Numerical Simulations for the Warm Rain Properties during RICO with a Newly Developed Triple-moment Warm Cloud Scheme

2020 ◽  
Author(s):  
Wei Deng
Keyword(s):  
Size Distribution ◽  
Water Drop ◽  
Cloud Droplet ◽  
Condensation Process ◽  
Observation Study ◽  
Warm Cloud ◽  
Raindrop Size Distribution ◽  
Double Moment ◽  
Simulation Results ◽  
Raindrop Size

<p>    Double-moment schemes with a constant shape parameter cannot describe the condensation process and the collision coalescence processes properly. Evolutions of cloud droplet spectra and raindrop spectra simulated with different current bulk microphysical schemes also showed big differences. The newly developed triple-moment scheme (IAP-LACS scheme) considered the variation of the shape parameters of water drop distributions by means of the radar reflectivities of cloud droplets and raindrops, respectively, during the condensation process and collision-coalescence processes. In order to evaluate the performance of our new scheme, we use large-eddy simulation in WRF to research the precipitation formation in Rain in Cumulus over the Ocean (RICO) observation study with new triple-moment warm cloud scheme. This paper will show the simulation results for the microphysical characteristic, specical for the evolution of warm raindrop size distribution in comparison with aircarft measurement. Our simulations show that our new triple-moment scheme can grasp the main characteristic of raindrop size distribution as observation and there must be difference exsiting in simulation results between new scheme and other microphysical bulk schemes.</p>

scholarly journals Invariance of the Double-Moment Normalized Raindrop Size Distribution through 3D Spatial Displacement in Stratiform Rain

2017 ◽  
Vol 56 (6) ◽  
pp. 1663-1680 ◽  
Author(s):  
Timothy H. Raupach ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Vertical Displacement ◽  
Statistical Moments ◽  
Stratiform Rain ◽  
Vertical Range ◽  
Raindrop Size Distribution ◽  
Double Moment ◽  
Raindrop Size ◽  
Polarimetric Weather Radar ◽  
Using Data

AbstractDouble-moment normalization of the drop size distribution (DSD) summarizes the DSD in a compact way, using two of its statistical moments and a “generic” double-moment normalized DSD function. Results are presented of an investigation into the invariance of the double-moment normalized DSD through horizontal and vertical displacement in space, using data from disdrometers, vertically pointing K-band Micro Rain Radars, and an X-band polarimetric weather radar. The invariance of the double-moment normalized DSD is tested over a vertical range of up to 1.8 km and a horizontal range of up to approximately 100 km. The results suggest that for practical use, with well-chosen input moments, the double-moment normalized DSD can be assumed invariant in space in stratiform rain. The choice of moments used to characterize the DSD affects the amount of DSD variability captured by the normalization. It is shown that in stratiform rain, it is possible to capture more than 85% of the variability in DSD moments zero to seven using the technique. Most DSD variability in stratiform rain can thus be explained through the variability of two of its statistical moments. The results suggest similar behavior exists in transition and convective rain, but the limited data samples available do not allow for robust conclusions for these rain types. The results have implications for practical uses of double-moment DSD normalization, including the study of DSD variability and microphysics, DSD-retrieval algorithms, and DSD models used in rainfall retrieval.

scholarly journals Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

2016 ◽  
Author(s):  
Timothy H. Raupach ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Climatic Regions ◽  
Retrieval Technique ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Double Moment ◽  
Raindrop Size ◽  
A New Technique

Abstract. A new technique for estimating the raindrop size distribution (DSD) from polarimetric radar data is proposed. Two statistical moments of the DSD are estimated from polarimetric variables, and the DSD is reconstructed. The technique takes advantage of the relative invariance of the double-moment normalised DSD. The method was tested using X-band radar data and networks of disdrometers in three different climatic regions. Radar-derived estimates of the DSD compare reasonably well to observations. In the three tested domains, the proposed method performs similarly to and often better than a state-of-the-art DSD-retrieval technique. The approach is flexible because no specific double-normalised DSD model is prescribed. In addition, a method is proposed to treat noisy radar data to improve DSD-retrieval performance with radar measurements.

scholarly journals Measurements and Modeling of the Full Rain Drop Size Distribution

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 39 ◽  
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
Patrick Gatlin ◽  
Walter Petersen ◽  
Matthew Wingo
Keyword(s):  
Size Distribution ◽  
Small Drop ◽  
Dual Frequency ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Rain Drop ◽  
Double Moment ◽  
Raindrop Size ◽  
Microphysical Processes ◽  
Global Precipitation

The raindrop size distribution (DSD) is fundamental for quantitative precipitation estimation (QPE) and in numerical modeling of microphysical processes. Conventional disdrometers cannot capture the small drop end, in particular the drizzle mode which controls collisional processes as well as evaporation. To overcome this limitation, the DSD measurements were made using (i) a high-resolution (50 microns) meteorological particle spectrometer to capture the small drop end, and (ii) a 2D video disdrometer for larger drops. Measurements were made in two climatically different regions, namely Greeley, Colorado, and Huntsville, Alabama. To model the DSDs, a formulation based on (a) double-moment normalization and (b) the generalized gamma (GG) model to describe the generic shape with two shape parameters was used. A total of 4550 three-minute DSDs were used to assess the size-resolved fidelity of this model by direct comparison with the measurements demonstrating the suitability of the GG distribution. The shape stability of the normalized DSD was demonstrated across different rain types and intensities. Finally, for a tropical storm case, the co-variabilities of the two main DSD parameters (normalized intercept and mass-weighted mean diameter) were compared with those derived from the dual-frequency precipitation radar onboard the global precipitation mission satellite.

scholarly journals Aerosol impacts on warm-cloud microphysics and drizzle in a moderately polluted environment

2021 ◽  
Vol 21 (6) ◽  
pp. 4487-4502
Author(s):  
Ying-Chieh Chen ◽  
Sheng-Hsiang Wang ◽  
Qilong Min ◽  
Sarah Lu ◽  
Pay-Liam Lin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Indirect Effects ◽  
Cloud Microphysics ◽  
Cloud Water ◽  
Aerosol Indirect Effects ◽  
Low Level ◽  
Warm Cloud ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Northern Taiwan

Abstract. Climate is critically affected by aerosols, which alter cloud lifecycles and precipitation distribution through radiative and microphysical effects. In this study, aerosol and cloud property datasets from MODIS (Moderate Resolution Imaging Spectroradiometer), onboard the Aqua satellite, and surface observations, including aerosol concentrations, raindrop size distribution, and meteorological parameters, were used to statistically quantify the effects of aerosols on low-level warm-cloud microphysics and drizzle over northern Taiwan during multiple fall seasons (from 15 October to 30 November of 2005–2017). Our results indicated that northwestern Taiwan, which has several densely populated cities, is dominated by low-level clouds (e.g., warm, thin, and broken clouds) during the fall season. The observed effects of aerosols on warm clouds indicated aerosol indirect effects (i.e., increased aerosol loading caused a decrease in cloud effective radius (CER)), an increase in cloud optical thickness, an increase in cloud fraction, and a decrease in cloud-top temperature under a fixed cloud water path. Quantitatively, aerosol–cloud interactions (ACI=-∂ln⁡CER∂ln⁡α|CWP, changes in CER relative to changes in aerosol amounts) were 0.07 for our research domain and varied between 0.09 and 0.06 in the surrounding remote (i.e., ocean) and polluted (i.e., land) areas, respectively, indicating aerosol indirect effects were stronger in the remote area. From the raindrop size distribution analysis, high aerosol loading resulted in a decreased frequency of drizzle events, redistribution of cloud water to more numerous and smaller droplets, and reduced collision–coalescence rates. However, during light rain (≤1 mm h−1), high aerosol concentrations drove raindrops towards smaller droplet sizes and increased the appearance of drizzle drops. This study used long-term surface and satellite data to determine aerosol variations in northern Taiwan, effects on clouds and precipitation, and observational strategies for future research on aerosol–cloud–precipitation interactions.

scholarly journals WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 36
Author(s):  
Qiqi Yang ◽  
Shuliang Zhang ◽  
Qiang Dai ◽  
Hanchen Zhuang
Keyword(s):  
United Kingdom ◽  
Size Distribution ◽  
Gamma Distribution ◽  
Shape Parameter ◽  
Rainfall Intensity ◽  
Post Processing ◽  
The United Kingdom ◽  
Raindrop Size Distribution ◽  
Double Moment ◽  
Raindrop Size

Raindrop size distribution (RSD) is a key parameter in the Weather Research and Forecasting (WRF) model for rainfall estimation, with gamma distribution models commonly used to describe RSD under WRF microphysical parameterizations. The RSD model sets the shape parameter (μ) as a constant of gamma distribution in WRF double-moment bulk microphysics schemes. Here, we propose to improve the gamma RSD model with an adaptive value of μ based on the rainfall intensity and season, designed using a genetic algorithm (GA) and the linear least-squares method. The model can be described as a piecewise post-processing function that is constant when rainfall intensity is <1.5 mm/h and linear otherwise. Our numerical simulation uses the WRF driven by an ERA-interim dataset with three distinct double-moment bulk microphysical parameterizations, namely, the Morrison, WDM6, and Thompson aerosol-aware schemes for the period of 2013–2017 over the United Kingdom at a 5 km resolution. Observations were made using a disdrometer and 241 rain gauges, which were used for calibration and validation. The results show that the adaptive-μ model of the gamma distribution was more accurate than the gamma RSD model with a constant shape parameter, with the root-mean-square error decreasing by averages of 23.62%, 11.33%, and 22.21% for the Morrison, WDM6, and Thompson aerosol-aware schemes, respectively. This model improves the accuracy of WRF rainfall simulation by applying adaptive RSD parameterization and can be integrated into the simulation of WRF double-moment microphysics schemes. The physical mechanism of the RSD model remains to be determined to improve its performance in WRF bulk microphysics schemes.

scholarly journals Aerosol impacts on warm-cloud microphysics and drizzle in a moderately polluted environment

