Some statistical characteristics of occurrence of fog over Patna airport

bl 'kks/k i= esa o"kZ 2000 ls 2010 rd 10 o"kksZa ds uoacj ls Qjojh ekg dh vof/k ds vk¡dMksa dk mi;ksx djrs gq, iVuk gokbZ vMMs ij Nkus okys dqgjs dh lkaf[;dh; fo’ks"krkvksa tSls & dksgjk Nkus dh ckjEckjrk] Nkus dk le;] vof/k] l?kurk rFkk folfjr gksus ds le; dk v/;;u fd;k x;k gSA bl v/;;u ls izkIr gq, ifj.kke ls irk pyk gS fd foxr 10 o"kksZa ds nkSjku iVuk gokbZ vMMs ij pkjksa gh eghuksa esa dqgjs dh ckjEckjrk esa o"kZ 1961&90 rFkk 1951&80 ds tyok;fodh esa miyC/k flukWfIVd rFkk rRdkfyd ekSkle izs{k.kksa dh rqyuk esa fo’ks"k :i ls o`f) gqbZ gSA iVuk gokbZ vMMs ij dqgjk Nkus dk lcls vuqdwy eghuk fnlEcj vkSj mlds ckn tuojh dk ekuk x;k gsA fnlacj vkSj tuojh ds eghuksa esa 5 ?kaVsa ls vf/kd vof/k rd dqgjk Nkus dh vko`fRr dh izfr’kr~rk vf/kdre jgh gS tcfd uoacj ,oa Qjojh ds eghuksa esa 2 ?kaVs ls de vof/k dh vko`fRr lcls vf/kd jgh gSA dqgjk dk cuuk vDlj 0000&0200 ;w-Vh-lh- ds nkSjku vkSj bldk {k; gksuk 0200&0500 ;w- Vh- lh- ds nkSjku ns[kk x;k gSA cgqr ?kus dqgjs dh vf/kdre vko`fRr & izfr’krrk uoacj ekg esa ns[kh xbZ gSA fnlacj vkSj tuojh ds eghuksa dh vf/kdrj fLFkfr;ksa esa 1200 ;w-Vh-ij vxyh jkr@lqcg ds le; iMs+ dksgjs ds jsfM;ksa lkSans ds vk¡dMksa ds vk/kkj ij rS;kj fd, x, dqgjk LFkkf;Ro lwpdkad ¼,Q-,l-vkbZ-½ 40 ls de ik;k x;k gSA In this paper some statistical characteristics of fog, such as frequencies of occurrence, time of onset, duration, intensity and time of dispersal over Patna airport are studied making use of 10 years data for the period November-February, 2000-2010. The result shows that during the last ten years frequency of fog over Patna airport has increased significantly in all the four months as compared to the climatology based on available synoptic and current weather observations during 1961-90 and 1951-80. The most favourable month for occurrence of fog over Patna airport has been identified as December followed by January. Percentage frequency is highest for duration of fog for more than 5 hours in the months of December and January whereas in the months of November and February frequency is highest for duration less than 2 hours. The formation of fog mostly observed during 0000-0200 UTC and dissipation during 0200-0500 UTC. Percentage frequency of very thick fog was found to be highest in the month of November. In the months of December and January in most of the cases Fog Stability Index (FSI) based on 1200 UTC radiosonde data leading to occurrence of fog during following night/morning has been found to be less than 40.

An account of fog over Chennai

Climatologically (based on 1951-1980) the annual fog frequency of Chennai airport is 4.3 days. But, the operational aviation meteorological forecasters often experienced more number of foggy days during the past decade. Hence the fog frequency has been critically analysed based on current weather observations made by aerodrome meteorological office, Chennai during 1981-2002 (barring 1984 for which data is not readily available). It has been found that the annual frequency based on the present study has shot up to 21.5 days. The most favourable period for fog over Chennai airport has been identified as January followed by February and March. The formation of fog has been mostly observed during 0000-0200 UTC although in good number of cases it was during 2200-2400 UTC. The most common duration of fog is 60-120 minutes albeit duration as high as 540-570 minutes are also probable. The low level (surface) nocturnal inversion frequency has alarmingly increased during 1990s and the inversion is almost a day-to-day phenomenon during 2000s. Rapid urbanisation, vehicular traffic and industrial growth could be the cause for the increased atmospheric pollution which has increased the nocturnal stability conditions as well the fog frequency. Visibility as low as zero had been recorded on a number of cases and their causes have been analysed. Neutral or absolutely unstable stratification at 1200 UTC coupled with high relative humidity and high concentration of pollution cause the fog to form from 2200 UTC onwards and the nocturnal surface inversion / isotherm at 0000 UTC maintains the fog. Though the low level inversion maintains the fog once it is formed already, inversion alone is not a sufficient condition for the formation of fog.

Multi-decadal increase of forest burned area in Australia is linked to climate change

Fire activity in Australia is strongly affected by high inter-annual climate variability and extremes. Through changes in the climate, anthropogenic climate change has the potential to alter fire dynamics. Here we compile satellite (19 and 32 years) and ground-based (90 years) burned area datasets, climate and weather observations, and simulated fuel loads for Australian forests. Burned area in Australia’s forests shows a linear positive annual trend but an exponential increase during autumn and winter. The mean number of years since the last fire has decreased consecutively in each of the past four decades, while the frequency of forest megafire years (>1 Mha burned) has markedly increased since 2000. The increase in forest burned area is consistent with increasingly more dangerous fire weather conditions, increased risk factors associated with pyroconvection, including fire-generated thunderstorms, and increased ignitions from dry lightning, all associated to varying degrees with anthropogenic climate change.

