Impact of Cloud Model Microphysics on Passive Microwave Retrievals of Cloud Properties. Part I: Model Comparison Using EOF Analyses

Abstract The impact of model microphysics on the relationships among hydrometeor profiles, latent heating, and derived satellite microwave brightness temperatures TB have been examined using a nonhydrostatic, adaptive-grid cloud model to simulate a mesoscale convective system over water. Two microphysical schemes (each employing three-ice bulk parameterizations) were tested for two different assumptions in the number of ice crystals assumed to be activated at 0°C to produce simulations with differing amounts of supercooled cloud water. The model output was examined using empirical orthogonal function (EOF) analysis, which provided a quantitative framework in which to compare the simulations. Differences in the structure of the vertical anomaly patterns were related to physical processes and attributed to different approaches in cloud microphysical parameterizations in the two schemes. Correlations between the first EOF coefficients of cloud properties and TB at frequencies associated with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) showed additional differences between the two parameterization schemes that affected the relationship between hydrometeors and TB. Classified in terms of TB, the microphysical schemes produced significantly different mean vertical profiles of cloud water, cloud ice, snow, vertical velocity, and latent heating. The impact of supercooled cloud water on the 85-GHz TB led to a 15% variation in mean convective rain mass at the surface. The variability in mean profiles produced by the four simulations indicates that the retrievals of cloud properties, especially latent heating, based on TMI frequencies are dependent on the particular microphysical parameterizations used to construct the retrieval database.

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Impact of Cloud Model Microphysics on Passive Microwave Retrievals of Cloud Properties. Part II: Uncertainty in Rain, Hydrometeor Structure, and Latent Heating Retrievals

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2374.1 ◽

2006 ◽

Vol 45 (7) ◽

pp. 955-972 ◽

Cited By ~ 13

Author(s):

Eun-Kyoung Seo ◽

Michael I. Biggerstaff

Keyword(s):

Cloud Model ◽

Mesoscale Convective System ◽

Cloud Water ◽

Passive Microwave ◽

Convective System ◽

Latent Heating ◽

Cloud Property ◽

Property Relations ◽

Cloud Properties ◽

The Impact

Abstract The impact of model microphysics on the retrieval of cloud properties based on passive microwave observations was examined using a three-dimensional, nonhydrostatic, adaptive-grid cloud model to simulate a mesoscale convective system over ocean. Two microphysical schemes, based on similar bulk two-class liquid and three-class ice parameterizations, were used to simulate storms with differing amounts of supercooled cloud water typical of both the tropical oceanic environment, in which there is little supercooled cloud water, and midlatitude continental environments in which supercooled cloud water is more plentiful. For convective surface-level rain rates, the uncertainty varied between 20% and 60% depending on which combination of passive and active microwave observations was used in the retrieval. The uncertainty in surface rain rate did not depend on the microphysical scheme or the parameter settings except for retrievals over stratiform regions based on 85-GHz brightness temperatures TB alone or 85-GHz TB and radar reflectivity combined. In contrast, systematic differences in the treatment of the production of cloud water, cloud ice, and snow between the parameterization schemes coupled with the low correlation between those properties and the passive microwave TB examined here led to significant differences in the uncertainty in retrievals of those cloud properties and latent heating. The variability in uncertainty of hydrometeor structure and latent heating associated with the different microphysical parameterizations exceeded the inherent variability in TB–cloud property relations. This was true at the finescales of the cloud model as well as at scales consistent with satellite footprints in which the inherent variability in TB–cloud property relations are reduced by area averaging.

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Evidence for the breakdown of an Angkorian hydraulic system, and its historical implications for understanding the Khmer Empire

10.31219/osf.io/7p6gw ◽

2021 ◽

Author(s):

Terry Lustig ◽

sarah klassen ◽

Damian Evans ◽

Robert French ◽

Ian Moffat

Keyword(s):

