Articles | Volume 6, issue 2
https://doi.org/10.5194/ascmo-6-79-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-79-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A statistical approach to fast nowcasting of lightning potential fields
Joshua North
CORRESPONDING AUTHOR
Department of Statistics, University of Missouri, Columbia, MO, USA
Zofia Stanley
Department of Applied Mathematics, University of Colorado, Boulder, CO, USA
William Kleiber
Department of Applied Mathematics, University of Colorado, Boulder, CO, USA
Wiebke Deierling
Research Applications Laboratory,
National Center for Atmospheric Research, Boulder, CO, USA
Eric Gilleland
Research Applications Laboratory,
National Center for Atmospheric Research, Boulder, CO, USA
Matthias Steiner
Research Applications Laboratory,
National Center for Atmospheric Research, Boulder, CO, USA
Related authors
No articles found.
Matthew LeDuc, Tomoko Matsuo, and William Kleiber
EGUsphere, https://doi.org/10.5194/egusphere-2025-5570, https://doi.org/10.5194/egusphere-2025-5570, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
We propose a new approach for inverse problems involving ratios of photon counts. We show that the method is computationally efficient and accurately handles the uncertainty introduced by count data. We demonstrate the method by estimating the temperature in the upper atmosphere in both calm and geomagnetically active conditions. We also present results that suggest this method can allow extension of these temperature retrievals to more times of day than current techniques.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
Short summary
Short summary
To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
Short summary
Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021, https://doi.org/10.5194/npg-28-565-2021, 2021
Short summary
Short summary
In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
Short summary
Short summary
Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Chiara Marsigli, Elizabeth Ebert, Raghavendra Ashrit, Barbara Casati, Jing Chen, Caio A. S. Coelho, Manfred Dorninger, Eric Gilleland, Thomas Haiden, Stephanie Landman, and Marion Mittermaier
Nat. Hazards Earth Syst. Sci., 21, 1297–1312, https://doi.org/10.5194/nhess-21-1297-2021, https://doi.org/10.5194/nhess-21-1297-2021, 2021
Short summary
Short summary
This paper reviews new observations for the verification of high-impact weather and provides advice for their usage in objective verification. New observations include remote sensing datasets, products developed for nowcasting, datasets derived from telecommunication systems, data collected from citizens, reports of impacts and reports from insurance companies. This work has been performed in the framework of the Joint Working Group on Forecast Verification Research (JWGFVR) of the WMO.
James O. Pinto, Anders A. Jensen, Pedro A. Jiménez, Tracy Hertneky, Domingo Muñoz-Esparza, Arnaud Dumont, and Matthias Steiner
Earth Syst. Sci. Data, 13, 697–711, https://doi.org/10.5194/essd-13-697-2021, https://doi.org/10.5194/essd-13-697-2021, 2021
Short summary
Short summary
The dataset produced here was generated as part of a real-time demonstration of a new capability to provide fine-scale weather guidance to support small UAS operations. The nested model configuration enabled us to resolve large turbulent eddies that developed in response to daytime heating and demonstrated the current state of the science in coupling mesoscale forcing with a large eddy simulation (LES) model. Output from these real-time simulations was used for planning IOPs during LAPSE-RATE.
Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
Short summary
Short summary
Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
Cited articles
Barthe, C., Deierling, W., and Barth, M. C.: Estimation of total lightning from
various storm parameters: A cloud-resolving model study, J.
