Synoptic conditions for the formation of thundersnow in the territory of Western Siberia

The article presents the results of work on the study of synoptic conditions for the formation of a dangerous weather phenomenon for the territory of Western Siberia – a thundersnow. The material for the study was the archive of aerosynoptic material from the Khanty-Mansiysk Air Meteorological Center.

A Climate Perspective of the Quasi-Stationary Front in Southwestern China: Structure, Variation and Impact

Yunnan-Guizhou quasi-stationary front (YGQSF) is a unique weather phenomenon that frequently occurs during winter half year over the Yunnan-Guizhou Plateau in southwestern China. Most of previous studies analyzed it only with synoptic cases. This study investigates the structure, variation, and impact of YGQSF from a climate perspective, using long-term high-resolution atmospheric reanalysis and high-density station records for 1981-2016. An objective method quantifying YGQSF is proposed and three indexes are defined to measure the intensity, frequency, and location of YGQSF, respectively, with the horizontal gradient of air potential temperature at a terrain-following level of sigma 0.995. With these indexes, climatological structure, subseasonal variability as well as climatic impact of YGQSF are comprehensively examined. In climatology, YGQSF as a north-south-oriented low-level front is found to occur the most frequently during January-February-March (JFM), determined predominately by the coldness from the east of the front. The structure of YGQSF identified essentially reflects an obstruction of high-terrain Yunnan (the western part of the plateau) to the low-level cold air mass, which makes near-surface cold northeasterly winds cease westward intruding and veer upward over relatively low-terrain Guizhou, transporting moisture upward and forming low clouds. A sharp climate contrast is thus formed between two sides of YGQSF: cold, sunless, and continuously rainy Guizhou versus warm and sunny Yunnan. Furthermore, YGQSF features significant subseasonal variations with periods at around 30d and 60d largely in its intensity. Anomalously strong YGQSF events which are caused 75% by the cold anomaly from the east but less than 17% by the warm anomaly from the west yield different anomalous structures, but consistently amplify the sharp climate contrast between Yunnan and Guizhou.

Sensitivity Analysis of PBL Physics Schemes of WRF-ARW Model in Simulating the Tropical Cyclone ‘TITLI’ (2018) Over the Bay of Bengal

Tropical Cyclone (TC) is the most destructive weather phenomenon in the Indian sub-continent. To mitigate the destruction due to TC better prediction is needed. So, the study of sensitivity of different physical schemes in WRF-ARW model with intensification and track of TC is important. In this study, sensitivity of Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Bougeault-Lacarrere (Boulac), Medium-Range Forecast (MRF), Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2.5) and Level 3 (MYNN3) Planetary Boundary Layer (PBL) schemes are used to simulate the TC ‘Titli’ which made land fall near Palasa in North Andrha Pradesh and South Odhisha coasts at 0000 UTC of 11th October. National center for environmental prediction Global Final Reanalysis (FNL) data have been used as an initial and lateral boundary conditions. Variation of heat flux, latent heat flux and moisture flux with time for these schemes are shown which are responsible to intensify the TC. Model simulated intensity i.e., minimum central pressure, maximum sustained wind speed at the surface (10 m) and track are compared with the India Meteorological Department (IMD) estimated value. It can be specified that the Boulac, MYNN2.5 and MYNN3 schemes simulate the better intensity and track of TC ‘Titli’.

Latent cooling of atmosphere as an indicator of lowered snow line: Case study from Planica and Vrata valleys

A lowered snow line in Alpine valleys as a local weather phenomenon often varies from one valley to another. The relief morphology of the valleys and the intensity of precipitation play a crucial role in the variation. In Slovenia certain valleys are more susceptible to this phenomenon than others, one such example being the Planica Valley. This article examines the occurrence of a lowered snow line in the Planica Valley and the Vrata Valley during the winter seasons of 2015/2016 and 2016/2017. Precipitation events accompanying the occurrence of a lowered snow line were analyzed, and data on temperature and precipitation were included in the analysis. Results showed a striking degree of congruence of the phenomenon in both valleys.

Time-Delay Identification Using Multiscale Ordinal Quantifiers

Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.

Australia’s Black Summer pyrocumulonimbus super outbreak reveals potential for increasingly extreme stratospheric smoke events

The Black Summer fire season of 2019–2020 in southeastern Australia contributed to an intense ‘super outbreak’ of fire-induced and smoke-infused thunderstorms, known as pyrocumulonimbus (pyroCb). More than half of the 38 observed pyroCbs injected smoke particles directly into the stratosphere, producing two of the three largest smoke plumes observed at such altitudes to date. Over the course of 3 months, these plumes encircled a large swath of the Southern Hemisphere while continuing to rise, in a manner consistent with existing nuclear winter theory. We connect cause and effect of this event by quantifying the fire characteristics, fuel consumption, and meteorology contributing to the pyroCb spatiotemporal evolution. Emphasis is placed on the unusually long duration of sustained pyroCb activity and anomalous persistence during nighttime hours. The ensuing stratospheric smoke plumes are compared with plumes injected by significant volcanic eruptions over the last decade. As the second record-setting stratospheric pyroCb event in the last 4 years, the Australian super outbreak offers new clues on the potential scale and intensity of this increasingly extreme fire-weather phenomenon in a warming climate.

