Bibliographic record
Abstract
The strong wind and hailstorms that hit Bara and Parsa districts on 31 March 2019 killed 30, injured more than 1150 people and made more than 2890 families homeless. A mosque, schools, industries, agricultural lands, businesses were damaged along with utility services including water supply and electricity. The impact of the severe storm in the non-residential areas is still unknown. Department of Hydrology and Meteorology (DHM) with the support from the Small Earth Nepal (SEN) carried out a scientific study and declared that most of the damages were due to the tornado formed within the storm system in their joint press release on 5 April 2019 based on the preliminary assessment. This was the first ever officially recorded tornado in Nepal. To investigate the field situation, a team of seven researchers and experts from DHM, SEN and International Centre for Integrated Mountain Development (ICIMOD) visited the affected areas to gather in-situ information on the impacts of the Bara-Parsa tornado. The evidences gathered during the field study led the researchers to reconfirm that it was a tornado. The findings from the desk and field studies on this tornado event were shared to general public and stakeholders through various means, including press releases and news media, meetings and workshops by DHM, SEN, and other organizations in several separate occasions. DHM formed an eight-member committee from DHM, SEN and ICIMOD under chairmanship of the Director General of DHM to prepare this report. The committee in consultation with other experts in meteorology also coined “घुम्रपात” (Ghumrapaat) as a Nepali name for tornado. This report presents the findings of the studies from the four sources: meteorological analysis, social and news media, satellite images, and field studies. This is also the first of its kind of endeavor in Nepal to identify tornadic event by evidences gathered from combination of these sources. Interviews with tornado affected people, eyewitnesses, photographs and videos, aerial photos from an unmanned aerial vehicle (UAV) flights were collected during the field visit. Geographical information system (GIS), statistical analysis and data analysis software were used to analyze signatures of the tornado. Meteorological analysis included synoptic analysis of surface and upper air weather charts, infrared images of Himawari-8 satellite, lightning data, station pressure and wind observations, and radiosonde point observation data. Based on the destructions, the strength of Bara-Parsa tornado was estimated to be of EF3 category (Enhanced Fujita scale number 3 category) with the wind speed between 180 to 265 km/h. The tornado travel speed was estimated to be about 34 km/h considering its stretch in Bara-Parsa districts, from Sakhuwa-Parsauni, Parsa to Bairiya, Bara. Based on the satellite images, the length of tornado track in Bara-Parsa districts was estimated to be 44 km and the width ranged between 200 to 750 m. The tornado also affected forest area inside Chitwan National Park, but the details on the impact inside the park was not investigated for this report. However, based on the satellite images, the path of the tornado inside the park was estimated to be 9 km. To save lives from such severe weather events in future the report recommends establishing a severe weather warning system (nowcasting) along with resources and tools needed. To better understand the Bara-Parsa tornado event in entirety, the immediate focus should be on the investigation of its impact in Chitwan National Park. This report only presents preliminary analysis of this tornado; therefore, it is necessary to conduct further scientific research along with modeling of the Bara-Parsa tornado and other severe weather events in Nepal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".