Goddard Cumulus Ensemble Model Research Articles

The Next-Generation Goddard Convective–Stratiform Heating Algorithm: New Tropical and Warm-Season Retrievals for GPM

The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dB Z values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.

The impact of simulated mesoscale convective systems on global precipitation: A multiscale modeling study.

The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. MCSs usually have horizontal scales of a few hundred kilometers (km); therefore, a large domain with several hundred km is required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs. Typical multiscale modeling frameworks (MMFs) may also lack the resolution (4 km grid spacing) and domain size (128 km) to realistically simulate MCSs. The impact of MCSs on precipitation is examined by conducting model simulations using the Goddard Cumulus Ensemble (GCE, a CRM) model and Goddard MMF that uses the GCEs as its embedded CRMs. Both models can realistically simulate MCSs with more grid points (i.e., 128 and 256) and higher resolutions (1 or 2 km) compared to those simulations with fewer grid points (i.e., 32 and 64) and low resolution (4 km). The modeling results also show the strengths of the Hadley circulations, mean zonal and regional vertical velocities, surface evaporation, and amount of surface rainfall are weaker or reduced in the Goddard MMF when using more CRM grid points and higher CRM resolution. In addition, the results indicate that large-scale surface evaporation and wind feedback are key processes for determining the surface rainfall amount in the GMMF. A sensitivity test with reduced sea surface temperatures shows both reduced surface rainfall and evaporation.

High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations.

The Goddard microphysics was recently improved by adding a fourth ice class (frozen drops/hail). This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of ice/snow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries.

Massive Parallelization of the WRF GCE Model Toward a GPU-Based End-to-End Satellite Data Simulator Unit

Modern weather satellites provide more detailed observations of cloud and precipitation processes. To harness these observations for better satellite data assimilations, a cloud-resolving model, known as the Goddard Cumulus Ensemble (GCE) model, was developed and used by the Goddard Satellite Data Simulator Unit (G-SDSU). The GCE model has also been incorporated as part of the widely used weather research and forecasting (WRF) model. The computation of the cloud-resolving GCE model is time-consuming. This paper details our massively parallel design of GPU-based WRF GCE scheme. With one NVIDIA Tesla K40 GPU, the GPU-based GCE scheme achieves a speedup of 361× as compared to its original Fortran counterpart running on one CPU core, whereas the speedup for one CPU socket (four cores) with respect to one CPU core is only 3.9×.

Sensitivity of convection to observed variation in aerosol size distributions and composition at a rural site in the southeastern United States

We present a sensitivity analysis to determine the impact of variations in aerosol physical and chemical characteristics, as observed in the field in the southeastern United States, on convective cloud microphysics. Scenarios reflecting changes in aerosol properties, observed over the Piedmont of Virginia and associated with new particle formation and growth events, were evaluated using the two-dimensional version of the Goddard Cumulus Ensemble Model with detailed spectral-bin microphysics. Two aerosol size distributions represented early and late stages of particle growth, and two aerosol chemical compositions (norpinic acid and ammonium sulfate) represented extremes in aerosol hygroscopicity observed at the field site, for a total of four scenarios. The chosen compositions reflect inferred local changes in aerosol composition over short time scales. Variations in the aerosol size distribution and composition resulted in substantial variation in the total number of cloud condensation nuclei (CCN) produced in the four case studies. Cases with high CCN concentrations developed larger, more vigorous clouds with more precipitation generated by both warm and cold rain processes. Greater numbers of drops were propelled aloft and formed an extensive ice anvil that produced a large area of stratiform rain. Convection was enhanced by increasing aerosols despite decreases in precipitation efficiency. In contrast, lower CCN concentrations developed smaller clouds with suppressed cold rain processes and less total precipitation. The relatively small increase in CCN concentration associated with the increase in aerosol hygroscopicity resulted in an increase in accumulated modeled precipitation of 12 % after 180 min of simulation time in both high and low CCN cases. The increase in accumulated precipitation due to the substantial increase in CCN concentration associated with growth of the aerosol size distribution was 93 % for both aerosol compositions. The timing of the onset of precipitation was not affected by aerosol concentration or composition.