Analysis of Weather Forecasting Techniques

Weather forecasting is one of the important science application in our daily planning activities. This application has played a significant role to humans from a long time. Every human relied on their philosophical experience and other weather phenomenon to predict the weather and infer what was coming their way. This was the knowledge gathered over many years observations and has been passed from one generation to another. To predict the future’s weather condition, the variation in the conditions in past year must be utilized. The probability that the weather condition of the day in consideration will match the same day in previous year is very less. But the probability that it will match within the spam of adjacent fortnight of previous year is very high. In this paper, we analyse the use of various data mining techniques in forecasting maximum temperature, rainfall and wind speed.

What can we learn from weather apps user complaints?

<p>Meteored’s goal is to provide a weather forecast to an heterogeneous audience around the world through its websites and mobile apps.  Although the meteorological information is available through several products such as radar, satellite or weather field maps, most of the views are focused on checking the forecast for a specific location. Our weather forecast is built on the HRES model from ECWMF, which is post processed, spatially interpolated to the interested coordinates,  and, finally, summarized in several weather symbols. If any user doesn’t agree with the symbol that represents the forecast, she/he can select which symbol better represents its weather perception.</p><p>Using this simplification to validate forecast entails several challenges: 1) Spatial representativeness; there aren’t weather stations at each location where users demand to validate, 2) timing; sometimes there is lack of concurrency between a weather phenomenon and the user weather check,  3) user perception; same symbol can represent different weather for different users, and 4)  population density; most of the user complaints are focused on the most populated regions but this doesn't mean the performance is worse there.</p><p>Last year more than 374000 symbol suggestions were recorded from worldwide users, mainly from Europe and Southamerica. The percentage of complaints were 39% cloudy, 24% rainy, 21% suny, 8% storm, 5% snow and 3 % foggy. 16 % of the complaints happen when a cloudy symbol is shown but the user suggests a rainy symbol. Temporal series show more feedback during summer and slightly lower during March (maybe due to the pandemic). Complaints about snow significantly increased due to the historical event in Spain during January. From weather feedback, the straightforward question is: why most frequent complaining is about cloudiness? We can find several answers: there is an important error in the weather modelling, there is an error in the symbol representation, it is a frequent meteorology event or it is one of the most relevant in users daily life.</p><p>In order to understand how reliable the user’s feedback is, our forecast is compared against almost 10000 SYNOP observations, assessing 2 m temperature, 10 m wind speed, precipitation, fog and also, symbols. Preliminary results show a pronounced dependence of the bias with the orography with larger errors over some islands and over main mountain systems. This spatial variability for bias is smoothed in Meteored forecast due to biquadratic interpolation. However, the Meteored forecast has a diurnal cycle bias error with higher temperatures during the daytime and lower temperature at nighttime due to the temporal interpolation approach.  Regarding to the weather symbols validation is difficult to extract conclusion since failures and hits are hetereogenously distributed. In addition,  most discrepancies are related to fog although it has a low percentage of complaints. </p>

Equatorial Ionospheric Irregularities Observed by COSMIC 2 and GOLD Missions

<p>Equatorial ionospheric irregularities is an important space weather phenomenon, which can disrupt GNSS and communication systems. COSMIC 2 GNSS RO observations are affected via scintillations in signal amplitudes and phases. At the same time, we can use these scintillations to monitor and geolocate the ionospheric irregularities, which are of great value to the space weather services. Geolocation of the irregularities based on the RO signals is difficult, as any irregularities along the line between the GNSS and RO satellite can cause scintillation. Several geolocation methods are known. A back propagation (BP) method to geolocate the irregularities originally developed in 2001 and applied for GPS/MET RO data is being modified and applied for COSMIC 2 scintillation data. Because the equatorial irregularities are often associated with plasma bubbles, which are visible to the NASA UV imager GOLD, we have been using the GOLD images to validate the BP geolocation method.    In this presentation, we will show the progress of recent validation effort of the BP geolocation method by comparing the COSMIC 2 geolocated irregularities with plasma bubbles in GOLD UV observations. Though, GOLD observations are only available in the American sector, COSMIC 2 observations can be used geolocate ionospheric irregularities throughout the equatorial and low latitudes</p>