Scientists Hope Atmospheric Modeling Can Predict Meteotsunamis

The first extensively documented air pressure–driven meteotsunami on one of the Great Lakes presents an opportunity to use existing weather models to predict when these potentially deadly waves will strike.

Irradiance and atmospheric optical properties from photovoltaic power data: model improvements and first results

<p>Photovoltaic (PV) power data are a valuable but as yet under-utilised resource that could be used to characterise global irradiance with unprecedented spatio-temporal resolution. The resulting knowledge of atmospheric conditions can then be fed back into weather models and will ultimately serve to improve forecasts of PV power itself. This provides a data-driven alternative to statistical methods that use post-processing to overcome inconsistencies between ground-based irradiance measurements and the corresponding predictions of regional weather models (see for instance Frank et al., 2018). This work reports first results from an algorithm developed to infer global horizontal irradiance as well as atmospheric optical properties such as aerosol or cloud optical depth from PV power measurements.</p><p>Building on previous work (Buchmann, 2018), an improved forward model of PV power as a function of atmospheric conditions was developed. As part of the BMWi-funded project MetPVNet, PV power data from twenty systems in the Allgäu region were made available, and the corresponding irradiance, temperature and wind speed were measured during two measurement campaigns in autumn 2018 and summer 2019. System calibration was performed using all available clear sky days; the corresponding irradiance was simulated using libRadtran (Emde et al., 2016). Particular attention was paid to describing the dynamic variations in PV module temperature in order to correctly take into account the heat capacity of the solar panels.</p><p>PV power data from the calibrated systems were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde et al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions.  The results were compared to predictions from the COSMO weather model, and the accuracy of the inverted quantities was compared using both a simple and more complex forward model. The potential of the method to extract irradiance data over a larger area as well as the increase in information from combining neighbouring PV systems will be explored in future work.</p><p><strong>References</strong><br>  <br>Buchmann, T., 2018: Potenzial von Photovoltaikanlagen zur Ableitung raum-zeitlich hoch aufgelöster Globalstrahlungsdaten. Heidelberg University, http://archiv.ub.uni-heidelberg.de/volltextserver/24687/.<br>Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). <em>Geosci. Model Dev.</em>, 9, 1647–1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/.<br>Frank, C. W., S. Wahl, J. D. Keller, B. Pospichal, A. Hense, and S. Crewell, 2018: Bias correction of a novel European reanalysis data set for solar energy applications.<em> Sol. Energy</em>, 164, 12–24, doi:10.1016/j.solener.2018.02.012. https://doi.org/10.1016/j.solener.2018.02.012.</p>

Validation of tropospheric ties at the test setup GNSS co-location site in Potsdam

<p>Atmospheric ties are induced by differences between the set-up of observing geodetic systems at co-location sites, are mainly attributed to frequency and position, and are usually quantified by zenith delay and gradient component offsets derived by weather models or in situ instuments.. Similar to local ties, they could be applied to combine datasets from several space geodetic techniques, thus contributing to the improvement of the realization of terrestrial reference frames (TRF). Theoretically, atmospheric ties are affected only by the height differences between antennas at the same site and meteorological conditions. Therefore, atmospheric ties could be determined analytically based on meteorological information from in situ measurements or weather models. However, there is often a discrepancy between the expected zenith delay differences and those estimated from geodetic analysis, potentially degrading a combined atmospheric ties solution should tight constraints be used. In this study, we set up a GNSS experiment campaign on the rooftop of a building in Telegrafernberg that offers unobscured data coverage for one month. We compared the estimated zenith delay and gradients from GNSS stations in this experiment, applying atmospheric ties from (1) meteorological data from the Global Pressure and Temperature model 3 (GPT3), (2) ERA5 reanalysis, and (3) in-situ measurements, as well as corrections derived from ray tracing (Potsdam Mapping Functions, PMF). The results show that atmospheric ties employing GPT3, ERA5, in-situ measurements, and ray tracing has an excellent and comparable performance in term of bias mitigation, but not in term of standard deviation, for zenith delay. Moreover, the unexpected bias in zenith delay was identified in the antenna with radome installation. A significantly large bias was identified in estimated gradients; the source of this discrepancy has been traced back to unmitigated multipath effects in this experiment.</p>

Combination of ground-based and space-based GNSS tomography (2021-2025)

<p>Global Navigation Satellite Systems (GNSS) sense the atmosphere remotely and provide low-cost, high-quality information about its state. Nowadays, radio occultation (RO) profiles from space platforms and tropospheric delays from ground-based stations are routinely assimilated in Numerical Weather Models  (NWM).</p><p>In spite of provision of valuable information for weather forecasting, both space- and ground-based data have significant limitations. The RO technique has low horizontal resolution and does not provide reliable profiles in the first 3-5km of the troposphere. Whereas, the station-specific integrated value of troposphere are sparse and pose a problem to NWM adjoint operator for correcting model fields at different heights. These deficiencies could be resolved by the GNSS tomography technique that utilizes an inverse Radon transform to derive the 3D refractivity distribution over certain troposphere space. The combination of space-based and ground-based observations in the tomographic model will enable us to increase the number of intersections of GNSS signals and improve the refractivity solution within individual model locations. </p><p>The aim of this research is to harness the full potential of Space 4.0 era, rapidly growing numbers of RO and GNSS satellite constellations as well as low-cost GNSS ground-based networks worldwide. We will not only use current infrastructure but also examine impact of future constellations on model performance. 3D model of refractivity from dense observations should be an excellent tool in weather prediction. Our previous research proves that the assimilation of the GNSS tomography outputs into the NWM improves relative humidity and the short-term weather forecasts. Therefore, the research goal of this project is to assess the benefit of integrated tomography model on the severe weather prediction and urban scale weather models.</p>

Can deep learning beat numerical weather prediction?

The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Improving acces to aircraft meteorological data relay AMDAR

Paper deals with the use of aircraft sensors to obtain meteorological parameters required for predictive weather models, using parameters in ATM and meteorological research. It focuses on the current state of the problem of data acquisition, using the AMDAR system. Comparative use of data from other data sources such as Mode S EHS and Mode S MRAR. By building the necessary infrastructure or equipping of commercial aircraft. Within the paper is developed a methodology, which deals with obtaining data from LPS SR š.p., necessary for the comparison of different systems. The impact of the AMDAR system on aircraft instruments and on the safety of air traffic. Based on the analyzed facts, examples of the use of the system are pointed out, but also of the problems associated with this system.

E-GVAP Status and future

<p>E-GVAP (the EIG EUMETNET GNSS Water Vapour Programme) is an operational service providing atmospheric delay estimates for use in operational meteorology in near real-time. This is done in a close collaboration between geodetic and meteorological institutions. The use of the GNSS delay estimates is found to increase the skill of weather forecasts. By the start of 2019 E-GVAP did, along with EUMETNET itself, entered a new phase. In E-GVAP 4 the main product will still be zenith total delays (ZTD), with a focus on improving timeliness, in support of the high resolution, local weather models with frequent updates being set up these years. But in addition there will be focus on GNSS derived slant total delay (STD) estimates. Several of the weather models used in Europe are being prepared for STD assimilation. The STDs provide additional information, on atmospheric asymmetries, on top of the information contained in a single ZTD estimate</p>