Structural Integrity ◽

Mesoscale Convective System ◽

Tropical Rainfall Measuring Mission ◽

Convective System ◽

Royal Court ◽

Access Road ◽

Gis Tools ◽

Mesoscale Convective ◽

Analysis System ◽

Ground Penetrating

This paper examines the construction and design of a 7-km long embankment, probably builtfor King Jayavarman IV between 928 and 941 CE, as part of a new capital. We calculate thatthe capacities of the outlets were too small, and conclude that the embankment failed, probablywithin a decade of construction, so that the benefits of the reservoir stored by the embankmentand the access road on top of it were lessened substantially. We explain how the design wassub-optimal for construction, and that while the layout had a high aesthetic impact, theprocesses for ensuring structural integrity were poor. Simple and inexpensive steps to securethe weir were not undertaken. We speculate that this early failure may have contributed to thedecision to return the royal court and the capital of the Khmer Empire to the Angkor region,marking a critically important juncture in regional history.Abbreviations: APHRODITE, Asian Precipitation – Highly Resolved Observational DataIntegration Towards Evaluation (of Water Resources); ARI, annual recurrence interval; ASL,above sea level; DIAS, Data Integration and Analysis System; EFEO, École françaised'Extrême-Orient; GPR, ground penetrating radar; HEC-GeoRAS, Hydrologic EngineeringCenter: GIS tools for support of HEC-RAS; HEC-RAS, Hydrologic Engineering Center: RiverAnalysis System; HEC-HMS, Hydrologic Engineering Center: Hydrologic Modeling System;MCS, mesoscale convective system; RMSE, root mean square error; SRTM, NASA ShuttleRadar Topography Mission; TRMM, Tropical Rainfall Measuring Mission

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A Numerical Study of the Beijing Extreme Rainfall of 21 July 2012 and the Impact of Topography

Advances in Meteorology ◽

10.1155/2015/980747 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Hongxiong Xu ◽

Wenqing Yao

Keyword(s):

Numerical Study ◽

Deep Convection ◽

Mesoscale Convective System ◽

Extreme Rainfall ◽

Convective System ◽

Stratiform Region ◽

Moist Environment ◽

The Right ◽

Convective Cells ◽

The Impact

The extreme rainfall on 21 July 2012 is the heaviest rainfall that has occurred in Beijing since 1961. Observations and WRF (Weather Research and Forecasting) model are used to study the effect of MCS (mesoscale convective system) and topography on the rainfall. In this high-impact event, a quasi-stationary MCS developed in a favorable moist environment. The numerical simulation successfully reproduced the amount, location, and time evolution of the rainfall despite 4–6 h delay. In particular, the model reproduced the repeat passage of convective cells at the leading convergence line region along Taihang Mountains and the trailing stratiform region, producing the rainfall at nearly the right position. Results indicate the important roles of mesolow and low-level jet in maintaining the conditional instability that lifted the moist air to trigger deep convection and the repeated initiation and movement of the line shaped convective cells that produced the rainfall. The sensitive experiment was then further carried out to examine the effect of topography on this heavy rainfall. The reduction in model elevation field significantly influenced the above mesoscale systems, which lead to convective cells becoming less organized, and the peak rainfall amount in Beijing decreased by roughly 50%.

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Multiscale Characteristics and Evolution of Perturbations for Warm Season Convection-Allowing Precipitation Forecasts: Dependence on Background Flow and Method of Perturbation

Monthly Weather Review ◽

10.1175/mwr-d-13-00204.1 ◽

2014 ◽

Vol 142 (3) ◽

pp. 1053-1073 ◽

Cited By ~ 34

Author(s):

Aaron Johnson ◽

Xuguang Wang ◽

Ming Xue ◽

Fanyou Kong ◽

Gang Zhao ◽

...

Keyword(s):

Warm Season ◽

Mesoscale Convective System ◽

Haar Wavelet ◽

Precipitation Forecast ◽

Initial Time ◽

Small Scale ◽

Convective System ◽

Mesoscale Convective ◽

Perturbation Energy ◽

The Impact

Abstract Multiscale convection-allowing precipitation forecast perturbations are examined for two forecasts and systematically over 34 forecasts out to 30-h lead time using Haar Wavelet decomposition. Two small-scale initial condition (IC) perturbation methods are compared to the larger-scale IC and physics perturbations in an experimental convection-allowing ensemble. For a precipitation forecast driven primarily by a synoptic-scale baroclinic disturbance, small-scale IC perturbations resulted in little precipitation forecast perturbation energy on medium and large scales, compared to larger-scale IC and physics (LGPH) perturbations after the first few forecast hours. However, for a case where forecast convection at the initial time grew upscale into a mesoscale convective system (MCS), small-scale IC and LGPH perturbations resulted in similar forecast perturbation energy on all scales after about 12 h. Small-scale IC perturbations added to LGPH increased total forecast perturbation energy for this case. Averaged over 34 forecasts, the small-scale IC perturbations had little impact on large forecast scales while LGPH accounted for about half of the error energy on such scales. The impact of small-scale IC perturbations was also less than, but comparable to, the impact of LGPH perturbations on medium scales. On small scales, the impact of small-scale IC perturbations was at least as large as the LGPH perturbations. The spatial structure of small-scale IC perturbations affected the evolution of forecast perturbations, especially at medium scales. There was little systematic impact of the small-scale IC perturbations when added to LGPH. These results motivate further studies on properly sampling multiscale IC errors.

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A new parameterization of the accretion of cloud water by snow and its evaluation through simulations of mesoscale convective systems

10.5194/egusphere-egu2020-1994 ◽

2020 ◽

Author(s):

Han-Gyul Jin ◽

Jong-Jin Baik

Keyword(s):

Mesoscale Convective System ◽

Cloud Droplet ◽

Spherical Shape ◽

Heavy Precipitation ◽

Cloud Water ◽

Squall Line ◽

Convective System ◽

Cloud Droplets ◽

Convective Systems ◽

Mesoscale Convective

<p>A new parameterization of the accretion of cloud water by snow for use in bulk microphysics schemes is derived by analytically solving the stochastic collection equation (SCE), where the theoretical collision efficiency for individual snowflake&#8211;cloud droplet pairs is applied. The snowflake shape is assumed to be nonspherical with the mass- and area-size relations suggested by an observational study. The performance of the new parameterization is compared to two parameterizations based on the continuous collection equation, one with the spherical shape assumption for snowflakes (SPH-CON), and the other with the nonspherical shape assumption employed in the new parameterization (NSP-CON). In box model simulations, only the new parameterization reproduces a relatively slow decrease in the cloud droplet number concentration which is predicted by the direct SCE solver. This results from considering the preferential collection of cloud droplets depending on their sizes in the new parameterization based on the SCE. In idealized squall-line simulations using a cloud-resolving model, the new parameterization predicts heavier precipitation in the convective core region compared to SPH-CON, and a broader area of the trailing stratiform rain compared to NSP-CON due to the horizontal advection of greater amount of snow in the upper layer. In the real-case simulations of a line-shaped mesoscale convective system that passed over the central Korean Peninsula, the new parameterization predicts higher frequencies of light precipitation rates and lower frequencies of heavy precipitation rates. The relatively large amount of upper-level snow in the new parameterization contributes to a broadening of the area with significant snow water path.</p>

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The Impact of Low-Level Moisture Errors on Model Forecasts of an MCS Observed during PECAN

Monthly Weather Review ◽

10.1175/mwr-d-16-0296.1 ◽

2017 ◽

Vol 145 (9) ◽

pp. 3599-3624 ◽

Cited By ~ 17

Author(s):

John M. Peters ◽

Erik R. Nielsen ◽

Matthew D. Parker ◽

Stacey M. Hitchcock ◽

Russ S. Schumacher

Keyword(s):

Convective Available Potential Energy ◽

Mesoscale Convective System ◽

Squall Line ◽

Convective System ◽

Low Level ◽

Eastern Flank ◽

Western Flank ◽

Mesoscale Convective ◽

Outflow Boundary ◽

The Impact

This article investigates errors in forecasts of the environment near an elevated mesoscale convective system (MCS) in Iowa on 24–25 June 2015 during the Plains Elevated Convection at Night (PECAN) field campaign. The eastern flank of this MCS produced an outflow boundary (OFB) and moved southeastward along this OFB as a squall line. The western flank of the MCS remained quasi stationary approximately 100 km north of the system’s OFB and produced localized flooding. A total of 16 radiosondes were launched near the MCS’s eastern flank and 4 were launched near the MCS’s western flank. Convective available potential energy (CAPE) increased and convective inhibition (CIN) decreased substantially in observations during the 4 h prior to the arrival of the squall line. In contrast, the model analyses and forecasts substantially underpredicted CAPE and overpredicted CIN owing to their underrepresentation of moisture. Numerical simulations that placed the MCS at varying distances too far to the northeast were analyzed. MCS displacement error was strongly correlated with models’ underrepresentation of low-level moisture and their associated overrepresentation of the vertical distance between a parcel’s initial height and its level of free convection ([Formula: see text], which is correlated with CIN). The overpredicted [Formula: see text] in models resulted in air parcels requiring unrealistically far northeastward travel in a region of gradual meso- α-scale lift before these parcels initiated convection. These results suggest that erroneous MCS predictions by NWP models may sometimes result from poorly analyzed low-level moisture fields.

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Morphology, Intensity, and Rainfall Production of MJO Convection: Observations from DYNAMO Shipborne Radar and TRMM

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-14-0130.1 ◽

2015 ◽

Vol 72 (2) ◽

pp. 623-640 ◽

Cited By ~ 24

Author(s):

Weixin Xu ◽

Steven A. Rutledge

Keyword(s):

Mesoscale Convective System ◽

Tropical Rainfall Measuring Mission ◽

Radar Data ◽

Convective System ◽

Precipitation Radar ◽

Convective Systems ◽

Trmm Pr ◽

Mesoscale Convective ◽

Trmm Precipitation

Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.