Geophys. Res.-Atmos., 115, D24202, https://doi.org/10.1029/2010JD014405, 2010. a
Bookstein, F. L.: Principal warps: Thin-plate splines and the decomposition of
deformations, IEEE T. Pattern Anal.,
11, 567–585, 1989. a
Buechler, D. E. and Goodman, S. J.: Echo size and asymmetry: Impact on NEXRAD
storm identification, J. Appl. Meteorol., 29, 962–969, 1990. a
Deierling, W. and Petersen, W. A.: Total lightning activity as an indicator of
updraft characteristics, J. Geophys. Res.-Atmos., 113, D16210, https://doi.org/10.1029/2007JD009598,
2008. a
Deierling, W., Petersen, W. A., Latham, J., Ellis, S., and Christian, H. J.:
The relationship between lightning activity and ice fluxes in thunderstorms,
J. Geophys. Res.-Atmos., 113, D15210, https://doi.org/10.1029/2007JD009700, 2008. a
Deierling, W., Steiner, M., Ikeda, K., Kessinger, C., and Bass, R.: Short term
lightning hazard predictions, in: XV International Conference on Atmospheric
Electricity, Norman, OK, 2014. a
Dixon, M. and Wiener, G.: TITAN: Thunderstorm identification, tracking,
analysis, and nowcasting – A radar-based methodology, J. Atmos.
Ocean. Technol., 10, 785–797, 1993. a
Fierro, A. O., Mansell, E. R., MacGorman, D. R., and Ziegler, C. L.: The
implementation of an explicit charging and discharge lightning scheme within
the WRF-ARW model: Benchmark simulations of a continental squall line, a
tropical cyclone, and a winter storm, Mon. Weather Rev., 141,
2390–2415, 2013. a
Gilleland, E., Lindström, J., and Lindgren, F.: Analyzing the image warp
forecast verification method on precipitation fields from the ICP, Weather
Forecast., 25, 1249–1262, 2010. a
Harris, R. J., Mecikalski, J. R., MacKenzie Jr., W. M., Durkee, P. A., and
Nielsen, K. E.: The definition of GOES infrared lightning initiation interest
fields, J. Appl. Meteorol. Climatol., 49, 2527–2543, 2010. a
Holle, R. L., Demetriades, N. W., and Nag, A.: Objective airport warnings over
small areas using NLDN cloud and cloud-to-ground lightning data, Weather Forecast., 31, 1061–1069, 2016. a
Joe, P., Dance, S., Lakshmanan, V., Heizenreder, D., James, P., Lang, P., Hengstebeck, T., Feng, Y., Li, P. W., Yeung, H.-Y., Suzuki, O., Doi, K., and Dai, J.: Automated Processing of
Doppler Radar Data for Severe Weather Warnings, INTECH Open Access Publisher, 2012. a
Johnson, J., MacKeen, P. L., Witt, A., Mitchell, E. D. W., Stumpf, G. J.,
Eilts, M. D., and Thomas, K. W.: The storm cell identification and tracking
algorithm: An enhanced WSR-88D algorithm, Weather Forecast., 13,
263–276, 1998. a
Kicinger, R., Chen, J.-T., Steiner, M., and Pinto, J.: Airport capacity
prediction with explicit consideration of weather forecast uncertainty,
J. Air Transp., 24, 18–28, 2016. a
Li, P. and Lau, D.: Development of a lightning nowcasting system for Hong Kong
International Airport, in: 13th Conference on Aviation, Range and Aerospace
Meteorology, New Orleans, Louisiana, USA, 20–24 January, 2008. a
MacGorman, D. R., Apostolakopoulos, I. R., Lund, N. R., Demetriades, N. W.,
Murphy, M. J., and Krehbiel, P. R.: The timing of cloud-to-ground lightning
relative to total lightning activity, Mon. Weather Rev., 139,
3871–3886, 2011. a
Mansell, E. R., MacGorman, D. R., Ziegler, C. L., and Straka, J. M.: Charge
structure and lightning sensitivity in a simulated multicell thunderstorm,
J. Geophys. Res.-Atmos., 110, D12101, https://doi.org/10.1029/2004JD005287, 2005. a, b
Mecikalski, J. R., Williams, J. K., Jewett, C. P., Ahijevych, D., LeRoy, A.,
and Walker, J. R.: Probabilistic 0–1-h convective initiation nowcasts that
combine geostationary satellite observations and numerical weather prediction
model data, J. Appl. Meteorol. Climatol., 54, 1039–1059,
