Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2699-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2699-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA
Christian D. Kummerow
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA
Cooperative Institute for Research in the Atmosphere, Fort Collins,
Colorado, USA
Imme Ebert-Uphoff
Cooperative Institute for Research in the Atmosphere, Fort Collins,
Colorado, USA
Department of Electrical and Computer Engineering, Colorado State
University, Fort Collins, Colorado, USA
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Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
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Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-97, https://doi.org/10.5194/amt-2021-97, 2021
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Latent heating released from phase changes of water is an important factor in driving convection, and thus used in short-term weather forecast models to initiate convection. Typically, radars have been used to retrieve latent heating to be used in the forecast model, but continuous radar data are only available over land. Therefore, this study uses geostationary satellite data to retrieve latent heating so that it can be used to initiate convection in regions where radar data are not available.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 14, 3755–3771, https://doi.org/10.5194/amt-14-3755-2021, https://doi.org/10.5194/amt-14-3755-2021, 2021
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This study suggests two methods to detect convection using 1 min data from GOES-16: one method detects early convective clouds using their vertical growth rate and the other method detects mature convective clouds using their lumpy cloud top surfaces. Applying the two methods to 1-month data showed that the accuracy of the combined methods was 85.8 % and showed their potential to be used in regions where radar data are not available.
Chia-Pang Kuo and Christian Kummerow
Atmos. Meas. Tech., 17, 5637–5653, https://doi.org/10.5194/amt-17-5637-2024, https://doi.org/10.5194/amt-17-5637-2024, 2024
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A small satellite about the size of a shoe box, named TEMPEST, carries only a microwave sensor and is designed to measure the water cycle of the Earth from space in an economical way compared with traditional satellites, which have additional infrared sensors. To overcome the limitation, extra infrared signals from GOES-R ABI are combined with TEMPEST microwave measurements. Compared with ground observations, improved humidity information is extracted from the merged TEMPEST and ABI signals.
Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson
Atmos. Meas. Tech., 17, 515–538, https://doi.org/10.5194/amt-17-515-2024, https://doi.org/10.5194/amt-17-515-2024, 2024
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The latest version of the GPROF retrieval algorithm that produces global precipitation estimates using observations from the Global Precipitation Measurement mission is validated against ground-based radars. The validation shows that the algorithm accurately estimates precipitation on scales ranging from continental to regional. In addition, we validate candidates for the next version of the algorithm and identify principal challenges for further improving space-borne rain measurements.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
Short summary
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Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad
Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022, https://doi.org/10.5194/amt-15-5033-2022, 2022
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The Global Precipitation Measurement mission is an international satellite mission providing regular global rain measurements. We present two newly developed machine-learning-based implementations of one of the algorithms responsible for turning the satellite observations into rain measurements. We show that replacing the current algorithm with a neural network improves the accuracy of the measurements. A neural network that also makes use of spatial information unlocks further improvements.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-97, https://doi.org/10.5194/amt-2021-97, 2021
Preprint withdrawn
Short summary
Short summary
Latent heating released from phase changes of water is an important factor in driving convection, and thus used in short-term weather forecast models to initiate convection. Typically, radars have been used to retrieve latent heating to be used in the forecast model, but continuous radar data are only available over land. Therefore, this study uses geostationary satellite data to retrieve latent heating so that it can be used to initiate convection in regions where radar data are not available.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 14, 3755–3771, https://doi.org/10.5194/amt-14-3755-2021, https://doi.org/10.5194/amt-14-3755-2021, 2021
Short summary
Short summary
This study suggests two methods to detect convection using 1 min data from GOES-16: one method detects early convective clouds using their vertical growth rate and the other method detects mature convective clouds using their lumpy cloud top surfaces. Applying the two methods to 1-month data showed that the accuracy of the combined methods was 85.8 % and showed their potential to be used in regions where radar data are not available.
Cited articles
Afzali Gorooh, V., Kalia, S., Nguyen, P., Hsu, K. L., Sorooshian, S.,
Ganguly, S., and Nemani, R. R.: Deep Neural Network Cloud-Type
Classification (DeepCTC) Model and Its Application in Evaluating
PERSIANN-CCS, Remote Sens., 12, 316, https://doi.org/10.3390/rs12020316,
2020.