Benefits of a Fourth Ice Class in the Simulated Radar Reflectivities of Convective Systems Using a Bulk Microphysics Scheme

Abstract Current cloud microphysical schemes used in cloud and mesoscale models range from simple one-moment to multimoment, multiclass to explicit bin schemes. This study details the benefits of adding a fourth ice class (frozen drops/hail) to an already improved single-moment three-class ice (cloud ice, snow, graupel) bulk microphysics scheme developed for the Goddard Cumulus Ensemble model. Besides the addition and modification of several hail processes from a bulk three-class hail scheme, further modifications were made to the three-ice processes, including allowing greater ice supersaturation and mitigating spurious evaporation/sublimation in the saturation adjustment scheme, allowing graupel/hail to transition to snow via vapor growth and hail to transition to graupel via riming, wet graupel to become hail, and the inclusion of a rain evaporation correction and vapor diffusivity factor. The improved three-ice snow/graupel size-mapping schemes were adjusted to be more stable at higher mixing ratios and to increase the aggregation effect for snow. A snow density mapping was also added. The new scheme was applied to an intense continental squall line and a moderate, loosely organized continental case using three different hail intercepts. Peak simulated reflectivities agree well with radar for both the intense and moderate cases and were superior to earlier three-ice versions when using a moderate and large intercept for hail, respectively. Simulated reflectivity distributions versus height were also improved versus radar in both cases compared to earlier three-ice versions. The bin-based rain evaporation correction affected the squall line more but overall the agreement among the reflectivity distributions was unchanged. The new scheme also improved the simulated surface rain-rate histograms.

Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study

One of the current challenges in tropical cyclone (TC) research is how to improve our understanding of TC interannual variability and the impact of climate change on TCs. Recent advances in global modeling, visualization, and supercomputing technologies at NASA show potential for such studies. In this article, the authors discuss recent scalability improvement to the multiscale modeling framework (MMF) that makes it feasible to perform long-term TC-resolving simulations. The MMF consists of the finite-volume general circulation model (fvGCM), supplemented by a copy of the Goddard cumulus ensemble model (GCE) at each of the fvGCM grid points, giving 13,104 GCE copies. The original fvGCM implementation has a 1D data decomposition; the revised MMF implementation retains the 1D decomposition for most of the code, but uses a 2D decomposition for the massive copies of GCEs. Because the vast majority of computation time in the MMF is spent computing the GCEs, this approach can achieve excellent speedup without incurring the cost of modifying the entire code. Intelligent process mapping allows differing numbers of processes to be assigned to each domain for load balancing. The revised parallel implementation shows highly promising scalability, obtaining a nearly 80-fold speedup by increasing the number of cores from 30 to 3,335.

Comparing the Convective Structure and Microphysics in Two Sahelian Mesoscale Convective Systems: Radar Observations and CRM Simulations

Abstract Two mesoscale convective systems (MCSs) observed during the African Monsoon Multidisciplinary Analyses (AMMA) experiment are simulated using the three-dimensional (3D) Goddard Cumulus Ensemble model. This study was undertaken to determine the performance of the cloud-resolving model in representing distinct convective and microphysical differences between the two MCSs over a tropical continental location. Simulations are performed using 1-km horizontal grid spacing, a lower limit on current embedded cloud-resolving models within a global multiscale modeling framework. Simulated system convective structure and microphysics are compared to radar observations using contoured frequency-by-altitude diagrams (CFADs), calculated ice and water mass, and identified hydrometeor variables. Vertical distributions of ice hydrometeors indicate underestimation at the mid- and upper levels, partially due to the inability of the model to produce adequate system heights. The abundance of high-reflectivity values below and near the melting level in the simulation led to a broadening of the CFAD distributions. Observed vertical reflectivity profiles show that high reflectivity is present at greater heights than the simulations produced, thought to be a result of using a single-moment microphysics scheme. Relative trends in the population of simulated hydrometeors are in agreement with observations, though a secondary convective burst is not well represented. Despite these biases, the radar-observed differences between the two cases are noticeable in the simulations as well, suggesting that the model has some skill in capturing observed differences between the two MCSs.

