnitude. A further advantage is that tangent-linear and adjoint versions can easily be derived for arbitrary network structures and do not have to be changed when the network is trained with different data.

We will also discuss two ways to use the forward operator to improve forecasts. First, we show that observed and synthetic visible  Meteosat SEVIRI images can be used to detect systematic errors in the model clouds that can cause severe problems for data assimilation. Second, based on assimilation experiments using the ICON-D2 model and the local ensemble transformation Kalman filter implemented in DWDs data assimilation coding environment (DACE) we demonstrate for test periods of several weeks that errors in the cloud distribution and the surface radiation can be significantly reduced by assimilating visible SEVIRI images.

How to cite: Scheck, L., Geiss, S., Bach, L., de Lozar, A., and Weissmann, M.: Using visible satellite images for model evaluation and data assimilation, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-308, https://doi.org/10.5194/ems2021-308, 2021.

16:45–17:15
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EMS2021-498
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solicited
Susan Joslyn

This talk will describe an experimental research demonstrating the benefit of including numeric uncertainty information in weather forecasts intended for non-experts. Our results suggest that numeric uncertainty estimates (e.g. 30% chance) allow users to better differentiate situations that do and do not require precautionary action while also increasing understanding and trust in the forecast. People appear to understand that all forecasts involve uncertainty and find forecasts that acknowledge it explicitly more plausible.  Moreover, these benefits are not dependent on higher education or special abilities—they extend to a broad range of users. However, this work also suggests that it is important to present numeric uncertainty estimates in a manner that is compatible with the way in which people process information and with their decision goal. 

 

How to cite: Joslyn, S.: Uncertainty Information & Non-Expert Decisions, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-498, https://doi.org/10.5194/ems2021-498, 2021.

PSE10 | The EU Horizon Europe programme: Funding opportunities for the meteorological and climate community – basic and application areas

09:00–09:30
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EMS2021-511
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solicited
Adrian Broad

The EU has recently adopted its new Framework Programme for Research & Innovation called Horizon Europe (HE) with a budget of 95 billion euros over 7 years (2021-2027). It includes many fundamental and application science areas where the meteorological and climate community at large can contribute to and benefit from funding.

The event will include an introduction by Dr. Adrian Broad (UK Met Office and EUMETNET) to the three pillars of HE, followed by more insights for our community into the most relevant clusters and connections with the important HE missions. This will include among others: climate systems and extreme weather; sustainable and intelligent transport systems in the air, on the road or seas; space weather and earth observation; renewable energy and agriculture sectors; the ocean and marine environment; heat waves and air pollution impact on health; technology and the digital economy; or safe systems for critical infrastructures. To conclude, links between HE and other relevant EU programmes will be made, focussing on Copernicus, Digital Europe and the Civil Protection Mechanism.

How to cite: Broad, A.: The EU Horizon Europe programme: Funding opportunities for the meteorological and climate community – basic and application areas, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-511, https://doi.org/10.5194/ems2021-511, 2021.

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