References
- 1Allaire, J. 2017. Flexdashboard: R markdown format for flexible dashboards.
- 2Bonifacio, C, Barchyn, TE, Hugenholtz, CH and Kienzle, SW. 2015. CCDST: A free Canadian climate data scraping tool. Computers & Geosciences, 75: 13–16. DOI: 10.1016/j.cageo.2014.10.010
- 3Bopape, M-JM, Waitolo, D, Plant, RS, Phaduli, E, Nkonde, E, Simfukwe, H, Mkandawire, S, Rakate, E and Maisha, R. 2021. Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes. Climate, 9(2): 38. DOI: 10.3390/cli9020038
- 4Bradley, A and James, RJ. 2019. Web scraping using R. Advances in Methods and Practices in Psychological Science, 2(3): 264–270. DOI: 10.1177/2515245919859535
- 5Chang, EK, Peña, M and Toth, Z. 2013. International research collaboration in high-impact weather prediction. Bulletin of the American Meteorological Society, 94(11): ES149–ES151. DOI: 10.1175/BAMS-D-13-00057.1
- 6Chang, W, Cheng, J, Allaire, J, Xie, Y and McPherson, J. 2015. Package ‘shiny’. See
http://citeseerx.ist.psu.edu/viewdoc/download . - 7Dowle, M, Srinivasan, A, Gorecki, J, Chirico, M, Stetsenko, P, Short, T, Lianoglou, S, Antonyan, E, Bonsch, M, Parsonage, H, et al. 2019. Package ‘data. table’. Extension of ‘data.frame’.
- 8Dreyer, A and Stockton, J. 2013. Internet “data scraping”: A primer for counseling clients. New York Law Journal, 7: 1–3.
- 9Fundel, VJ, Fleischhut, N, Herzog, SM, Göber, M and Hagedorn, R. 2019. Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end-users. Quarterly Journal of the Royal Meteorological Society, 145: 210–231. DOI: 10.1002/qj.3482
- 10Gibert, K, Izquierdo, J, Sànchez-Marrè, M, Hamilton, SH, Rodríguez-Roda, I and Holmes, G. 2018. Which method to use? An assessment of data mining methods in Environmental Data Science. Environmental Modelling & Software, 110: 3–27. Special Issue on Environmental Data Science. Applications to Air quality and Water cycle. 2. DOI: 10.1016/j.envsoft.2018.09.021
- 11Graul, C and Graul, MC. 2016. ‘Package ‘leafletr”.
- 12Hadley, W and Garrett, G. 2016. R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc.
- 13Helmschrot, J, Muche, G, Hillmann, T, Kanyanga, J, Butale, M, Nascimento, D, Kruger, S, Strohbach, B, Seely, M, Ribeiro, C, others. 2015. SASSCAL WeatherNet to support regional weather monitoring and climate-related research in Southern Africa. Proceedings of the International Association of Hydrological Sciences, 366: 170–171. DOI: 10.5194/piahs-366-170-2015
- 14Hirschey, JK. 2014. Symbiotic relationships: Pragmatic acceptance of data scraping. Berkeley Tech. LJ, 29: 897. DOI: 10.2139/ssrn.2419167
- 15Ives, B and Krotov, V. 2006. Anything you search can be used against you in a court of law: Data mining in search archives. Communications of the Association for Information Systems, 18(1): 29. DOI: 10.17705/1CAIS.01829
- 16Kaspar, F, Helmschrot, J, Mhanda, A, Butale, M, de Clercq, W, Kanyanga, J, Neto, F, Kruger, S, Castro Matsheka, M, Muche, G, et al. 2015. The SASSCAL contribution to climate observation, climate data management and data rescue in Southern Africa. Advances in science and research, 12: 171–177. DOI: 10.5194/asr-12-171-2015
- 17Katz, RW and Murphy, AH. 2005. Economic value of weather and climate forecasts. Cambridge University Press.
- 18Krotov, V, Leigh, J and Leiser, S. 2020. Tutorial: Legality and Ethics of Web Scraping. Communications of the Association for Information Systems, 47(1): 22. DOI: 10.17705/1CAIS.04724
- 19Lang, DT and Lang, MDT. 2013. Package ‘xml’.
