The relationship between precipitation and its spatial pattern in the trades observed during EUREC 4 A

Trade wind convection organises into a rich spectrum of spatial patterns, often in conjunction with precipitation development. Which role spatial organisation plays for precipitation and vice versa is not well understood. We analyse scenes of trade-wind convection scanned by the C-band radar Poldirad during the EUREC4A field campaign to investigate how trade-wind precipitation fields are spatially organised, quantified by the cells' number, mean size, and spatial arrangement, and how this matters for precipitation characteristics. We find that the mean rain rate (i.e., the amount of precipitation in a scene) and the intensity of precipitation (mean conditional rain rate) relate differently to the spatial pattern of precipitation. Whereas the amount of precipitation increases with mean cell size or number, as it scales well with the precipitation fraction, the intensity increases predominantly with mean cell size. In dry scenes, the increase of precipitation intensity with mean cell size is stronger than in moist scenes. Dry scenes usually contain fewer cells with a higher degree of clustering than moist scenes do. High precipitation intensities hence typically occur in dry scenes with rather large, few, and strongly clustered cells, whereas high precipitation amounts typically occur in moist scenes with rather large, numerous, and weakly clustered cells. As cell size influences both the intensity and amount of precipitation, its importance is highlighted. Our analyses suggest that the cells' spatial arrangement, correlating mainly weakly with precipitation characteristics, is of second-order importance for precipitation across all regimes, but it could be important for high precipitation intensities and to maintain precipitation amounts in dry environments

Shallow cumulus cloud feedback in large eddy simulations - bridging the gap to storm-resolving models

The response of shallow trade cumulus clouds to global warming is a leading source of uncertainty in projections of the Earth's changing climate. A setup based on the Rain In Cumulus over the Ocean field campaign is used to simulate a shallow trade wind cumulus field with the Icosahedral Nonhydrostatic Large Eddy Model in a control and a perturbed 4K warmer climate, while degrading horizontal resolution from 100 m to 5 km. As the resolution is coarsened, the base-state cloud fraction increases substantially, especially near cloud base, lateral mixing is weaker, and cloud tops reach higher. Nevertheless, the overall vertical structure of the cloud layer is surprisingly robust across resolutions. In a warmer climate, cloud cover reduces, alone constituting a positive shortwave cloud feedback: the strength correlates with the amount of base-state cloud fraction and thus is stronger at coarser resolutions. Cloud thickening, resulting from more water vapour availability for condensation in a warmer climate, acts as a compensating feedback, but unlike the cloud cover reduction it is largely resolution independent. Therefore, refining the resolution leads to convergence to a near-zero shallow cumulus feedback. This dependence holds in experiments with enhanced realism including precipitation processes or warming along a moist adiabat instead of uniform warming. Insofar as these findings carry over to other models, they suggest that storm-resolving models may exaggerate the trade wind cumulus cloud feedback

The relationship between precipitation and its spatial pattern in the trades observed during EUREC4A

Trade wind convection organises into a rich spectrum of spatial patterns, often in conjunction with precipitation development. Which role spatial organisation plays for precipitation and vice versa is not well understood. We analyse scenes of trade‐wind convection scanned by the C‐band radar Poldirad during the EUREC4A field campaign to investigate how trade‐wind precipitation fields are spatially organised, quantified by the cells' number, mean size, and spatial arrangement, and how this matters for precipitation characteristics. We find that the mean rain rate (i.e., the amount of precipitation in a scene) and the intensity of precipitation (mean conditional rain rate) relate differently to the spatial pattern of precipitation. Whereas the amount of precipitation increases with mean cell size or number, as it scales well with the precipitation fraction, the intensity increases predominantly with mean cell size. In dry scenes, the increase of precipitation intensity with mean cell size is stronger than in moist scenes. Dry scenes usually contain fewer cells with a higher degree of clustering than moist scenes do. High precipitation intensities hence typically occur in dry scenes with rather large, few, and strongly clustered cells, whereas high precipitation amounts typically occur in moist scenes with rather large, numerous, and weakly clustered cells. As cell size influences both the intensity and amount of precipitation, its importance is highlighted. Our analyses suggest that the cells' spatial arrangement, correlating mainly weakly with precipitation characteristics, is of second‐order importance for precipitation across all regimes, but it could be important for high precipitation intensities and to maintain precipitation amounts in dry environments.We analyse scenes of trade‐wind convection scanned by the C‐band radar Poldirad during the EUREC4A field campaign to investigate how trade‐wind precipitation fields are spatially organised, quantified by the cells' number, mean size, and spatial arrangement, and how this matters for precipitation characteristics. We conclude that the cells' size is important for both the amount and intensity of precipitation, whereas the cells' spatial arrangement is of second‐order importance for precipitation across all regimes, but possibly important for precipitation in dry environments.Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy—EXC 2037 'CLICCS—Climate, Climatic Change, and Society'https://doi.org/10.25326/217https://doi.org/10.25326/7