Bankert, R. L., Mitrescu, C., Miller, S. D., and Wade, R. H.: Comparison of
GOES cloud classification algorithms employing explicit and implicit
physics, J. Appl. Meteorol. Clim., 48, 1411–1421,
https://doi.org/10.1175/2009JAMC2103.1, 2009.
Bedka, K. M. and Khlopenkov, K.: A probabilistic multispectral pattern
recognition method for detection of overshooting cloud tops using passive
satellite imager observations, J. Appl. Meteorol.
Climatol., 55, 1983–2005, https://doi.org/10.1175/JAMC-D-15-0249.1, 2016.
Bedka, K. M., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T.: Objective satellite-based detection of overshooting tops using infrared
window channel brightness temperature gradients, J. Appl. Meteorol. Clim., 49, 181–202,
https://doi.org/10.1175/2009JAMC2286.1, 2010.
Bedka, K. M., Dworak, R., Brunner, J., and Feltz, W.: Validation of
satellite-based objective overshooting cloud-top detection methods using
CloudSat cloud profiling radar observations, J. Appl. Meteorol. Clim., 51, 1811–1822,
https://doi.org/10.1175/JAMC-D-11-0131.1, 2012.
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R.,
Smirnova, T. G., Olson, J. B., James, E. P., Dowell,
D. C., Grell, G. A., Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R.,
Kenyon, J. S., and Manakin, G. S.: A North
American hourly assimilation and model forecast cycle: The rapid refresh,
Mon. Weather. Rev., 144, 1669–1694,
https://doi.org/10.1175/MWR-D-15-0242.1, 2016.
Beucler, T., Rasp, S., Pritchard, M., and Gentine, P.: Achieving
Conservation of Energy in Neural Network Emulators for Climate
Modeling, arXiv [preprint], arXiv:1906.06622, 15 June 2019.
Boukabara, S. A., Krasnopolsky, V., Stewart, J. Q., Maddy, E. S., Shahroudi,
N., and Hoffman, R. N.: Leveraging Modern Artificial Intelligence for Remote
Sensing and NWP: Benefits and Challenges, B. Am.
Meteorol. Soc., 100, ES473–ES491, https://doi.org/10.1175/BAMS-D-18-0324.1, 2019.
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural
network unified physics parameterization, Geophys. Res. Lett., 45,
6289–6298, https://doi.org/10.1029/2018GL078510, 2018.
Brunner, J. C., Ackerman, S. A., Bachmeier, A. S., and Rabin, R. M.: A
quantitative analysis of the enhanced-V feature in
relation to severe weather, Weather Forecast., 22, 853–872,
https://doi.org/10.1175/WAF1022.1, 2007.
Bu, J., Elhamod, M., Singh, C., Redell, M., Lee, W. C., and Karpatne, A.:
Learning neural networks with competing physics objectives: An application
in quantum mechanics, arXiv [preprint], arXiv:2007.01420, 2 July 2020.
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
machine learning break the convection parameterization
deadlock?, Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018.
Hayatbini, N., Kong, B., Hsu, K. L., Nguyen, P., Sorooshian, S., Stephens,
G., Fowlkes, C., Nemani, R., and Ganguly, S.: Conditional Generative
Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation
from Multispectral GOES-16 Satellite Imageries – PERSIANN-cGAN, Remote
Sens., 11, 2193, https://doi.org/10.3390/rs11192193, 2019.
Hilburn, K. A., Ebert-Uphoff, I., and Miller, S. D.: Development and
Interpretation of a Neural Network-Based Synthetic Radar Reflectivity
Estimator Using GOES-R Satellite Observations,
arXiv [preprint], arXiv:2004.07906, 16 April 2020.
Hirose, H., Shige, S., Yamamoto, M. K., and Higuchi, A.: High temporal
rainfall estimations from Himawari-8 multiband observations using the
random-forest machine-learning method, J. Meteorol. Soc. Jpn. II, 97, 689–710, https://doi.org/10.2151/jmsj.2019-040, 2019.
