scholarly journals The Mount Everest plume in winter

2022 ◽  
Author(s):  
Edward Hindman ◽  
Scott Lindstrom
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Jet Stream ◽  
Moist Air ◽  
Mount Everest ◽  
Cloud Droplets ◽  
Meteorological Model ◽  
Ice Particles ◽  
Meteorological Satellite

Abstract. Mt. Everest’s summit pyramid is the highest obstacle on earth to the wintertime jet-stream winds. Downwind, in its wake, a visible plume often forms. The meteorology and composition of the plume are unknown. Accordingly, we observed real-time images from a geosynchronous meteorological satellite from November 2020 through March 2021 to identify plumes and collect the corresponding meteorological data. We used the data with a basic meteorological model to show the plumes formed when sufficiently moist air was drawn into the wake. We conclude the plumes were composed initially of either cloud droplets or ice particles depending on the temperature. One plume was observed to glaciate downwind. Thus, Everest plumes may be a source of snowfall formed insitu. The plumes, however, were not composed of resuspended snow.

scholarly journals Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data

2013 ◽  
Vol 5 (4) ◽  
pp. 1734-1753 ◽  
Author(s):  
Yonglin Shen ◽  
Lixin Wu ◽  
Liping Di ◽  
Genong Yu ◽  
Hong Tang ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Meteorological Data ◽  
Time Estimation ◽  
Real Time Estimation

scholarly journals Verification of impact-based operational weather warnings at ZAMG using real-time fire brigade and eye-witness data

2021 ◽  
Author(s):  
Georg Pistotnik ◽  
Hannes Rieder ◽  
Simon Hölzl ◽  
Rainer Kaltenberger ◽  
Thomas Krennert ◽  
...  
Keyword(s):  
Real Time ◽  
General Public ◽  
Meteorological Data ◽  
State Level ◽  
Safety Net ◽  
Federal State ◽  
Free Text ◽  
Civil Protection ◽  
Fire Brigade ◽  
Primary Basis

<p>Development, verification and feedback of impact-based weather warnings require novel data and methods. Unlike meteorological data, impact information is often qualitative and subjective, and therefore needs some sort of quantification and objectivation. It is also inherently incomplete: an absence of reporting does not automatically imply an absence of impacts.<br>The reconciliation of impact information with conventional meteorological data demands a paradigm change. We designed and implemented a verification scheme around a backbone of weather-related fire brigade operations and eye-witness reports at ZAMG, the national meteorological service of Austria. Meteorological stations, radar and derived gridded data are conceptualized as a backstop to mitigate impact voids (possibly arising from a lack of vulnerability, exposure or simply a lack of reporting), but are not the primary basis anymore.<br>Operation data from fire brigade units across Austria are stored at civil protection authorities at federal state level and copied to ZAMG servers in real-time. Their crucial information is condensed into a few components: time, place, a keyword (from a predefined list of operations) and an optional free text field. This compact information is cross-checked with meteorological data to single out weather-related operations, which are then assigned to event types (rain, wind, snow, ice, or thunderstorm) and categorized into three different intensity levels („remarkable”, „severe” and „extreme”) according to an elaborated criteria catalogue. This quality management and refinement is performed in a three-stage procedure to utilize the dataset for different time scales and applications:<br> „First guess” based on automatic filtering: available in real-time and used for an immediate adjustment of active warnings, if necessary;<br> „Educated guess” based on a semi-manual plausibility check: timely available (ideally within a day) and used for an evaluation of latest warnings (including possible implications for follow-up warnings);<br> Final classification based on a thorough manual quality control: available some days to weeks later and used for objective verification.<br>Eye-witnesses can report weather events and their impacts in real-time via a reporting app implemented at ZAMG (wettermelden.at). Reports from different sources and trustworthiness are funneled into a standardized API. Observations from the general public are treated like a „first guess”, those from trained observers like an „educated guess”, and are merged with the refined fire brigade data at the corresponding stages.<br>The weather event types are synchronized with our warning parameters to allow an objective verification of impact-based warnings. We illustrate our measures to convert these point-wise impact data into spatial impact information, to circumvent artifacts due to varying population density and to include the “safety net” of conventional meteorological data. Yellow, orange and red warnings are thereby translated into probabilities for certain scenarios, which are meaningful and intuitive for the general public and for civil protection authorities.</p>

scholarly journals Cirrus, contrails, and ice supersaturated regions in high pressure systems at northern mid latitudes

2007 ◽  
Vol 7 (5) ◽  
pp. 13175-13201 ◽  
Author(s):  
F. Immler ◽  
R. Treffeisen ◽  
D. Engelbart ◽  
K. Krüger ◽  
O. Schrems
Keyword(s):  
High Pressure ◽  
Optical Depth ◽  
Meteorological Data ◽  
Large Fraction ◽  
Close Correlation ◽  
Cirrus Clouds ◽  
Radiosonde Data ◽  
Medium Range Weather Forecast ◽  
Ice Particles ◽  
Lidar Observations

