Tools from the Indian space programme for observing and forecasting extreme weather events - Retrospect and prospect

Extreme weather events (EWEs) cause hardship, economic loss and have severe socio-economic consequences. It is necessary to develop forecast capability for such events so as to minimise losses and take appropriate measures for combating. Methods relying on only ground based weather observation networks are not adequate. Observations from space platforms offer regular and frequent measurements over a wide area. Observations from geostationary platforms provide information on atmospheric parameters and processes, while low earth orbiting platforms provide global observations at higher spatial resolutions. This paper describes the evolution of space programme in India, consisting of the space segment, data processing and products, and science and applications for observing and monitoring weather systems. Emphasis is on developing end-to-end capacity in weather monitoring. Forecasting of EWEs is illustrated with a few case studies. With the integration of space technology inputs, it is expected that forecast skill and lead time of the forecast will improve. Some of the future Indian space missions planned to enhance the capacity are also described. A multidisciplinary approach comprising the use of space technology, ground based measurement network and high speed computing power, can form a backbone for observing and forecasting EWEs.

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.

Comparison of Temperatures and Spatial Resolutions between Urban Sensors and National Weather Observations (ASOS, AWS) for Urban Heat Island Intensity Analysis

Urban temperatures have become more important because the frequency and intensity of heatwaves will increase with the worsening urban heat island effects due to climate change. Recently, urban sensors are emerging as a new means of heat island analysis, and urban sensor data including temperatures were opened to the public in Seoul in 2020. However, there are insufficient cases of analysis and the differences from national weather observation are unknown. Accordingly, this study introduces the S⋅DoT (Smart Seoul Data of Things), an urban sensor in Seoul, and compared S⋅DoT temperatures with national weather observation (such as ASOS and AWS) temperature in order to facilitate heat island analysis using urban sensors. The result shows the S⋅DoT temperature is about 1-1.15 ℃ higher than ASOS⋅AWS during May to August 2020, which is due to differences in the height and installation environment, not in the performance of the equipment. S⋅DoT is installed in the urban environments at the height of approximately 3 m and measures the actual living temperature, whereas ASOS measures temperature in the controlled environment and AWS measures temperature from the higher position due to being installed on the top of a building. Therefore, we suggest that when using S⋅DoT together with rural AWS temperature to analyze the intensity of heat islands, calibration should be considered taking into account the difference between the two observations, and a better method is to calculate the heat island intensity only using S⋅DoT temperature in urbanized and non-urbanized areas without using rural temperature.

Meteorological Pursuit a case study of Hafiz Hayat Campus, University of Gujrat, Pakistan

Being an important phenomenon “Weather Observation” or “Meteorology” is not only interesting for Meteorologists but also for environmentalists and geologists as well. So for this an internship program was held at University of Gujrat Hafiz Hayat Campus, in a newly maintained Met Office, where appointed Official Weather Observers supervised this work. So in this way this is a wonderful chance to become a part of Starting Weather observation at UOG, being the students of Environmental Sciences. A group of students worked as internees.

Meteorological Pursuit in University of Gujrat, Pakistan (Part 1)

Being an important phenomenon “Weather Observation” or “Meteorology” is not only interesting for Meteorologists but also for environmentalists and geologists as well. So for this an internship program was held at University of Gujrat Hafiz Hayat Campus, in a newly maintained Met Office, where appointed Official Weather Observers supervised this work. So in this way this is a wonderful chance to become a part of Starting Weather observation at UOG, being the students of Environmental Sciences. A group of students worked as internees. Observation under certain parameters, by taking scheduled readings of outdoor installed instruments, then accordingly filling of Pocket Register, by consulting Hydrometric table, Aeronautical Code book 1995, & Surface Weather code book 1995 of Pakistan Meteorological Department 2008. Making Metar (after 1 hour reporting) and Synaptic (after 3 hours reporting), then reporting it to the Regional Meteorological headquarter Lahore through cell phone message sending or directly uploading it to their website. Discussion sections on different weather changes as shown by readings, along with instruments working accordingly, are the different steps of Starting of Weather Observation at UOG. Chapter one and two are included in this part 1 of this Meteorological pursuit.

Aviation infrastructures in the Republic of China, 1920–37

This essay investigates technical aspects of the history of aviation in the Republic of China, focusing on the period between 1920 and 1937. It suggests that Chinese authors and administrators came to see the establishment of technical infrastructure as dependent on the education of personnel who could assume responsibility for maintaining and expanding Chinese aviation ventures, rather than on specific technologies or practices. Magazines and journals in the 1920s reflected concerns with the establishment of weather observation and reporting, radio communications, and technical education in service of aviation; the last of these was critical for the first two. Provisions for technical work and training were reflected in contracts that were drawn up in the years around 1930 to establish three aviation projects in the Republic: the China National Aviation Corporation (CNAC), the Eurasia Aviation Corporation, and Southwest Airlines. Subsequent contracts and reports for CNAC and Eurasia in the years before the 1937 outbreak of war with Japan suggested a particular emphasis on the technical education of personnel as an important step in building Chinese aviation infrastructures.

International Cooperation: A Brief History We've Experienced

The solar-terrestrial space is of considerable significance for human activities. Since the first artificial satellite Sputnik 1 was launched in 1957, more knowledge about the dynamic conditions of the space environment has been acquired. With growing dependence on modern technology — both in space and on the ground, the vulnerability of the modern society and its infrastructure to space weather has increased dramatically. To better understand, forecast and reduce the adverse effects of space weather, science programs on space weather always prioritize the measurement or acquisition of the data from different locations of the geo-space, such as in magnetopause, polar cusps, and the magnetic tail. For the ground observations, it is necessary to locate the instruments in different longitudes and latitudes. For a single country, it is impossible to cover all these observation points. Therefore, international cooperation is very much needed. The paper reviews some of the international space weather observation programs we have experienced at the system design level. It may provide lessons learned for the community that may enable such kind of cooperative programs in the future.

New Normal Weather Breaks a Traditional Clothing Retail Calendar

Background: Clothing businesses have complained of sluggish sales because of new normal weather, an increased variation of temperature and precipitation and the higher occurrence of extreme weather events. Traditionally, the business runs tied to calendar dates or retailing events, and the previous year's sales draw up a sales plan. This study questioned whether the sales planning method of the clothing business is valid and reliable for today. Results: Using weather observation data and Google Trends for the past 11 years, consumers' responses to weather changes were analyzed through the decision tree to learn about consumer insights. The month is the most significant predictor of seasonal clothing demand during a season, and consumers' responses to weather vary from month to month. Minimum temperature and maximum temperature were significant predictors in a particular month. Conclusions: Our results have important managerial implications. Rapid weather changes affect consumers’ demand. Clothing retailers can apply the predictive model to quickly respond to unexpected weather changes, prepare products with rapidly increasing demand not to miss sales opportunities, and adjust quantities and prices for products with sharp declines in demand.