Aerosol Effects on Cumulus Congestus Population over the Tropical Pacific: A Cloud-Resolving Modeling Study

This study examines the significance of aerosol serving as cloud condensation nuclei (CCN) in modulating strengths of tropical maritime convection. Through a Tropical Ocean Global Atmosphere Couple OceanAtmosphere Response Experiment (TOGA COARE) case study using a cloud-resolving model (the Goddard Cumulus Ensemble Model with a horizontal mesh interval of 750 m) anda d etailedspectral bin microphysical scheme, it is foundthat low aerosol concentration acts to reduce convection strengths. Over the tropical western Pacific where low-level water vapor is abundant and a ubiquitous weak stable level exists near 0° C, the low backgroundmaritime aerosol concentration is conducive for forming cumulus congestus. Sensitivity tests show that the main mechanism of convection damping in a clean maritime environment is through reduced condensational growth, although the freezing of supercooled water, cloudtop evaporation, andrain evaporation also contribute to the simulatedeffects. Consid ering the importance of congestus in tropical dynamics and the MaddenJulian oscillation (MJO) lifecycle, we further propose a hypothesis that aerosolcloudprecipitation interactions in an ultraclean marine environment may serve as a damping mechanism for tropical convection.

Temporal and spatial scale dependence of precipitation analysis over the tropical deep convective regime

Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE). The analysis shows that the calculations of model domain mean or time-mean grid-scale mean simulation data overestimate the rain rates of the two rainfall types associated with net condensation but they severely underestimate the rain rate of the rainfall type associated with net evaporation and hydrometeor convergence.

Reducing the Biases in Simulated Radar Reflectivities from a Bulk Microphysics Scheme: Tropical Convective Systems

Abstract A well-known bias common to many bulk microphysics schemes currently being used in cloud-resolving models is the tendency to produce excessively large reflectivity values (e.g., 40 dBZ) in the middle and upper troposphere in simulated convective systems. The Rutledge and Hobbs–based bulk microphysics scheme in the Goddard Cumulus Ensemble model is modified to reduce this bias and improve realistic aspects. Modifications include lowering the efficiencies for snow/graupel riming and snow accreting cloud ice; converting less rimed snow to graupel; allowing snow/graupel sublimation; adding rime splintering, immersion freezing, and contact nucleation; replacing the Fletcher formulation for activated ice nuclei with that of Meyers et al.; allowing for ice supersaturation in the saturation adjustment; accounting for ambient RH in the growth of cloud ice to snow; and adding/accounting for cloud ice fall speeds. In addition, size-mapping schemes for snow/graupel were added as functions of temperature and mixing ratio, lowering particle sizes at colder temperatures but allowing larger particles near the melting level and at higher mixing ratios. The modifications were applied to a weakly organized continental case and an oceanic mesoscale convective system (MCS). Strong echoes in the middle and upper troposphere were reduced in both cases. Peak reflectivities agreed well with radar for the weaker land case but, despite improvement, remained too high for the MCS. Reflectivity distributions versus height were much improved versus radar for the less organized land case but not for the MCS despite fewer excessively strong echoes aloft due to a bias toward weaker echoes at storm top.

