AS1.26

Fog is a critical weather phenomenon for safety and operations in aviation. Unfortunately, the forecasting of radiation fog remains challenging due to the numerous physical processes that play a role and their complex interactions, in addition to the vertical and horizontal resolution of the numerical models. In this study we evaluate the performance of the Weather Research and Forecasting (WRF) model for a radiation fog event at Schiphol Amsterdam Airport (The Netherlands) and further develop the model towards a 100 m grid spacing. Hence we introduce high resolution land use and land elevation data. In addition we study the role of gravitational droplet settling, advection of TKE, top-down diffusion caused by strong radiative cooling at the fog top. Finally the impact of heat released by the terminal areas on the fog formation is studied. The model outcomes are evaluated against 1-min weather observations near multiple runways at the airport.

Overall we find the WRF model shows an reasonable timing of the fog onset and is well able to reproduce the visibility and meteorological conditions as observed during the case study. The model appears to be relatively insensitive to the activation of the individual physical processes. An increased spatial resolution to 100 m generally results in a better timing of the fog onset differences up to three hours, though not for all runways. The effect of the refined landuse dominates over the effect of refined elevation data. The modelled fog dissipation systematically occurs 3-4 h hours too early, regardless of physical processes or spatial resolution. Finally, the introduction of heat from terminal buildings delays the fog onset with a maximum of two hours, an overestimated visibility of 100-200 m and a decrease of the LWC with 0.10-0.15 g/kg compared to the reference.

How to cite: Steeneveld, G.-J. and Knol, R.: Fog forecasting for Schiphol airport at sub-kilometre scale. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-155, https://doi.org/10.5194/egusphere-egu21-155, 2021.

Correlation between Low-level Jets and Fog Events in Tianjin  

Bingui Wu^1,2 Zongfei Li^1,2 , Tingting Ju^3,&, Hongsheng Zhang⁴

¹ Affiliation:Tianjin Key Labarotory of Marine Meteorology, Tianjin 300074, China.

² Affiliation: Tianjin Meteorological Bureau, Tianjin 300074, China.

Address: Tianjin Meteorological Bureau, No.100 Weather Station Road Hexi District, Tianjin, 300074, China.

³ Affiliation: Institute of Navigation College, Dalian Maritime University, Dalian 116026, China

Address: Dalian Maritime University, No.1 LinghaiRoad Ganjingzi District, Dalian 116026, China

⁴ Affiliation: Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China

Address: Peking University, No.5 Yiheyuan Road Haidian District, Beijing 100871, China

KEY WORDS: low level jet, fog, Tianjin

Abstract

Fog and low level jet (LLJ) greatly affect aviation. Both of them are cared more, while not for their relationship. In this study, the relationship between the LLJs and fog are studied using the observational hourly wind profile data and the automatic meteorological observation data from Xiqing in 2016. The results show that LLJs play an important role in the fog events. The fog events tend to occur frequently with the occurrence of LLJs, especially in spring and summer, which suggest the LLJs seem to be more important for triggering advection fogs. In addition, the relationship between LLJs and fog events occur simultaneously and one, two and three days after the occurrence of LLJs are compared, and a pronounced relation are observed between LLJs and fog events one day after, a lag effect of LLJs on fog events is verified. For the condition that the LLJ and fog event occur on the same day, the differences of specific humidity between the occurrence of LLJs and fogs. In the case that the occurrence of LLJ is prior to fog, persistent southwest wind support the fog formation. While the differences of specific humidity between the occurrence of LLJs and fogs, in the case that the occurrence of LLJ is posterior to fog are always larger or close to zero, and the prevailing wind direction is north wind, which suggest that the main contribution of LLJs to fog is leading to fog dissipation and short duration in this condition. For the condition that the occurrence of LLJ one day prior to fog event, a pronounced negative correlation between the height of LLJs and the duration of fog is observed.

How to cite: Wu, B.: correlation between Low-level Jets and fog events in Tianjin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1966, https://doi.org/10.5194/egusphere-egu21-1966, 2021.

Unmanned Aircraft System (UAS) operations have spread rapidly worldwide performing a variety of military and civilian applications. The ability and performance of UAS to carry out these applications are strongly affected by poor weather conditions. Fog is one of the critical issues that threaten the safety of UAS missions by altering visibility. Therefore, the mission planning based on accurate visibility nowcasts prior to Beyond Visual Line Of Sight (BVLOS) UAS missions will be mandatory to ensure safer UAS operations.

Two types of models are generally considered for visibility nowcasting: physics-based or data-driven models. However, physics-based visibility forecasts remain expensive and difficult to use operationally. Recently, with the increase of the number of available historical data, data-driven models, especially those using deep learning approaches in particular, have attracted increasing attention in weather forecasting and have proven themselves as a powerful prediction tool.