- 20Lang, DT and Lang, MDT. 2015. Package ‘XML’. DOI: 10.2307/248873
- 21Mason, RO. 1986. Four ethical issues of the information age. MIS quarterly, 5–12. DOI: 10.2307/248873
- 22Molongwane, C, Bopape, M-JM, Fridlind, A, Motshegwa, T, Matsui, T, Phaduli, E, Sehurutshi, B and Maisha, R. 2020. Sensitivity of Botswana Ex-Tropical Cyclone Dineo rainfall simulations to cloud microphysics scheme. AAS Open Research, 3(30): 30. DOI: 10.12688/aasopenres.13062.1
- 23Moses, O. 2017. Heat wave characteristics in the context of climate change over past 50 years in Botswana. Botswana Notes and Records;
ub.bw/index.php/bnr/ . - 24Muche, G, Kruger, S, Hillmann, T, Josenhans, K, Ribeiro, C, Bazibi, M, Seely, M, Nkonde, E, de Clercq, W, Strohbach, B, others. 2018. SASSCAL WeatherNet: present state, challenges, and achievements of the regional climatic observation network and database. Biodiversity & Ecology, 6: 34–43. DOI: 10.7809/b-e.00302
- 25Munzert, S, Rubba, C, Meissner, P and Nyhuis, D. 2014. Automated data collection with R: A practical guide to web scraping and text mining. John Wiley & Sons. DOI: 10.1002/9781118834732
- 26Nkemelang, T, New, M and Zaroug, M. 2018. Temperature and precipitation extremes under current, 1.5°C and 2.0°C global warming above pre-industrial levels over Botswana, and implications for climate change vulnerability. Environmental Research Letters, 13(6):
065016 . DOI: 10.1088/1748-9326/aac2f8 - 27Oliver, M and Hambira, WL. 2018. Effects of climate change on evapotranspiration over the Okavango Delta water resources. Physics and Chemistry of the Earth, Parts A/B/C, 105: 98–103. DOI: 10.1016/j.pce.2018.03.011
- 28Robert, S and Paul, S. 2020. Making health economic models Shiny: A tutorial. Wellcome Open Research, 5(69): 69. DOI: 10.12688/wellcomeopenres.15807.2
- 29Schuol, J and Abbaspour, K. 2007. Using monthly weather statistics to generate daily data in a SWAT model application to West Africa. Ecological modeling, 201(3–4): 301–311. DOI: 10.1016/j.ecolmodel.2006.09.028
- 30Singh, J and Singh, O. 2020. Assessing rainfall erosivity and erosivity density over a western Himalayan catchment, India. Journal of Earth System Science, 129(1): 1–22. 2. DOI: 10.1007/s12040-020-1362-8
- 31Sitterson, J, Sinnathamby, S, Parmar, R, Koblich, J, Wolfe, K and Knightes, CD. 2020. Demonstration of an online web services tool incorporating automatic retrieval and comparison of precipitation data. Environmental Modelling & Software, 123:
104570 . DOI: 10.1016/j.envsoft.2019.104570 - 32Somses, S, Bopape, M-JM, Ndarana, T, Fridlind, A, Matsui, T, Phaduli, E, Limbo, A, Maikhudumu, S, Maisha, R and Rakate, E. 2020. Convection Parametrization and Multi-Nesting Dependence of a Heavy Rainfall Event over Namibia with Weather Research and Forecasting (WRF) Model. Climate, 8(10): 112. DOI: 10.3390/cli8100112
- 33Thapelo, ST. 2014.
Técnicas de aprendizaje automatizado para el pronóstico de temperaturas minímas en el Centro Meteorológico de Villa Clara, Santa Clara , PhD thesis, Universidad Central “Marta Abreu” de Las Villas. - 34Thapelo, TS and Jamisola, RS. 2019. Machine learning for maximum and minimum temperature analytics and prediction at local level.
- 35Tufa, D, Paul, B, Jessica, S, Kinfe, H, Daniel, O, del Corral, J, Cousin, R and Thomson, MC. 2014. Bridging critical gaps in climate services and applications in Africa. Earth Perspectives, 1(1): 15. DOI: 10.1186/2194-6434-1-15
- 36Vanderkam, D, Allaire, J, Owen, J, Gromer, D, Shevtsov, P and Thieurmel, B. 2015. dygraphs: Interface to’Dygraphs’ Interactive Time Series Charting Library. R package version 0.5.
- 37Vyacheslav, L, Andrew, R and Samuel, S. 2019. Statistics for climate informatics. Environmetrics, 30(4). DOI: 10.1002/env.2567
- 38Wickham, H. 2011. ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics, 3(2): 180–185.
- 39Wickham, H and Wickham, MH. 2016. Package ‘rvest’. URL:
https://cran.r-project.org/web/packages/rvest/rvest.pdf . DOI: 10.1002/wics.147 - 40Wickham, H and Wickham, MH. 2019. Package ‘stringr’.
- 41Yang, Y, Wilson, L and Wang, J. 2010. Development of an automated climatic data scraping, filtering and display system. Computers and Electronics in Agriculture, 71(1): 77–87. DOI: 10.1016/j.compag.2009.12.006