Articles | Volume 24, issue 2
https://doi.org/10.5194/nhess-24-567-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-567-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data
Joseph Smith
CORRESPONDING AUTHOR
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Cathryn Birch
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
John Marsham
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Simon Peatman
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Massimo Bollasina
School of GeoSciences, University of Edinburgh, Edinburgh, EH8 9YL, UK
George Pankiewicz
UK Met Office, Exeter, EX1 3PB, UK
Related authors
No articles found.
Weihao Sun, Massimo Bollasina, Ioana Colfescu, Guoxiong Wu, and Yimin Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-3389, https://doi.org/10.5194/egusphere-2025-3389, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Observational records show that the Asian monsoon underwent substantial changes during the early 20th century, including a wetting trend over South Asia and a southward shift in rainfall over East Asia. We show that increasing European sulphate aerosol emissions played a crucial role in shaping the monsoon rainfall trends. This has important implications for reducing uncertainties in monsoon projections, particularly in light of the diverse future aerosol emission scenarios for the region.
Zixuan Jia, Massimo A. Bollasina, Wenjun Zhang, and Ying Xiang
Atmos. Chem. Phys., 25, 8805–8820, https://doi.org/10.5194/acp-25-8805-2025, https://doi.org/10.5194/acp-25-8805-2025, 2025
Short summary
Short summary
Using multi-model mean data from regional aerosol perturbation experiments, we find that increased Asian sulfate aerosols strengthen the link between ENSO (El Niño–Southern Oscillation) and the East Asian winter monsoon. In coupled simulations, aerosol-induced broad cooling increases the ENSO amplitude by affecting the tropical Pacific mean state, contributing to the increase in monsoon interannual variability. These results provide important implications to reduce uncertainties in future projections of regional extreme variability.
Duncan Watson-Parris, Laura J. Wilcox, Camilla W. Stjern, Robert J. Allen, Geeta Persad, Massimo A. Bollasina, Annica M. L. Ekman, Carley E. Iles, Manoj Joshi, Marianne T. Lund, Daniel McCoy, Daniel M. Westervelt, Andrew I. L. Williams, and Bjørn H. Samset
Atmos. Chem. Phys., 25, 4443–4454, https://doi.org/10.5194/acp-25-4443-2025, https://doi.org/10.5194/acp-25-4443-2025, 2025
Short summary
Short summary
In 2020, regulations by the International Maritime Organization aimed to reduce aerosol emissions from ships. These aerosols previously had a cooling effect, which the regulations might reduce, revealing more greenhouse gas warming. Here we find that, while there is regional warming, the global 2020–2040 temperature rise is only +0.03 °C. This small change is difficult to distinguish from natural climate variability, indicating the regulations have had a limited effect on observed warming to date.
Zhen Liu, Massimo A. Bollasina, and Laura J. Wilcox
Atmos. Chem. Phys., 24, 7227–7252, https://doi.org/10.5194/acp-24-7227-2024, https://doi.org/10.5194/acp-24-7227-2024, 2024
Short summary
Short summary
The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model-simulated spatio-temporal variability in the climatological monsoon precipitation across Asia, which critically modulates the efficacy of aerosol–cloud–precipitation interactions, the predominant driver of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Ben Maybee, Cathryn E. Birch, Steven J. Böing, Thomas Willis, Linda Speight, Aurore N. Porson, Charlie Pilling, Kay L. Shelton, and Mark A. Trigg
Nat. Hazards Earth Syst. Sci., 24, 1415–1436, https://doi.org/10.5194/nhess-24-1415-2024, https://doi.org/10.5194/nhess-24-1415-2024, 2024
Short summary
Short summary
This paper presents the development and verification of FOREWARNS, a novel method for regional-scale forecasting of surface water flooding. We detail outcomes from a workshop held with UK forecast users, who indicated they valued the forecasts and would use them to complement national guidance. We use results of objective forecast tests against flood observations over northern England to show that this confidence is justified and that FOREWARNS meets the needs of UK flood responders.
