Understanding the model representation of clouds based on visible and infrared satellite observations

There is a rising interest in improving the representation of clouds in numerical weather prediction models. This will directly lead to improved radiation forecasts and, thus, to better predictions of the increasingly important production of photovoltaic power. Moreover, a more accurate representation of clouds is crucial for assimilating cloud-affected observations, in particular high-resolution observations from instruments on geostationary satellites. These observations can also be used to diagnose systematic errors in the model clouds, which are influenced by multiple parameterisations with many, often not well-constrained, parameters. In this study, the benefits of using both visible and infrared satellite channels for this purpose are demonstrated. We focus on visible and infrared Meteosat SEVIRI (Spinning Enhanced Visible InfraRed Imager) images and their model equivalents computed from the output of the ICON-D2 (ICOsahedral Non-hydrostatic, development version based on version 2.6.1; Zängl et al., 2015) convection-permitting, limited area numerical weather prediction model using efficient forward operators. We analyse systematic deviations between observed and synthetic satellite images derived from semi-free hindcast simulations for a 30 d summer period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible reflectance facilitates the attribution of individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived visible and infrared observation equivalents to modified model and visible forward operator settings to identify dominant error sources. Estimates of the uncertainty of the visible forward operator turned out to be sufficiently low; thus, it can be used to assess the impact of model modifications. Results obtained for various changes in the model settings reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible satellite images. Visible observations are, therefore, well-suited to constrain subgrid cloud settings. In contrast, infrared channels are much less sensitive to the subgrid clouds, but they can provide information on errors in the cloud-top height.

Simulation of the Operation of a Single Pixel Camera with Compressive Sensing in the Long-Wave Infrared

Imaging with the use of a single pixel camera and based on compressed sensing (CS) is a new and promising technology. The use of CS allows reconstruction of images in various spectrum ranges depending on the spectrum sensibility of the used detector. During the study image reconstruction was performed in the LWIR range based on a thermogram from a simulated single pixel camera. For needs of reconstruction CS was used. A case analysis showed that the CS method may be used for construction of infrared-based observation single pixel cameras. This solution may also be applied in measuring cameras. Yet the execution of a measurement of radiation temperature requires calibration of results obtained by CS reconstruction. In the study a calibration method of the infrared observation camera was proposed and studies were carried out of the impact exerted by the number of measurements made on the quality of reconstruction. Reconstructed thermograms were compared with reference images of infrared radiation. It has been ascertained that the reduction of the reconstruction error is not directly in proportion to the number of collected samples being collected. Based on a review of individual cases it has been ascertained that apart from the number of collected samples, an important factor that affects the reconstruction fidelity is the structure of the image as such. It has been proven that estimation of the error for reconstructed thermograms may not be based solely on the quantity of executed measurements.

Multi-Task Collaboration Deep Learning Framework for Infrared Precipitation Estimation

Infrared observation is an all-weather, real-time, large-scale precipitation observation method with high spatio-temporal resolution. A high-precision deep learning algorithm of infrared precipitation estimation can provide powerful data support for precipitation nowcasting and other hydrological studies with high timeliness requirements. The “classification-estimation” two-stage framework is widely used for balancing the data distribution in precipitation estimation algorithms, but still has the error accumulation issue due to its simple series-wound combination mode. In this paper, we propose a multi-task collaboration framework (MTCF), i.e., a novel combination mode of the classification and estimation model, which alleviates the error accumulation and retains the ability to improve the data balance. Specifically, we design a novel positive information feedback loop composed of a consistency constraint mechanism, which largely improves the information abundance and the prediction accuracy of the classification branch, and a cross-branch interaction module (CBIM), which realizes the soft feature transformation between branches via the soft spatial attention mechanism. In addition, we also model and analyze the importance of the input infrared bands, which lay a foundation for further optimizing the input and improving the generalization of the model on other infrared data. Extensive experiments based on Himawari-8 demonstrate that compared with the baseline model, our MTCF obtains a significant improvement by 3.2%, 3.71%, 5.13%, 4.04% in F1-score when the precipitation intensity is 0.5, 2, 5, 10 mm/h, respectively. Moreover, it also has a satisfactory performance in identifying precipitation spatial distribution details and small-scale precipitation, and strong stability to the extreme-precipitation of typhoons.

Spatiotemporal geostatistical analysis of precipitation combining ground and satellite observations

Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space–time regression kriging. The case study is referred to the island of Crete, Greece, for the time period of 2010–2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping.