Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7681-2021
© 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-14-7681-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2021
Research article |
| 08 Dec 2021
| 08 Dec 2021
Idealized simulation study of the relationship of disdrometer sampling statistics with the precision of precipitation rate measurement
Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA
Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA
Related authors
Thomas D. DeWitt, Timothy J. Garrett, and Karlie N. Rees
EGUsphere, https://doi.org/10.5194/egusphere-2025-3486, https://doi.org/10.5194/egusphere-2025-3486, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Clouds appear chaotic, but they in fact follow fractal mathematical patterns similar to coastlines. We measured their fractal properties using satellite images and found two key numbers that describe cloud shapes: one for how rough individual cloud edges are, and another for how clouds of different sizes organize together. We recommend methodology that provides objective ways to verify whether climate models accurately simulate real clouds.
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Karlie N. Rees, Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, https://doi.org/10.5194/acp-21-14235-2021, 2021
Short summary
Short summary
Accurate predictions of weather and climate require descriptions of the mass and density of snowflakes as a function of their size. Few measurements have been obtained to date because snowflakes are so small and fragile. This article describes results from a new instrument that automatically measures individual snowflake size, mass, and density. Key findings are that small snowflakes have much lower densities than is often assumed and that snowflake density increases with temperature.
Thomas D. DeWitt, Timothy J. Garrett, and Karlie N. Rees
EGUsphere, https://doi.org/10.5194/egusphere-2025-3486, https://doi.org/10.5194/egusphere-2025-3486, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Clouds appear chaotic, but they in fact follow fractal mathematical patterns similar to coastlines. We measured their fractal properties using satellite images and found two key numbers that describe cloud shapes: one for how rough individual cloud edges are, and another for how clouds of different sizes organize together. We recommend methodology that provides objective ways to verify whether climate models accurately simulate real clouds.
Spencer Donovan, Dhiraj K. Singh, Timothy J. Garrett, and Eric R. Pardyjak
EGUsphere, https://doi.org/10.5194/egusphere-2025-3060, https://doi.org/10.5194/egusphere-2025-3060, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Accurate snowfall prediction requires quantifying how snowflakes interact with atmospheric turbulence. Using field-based imaging techniques, we directly measured the mass, size, density, and fall speed of snowflakes in surface-layer turbulence. We found that turbulence and microstructure jointly modulate fall speed, often deviating from the terminal velocity in still air. These results inform new parameterizations for numerical weather and climate models.
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 17, 4581–4598, https://doi.org/10.5194/amt-17-4581-2024, https://doi.org/10.5194/amt-17-4581-2024, 2024
Short summary
Short summary
Accurate measurements of the properties of snowflakes are challenging to make. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), is shown to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density. The method exploits the rate of heat transfer during the melting of a snowflake on a hotplate.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
Short summary
Short summary
There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Timothy J. Garrett, Matheus R. Grasselli, and Stephen Keen
Earth Syst. Dynam., 13, 1021–1028, https://doi.org/10.5194/esd-13-1021-2022, https://doi.org/10.5194/esd-13-1021-2022, 2022
Short summary
Short summary
Current world economic production is rising relative to energy consumption. This increase in
production efficiencysuggests that carbon dioxide emissions can be decoupled from economic activity through technological change. We show instead a nearly fixed relationship between energy consumption and a new economic quantity, historically cumulative economic production. The strong link to the past implies inertia may play a more dominant role in societal evolution than is generally assumed.
Dhiraj K. Singh, Spencer Donovan, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, https://doi.org/10.5194/amt-14-6973-2021, 2021
Short summary
Short summary
This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.
Karlie N. Rees, Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, https://doi.org/10.5194/acp-21-14235-2021, 2021
Short summary
Short summary
Accurate predictions of weather and climate require descriptions of the mass and density of snowflakes as a function of their size. Few measurements have been obtained to date because snowflakes are so small and fragile. This article describes results from a new instrument that automatically measures individual snowflake size, mass, and density. Key findings are that small snowflakes have much lower densities than is often assumed and that snowflake density increases with temperature.
Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021, https://doi.org/10.5194/amt-14-1127-2021, 2021
Short summary
Short summary
Snow measurements are very sensitive to wind. Here, we compare airflow and snowfall simulations to Arctic observations for a Multi-Angle Snowflake Camera to show that measurements of fall speed, orientation, and size are accurate only with a double wind fence and winds below 5 m s−1. In this case, snowflakes tend to fall with a nearly horizontal orientation; the largest flakes are as much as 5 times more likely to be observed. Adjustments are needed for snow falling in naturally turbulent air.
Cited articles
Andrey, J. and Yagar, S.: A temporal analysis of rain-related crash risk,
Accident Anal. Prev., 25, 465–472,
https://doi.org/10.1016/0001-4575(93)90076-9, 1993. a
Andrey, J. C. and Mills, B. E.: Collisions, Casualties, and Costs: Weathering
the Elements on Canadian Roads, Institute for Catastrophic Loss Reduction
Canada, 2003. a
Bartholomew, M. J.: Laser Disdrometer Instrument Handbook, Tech. rep., Department of Energy Atmospheric Radiation Measurement Program, available at: https://www.arm.gov/publications/tech_reports/handbooks/\\ldis_handbook.pdf (last access: 3 March 2020), 2014. a
Battaglia, A., Rustemeier, E., Tokay, A., Blahak, U., and Simmer, C.: PARSIVEL
snow observations: A critical assessment, J. Atmos. Ocean.
Tech., 27, 333–344, https://doi.org/10.1175/2009JTECHA1332.1,
2010. a
Bergel-Hayat, R., Debbarh, M., Antoniou, C., and Yannis, G.: Explaining the
road accident risk: Weather effects, Accident Anal. Prev., 60,
456–465, https://doi.org/10.1016/j.aap.2013.03.006, 2013. a
Brandes, E. A., Ikeda, K., Zhang, G., Schönhuber, M., and Rasmussen, R. M.:
A Statistical and Physical Description of Hydrometeor Distributions in
Colorado Snowstorms Using a Video Disdrometer, J. Appl.
Meteorol. Clim., 46, 634–650, https://doi.org/10.1175/JAM2489.1, 2007. a
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate
snow-cover stratigraphy for operational avalanche forecasting, J.
Glaciol., 38, 13–22, https://doi.org/10.3189/S0022143000009552, 1992. a
Campbell, J. F. and Langevin, A.: Operations management for urban snow removal
and disposal, Transportation Res. A-Pol., 29, 359–370, https://doi.org/10.1016/0965-8564(95)00002-6, 1995. a
Colle, B. A., Garvert, M. F., Wolfe, J. B., Mass, C. F., and Woods, C. P.: The
13–14 December 2001 IMPROVE-2 Event. Part III: Simulated
Microphysical Budgets and Sensitivity Studies, J. Atmos.
Sci., 62, 3535–3558, 2005. a
Estévez, J., Gavilán, P., and García-Marín, A. P.: Data validation procedures in agricultural meteorology – a prerequisite for their use, Adv. Sci. Res., 6, 141–146, https://doi.org/10.5194/asr-6-141-2011, 2011. a
Fulton, R. A., Breidenbach, J. P., Seo, D.-J., Miller, D. A., and O'Bannon, T.:
The WSR-88D Rainfall Algorithm, Weather Forecast., 13,
377–395, 1998. a
Garrett, T. J.: Analytical solutions for precipitation size distributions at
steady-state, J. Atmos. Sci., 76, 1031–1037,
https://doi.org/10.1175/JAS-D-18-0309.1, 2019. a
Gultepe, I.: Measurements of light rain, drizzle and heavy fog, in:
Precipitation: Advances in measurement, estimation and prediction,
Springer, 59–82, 2008. a
Horel, J., Splitt, M., Dunn, L., Pechmann, J., White, B., Ciliberti, C.,
Lazarus, S., Slemmer, J., Zaff, D., and Burks, J.: Mesowest: Cooperative
Mesonets in the Western United States, B. Am.
Meteorol. Soc., 83, 211–226, 2002. a
Jameson, A. R. and Kostinski, A. B.: Fluctuation Properties of Precipitation.
Part V: Distribution of Rain Rates–Theory and Observations in
Clustered Rain, J. Atmos. Sci., 56, 3920–3932, 1999. a
Jameson, A. R. and Kostinski, A. B.: Reconsideration of the physical and
empirical origins of Z–R relations in radar meteorology, Q.
J. Roy. Meteor. Soc., 127, 517–538,
2001a. a
Jameson, A. R. and Kostinski, A. B.: What is a Raindrop Size
Distribution?, B. Am. Meteorol. Soc., 82,
1169–1178, 2001b. a
Jameson, A. R. and Kostinski, A. B.: Spurious power-law relations among
rainfall and radar parameters, Q. J. Roy. Meteor.
Soc., 128, 2045–2058, 2002. a
Joss, J. and Waldvogel, A.: Ein spektrograph für niederschlagstropfen mit
automatischer auswertung, Pure Appl. Geophys., 68, 240–246, 1967. a
Kummerow, C., Simpson, J., Thiele, O., Barnes, W., Chang, A. T. C., Stocker,
E., Adler, R. F., Hou, A., Kakar, R., Wentz, F., Ashcroft, P., Kozu, T.,
Hong, Y., Okamoto, K., Iguchi, T., Kuroiwa, H., Im, E., Haddad, Z., Huffman,
G., Ferrier, B., Olson, W. S., Zipser, E., Smith, E. A., Wilheit, T. T.,
North, G., Krishnamurti, T., and Nakamura, K.: The Status of the Tropical
Rainfall Measuring Mission (TRMM) after Two Years in Orbit, J.
Appl. Meteorol., 39, 1965–1982, 2000. a
Lamb, D. and Verlinde, J.: Physics and Chemistry of Clouds, Cambridge
University Press, 2011. a
Lane, J. E., Kasparis, T., Metzger, P. T., and Jones, W. L.: Spatial and
Temporal Extrapolation of Disdrometer Size Distributions Based on a
Lagrangian Trajectory Model of Falling Rain, Open Atmos. Sci.
J., 3, 172–186, https://doi.org/10.2174/1874282300903010172, 2009. a
Liu, X., Gao, T., Hu, Y., and Shu, X.: Measuring hydrometeors using a
precipitation microphysical characteristics sensor: Sampling effect of
different bin sizes on drop size distribution parameters, Adv.
Meteorol., 2018, 1–15, https://doi.org/10.1155/2018/9727345, 2018. a
Liu, X. C., Gao, T. C., and Liu, L.: Effect of sampling variation on error of rainfall variables measured by optical disdrometer, Atmos. Meas. Tech. Discuss., 5, 8895–8924, https://doi.org/10.5194/amtd-5-8895-2012, 2012. a
Marshall, J. S. and Palmer, W.: The distribution of raindrops with size,
J. Meteorol., 5, 165–166, 1948. a
Marshall, J. S., Langille, R. C., and Palmer, W. M. K.: Measurement of Rainfall
by Radar, J. Meteorol., 4, 186–192, 1947. a
Miller, P. A. and Barth, M. F.: Ingest, integration, quality control, and
distribution of observations from state transportation departments using
MADIS, in: 19th International Conference on Interactive Information and
Processing Systems, 2003. a
Pollock, M. D., O'Donnell, G., Quinn, P., Dutton, M., Black, A., Wilkinson,
M. E., Colli, M., Stagnaro, M., Lanza, L. G., Lewis, E., Kilsby, C. G., and
O'Connell, P. E.: Quantifying and Mitigating Wind-Induced Undercatch in
Rainfall Measurements, Water Resour. Res., 54, 3863–3875,
https://doi.org/10.1029/2017WR022421,
2018. a
Rasmussen, R. M., Hallett, J., Purcell, R., Landolt, S. D., and Cole, J.: The
Hotplate Precipitation Gauge, J. Atmos. Ocean. Tech.,
28, 148–164, https://doi.org/10.1175/2010JTECHA1375.1, 2010. a, b