2015. a
Metta, S., von Hardenberg, J., Ferraris, L., Rebora, N., and Provenzale, A.:
Precipitation nowcasting by a spectral-based nonlinear stochastic model,
J. Hydrometeorol., 10, 1285–1297, 2009. a
Murphy, M., Demetriades, N., and Cummins, K.: The value of cloud lightning in
probabilistic thunderstorm warning, 16th Conf. on Probability and Statistics in the Atmospheric Sciences, American Meteorological Society, Orlando, FL,
134–139, 2002. a
Murphy, M. J., Holle, R. L., and Demetriades, N. W.: Cloud-to-ground lightning
warnings using electric field mill and lightning observations, in: 20th
international lightning detection conference, Tucson, AZ, 21–23, 2008. a
North, J.:
nowcasting, GitHub, available at:
https://github.com/jsnowynorth/nowcasting, last access: 19 May 2020. a
Pierce, C., Seed, A., Ballard, S., Simonin, D., and Li, Z.: Nowcasting, in:
Doppler Radar Observations-Weather Radar, Wind Profiler, Ionospheric Radar,
and Other Advanced Applications, edited by: Bech, J., 2012. a
Potts, R. J.: A thunderstorm and lightning alert service for airport
operations, American Meteorological Society, Phoenix, AZ, 12–15, 2009. a
Ruzanski, E., Chandrasekar, V., and Wang, Y.: The CASA nowcasting system,
J. Atmos. Ocean. Technol., 28, 640–655, 2011. a
Saxen, T. R., Mueller, C. K., Warner, T. T., Steiner, M., Ellison, E. E.,
Hatfield, E. W., Betancourt, T. L., Dettling, S. M., and Oien, N. A.: The
operational mesogamma-scale analysis and forecast system of the US army test
and evaluation command. Part IV: The White Sands Missile Range auto-nowcast
system, J. Appl. Meteorol. Climatol., 47, 1123–1139, 2008. a, b
Steiner, M., Bateman, R., Megenhardt, D., Liu, Y., Xu, M., Pocernich, M., and
Krozel, J.: Translation of ensemble weather forecasts into probabilistic air
traffic capacity impact, Air Traffic Control Quarterly, 18, 229–254, 2010. a
Steiner, M., Deierling, W., and Bass, R.: Balancing safety and efficiency of
airport operations under lightning threats: a look inside the race closure
decision-making process, Air Traffic Control, 55, 16–23, 2013. a
Steiner, M., Deierling, W., Ikeda, K., and Bass, R. G.: Ground delays from
lightning ramp closures and decision uncertainties, Air Traffic Control
Quarterly, 22, 223–249, 2014. a
Steiner, M., Deierling, W., Ikeda, K., Robinson, M., Klein, A., Bewley, J., and
Bass, R.: Air Traffic Impacts Caused by Lightning Safety Procedures, in: 16th
AIAA Aviation Technology, Integration, and Operations Conference, 1–19,
2016. a
Thomas, R. J., Krehbiel, P. R., Rison, W., Hunyady, S. J., Winn, W. P., Hamlin,
T., and Harlin, J.: Accuracy of the lightning mapping array, J.
Geophys. Res.-Atmos., 109, D14207, https://doi.org/10.1029/2004JD004549, 2004.
a
Wang, Y., Coning, E., Harou, A., Jacobs, W., Joe, P., Nikitina, L., Roberts,
R., Wang, J., and Wilson, J.: Guidelines for nowcasting techniques, WMO
publication, available at: https://library.wmo.int/doc_num.php?explnum_id=3795 (last access: 20 March 2020),
2017. a
Williams, E. R. and Lhermitte, R. M.: Radar tests of the precipitation
hypothesis for thunderstorm electrification, J. Geophys. Res.-Oceans, 88, 10984–10992, 1983. a
Workman, E. and Reynolds, S.: Electrical activity as related to thunderstorm
cell growth, B. Am. Meteorol. Soc., 30, 142–144,
1949. a
Zipser, E. J. and Lutz, K. R.: The vertical profile of radar reflectivity of
convective cells: A strong indicator of storm intensity and lightning
probability?, Mon. Weather Rev., 122, 1751–1759, 1994. a
Short summary
Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Very short-term forecasting, called nowcasting, is used to monitor storms that pose a...