Assessing the weather conditions for urban cyclists by spatially dense measurements with an agent‐based approach

Abstract Convincing commuters to use a bike is a timely contribution to reach sustainability goals. However, more than other modes of transportation, cycling is heavily influenced by the current meteorological conditions. In this study, we assess the weather conditions experienced on individual cycling routes through an urban environment and how weather observations and forecasts may give guidance to a better cycling experience. We introduce an agent‐based model that simulates cycling trips in Hamburg, Germany, and a three‐category traffic light scheme for precipitation, wind and temperature comfort. We use these tools to evaluate the cycling weather based on the commonly used single‐station measurement approach versus spatially dense observations from an urban station network and radar measurements. Analysis of long‐term data from a single station shows that most frequently discomfort is caused by temperature with a probability of 33%. Wind and precipitation discomfort occur only for about 5% of the rides. While temperature conditions can be well assessed by a single station, only one‐third of critical precipitation events and less than 10% of critical wind events are captured. With perfect knowledge, temporal flexibility in start time of less than ±30 min reduces the risk of getting wet by 50%. For precipitation, nowcasting is able to predict 30% of the critical events correctly, which is significantly better than model forecasts. Operational ensemble forecast provides satisfactory guidance concerning temperature; however, the limited predictability of precipitation and wind renders these forecasts only useful for riders with a high risk‐awareness and small sensitivity to false alarms

EUREC⁴A's HALO

International audienceAs part of the EUREC 4 A field campaign, the German research aircraft HALO, configured as a cloud observatory, conducted 15 research flights in the trade wind region east of Barbados in January and February 2020. Narrative text, aircraft state data, and meta data describing HALO's operation during the campaign are provided. Each HALO research flight is segmented by time-stamp intervals into standard elements to aid the consistent analysis of the flight data. Photographs from HALO's cabin and animated satellite images synchronized with flight tracks are provided to visually document flight condi-5 tions. As a comprehensive product from the remote sensing observations, a multi-sensor cloud mask product is derived and quantifies the incidence of clouds observed during the flights. In addition, to lower the threshold for new users of HALO's data, a collection of use cases is compiled into an online book "How to EUREC 4 A", included as an asset with this paper. This online book provides easy access to most of EUREC 4 A's HALO data through an intake catalogue

EUREC(4)A's HALO

As part of the EUREC(4)A (Elucidating the role of cloud-circulation coupling in climate) field campaign, the German research aircraft HALO (High Altitude and Long Range Research Aircraft), configured as a cloud observatory, conducted 15 research flights in the trade-wind region east of Barbados in January and February 2020. Narrative text, aircraft state data, and metadata describing HALO's operation during the campaign are provided. Each HALO research flight is segmented by timestamp intervals into standard elements to aid the consistent analysis of the flight data. Photographs from HALO's cabin and animated satellite images synchronized with flight tracks are provided to visually document flight conditions. As a comprehensive product from the remote sensing observations, a multi-sensor cloud mask product is derived and quantifies the incidence of clouds observed during the flights. In addition, to lower the threshold for new users of HALO's data, a collection of use cases is compiled into an online book, How to EUREC(4)A, included as an asset with this paper. This online book provides easy access to most of EUREC(4)A's HALO data through an intake catalogue. Code and data are freely available at the locations specified in Table 6