Matthias Fischer, Peter Knippertz, Roderick van der Linden, Alexander Lemburg, Gregor Pante, Carsten Proppe, and John H. Marsham
Weather Clim. Dynam., 5, 511–536, https://doi.org/10.5194/wcd-5-511-2024, https://doi.org/10.5194/wcd-5-511-2024, 2024
Short summary
Short summary
Our research enhances the understanding of the complex dynamics within the West African monsoon system by analyzing the impact of specific model parameters on its characteristics. Employing surrogate models, we identified critical factors such as the entrainment rate and the fall velocity of ice. Precise definition of these parameters in weather models could improve forecast accuracy, thus enabling better strategies to manage and reduce the impact of weather events.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Andrew P. Schurer, Gabriele C. Hegerl, Hugues Goosse, Massimo A. Bollasina, Matthew H. England, Michael J. Mineter, Doug M. Smith, and Simon F. B. Tett
Clim. Past, 19, 943–957, https://doi.org/10.5194/cp-19-943-2023, https://doi.org/10.5194/cp-19-943-2023, 2023
Short summary
Short summary
We adopt an existing data assimilation technique to constrain a model simulation to follow three important modes of variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode. How it compares to the observed climate is evaluated, with improvements over simulations without data assimilation found over many regions, particularly the tropics, the North Atlantic and Europe, and discrepancies with global cooling following volcanic eruptions are reconciled.
Nora L. S. Fahrenbach and Massimo A. Bollasina
Atmos. Chem. Phys., 23, 877–894, https://doi.org/10.5194/acp-23-877-2023, https://doi.org/10.5194/acp-23-877-2023, 2023
Short summary
Short summary
We studied the monthly-scale climate response to COVID-19 aerosol emission reductions during January–May 2020 using climate models. Our results show global temperature and rainfall anomalies driven by circulation changes. The climate patterns reverse polarity from JF to MAM due to a shift in the main SO2 reduction region from China to India. This real-life example of rapid climate adjustments to abrupt, regional aerosol emission reduction has large implications for future climate projections.
Liang Guo, Laura J. Wilcox, Massimo Bollasina, Steven T. Turnock, Marianne T. Lund, and Lixia Zhang
Atmos. Chem. Phys., 21, 15299–15308, https://doi.org/10.5194/acp-21-15299-2021, https://doi.org/10.5194/acp-21-15299-2021, 2021
Short summary
Short summary
Severe haze remains serious over Beijing despite emissions decreasing since 2008. Future haze changes in four scenarios are studied. The pattern conducive to haze weather increases with the atmospheric warming caused by the accumulation of greenhouse gases. However, the actual haze intensity, measured by either PM2.5 or optical depth, decreases with aerosol emissions. We show that only using the weather pattern index to predict the future change of Beijing haze is insufficient.
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-46, https://doi.org/10.5194/wcd-2021-46, 2021
Preprint withdrawn
Short summary
Short summary
This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.
Francesco S. R. Pausata, Gabriele Messori, Jayoung Yun, Chetankumar A. Jalihal, Massimo A. Bollasina, and Thomas M. Marchitto
Clim. Past, 17, 1243–1271, https://doi.org/10.5194/cp-17-1243-2021, https://doi.org/10.5194/cp-17-1243-2021, 2021
Short summary
Short summary
Far-afield changes in vegetation such as those that occurred over the Sahara during the middle Holocene and the consequent changes in dust emissions can affect the intensity of the South Asian Monsoon (SAM) rainfall and the lengthening of the monsoon season. This remote influence is mediated by anomalies in Indian Ocean sea surface temperatures and may have shaped the evolution of the SAM during the termination of the African Humid Period.
Lixia Zhang, Laura J. Wilcox, Nick J. Dunstone, David J. Paynter, Shuai Hu, Massimo Bollasina, Donghuan Li, Jonathan K. P. Shonk, and Liwei Zou
Atmos. Chem. Phys., 21, 7499–7514, https://doi.org/10.5194/acp-21-7499-2021, https://doi.org/10.5194/acp-21-7499-2021, 2021
Short summary
Short summary
The projected frequency of circulation patterns associated with haze events and global warming increases significantly due to weakening of the East Asian winter monsoon. Rapid reduction in anthropogenic aerosol further increases the frequency of circulation patterns, but haze events are less dangerous. We revealed competing effects of aerosol emission reductions on future haze events through their direct contribution to haze intensity and their influence on the atmospheric circulation patterns.
