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Measurement and Modeling of the Precipitation Particle Size Distribution

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (15 June 2020) | Viewed by 38816

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Special Issue Editors

Dr. Patrick Gatlin

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Guest Editor

NASA Marshall Space Flight Center, AL 35808, USA
Interests: precipitation microphysics; microwave remote sensing; lightning

Dr. Merhala Thurai

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Guest Editor

Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
Interests: rain microstructure; polarimetric weather radar; electromagnetic scattering of rain

Dr. Christopher Williams

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Guest Editor

Research Professor, Colorado Center for Astrodynamics Research (CCAR), Ann and H.J. Smead Aerospace Engineering Sciences Department, University of Colorado at Boulder, Boulder, CO 80303, USA
Interests: radar; precipitation microphysics; cloud dynamics

Dr. Elisa Adirosi

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Guest Editor

National Research Council of Italy—Institute of Atmospheric Sciences and Climate (CNR—ISAC), 7, 00185 Roma, Italy
Interests: ground validation studies of precipitation; disdrometers and particle size distributions; retrieval techniques from radar and in situ devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Atmosphere dedicates this Special Issue to the precipitation particle size distribution (PSD). PSD is the fundamental metric that describes rain and snow. Knowledge of how raindrops and snowflakes as well as other hydrometeors are distributed within the atmosphere enables more precise hydrometeorological forecasts, more accurate remote sensing, and better characterization of their erosive effect on soil and materials. The measurement and modeling of precipitation particles dates to the mid-20th century, with much of this research scattered through various publications. This Special Issue brings together research on the PSD of both rain and snow, highlighting some key advances made in their measurement and modeling in the past decade, with a particular focus on remote sensing and cloud-resolving models.

Since precipitation plays a vital role within the Earth system, its depiction in remote sensing and numerical weather prediction is of great importance to better understanding and predicting weather and climate. Hence, we invite you to contribute articles to this Special Issue by reporting on current research entailing the measurement of precipitation particle sizes, both in situ and via remote sensing, as well as modeling of the precipitation PSD, including its representation by statistical models and parameterization in cloud-resolving models.

Dr. Patrick Gatlin
Dr. Merhala Thurai
Dr. Christopher Williams
Dr. Elisa Adirosi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Precipitation
Microphysics
Disdrometer
Radar
Numerical weather prediction
Cloud-resolving models

Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 169 KiB

Open AccessEditorial

Measurement and Modeling of the Precipitation Particle Size Distribution

by Patrick N. Gatlin, Merhala Thurai, Christopher Williams and Elisa Adirosi

Atmosphere 2021, 12(7), 819; https://doi.org/10.3390/atmos12070819 - 26 Jun 2021

Viewed by 1503

Abstract

Precipitation plays a vital role within the Earth system [...] Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

Research

Jump to: Editorial

14 pages, 5159 KiB

Open AccessArticle

Characteristics of Snow Particle Size Distribution in the PyeongChang Region of South Korea

by Tiantian Yu, V. Chandrasekar, Hui Xiao and Shashank S. Joshil

Atmosphere 2020, 11(10), 1093; https://doi.org/10.3390/atmos11101093 - 13 Oct 2020

Cited by 8 | Viewed by 2696

Abstract

Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel² disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow (

ρ

), snowfall rate (

S

), and ice water content (

I W C

) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors (

D_{m}

l o g_{10} N_{w}

, and

μ

) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The

D_{m}

decreases and

l o g_{10} N_{w}

and μ increase as the density increases. The

D_{m}

and

l o g_{10} N_{w}

increase and μ decreases with the increase of snowfall rate. The power-law relationship between

ρ

and

D_{m}

is obtained and the relationship between

S

and

I W C

is also derived. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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14 pages, 4195 KiB

Open AccessEditor’s ChoiceArticle

The GPM Validation Network and Evaluation of Satellite-Based Retrievals of the Rain Drop Size Distribution

by Patrick N. Gatlin, Walter A. Petersen, Jason L. Pippitt, Todd A. Berendes, David B. Wolff and Ali Tokay

Atmosphere 2020, 11(9), 1010; https://doi.org/10.3390/atmos11091010 - 21 Sep 2020

Cited by 27 | Viewed by 3941

Abstract

A unique capability of the Global Precipitation Measurement (GPM) mission is its ability to better estimate the raindrop size distribution (DSD) on a global scale. To validate the GPM DSD retrievals, a network of more than 100 ground-based polarimetric radars from across the globe are utilized within the broader context of the GPM Validation Network (VN) processing architecture. The GPM VN ensures quality controlled dual-polarimetric radar moments for use in providing reference estimates of the DSD. The VN DSD estimates are carefully geometrically matched with the GPM core satellite measurements for evaluation of the GPM algorithms. We use the GPM VN to compare the DSD retrievals from the GPM’s Dual-frequency Precipitation Radar (DPR) and combined DPR–GPM Microwave Imager (GMI) Level-2 algorithms. Results suggested that the Version 06A GPM core satellite algorithms provide estimates of the mass-weighted mean diameter (D_m) that are biased 0.2 mm too large when considered across all precipitation types. In convective precipitation, the algorithms tend to overestimate D_m by 0.5–0.6 mm, leading the DPR algorithm to underestimate the normalized DSD intercept parameter (N_w) by a factor of two, and introduce a significant bias to the DPR retrievals of rainfall rate for DSDs with large D_m. The GPM Combined algorithm performs better than the DPR algorithm in convection but provides a severely limited range of N_w estimates, highlighting the need to broaden its a priori database in convective precipitation. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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25 pages, 741 KiB

Open AccessArticle

Refinements to Data Acquired by 2-Dimensional Video Disdrometers

by Michael L. Larsen and Christopher K. Blouin

Atmosphere 2020, 11(8), 855; https://doi.org/10.3390/atmos11080855 - 13 Aug 2020

Cited by 3 | Viewed by 2197

Abstract

The 2-Dimensional Video Disdrometer (2DVD) is a commonly used tool for exploring rain microphysics and for validating remotely sensed rain retrievals. Recent work has revealed a persistent anomaly in 2DVD data. Early investigations of this anomaly concluded that the resulting errors in rain measurement were modest, but the methods used to flag anomalous data were not optimized, and related considerations associated with the sample sensing area were not fully investigated. Here, we (i) refine the anomaly-detecting algorithm for increased sensitivity and reliability and (ii) develop a related algorithm for refining the estimate of sample sensing area for all detected drops, including those not directly impacted by the anomaly. Using these algorithms, we explore the corrected data to measure any resulting changes to estimates of bulk rainfall statistics from two separate 2DVDs deployed in South Carolina combining for approximately 10 total years of instrumental uptime. Analysis of this data set consisting of over 200 million drops shows that the error induced in estimated total rain accumulations using the manufacturer-reported area is larger than the error due to considerations related to the anomaly. The algorithms presented here imply that approximately 4.2% of detected drops are spurious and the mean reported effective sample area for drops believed to be correctly detected is overestimated by ~8.5%. Simultaneously accounting for all of these effects suggests that the total accumulated rainfall in the data record is approximately 1.1% larger than the raw data record suggests. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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27 pages, 7611 KiB

Open AccessArticle

The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow

by Claire Pettersen, Larry F. Bliven, Annakaisa von Lerber, Norman B. Wood, Mark S. Kulie, Marian E. Mateling, Dmitri N. Moisseev, S. Joseph Munchak, Walter A. Petersen and David B. Wolff

Atmosphere 2020, 11(8), 785; https://doi.org/10.3390/atmos11080785 - 24 Jul 2020

Cited by 25 | Viewed by 5157

Abstract

Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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18 pages, 11862 KiB

Open AccessArticle

Characteristics of Orographic Rain Drop-Size Distribution at Cherrapunji, Northeast India

by Fumie Murata, Toru Terao, Kaustav Chakravarty, Hiambok Jones Syiemlieh and Laitpharlang Cajee

Atmosphere 2020, 11(8), 777; https://doi.org/10.3390/atmos11080777 - 23 Jul 2020

Cited by 17 | Viewed by 3494

Abstract

The rain drop size distribution (DSD) at Cherrapunji, Northeast India was observed by a laser optical disdrometer Parsivel

^{2}

from May to October 2017; this town is known for the world’s heaviest orographic rainfall recorded. The disdrometer showed a 30% underestimation of the [...] Read more.

The rain drop size distribution (DSD) at Cherrapunji, Northeast India was observed by a laser optical disdrometer Parsivel

^{2}

from May to October 2017; this town is known for the world’s heaviest orographic rainfall recorded. The disdrometer showed a 30% underestimation of the rainfall amount, compared with a collocated rain gauge. The observed DSD had a number of drops with a mean normalized intercept log

_{10} N_{w} > 4.0

for all rain rate categories, ranging from <5 to >80 mm h

^{- 1}

, comparable to tropical oceanic DSDs. These results differ from those of tropical oceanic DSDs, in that data with a larger

N_{w}

were confined to the stratiform side of a stratiform/convective separation line proposed by Bringi et al. (2009). A large number of small drops is important for quantitative precipitation estimates by in-situ radar and satellites, because it tends to miss or underestimate precipitation amounts. The large number of small drops, as defined by the second principal component (>+1.5) while using the principal component analysis approach of Dolan et al. (2018), was rare for the pre-monsoon season, but was prevalent during the monsoon season, accounting for 16% (19%) of the accumulated rainfall (precipitation period); it tended to appear over weak active spells or the beginning of active spells of intraseasonal variation during the monsoon season. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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18 pages, 5188 KiB

Open AccessArticle

Disdrometer, Polarimetric Radar, and Condensation Nuclei Observations of Supercell and Multicell Storms on 11 June 2018 in Eastern Nebraska

by Matthew Van Den Broeke

Atmosphere 2020, 11(7), 770; https://doi.org/10.3390/atmos11070770 - 21 Jul 2020

Cited by 3 | Viewed by 2236

Abstract

Disdrometer and condensation nuclei (CN) data are compared with operational polarimetric radar data for one multicell and one supercell storm in eastern Nebraska on 11 June 2018. The radar was located ~14.3 km from the instrumentation location and provided excellent observation time series with new low-level samples every 1–2 min. Reflectivity derived by the disdrometer and radar compared well, especially in regions with high number concentration of drops and reflectivity <45 dBZ. Differential reflectivity also compared well between the datasets, though it was most similar in the supercell storm. Rain rate calculated by the disdrometer closely matched values estimated by the radar when reflectivity and differential reflectivity were used to produce the estimate. Concentration of CN generally followed precipitation intensity for the leading convective cell, with evidence for higher particle concentration on the edges of the convective cell associated with outflow. The distribution of CN in the supercell was more complex and generally did not follow precipitation intensity. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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16 pages, 2247 KiB

Open AccessArticle

Dependence of Mass–Dimensional Relationships on Median Mass Diameter

by Saisai Ding, Greg M. McFarquhar, Stephen W. Nesbitt, Randy J. Chase, Michael R. Poellot and Hongqing Wang

Atmosphere 2020, 11(7), 756; https://doi.org/10.3390/atmos11070756 - 17 Jul 2020

Cited by 13 | Viewed by 3201

Abstract

Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form

m = a D^{b}

are widely used and usually universally applied across the complete range of particle sizes. For [...] Read more.

Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form

m = a D^{b}

are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (D_mm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, D_mm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference

χ^{2}

that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on D_mm are shown with both parameters significantly decreasing with D_mm, leading to smaller effective densities for larger D_mm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when D_mm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different D_mm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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15 pages, 4158 KiB

Open AccessArticle

Characteristics of DSD Bulk Parameters: Implication for Radar Rain Retrieval

by Liang Liao, Robert Meneghini, Toshio Iguchi and Ali Tokay

Atmosphere 2020, 11(6), 670; https://doi.org/10.3390/atmos11060670 - 25 Jun 2020

Cited by 16 | Viewed by 3074

Abstract

With the use of 213,456 one-minute measured data of droplet-size distribution (DSD) of rain collected during several National Aeronautics and Space Administration (NASA)-sponsored field campaigns, the relationships between rainfall rate R, mass-weighted diameter D_m and normalized intercept parameter N_w of the gamma DSD are studied. It is found, based on the simulations of the gamma DSD model, that R, D_m and N_w are closely interrelated, and that the ratio of R to N_w is solely a function of D_m, independent of the shape factor μ of the gamma distribution. Furthermore, the model-produced ratio agrees well with those from the DSD data. When a power-law equation is applied to fit the model data, we have:

R = {aN}_{w} D_{m}^{b}

, where

a = 1.588 \times 10^{- 4}

b = 4.706

. Analysis of two-parameter relationships such as R–D_m, N_w–R and N_w–D_m reveals that R and D_m are moderately correlated while N_w and D_m are negatively correlated. N_w and R, however, are uncorrelated. The gamma DSD model also reveals that variation of R–D_m relation is caused primarily by N_w. For the application of the Ku- and Ka-band dual-frequency radar for the retrieval of the DSD bulk parameters as well as the specific radar attenuations, the study is carried out to relate the dual-frequency radar reflectivity factors to the DSD and attenuation parameters. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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21 pages, 5451 KiB

Open AccessArticle

Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow

by Randy J. Chase, Stephen W. Nesbitt and Greg M. McFarquhar

Atmosphere 2020, 11(6), 619; https://doi.org/10.3390/atmos11060619 - 11 Jun 2020

Cited by 27 | Viewed by 4712

Abstract

The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (R) and the mass weighted mean diameter (

D_{m}

) with a mean absolute percent error (

M A P E

) on R of

29%

and

47%

and a mean bias percentage (

M B P

) of

- 6%

and

- 20%

for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of R, at

77%

and

- 52%

, respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of R and

D_{m}

illustrates that the variability found in hydrometeor mass causes a poor correlation between R and

D_{m}

in snowfall. The results presented here suggest that

R - D_{m}

retrieval is likely not optimal in snowfall, and other retrieval techniques for R should be explored. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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15 pages, 4916 KiB

Open AccessArticle

Drop Size Distribution Measurements in Outer Rainbands of Hurricane Dorian at the NASA Wallops Precipitation-Research Facility

by Merhala Thurai, Viswanathan N. Bringi, David B. Wolff, David A. Marks and Charanjit S. Pabla

Atmosphere 2020, 11(6), 578; https://doi.org/10.3390/atmos11060578 - 01 Jun 2020

Cited by 11 | Viewed by 3100

Abstract

Hurricane rainbands are very efficient rain producers, but details on drop size distributions are still lacking. This study focuses on the rainbands of hurricane Dorian as they traversed the densely instrumented NASA precipitation-research facility at Wallops Island, VA, over a period of 8 h. Drop size distribution (DSD) was measured using a high-resolution meteorological particle spectrometer (MPS) and 2D video disdrometer, both located inside a double-fence wind shield. The shape of the DSD was examined using double-moment normalization, and compared with similar shapes from semiarid and subtropical sites. Dorian rainbands had a superexponential shape at small normalized diameter values similar to those of the other sites. NASA’s S-band polarimetric radar performed range height-indicator (RHI) scans over the disdrometer site, showing some remarkable signatures in the melting layer (bright-band reflectivity peaks of 55 dBZ, a dip in the copolar correlation to 0.85 indicative of 12–15 mm wet snow, and a staggering reflectivity gradient above the 0 °C level of −10 dB/km, indicative of heavy aggregation). In the rain layer at heights < 2.5 km, polarimetric signatures indicated drop break-up as the dominant process, but drops as large as 5 mm were detected during the intense bright-band period. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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11 pages, 816 KiB

Open AccessArticle

Large Sample Comparison of Parameter Estimates in Gamma Raindrop Distributions

by Roger W. Johnson and Donna V. Kliche

Atmosphere 2020, 11(4), 333; https://doi.org/10.3390/atmos11040333 - 29 Mar 2020

Cited by 3 | Viewed by 2313

Abstract

Raindrop size distributions have been characterized through the gamma family. Over the years, quite a few estimates of these gamma parameters have been proposed. The natural question for the practitioner, then, is what estimation procedure should be used. We provide guidance in answering this question when a large sample size (>2000 drops) of accurately measured drops is available. Seven estimation procedures from the literature: five method of moments procedures, maximum likelihood, and a pseudo maximum likelihood procedure, were examined. We show that the two maximum likelihood procedures provide the best precision (lowest variance) in estimating the gamma parameters. Method of moments procedures involving higher-order moments, on the other hand, give rise to poor precision (high variance) in estimating these parameters. A technique called the delta method assisted in our comparison of these various estimation procedures. Full article

(This article belongs to the Special Issue Measurement and Modeling of the Precipitation Particle Size Distribution)

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