Unlocking weather observations from the Societas Meteorologica Palatina (1781–1792)

In recent years, instrumental observations have become increasingly important in climate research, allowing past daily-to-decadal climate variability and weather extremes to be explored in greater detail. The 18th century saw the formation of several short-lived meteorological networks of which the one organised by the Societas Meteorologica Palatina is arguably the most well known. This network stood out as one of the few that efficiently managed to control its members, integrating, refining, and publishing measurements taken from numerous stations around Europe and beyond. Although much has been written about the network in both history, science, and individual prominent series used for climatological studies, the actual measurements have not yet been digitised and published in extenso. This paper represents an important step towards filling this perceived gap in research. Here, we provide an inventory listing the availability of observed variables for the 37 stations that belonged to the society's network and discuss their historical context. Most of these observations have been digitised, and a considerable fraction has been converted and formatted. In this paper, we focus on the temperature and pressure measurements, which have been corrected and homogenised. We then demonstrate their potential for climate research by analysing two cases of extreme weather. The recovered series will have wide applications and could contribute to a better understanding of the mechanisms behind climatic variations and extremes as well as the societal reactions to adverse weather. Even the shorter series could be ingested into reanalyses and improve the quality of large-scale reconstructions.

RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations

Quantitative precipitation prediction is essential for managing water-related disasters, including floods, landslides, tsunamis, and droughts. Recent advances in data-driven approaches using deep learning techniques provide improved precipitation nowcasting performance. Moreover, it has been known that multi-modal information from various sources could improve deep learning performance. This study introduces the RAIN-F+ dataset, which is the fusion dataset for rainfall prediction, and proposes the benchmark models for precipitation prediction using the RAIN-F+ dataset. The RAIN-F+ dataset is an integrated weather observation dataset including radar, surface station, and satellite observations covering the land area over the Korean Peninsula. The benchmark model is developed based on the U-Net architecture with residual upsampling and downsampling blocks. We examine the results depending on the number of the integrated dataset for training. Overall, the results show that the fusion dataset outperforms the radar-only dataset over time. Moreover, the results with the radar-only dataset show the limitations in predicting heavy rainfall over 10 mm/h. This suggests that the various information from multi-modality is crucial for precipitation nowcasting when applying the deep learning method.

History of the Hydrometeorological Service of Belarus

The first weather observations within the modern territory of Belarus go back to ancient times and are found as mentions of weather conditions in chronicles. Hydrometeorology in those times was not a defined science but connected to the everyday needs of people in different regions. In the period from 1000 to 1800, there were first efforts to document outstanding weather conditions and phenomena. They are stored in chronicles, books, and reports. The first instrumental observations started in the early 1800s. They have varying observing practices and periods of observations. The hydrometeorological network saw the active expansion of observations in the following century, but the network was destroyed at the beginning of the civil war (1917–1922). Five years later, hydrometeorological activity resumed, and the foundation of meteorological services of the Russian Soviet Federal Socialist Republic (RSFSR) was initiated. The next years saw a complicated Belarusian hydrometeorological service formation and reorganization. The meteorological bureau was formed in 1924, and this year is considered the official date of the Hydrometeorological Service of Belarus foundation, despite multiple changes in title and functions during its course. During the Great Patriotic War (1941–1945) people’s courage and efforts were directed to saving the existing network of hydrometeorological observations and providing weather services for military purposes. The postwar period was characterized by the implementation of new methods of weather forecasting and new forms of hydrometeorological information. Later decades were marked by the invention and implementation of new observational equipment. The Hydrometeorological Service of Belarus in this period was a testing ground within the Soviet Union for the development of meteorological tools and devices. The current Hydrometeorological Service of Belarus is described as an efficient, modern-equipped, and constantly developing weather service.

A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations

Commercial microwave links (CMLs) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation, or humidity. The CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) mainly focused on the rainfall observations by monitoring a 38 GHz dual-polarized CML of 1.85 km path length at a high temporal resolution (4 s), as well as a co-located array of five disdrometers and three rain gauges over 1 year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4923125 (Špačková et al., 2021). The data quality is generally satisfactory for further analysis, and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

Optimizing Prepositioning of Equipment and Personnel for Los Angeles County Fire Department to Fight Wildland Fires

Every day, the Los Angeles County Fire Department uses weather forecasts and automated real-time weather observations, together with field-tested moisture content of soil and vegetation, to decide whether and where to position firefighting equipment and personnel, as well as what equipment to use, for the following day. Anticipating a particularly hazardous “red flag” day, they activate off-duty personnel and reserve equipment and add these to the total augmented, prepositioned force. Analysis of years of detailed daily data can advise these costly decisions. Three models, respectively, predict for each region of the county the probability of a fire start, the area burned by a fire given any particular package of equipment and personnel preassigned to fight it, and which packages to form and send to each position. The conflicting objectives are to minimize the expected number of citizens evacuated and the constrained augmentation cost for personnel and equipment.