Jessica C. A. Baker, Luis Garcia-Carreras, Manuel Gloor, John H. Marsham, Wolfgang Buermann, Humberto R. da Rocha, Antonio D. Nobre, Alessandro Carioca de Araujo, and Dominick V. Spracklen
Hydrol. Earth Syst. Sci., 25, 2279–2300, https://doi.org/10.5194/hess-25-2279-2021, https://doi.org/10.5194/hess-25-2279-2021, 2021
Short summary
Short summary
Evapotranspiration (ET) is a vital part of the Amazon water cycle, but it is difficult to measure over large areas. In this study, we compare spatial patterns, seasonality, and recent trends in Amazon ET from a water-budget analysis with estimates from satellites, reanalysis, and global climate models. We find large differences between products, showing that many widely used datasets and climate models may not provide a reliable representation of this crucial variable over the Amazon.
Laura J. Wilcox, Zhen Liu, Bjørn H. Samset, Ed Hawkins, Marianne T. Lund, Kalle Nordling, Sabine Undorf, Massimo Bollasina, Annica M. L. Ekman, Srinath Krishnan, Joonas Merikanto, and Andrew G. Turner
Atmos. Chem. Phys., 20, 11955–11977, https://doi.org/10.5194/acp-20-11955-2020, https://doi.org/10.5194/acp-20-11955-2020, 2020
Short summary
Short summary
Projected changes in man-made aerosol range from large reductions to moderate increases in emissions until 2050. Rapid reductions between the present and the 2050s lead to enhanced increases in global and Asian summer monsoon precipitation relative to scenarios with continued increases in aerosol. Relative magnitude and spatial distribution of aerosol changes are particularly important for South Asian summer monsoon precipitation changes, affecting the sign of the trend in the coming decades.
Cited articles
Ali, A., Supriatna, S., and Sa'adah, U.: Radar-Based Stochastic Precipitation Nowcasting Using The Short-Term Ensemble Prediction System (STEPS) (Case Study: Pangkalan Bun Weather Radar), International Journal of Remote Sensing and Earth Sciences, 18, 91, https://doi.org/10.30536/j.ijreses.2021.v18.a3527, 2021.
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9 — Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016.
Birch, C. E., Webster, S., Peatman, S. C., Parker, D. J., Matthews, A. J., Li, Y., and Hassim, M. E. E.: Scale Interactions between the MJO and the Western Maritime Continent, J. Climate, 29, 2471–2492, https://doi.org/10.1175/JCLI-D-15-0557.1, 2016.
Bowler, N. E., Pierce, C. E., and Seed, A. W.: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP, Q. J. Roy. Meteor. Soc., 132, 2127–2155, https://doi.org/10.1256/qj.04.100, 2006.
Burton, R. R., Blyth, A. M., Cui, Z., Groves, J., Lamptey, B. L., Fletcher, J. K., Marsham, J. H., Parker, D. J., and Roberts, A.: Satellite-based nowcasting of West African mesoscale storms has skill at up to four hours lead time, Weather Forecast., 37, 445–455, https://doi.org/10.1175/WAF-D-21-0051.1, 2022.
Dayem, K. E., Noone, D. C., and Molnar, P.: Tropical western Pacific warm pool and maritime continent precipitation rates and their contrasting relationships with the Walker Circulation, J. Geophys. Res., 112, D06101, https://doi.org/10.1029/2006JD007870, 2007.
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze, R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021.
Ferrett, S., Frame, T. H. A., Methven, J., Holloway, C. E., Webster, S., Stein, T. H. M., and Cafaro, C.: Evaluating convection-permitting ensemble forecasts of precipitation over Southeast Asia, Weather Forecast., 36, 1199–1217, https://doi.org/10.1175/WAF-D-20-0216.1, 2021.