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Evaluation of a Forward Operator to Assimilate Cloud Water Path into WRF-DART

Monthly Weather Review ◽

10.1175/mwr-d-12-00238.1 ◽

2013 ◽

Vol 141 (7) ◽

pp. 2272-2289 ◽

Cited By ~ 24

Author(s):

Thomas A. Jones ◽

David J. Stensrud ◽

Patrick Minnis ◽

Rabindra Palikonda

Keyword(s):

Cloud Water ◽

Cloud Base ◽

Limited Attention ◽

Water Path ◽

Cloud Properties ◽

Oklahoma Mesonet ◽

Cloud Water Path ◽

Mean Square Difference ◽

The Impact ◽

Forward Operator

Abstract Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH). Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.

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A Clustering Approach to Compare Cloud Model Simulations to Satellite Observations

Journal of Climate ◽

10.1175/jcli-d-11-00472.1 ◽

2012 ◽

Vol 25 (22) ◽

pp. 7896-7916 ◽

Cited By ~ 1

Author(s):

Fang Wang ◽

Christian Kummerow

Keyword(s):

Cluster Analysis ◽

Cloud Model ◽

Tropical Rainfall Measuring Mission ◽

Convective Cloud ◽

Atmospheric Modeling ◽

Satellite Observations ◽

Cloud Type ◽

Brightness Temperatures ◽

Microphysical Properties ◽

High Frequency Microwave

Abstract Cloud-resolving models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High-frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can therefore be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes (in other words, cloud types). A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the “clear sky/thin cirrus” cluster, the “cloudy” cluster, the “stratiform anvil” cluster, and the “convective” cluster. Cluster-by-cluster comparisons between the observations and the simulations disclose biases in the model that are consistent with an overproduction of supercooled water and an excess of large hail particles. While other problems cannot be completely ruled out, the method does provide some guidance to assess microphysical fidelity within each cluster or cloud type. Guided by the apparent model/observational discrepancies in the convective cloud cluster, the hail size parameter was adjusted in order to produce a greater number of smaller hail particles consistent with the observations. While the work cannot define microphysical errors in an unambiguously fashion, the cluster analysis is seen as useful to isolate individual microphysical inconsistencies that can then be addressed within each cluster of cloud type.

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Reply to “Comments on ‘Do Ultrafine Cloud Condensation Nuclei Invigorate Deep Convection?’”

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-20-0315.1 ◽

2021 ◽

Vol 78 (1) ◽

pp. 341-350

Author(s):

Wojciech W. Grabowski ◽

Hugh Morrison

Keyword(s):

Negative Impact ◽

Cloud Condensation Nuclei ◽

Deep Convection ◽

Diagnostic Technique ◽

Cloud Water ◽

Latent Heating ◽

Condensation Nuclei ◽

Modeling Framework ◽

Microphysics Scheme ◽

The Impact

AbstractThis is a rebuttal of Fan and Khain’s comments (hereafter FK21) on a 2020 paper by Grabowski and Morrison (hereafter GM20) that questions the impact of ultrafine cloud condensation nuclei (CCN) on deep convection. GM20 argues that “cold invigoration,” an increase of the updraft speed from lofting and freezing of additional cloud water in polluted environments, is unlikely because the latent heating from freezing of this cloud water approximately recovers the negative impact on the buoyancy from the weight of this water. FK21 suggest a variety of processes that could invalidate our claim. We maintain that our argument is valid and invite the authors to compare their microphysics scheme with ours in the same simplified modeling framework. However, pollution does affect the partitioning of latent heating within the column and likely leads to convection changes beyond a single diurnal cycle through larger-scale circulation changes. This argument explains impacts seen in our idealized mesoscale simulations and in convective–radiative equilibrium simulations by others. We agree with FK21 on the existence of a “warm invigoration” mechanism but question its interpretation. Consistent with the simulations in GM20, we argue that changes in the buoyancy can be explained by the response of the supersaturation to droplet microphysical changes induced by pollution. The buoyancy change is determined by supersaturation differences between pristine and polluted conditions, while condensation rate responds to these supersaturation changes. Finally, we agree with FK21 that the piggybacking modeling technique cannot prove or disprove invigoration; rather, it is a diagnostic technique that can be used to understand mechanisms driving simulation differences.

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