This study aims at developing a Visibility Nowcasting System (VNS) that improves the performance and the capability of nowcasting the visibility using deep learning over the U.S.. To that end,  a deep neural network, called an encoder-decoder convolutional neural network (CNN), is used to demonstrate specifically how basic NWP fields such as temperature, wind speed, relative humidity, etc. and visibility from surface observations can provide accurate visibility nowcasts. The VNS will be then tested in different geographical environments where UAS flights are deployed (for example, over North Dakota) since it can learn the time and space correlation according to the historical data.

To train the network, we created a labeled data set from available METAR reports and hourly reanalysis data from the High-Resolution Rapid Refresh (HRRR) model. This dataset will be also used to test the CNN and evaluate their nowcasting performance. The model will be then evaluated in operational use cases and compared to other available visibility observations during fog events.

How to cite: Majdi, M. and Delene, D.: Visibility Nowcasting For UAS Operations using Deep Learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3704, https://doi.org/10.5194/egusphere-egu21-3704, 2021.

The Madeira International Airport (MIA) lies on the island south-eastern coast and it is known to be exposed to wind hazards. A link between these adverse winds at MIA and the synoptic-scale circulation is established using a weather type (WT) classification. From April to September (summer period), five WTs prevail, cumulatively representing nearly 70% of days. These WTs reflect the presence of well-established Azores high, with some variations on location and strength. Although with a low frequency of occurrence (<5%), this anticyclone occasionally strengthens and extends towards Iberia, inducing anomalously strong NNE/NE up to 3-5 km over Madeira. The most severe and longer-lasting wind conditions at the MIA, with a higher frequency of gusts above 35 kt, are driven by this synoptic-scale pattern and are more common in summer. An episode of adverse winds at the MIA is analyzed, illustrating the occurrence of upstream stagnation, flow splitting, and lee wake formation. The upstream conditions include a low-level inversion, strong NNE/NE winds near and above the inversion and a Froude number less than 1. AROME model predicted the occurrence of downslope winds, in association with a large-amplitude mountain wave. At this time, the strongest wind gusts were registered and a missed approach occurred. The wind regime in different places of the island suggests that these conditions are relatively frequent, mostly in summer. Lastly, this study provides an objective verification of the AROME wind forecasts, for a 3-year period and from June to August.

How to cite: Belo-Pereira, M. and Santos, J.: Air-traffic restrictions at the Madeira International Airport due to adverse winds: links to synoptic-scale patterns and mountain wave phenomena, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6116, https://doi.org/10.5194/egusphere-egu21-6116, 2021.

Emphasizes some aspects of the aviation forecasting system under construction for use by the integrated meteorological center (CIMAER) in Brazil. It consists of a set of hybrid models based on determinism and machine learning that use remote sensing data (such as lighting sensor, SODAR, satellite and soon RADAR), in situ data (from the surface weather station and radiosonde) and aircraft data (such as retransmission of aircraft weather data and vertical acceleration). The idea is to gradually operationalize the system to assist CIMAER´s meteorologists in generating their nowcasting, for example, of visibility, ceiling, turbulence, convective weather, ice, etc. with objectivity and precision. Some test results of the developed nowcasting models are highlighted as examples of nowcasting namely: a) visibility and ceiling up to 1h for Santos Dumont airport; b) 6-8h convective weather forecast for the Rio de Janeiro area and the São Paulo-Rio de Janeiro route. Finally, the steps in development and the futures are superficially covered.

How to cite: Almeida, V., França, G., Albuquerque Neto, F., Campos Velho, H., Almeida, M., Menezes, W., Menegussi, C., and Cordeiro, F.: Brazilian aviation nowcasting system: current stage and some results, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6609, https://doi.org/10.5194/egusphere-egu21-6609, 2021.