Germann, U. and Zawadzki, I.: Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology, Mon. Weather Rev., 130, 2859–2873, https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2, 2002.
Gijben, M. and de Coning, E.: Using Satellite and Lightning Data to Track Rapidly Developing Thunderstorms in Data Sparse Regions, Atmosphere, 8, 67, https://doi.org/10.3390/atmos8040067, 2017.
Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., and Gruber, C.: The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region, Weather Forecast., 26, 166–183, https://doi.org/10.1175/2010WAF2222451.1, 2011.
Han, D., Lee, J., Im, J., Sim, S., Lee, S., and Han, H.: A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data, Remote Sens.-Basel, 11, 1454, https://doi.org/10.3390/rs11121454, 2019.
Han, L., Zhang, J., Chen, H., Zhang, W., and Yao, S.: Toward the Predictability of a Radar-Based Nowcasting System for Different Precipitation Systems, IEEE Geosci. Remote S., 19, 1–5, https://doi.org/10.1109/LGRS.2022.3185031, 2022.
Harjupa, W., Abdillah, M. R., Azura, A., Putranto, M. F., Marzuki, M., Nauval, F., Risyanto, Saufina, E., Jumianti, N., and Fathrio, I.: On the utilization of RDCA method for detecting and predicting the occurrence of heavy rainfall in Indonesia, Remote Sensing Applications: Society and Environment, 25, 100681, https://doi.org/10.1016/j.rsase.2021.100681, 2022.
Hill, P. G., Stein, T. H. M., Roberts, A. J., Fletcher, J. K., Marsham, J. H., and Groves, J.: How skilful are Nowcasting Satellite Applications Facility products for tropical Africa?, Meteorol. Appl., 27, 12, https://doi.org/10.1002/met.1966, 2020.
Horn, B. K. P. and Schunck, B. G.: Determining optical flow, Artif. Intell., 17, 185–203, https://doi.org/10.1016/0004-3702(81)90024-2, 1981.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Imhoff, R. O., Brauer, C. C., Overeem, A., Weerts, A. H., and Uijlenhoet, R.: Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events, Water Resour. Res., 56, e2019WR026723, https://doi.org/10.1029/2019WR026723, 2020.
Lagerquist, R., Stewart, J., Ebert-Uphoff, I., and Christina, K.: Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data, Mon. Weather Rev., 149, 3897–3921, https://doi.org/10.1175/MWR-D-21-0096.1, 2021.
Lebedev, V., Ivashkin, V., Rudenko, I., Ganshin, A., Molchanov, A., Ovcharenko, S., Grokhovetskiy, R., Bushmarinov, I., and Solomentsev, D.: Precipitation Nowcasting with Satellite Imagery, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, 25 July 2019, Anchorage, AK, USA, 2680–2688, https://doi.org/10.1145/3292500.3330762, 2019.
Line, W. E., Schmit, T. J., Lindsey, D. T., and Goodman, S. J.: Use of Geostationary Super Rapid Scan Satellite Imagery by the Storm Prediction Center, Weather Forecast., 31, 483–494, https://doi.org/10.1175/WAF-D-15-0135.1, 2016.
Lucas, B. D. and Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision, 10, in: Proceedings: 7th international joint conference on Artificial intelligence – Volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, https://dl.acm.org/doi/10.5555/1623264.1623280 (last access: 14 February 2024), 1981.
Machado, L. A. T. and Laurent, H.: The Convective System Area Expansion over Amazonia and Its Relationships with Convective System Life Duration and High-Level Wind Divergence, Mon. Weather Rev., 132, 714–725, https://doi.org/10.1175/1520-0493(2004)132<0714:TCSAEO>2.0.CO;2, 2004.
Marcos: NWC SAF convective precipitation product from MSG: A new day-time method based on cloud top physical properties, Tethys, 12, 3–11, https://doi.org/10.3369/tethys.2015.12.01, 2015.