Abstract. During the European heat wave summer 2003 with predominant high pressure conditions we performed a detailed study of upper tropospheric humidity and ice particles which yielded striking results concerning the occurrence of ice supersaturated regions (ISSR), cirrus, and contrails. Our study is based on lidar observations and meteorological data obtained at Lindenberg/Germany (52.2° N, 14.1° E) as well as the analysis of the European centre for medium range weather forecast (ECMWF). Cirrus clouds were detected in 55% of the lidar profiles and a large fraction of them were subvisible (optical depth <0.03). Thin ice clouds were particularly ubiquitous in high pressure systems. The radiosonde data showed that the upper troposphere was very often supersaturated with respect to ice. Relating the radiosonde profiles to concurrent lidar observations reveals that the ISSRs almost always contained ice particles. Persistent contrails observed with a camera were frequently embedded in these thin or subvisible cirrus clouds. The ECMWF cloud parametrisation reproduces the observed cirrus clouds consistently and a close correlation between the ice water path in the model and the measured optical depth of cirrus is demonstrated.

Inferring significant wave height from seismic data: A comparison between methods.

2021 ◽  
Author(s):  
Francisco Bolrão ◽  
Co Tran ◽  
Miguel Lima ◽  
Sheroze Sheriffdeen ◽  
Diogo Rodrigues ◽  
...  
Keyword(s):  
Real Time ◽  
Wave Height ◽  
Seismic Data ◽  
Significant Wave Height ◽  
Transfer Functions ◽  
Seismic Station ◽  
Meteorological Data ◽  
Ocean Waves ◽  
The United States ◽  
Significant Wave

&lt;p&gt;The most pervasive seismic signal recorded on our planet &amp;#8211; microseismic ambient noise -results from the coupling of energy between atmosphere, oceans and solid Earth. Because it carries information on ocean waves (source), the microseismic wavefield can be advantageously used to image ocean storms. This imaging is of interest both to climate studies &amp;#8211; by extending the record of oceanic activity back into the early instrumental seismic record &amp;#8211; and to real-time monitoring &amp;#8211; where real-time seismic data can potentially be used to complement the spatially dense but temporally sparse satellite meteorological data.&lt;br&gt;In our work, we develop empirical transfer functions between seismic observations and ocean activity observations, in particular, significant wave height. We employ three different approaches: 1) The approach of Ferretti et al (2013), who compute a seismic significant wave height and invert only for the empirical conversion parameters between oceanic and seismic significant wave heights; 2) The classical approach of Bromirski et al (1999), who computed an empirical transfer function between ground-motion recorded at a coastal seismic station and significant wave height measured at a nearby ocean buoy; and 3) A novel recurrent neural-network (RNN) approach to infer significant wave height from seismic data.&amp;#160;&lt;br&gt;We apply the three approaches to seismic and ocean buoy data recorded in the east coast of the United States. All three approaches are able to successfully predict ocean significant wave height from the seismic data. We compare the three approaches in terms of accuracy, computational effort and robustness. In addition, we investigate the regimes where each approach works best. &amp;#160;The results show that the RNN approach is able to predict well the significant wave height recorded at the buoy. The prediction is improved if several nearby seismic stations are used rather than just one.&amp;#160;&lt;br&gt;This work is supported by FCT through projects UIDB/50019/2020 &amp;#8211; IDL and UTAP-EXPL/EAC/0056/2017 - STORM.&lt;/p&gt;

scholarly journals Mechanism of Daytime Strong Winds on the Northern Slopes of Himalayas, near Mount Everest: Observation and Simulation

2018 ◽  
Vol 57 (2) ◽  
pp. 255-272 ◽  
Author(s):  
Fanglin Sun ◽  
Yaoming Ma ◽  
Zeyong Hu ◽  
Maoshan Li ◽  
Gianni Tartari ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Monsoon Season ◽  
Meteorological Data ◽  
Mount Everest ◽  
The North ◽  
China Academy ◽  
Westerly Winds ◽  
Meteorological Observations ◽  
Strong Winds ◽  
Academy Of Sciences

AbstractThe seasonal variability of strong afternoon winds in a northern Himalayan valley and their relationship with the synoptic circulation were examined using in situ meteorological data from March 2006 to February 2007 and numerical simulations. Meteorological observations were focused on the lower Rongbuk valley, on the north side of the Himalayas (4270 m MSL), where a wind profile radar was available. In the monsoon season (21 May–4 October), the strong afternoon wind was southeasterly, whereas it was southwesterly in the nonmonsoon season. Numerical simulations were performed using the Weather Research and Forecasting Model to investigate the mechanism causing these afternoon strong winds. The study found that during the nonmonsoon season the strong winds are produced by downward momentum transport from the westerly winds aloft, whereas those during the monsoon season are driven by the inflow into the Arun Valley east of Mount Everest. The air in the Arun Valley was found to be colder than that of the surroundings during the daytime, and there was a horizontal pressure gradient from the Arun Valley to Qomolangma Station (QOMS), China Academy of Sciences, at the 5200-m level. This explains the formation of the strong afternoon southeasterly wind over QOMS in the monsoon season. In the nonmonsoon season, the colder air from Arun Valley is confined below the ridge by westerly winds associated with the subtropical jet.

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