2020 ◽  
Author(s):  
Ying-Chieh Chen ◽  
Sheng-Hsiang Wang ◽  
Qilong Min ◽  
Sarah Lu ◽  
Pay-Liam Lin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Indirect Effects ◽  
Cloud Microphysics ◽  
Cloud Water ◽  
Aerosol Indirect Effects ◽  
Low Level ◽  
Warm Cloud ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Northern Taiwan

Abstract. Climate is critically affected by aerosols, which can alter cloud lifecycles and precipitation distribution through radiative and microphysical effects. In this study, aerosol and cloud properties datasets from MODIS onboard Aqua satellite and surface observations, including aerosol concentrations, raindrop size distribution, and meteorological parameters, were used to statistically quantify the effects of aerosols on low-level warm cloud microphysics and drizzle over northern Taiwan during fall seasons (from October 15 to November 30 of 2005–2017). Results indicated that clouds in northwestern Taiwan, which with active human activity is dominated by low-level clouds (e.g. warm, thin, and broken clouds). The observed effects of aerosols on warm clouds indicated aerosol indirect effects; increasing aerosol loading caused a decrease in cloud effective radius (CER), an increase in cloud optical thickness, an increase in cloud fraction, and a decrease in cloud top temperature under a fixed cloud water path. A quantitative value of aerosol–cloud interactions (ACI = (δ ln⁡ CER)/(δ  ln⁡ α), changes in CER depend on changes in aerosols) were calculated to be 0.07 for our research domain. ACI values varied between 0.09 and 0.06 in surrounding clean and heavily polluted areas, respectively, which indicated that aerosol indirect effects were more sensitive in the clean area. Analysis of raindrop size distribution observations during high aerosol loading resulted in a decreased frequency of drizzle events, redistributed cloud water to more numerous and smaller droplets, and reduced collision–coalescence rates. However, in the scenario of light precipitation (&amp;leq; 1 mm h−1), high aerosol concentrations drive raindrops towards smaller droplet sizes and increase the appearance of drizzle drops. This study used long-term surface and satellite data to determine aerosol variations in northern Taiwan, effects on the clouds and precipitations, and applications to observational strategy planning for future research on aerosol–cloud–precipitation interactions.

scholarly journals Reconstructing the Drizzle Mode of the Raindrop Size Distribution Using Double-Moment Normalization

2019 ◽  
Vol 58 (1) ◽  
pp. 145-164 ◽  
Author(s):  
Timothy H. Raupach ◽  
Merhala Thurai ◽  
V. N. Bringi ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Rain Rate ◽  
Drop Diameter ◽  
Light Rain ◽  
Raindrop Size Distribution ◽  
Shoulder Region ◽  
Double Moment ◽  
Best Fitting ◽  
Raindrop Size ◽  
Fitting Model

AbstractCommonly used disdrometers tend not to accurately measure concentrations of very small drops in the raindrop size distribution (DSD), either through truncation of the DSD at the small-drop end or because of large uncertainties on these measurements. Recent studies have shown that, as a result of these inaccuracies, many if not most ground-based disdrometers do not capture the “drizzle mode” of precipitation, which consists of large concentrations of small drops and is often separated from the main part of the DSD by a shoulder region. We present a technique for reconstructing the drizzle mode of the DSD from “incomplete” measurements in which the drizzle mode is not present. Two statistical moments of the DSD that are well measured by standard disdrometers are identified and used with a double-moment normalized DSD function that describes the DSD shape. A model representing the double-moment normalized DSD is trained using measurements of DSD spectra that contain the drizzle mode obtained using collocated Meteorological Particle Spectrometer and 2D video disdrometer instruments. The best-fitting model is shown to depend on temporal resolution. The result is a method to estimate, from truncated or uncertain measurements of the DSD, a more complete DSD that includes the drizzle mode. The technique reduces bias on low-order moments of the DSD that influence important bulk variables such as the total drop concentration and mass-weighted mean drop diameter. The reconstruction is flexible and often produces better rain-rate estimations than a previous DSD correction routine, particularly for light rain.

scholarly journals Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

2017 ◽  
Vol 10 (7) ◽  
pp. 2573-2594 ◽  
Author(s):  
Timothy H. Raupach ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Radar Data ◽  
Drop Diameter ◽  
Polarimetric Radar ◽  
Climatic Regions ◽  
Retrieval Technique ◽  
Raindrop Size Distribution ◽  
Double Moment ◽  
Raindrop Size ◽  
A New Technique

Abstract. A new technique for estimating the raindrop size distribution (DSD) from polarimetric radar data is proposed. Two statistical moments of the DSD are estimated from polarimetric variables, and the DSD is reconstructed using a double-moment normalisation. The technique takes advantage of the relative invariance of the double-moment normalised DSD. The method was tested using X-band radar data and networks of disdrometers in three different climatic regions. Radar-derived estimates of the DSD compare reasonably well to observations. In the three tested domains, in terms of DSD moments, rain rate, and characteristic drop diameter, the proposed method performs similarly to and often better than a state-of-the-art DSD-retrieval technique. The approach is flexible because no specific DSD model is prescribed. In addition, a method is proposed to treat noisy radar data to improve DSD-retrieval performance with radar measurements.

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