Rees, K. N., Singh, D. K., Pardyjak, E. R., and Garrett, T. J.: Mass and density of individual frozen hydrometeors and A differential emissivity imaging technique for measuring hydrometeors, The Hive: University of Utah Research Data Repository [data set], https://doi.org/10.7278/S50D-SPT1-FNHH, 2021. a
Singh, D. K., Donovan, S., Pardyjak, E. R., and Garrett, T. J.: A differential emissivity imaging technique for measuring hydrometeor mass and type, Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, 2021. a, b, c, d
Smith, R. B., Jiang, Q., Fearon, M. G., Tabary, P., Dorninger, M., Doyle,
J. D., and Benoit, R.: Orographic precipitation and air mass transformation:
An Alpine example, Q. J. Roy. Meteor. Soc.,
129, 433–454, https://doi.org/10.1256/qj.01.212,
2003. a
Theofilatos, A. and Yannis, G.: A review of the effect of traffic and weather
characteristics on road safety, Accident Anal. Prev., 72,
244–256, https://doi.org/10.1016/j.aap.2014.06.017, 2014. a
Thurai, M., Petersen, W. A., Tokay, A., Schultz, C., and Gatlin, P.: Drop size distribution comparisons between Parsivel and 2-D video disdrometers, Adv. Geosci., 30, 3–9, https://doi.org/10.5194/adgeo-30-3-2011, 2011. a
Tokay, A., Petersen, W. A., Gatlin, P., and Wingo, M.: Comparison of Raindrop
Size Distribution Measurements by Collocated Disdrometers, J.
Atmos. Ocean. Tech., 30, 1672–1690,
https://doi.org/10.1175/JTECH-D-12-00163.1, 2013. a
Ulbrich, C. W. and Atlas, D.: Assessment of the contribution of differential polarization to improved rainfall measurements, Radio Science, 19, 49–57, https://doi.org/10.1029/RS019i001p00049, 1984. a, b, c
Vaisala: Present Weather Detector PWD52 B211065EN-C, datasheet, available at: https://www.vaisala.com/sites/default/files/documents/\\PWD52-Datasheet-B211065EN.pdf (last access: 3 March 2020), 2018a. a
Vaisala: MAWS201M Datasheet B210730EN-F, datasheet, available at: https://www.vaisala.com/sites/default/files/documents/MAWS201-Datasheet-B211006EN-D.pdf
(last access: 28 November 2021), 2018b. a
Vaisala: Forward Scatter FD70 Series B211744EN-C, datasheet, available at: https://www.vaisala.com/sites/default/files/documents/\\FD70-Series-Datasheet-B211744EN.pdf (last access: 3 March 2020), 2019a. a
Villermaux, E. and Bossa, B.: Single-drop fragmentation determines size
distribution of raindrops, Nature Phys., 5, 697–702, 2009. a
Wade, C. G.: A Multisensor Approach to Detecting Drizzle on ASOS, J.
Atmos. Ocean. Tech., 20, 820–832, 2003. a
Yankee Environmental Systems, I.: TPS-3100 Total Precipitation Sensor,
Installation and User Guide, Version 2.0, Rev M, 2011. a
Short summary
Monte Carlo simulations are used to establish baseline precipitation measurement uncertainties according to World Meteorological Organization standards. Measurement accuracy depends on instrument sampling area, time interval, and precipitation rate. Simulations are compared with field measurements taken by an emerging hotplate precipitation sensor. We find that the current collection area is sufficient for light rain, but a larger collection area is required to detect moderate to heavy rain.
Monte Carlo simulations are used to establish baseline precipitation measurement uncertainties...