Mori, S., Jun-Ichi, H., Tauhid, Y. I., Yamanaka, M. D., Okamoto, N., Murata, F., Sakurai, N., Hashiguchi, H., and Sribimawati, T.: Diurnal Land–Sea Rainfall Peak Migration over Sumatera Island, Indonesian Maritime Continent, Observed by TRMM Satellite and Intensive Rawinsonde Soundings, Mon. Weather Rev., 132, 2021–2039, https://doi.org/10.1175/1520-0493(2004)132<2021:DLRPMO>2.0.CO;2, 2004.
Mueller, C., Saxen, T., Roberts, R., Wilson, J., Betancourt, T., Dettling, S., Oien, N., and Yee, J.: NCAR Auto-Nowcast System, Weather Forecast., 18, 545–561, https://doi.org/10.1175/1520-0434(2003)018<0545:NAS>2.0.CO;2, 2003.
Murphy, A. H. and Epstein, E. S.: Skill Scores and Correlation Coefficients in Model Verification, Mon. Weather Rev., 117, 572–582, https://doi.org/10.1175/1520-0493(1989)117<0572:SSACCI>2.0.CO;2, 1989.
Permana, D. S., Hutapea, T. D., Praja, A. S., Paski, J. A. I., Makmur, E. E. S., Haryoko, U., Umam, I. H., Saepudin, M., and Adriyanto, R.: The Indonesia In-House Radar Integration System (InaRAISE) of Indonesian Agency for Meteorology Climatology and Geophysics (BMKG): Development, Constraint, and Progress, IOP Conf. Ser.-Earth Environ. Sci., 303, 012051, https://doi.org/10.1088/1755-1315/303/1/012051, 2019.
Porson, A. N., Hagelin, S., Boyd, D. F. A., Roberts, N. M., North, R., Webster, S., and Lo, J. C.: Extreme rainfall sensitivity in convective-scale ensemble modelling over Singapore, Q. J. Roy. Meteor. Soc., 145, 3004–3022, https://doi.org/10.1002/qj.3601, 2019.
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., and Foresti, L.: Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0), Geosci. Model Dev., 12, 4185–4219, https://doi.org/10.5194/gmd-12-4185-2019, 2019.
Qian, J.-H.: Why Precipitation Is Mostly Concentrated over Islands in the Maritime Continent, J. Atmos. Sci., 65, 1428–1441, https://doi.org/10.1175/2007JAS2422.1, 2008.
Ramage, C. S.: ROLE OF A TROPICAL “MARITIME CONTINENT” IN THE ATMOSPHERIC CIRCULATION, Mon. Weather Rev., 96, 365–370, https://doi.org/10.1175/1520-0493(1968)096<0365:ROATMC>2.0.CO;2, 1968.
Reen, B. P., Cai, H., and Raby, J. W.: Preliminary Investigation of Assimilating Global Synthetic Weather Radar, United States Army Research Lab., https://apps.dtic.mil/sti/pdfs/AD1111072.pdf (last access: 14 February 2024), 2020.
Roberts, A. J., Fletcher, J. K., Groves, J., Marsham, J. H., Parker, D. J., Blyth, A. M., Adefisan, E. A., Ajayi, V. O., Barrette, R., de Coning, E., Dione, C., Diop, A., Foamouhoue, A. K., Gijben, M., Hill, P. G., Lawal, K. A., Mutemi, J., Padi, M., Popoola, T. I., Rípodas, P., Stein, T. H. M., and Woodhams, B. J.: Nowcasting for Africa: advances, potential and value, Weather, 77, 250–256, https://doi.org/10.1002/wea.3936, 2022.
Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events, Mon. Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2008.
Roca, R., Fiolleau, T., and Bouniol, D.: A Simple Model of the Life Cycle of Mesoscale Convective Systems Cloud Shield in the Tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017.
Rouault, E., Warmerdam, F., Schwehr, K., Kiselev, A., Butler, H., Łoskot, M., Szekeres, T., Tourigny, E., Landa, M., Miara, I., Elliston, B., Chaitanya, K., Plesea, L., Morissette, D., Jolma, A., Dawson, N., Baston, D., de Stigter, C., and Miura, H.: GDAL, Zenodo [code], https://doi.org/10.5281/ZENODO.5884351, 2023.
