Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

Harmonization and Verification of Three National European Icing Forecast Models Using Pilot Reports

The Single European Sky Air Traffic Management Research (SESAR) program aims at modernizing and harmonizing the European airspace, which currently has a strongly fragmented character. Besides turbulence and convection, in-flight icing is part of SESAR and can be seen as one of the most important meteorological phenomena, which may lead to hazardous flight conditions for aircraft. In this study, several methods with varying complexities are analyzed for combining three individual in-flight icing forecasts based on numerical weather prediction models from Deutscher Wetterdienst, Météo-France, and Met Office. The optimal method will then be used to operate one single harmonized in-flight icing forecast over Europe. As verification data, pilot reports (PIREPs) are used, which provide information about hazardous weather and are currently the only direct regular measure of in-flight icing events available. In order to assess the individual icing forecasts and the resulting combinations, the probability of detection skill score is calculated based on multicategory contingency tables for the forecast icing intensities. The scores are merged into a single skill score to give an overview of the quality of the icing forecast and enable comparison of the different model combination approaches. The concluding results show that the most complex combination approach, which uses iteratively optimized weighting factors for each model, provides the best forecast quality according to the PIREPs. The combination of the three icing forecasts results in a harmonized icing forecast that exceeds the skill of each individual icing forecast, thus providing an improvement to in-flight icing forecasts over Europe.

Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products

The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (N1 -https://media.proquest.com/media/hms/PFT/1/TPT6N?_a=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%3D%3D&_s=Os82DX%2BaBlhnLe1wnAEkKTgdQ4M%3D ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.

Validation of a Simulink Model for Simulating the Two Typical Controlled Ventilation Modes of Intensive Care Units Mechanical Ventilators

Mechanical ventilators are vital components of critical care services for patients with severe acute respiratory failure. In particular, pressure- and volume-controlled mechanical ventilation systems are the typical modes used in intensive care units (ICUs) to ventilate patients who cannot breathe adequately on their own. In this paper, a Simulink model is proposed to simulate these two typical modes employed in intensive care lung ventilators. Firstly, these two modes of ventilation are described in detail in the present paper. Secondly, the suggested Simulink model is analysed: it consists of using well-established subroutines already present in Simulink through the Simscape Fluids (gas) library, to simulate all the pneumatic components employed in some commercial ICU ventilators, such as pressure reducing valves, pressure relief valves, check valves, tanks, ON\OFF and proportional directional valves, etc. Finally, the simulation results of both modes in terms of pressure, tidal volume, and inspired/expired flow are compared with the real-life quantitative trends taken from previously recorded real-life experiments in order to validate the Simulink model. The accuracy of the model is high, as the numerical predictions are in good agreement with the real-life data, the percentage error being less than 10% in most comparisons. In this way, the model can easily be used by manufacturers and start-ups in order to produce new mechanical ventilators in the shortest time possible. Moreover, it can also be used by doctors and trainees to evaluate how the mechanical ventilator responds to different patients.