Iowa Environmental Mesonet: MRMS datasets, MRMS archiving, available at: http://mtarchive.geol.iastate.edu/, last access is 31 March 2021.
Kohler, J., Daneshmand, H., Lucchi, A., Zhou, M., Neymeyr, K., and Hofmann,
T.: Towards a theoretical understanding of batch normalization, stat, 1050,
arXiv [preprint], arXiv:1805.10694v2, 27 May 2018.
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Chalikov, D. V.: New
approach to calculation of atmospheric model physics: Accurate and fast
neural network emulation of longwave radiation in a climate model, Mon.
Weather Rev., 133, 1370–1383, https://doi.org/10.1175/MWR2923.1, 2005.
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Belochitski, A. A.: Using
ensemble of neural networks to learn stochastic convection parameterizations
for climate and numerical weather prediction models from data simulated by a
cloud resolving model, Advances in Artificial Neural Systems, 2013, 485913, https://doi.org/10.1155/2013/485913, 2013.
Lee, Y., Kummerow, C. D., and Zupanski, M.: A simplified method for the detection of convection using high resolution imagery from GOES-16, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-38, in review, 2020.
Liu, Q., Li, Y., Yu, M., Chiu, L. S., Hao, X., Duffy, D. Q., and Yang, C.:
Daytime rainy cloud detection and convective precipitation delineation based
on a deep neural Network method using GOES-16 ABI images, Remote
Sens., 11, 2555, https://doi.org/10.3390/rs11212555, 2019.
Mahajan, S. and Fataniya, B.: Cloud detection methodologies: Variants and
development-a review, Complex & Intelligent Systems, 6, 251–261, https://doi.org/10.1007/s40747-019-00128-0, 2020.
Mecikalski, J. R., MacKenzie Jr., W. M., Koenig, M., and Muller, S.:
Cloud-top properties of growing cumulus prior to convective initiation as
measured by Meteosat Second Generation. Part I: Infrared fields, J. Appl. Meteorol. Clim., 49, 521–534, https://doi.org/10.1175/2009JAMC2344.1, 2010.
O'Gorman, P. A. and Dwyer, J. G.: Using machine learning to parameterize
moist convection: Potential for modeling of climate, climate change, and
extreme events, J. Adv. Model. Earth Syst., 10,
2548–2563, https://doi.org/10.1029/2018MS001351, 2018.
Qi, Y., Zhang, J., and Zhang, P.: A real-time automated convective and
stratiform precipitation segregation algorithm in native radar coordinates,
Q. J. Roy. Meteor. Soc., 139, 2233–2240, https://doi.org/10.1002/qj.2095, 2013.
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent
subgrid processes in climate models, P. Natl. Acad.
Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018.
Roebber, P. J.: Visualizing multiple measures of forecast quality, Weather
Forecast., 24, 601–608, https://doi.org/10.1175/2008WAF2222159.1, 2009.
Sieglaff, J. M., Cronce, L. M., Feltz, W. F., Bedka, K. M., Pavolonis, M.
J., and Heidinger, A. K.: Nowcasting convective storm initiation using
satellite-based box-averaged cloud-top cooling and cloud-type
trends, J. Appl. Meteorol. Clim., 50, 110–126, https://doi.org/10.1175/2010JAMC2496.1, 2011.
Sun, R.: Optimization for deep learning: An overview, arXiv [preprint], arXiv:1912.08957, 19 December 2019.
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H.,
Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J.,
and Kitzmiller, D.: Multi-Radar Multi-Sensor (MRMS) quantitative
precipitation estimation: Initial operating capabilities, B.
Am. Meteorol. Soc., 97, 621–638, https://doi.org/10.1175/BAMS-D-14-00174.1, 2016.
Short summary
Convective clouds are usually associated with intense rain that can cause severe damage, and thus it is important to accurately detect convective clouds. This study develops a machine learning model that can identify convective clouds from five temporal visible and infrared images as humans can point at convective regions by finding bright and bubbling areas. The results look promising when compared to radar-derived products, which are commonly used for detecting convection.
Convective clouds are usually associated with intense rain that can cause severe damage, and...