Seed, A. W.: A Dynamic and Spatial Scaling Approach to Advection Forecasting, J. Appl. Meteorol., 42, 381–388, https://doi.org/10.1175/1520-0450(2003)042<0381:ADASSA>2.0.CO;2, 2003.
Shi, J. and Tomasi: Good features to track, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, 21–23 June 1994, Seattle, WA, USA, 593–600, https://doi.org/10.1109/CVPR.1994.323794, 1994.
Sieglaff, J. M., Hartung, D. C., Feltz, W. F., Cronce, L. M., and Lakshmanan, V.: A Satellite-Based Convective Cloud Object Tracking and Multipurpose Data Fusion Tool with Application to Developing Convection, J. Atmos. Ocean. Tech., 30, 510–525, https://doi.org/10.1175/JTECH-D-12-00114.1, 2013.
Sobajima, A.: Rapidly Developing Cumulus Areas Derivation Algorithm Theoretical Basis Document, Japanese Meteorological Agency, https://cwg.eumetsat.int/res/pdf/ATBD_RapidlyDevelopingCumulusAreas_CWG.pdf (last access: 14 February 2024), 2012.
Srivastava, K., Lau, Sharons. Y., Yeung, H. Y., Cheng, T. L., Bhardwaj, R., Kannan, A. M., Bhowmik, S. K. R., and Singh, H.: Use of SWIRLS nowcasting system for quantitative precipitation forecast using Indian DWR data, MAUSAM, 63, 1–16, https://doi.org/10.54302/mausam.v63i1.1442, 2021.
University of Lille: ICARE Data and Services Center, https://www.icare.univ-lille.fr/ (last access: 13 February 2024), 2024.
Venugopal, V., Foufoula-Georgiou, E., and Sapozhnikov, V.: Evidence of dynamic scaling in space-time rainfall, J. Geophys. Res., 104, 31599–31610, https://doi.org/10.1029/1999JD900437, 1999.
Vila, D. A., Machado, L. A. T., Laurent, H., and Velasco, I.: Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) Using Satellite Infrared Imagery: Methodology and Validation, Weather Forecast., 23, 233–245, https://doi.org/10.1175/2007WAF2006121.1, 2008.
Woodhams, B. J., Birch, C. E., Marsham, J. H., Bain, C. L., Roberts, N. M., and Boyd, D. F. A.: What Is the Added Value of a Convection-Permitting Model for Forecasting Extreme Rainfall over Tropical East Africa?, Mon. Weather Rev., 146, 2757–2780, https://doi.org/10.1175/MWR-D-17-0396.1, 2018.
World Meteorological Organization: Early Warnings For All Initiative scaled up into action on the ground, World Meteorological Organization, https://wmo.int/site/wmo-and-early-warnings-all-initiative (last access: 13 February 2024), 2023.
Yamanaka, M. D.: Physical climatology of Indonesian maritime continent: An outline to comprehend observational studies, Atmos. Res., 178–179, 231–259, https://doi.org/10.1016/j.atmosres.2016.03.017, 2016.
Yang, G.-Y. and Slingo, J.: The Diurnal Cycle in the Tropics, Mon. Weather Rev., 129, 784–801, https://doi.org/10.1175/1520-0493(2001)129<0784:TDCITT>2.0.CO;2, 2001.
Short summary
Nowcasting uses observations to make predictions of the atmosphere on short timescales and is particularly applicable to the Maritime Continent, where storms rapidly develop and cause natural disasters. This paper evaluates probabilistic and deterministic satellite nowcasting algorithms over the Maritime Continent. We show that the probabilistic approach is most skilful at small scales (~ 60 km), whereas the deterministic approach is most skilful at larger scales (~ 200 km).
Nowcasting uses observations to make predictions of the atmosphere on short timescales and is...
Altmetrics
Final-revised paper
Preprint