Despite of it is well known, it is always good to point that numerical weather prediction is an initial value problem and requires analysis of the initial conditions to begin a time dependent process (Richardson, 1922). Bergthorsson and Döös (1955), in that time, enunciated that analysis could be improved if they were not based solely on available observations, but also on forecasts made by model from previous observations, with background on data assimilation defined usually by a model forecast with errors. Airports are the most weather info powered locations, although all infrastructure, most of the moisture, turbulence, and convective processes circle around 25,000 feet and below, what turns rawinsonde observations an important source, besides, off course, data observations obtained from aircrafts. The aircraft data universe includes the Aircraft Communications Addressing and Reporting System (ACARS) reporting temperature and wind collected during all phases of flight, which composes the subset named Meteorological Data Collection and Reporting System (MDCRS), which has been used by several air carriers. For example, the AMDAR (Aircraft Meteorological Data Relay) program delivers more than 680,000 wind and temperature reports daily (Petersen et al., 2015), and with the advent of humidity sensor (Water Vapor Sensing System - WVSS-II in Hoover et al., 2017), vertical profiles of moisture (ascent and descent) are included in that. Based on current ECMWF numbers, FM-35 WMO provides 413 thousand information’s in the assimilation cycle for a typical day, otherwise, aircraft observations provide 1,234 thousands information’s (Bonavita in ECMWF, 2020). Numbers obtained from MADIS support page (amdar.noaa.gov/new_soundings) shows that on Guarulhos Airport receiving 835 (eight hundred and third five) profiles on period from July, 14 to 20, 2019. It takes a more important role, when it comes to mind that satellites profiles cannot resolve sharp vertical structures, as an example, warming-moisture combination to thunderstorm development. For testing the forecast sensitivity of the aircraft observations impact in the WRF 3DVAR Data Assimilation Systems, the WRF 4.2.1 has been installed without any source code modification, and configured for a 36 hour simulation period in forecast mode, starting in 12Z January, 2^nd 2020, applying Global Forecast System (GFS) model as initial and boundary condition, for a centred area in Guarulhos Airport, with 9 km spatial resolution. The results were compared against a simulation including aircraft data observation obtained from MADIS for Guarulhos International Airport Forecast, for the same period. That date was marked with strong precipitation starting around 19Z, with damages to the Airport infrastructure, as well, causing flight operations impact. For this period two profiles have been obtained and applied in the window time around analysis (12Z January, 2^nd 2020), and both assimilated using 3DVar WRF System. Analysis based on the results obtained demonstrates that there was an increase in precipitation amount forecasted by assimilation experiment and cooling temperature in cloud base, against no-assimilation, leading to conclusion that the aircraft profile data assimilation process can impact a precipitation forecast even 7 hours after analysis, encouraging to apply a 4DVar, in short range forecast and more assimilation experiments.

How to cite: Da Silva, R. and França, G.: Forecast Sensitivity of the Aircraft Observations Impact in the WRF 3DVAR Data Assimilation Systems on Guarulhos International Airport Forecast, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8118, https://doi.org/10.5194/egusphere-egu21-8118, 2021.

Clear-Air Turbulence (CAT) is small-scale eddies in clear sky conditions that affect cruising aircraft directly. The current turbulence prediction systems mostly produce deterministic forecasts. To consider inherent uncertainties and provide reliable probabilities for turbulence forecasts, it is also essential to produce probabilistic turbulence forecasts. In this study, we calculate multi-model-based ensemble mean CAT forecast (MMEM) and multi-model-based probabilistic CAT forecast (MMP) based on Ellrod-Knox Index (EKI) diagnostic using seven global NWP model outputs with a 0.5^o x 0.5^o resolution from The International Grand Global Ensemble (TIGGE) database. The EKI is a representative CAT diagnostic, which adding a divergence trend term for detecting CAT related to inertia gravity waves and anticyclonic flows to a combination term of vertical wind shear and total horizontal deformation to detect CAT related to frontogenesis. The 24-h and 30-h forecasts at 200, 250, and 300 hPa levels valid at 1800 UTC for a 6-month period (2016.10–2017.03) are used to calculate the 30-h EKI forecast at 250 hPa. MMEM is simply derived by averaging EKIs from seven TIGGE NWP models at a given grid point, while MMP is derived by calculating the percentage agreement of how often EKIs exceed a certain EKI threshold for moderate-or-greater (MOG)-level turbulence among seven EKIs based on different TIGGE model outputs at a given grid point. Three EKI thresholds based on the 95^th-, 98^th-, and 99^th-percentile values of each of the seven EKI probability density functions are tested to represent better reliability and spread of the MMP. The performance skills of MMEM and MMP are validated based on the probability of detection method and reliability test, respectively, against turbulence observations from pilot reports and in-situ flight eddy dissipation rate (EDR) data. In the validation result of deterministic forecasts, the MMEM has better performance skills than any single-model-based EKI forecasts. In the validation result of probabilistic forecasts, all MMPs show an over-forecasting, although with better reliability when applying a higher-percentile of EKI values as a threshold.

How to cite: Lee, D.-B., Chun, H.-Y., and Kim, J.-H.: Validation of Multi-Model-based Deterministic and Probabilistic Clear-Air Turbulence (CAT) Forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8598, https://doi.org/10.5194/egusphere-egu21-8598, 2021.

How to cite: Lloyd, G., Choularton, T., Gallagher, M., kraemer, M., Petzold, A., and Baumgardner, D.: In-Situ Measurements of Cirrus Clouds on a Global Scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10885, https://doi.org/10.5194/egusphere-egu21-10885, 2021.