A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations

Abstract In the near future, the Global Precipitation Measurement (GPM) mission will provide precipitation observations with unprecedented accuracy and spatial/temporal coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer-resolution precipitation fields. This paper explores a dynamic downscaling method using ensemble data assimilation techniques and cloud-resolving models. A prototype ensemble data assimilation system using the Weather Research and Forecasting Model (WRF) has been developed. A high-resolution regional WRF with multiple nesting grids is used to provide the first-guess and ensemble forecasts. An ensemble assimilation algorithm based on the maximum likelihood ensemble filter (MLEF) is used to perform the analysis. The forward observation operators from NOAA–NCEP’s gridpoint statistical interpolation (GSI) are incorporated for using NOAA–NCEP operational datastream, including conventional data and clear-sky satellite observations. Precipitation observation operators are developed with a combination of the cloud-resolving physics from NASA Goddard cumulus ensemble (GCE) model and the radiance transfer schemes from NASA Satellite Data Simulation Unit (SDSU). The prototype of the system is used as a test bed to optimally combine observations and model information to produce a dynamically downscaled precipitation analysis. A case study on Tropical Storm Erin (2007) is presented to investigate the ability of the prototype of the WRF Ensemble Data Assimilation System (WRF-EDAS) to ingest information from in situ and satellite observations including precipitation-affected radiance. The results show that the analyses and forecasts produced by the WRF-EDAS system are comparable to or better than those obtained with the WRF-GSI analysis scheme using the same set of observations. An experiment was also performed to examine how the analyses and short-term forecasts of microphysical variables and dynamical fields are influenced by the assimilation of precipitation-affected radiances. The results highlight critical issues to be addressed in the next stage of development such as model-predicted hydrometeor control variables and associated background error covariance, bias estimation, and correction in radiance space, as well as the observation error statistics. While further work is needed to optimize the performance of WRF-EDAS, this study establishes the viability of developing a cloud-scale ensemble data assimilation system that has the potential to provide a useful vehicle for downscaling satellite precipitation information to finer scales suitable for hydrological applications.

Simulations of microwave brightness temperatures at AMSU-B frequencies over a 3D convective cloud system

Numerical simulations have been carried out to understand the effects of clouds associated with a tropical deep convective cloud system on the Advanced Microwave Sensor Unit-B (AMSU-B) channels at 89, 150, 183.3 ± 7, 183.3 ± 3, and 183.3 ± 1 GHz. The hydrometeor profiles including cloud liquid water, cloud ice, snow, graupel, and rain water for a deep convective cloud system simulated by a realistic dynamical cloud model, the Goddard Cumulus Ensemble model, have been input to a Vector Discrete Ordinate Radiative Transfer model to simulate the nadir down-looking microwave brightness temperatures at the top of the atmosphere. It is found that the AMSU-B channels have large brightness temperature depressions occurring over the clouds with large ice water paths. Moreover, for the three water vapour sounding frequencies around 183.3 GHz, the frequencies broader and further away from the centre of the water vapour absorption line show stronger depressions. The three water vapour channels, particularly the channels closer to the absorption line centre, essentially have negligible influence from liquid water. However, the window frequencies at 89 and 150 GHz have distinct influence from liquid water, particularly the 150 GHz, although they are also strongly influenced by frozen hydrometeors. The AMSU-B frequencies at 150 GHz and water vapour channels of 183.3 ± 7 and 183.3 ± 3 GHz are sensitive to cirrus clouds with total ice water paths above 0.1–0.2 kg m−2. The influence of deep convective clouds and thick cirrus clouds on the AMSU-B water vapour channels demonstrates that they have a potential to estimate ice water paths in thick cirrus clouds and in the upper parts of deep convective clouds, which can complement the retrievals from the 89 and 150 GHz channels.

Characterization of Precipitating Clouds by Ground-Based Measurements with the Triple-Frequency Polarized Microwave Radiometer ADMIRARI

AbstractA groundbreaking new-concept multiwavelength dual-polarized Advanced Microwave Radiometer for Rain Identification (ADMIRARI) has been built and continuously operated in two field campaigns: the Convective and Orographically Induced Precipitation Study (COPS) and the European Integrated Project on Aerosol Cloud Climate Air Quality Interactions (EUCAARI). The radiometer has 6 channels working in horizontal and vertical polarization at 10.65, 21.0, and 36.5 GHz, and it is completely steerable both in azimuth and in elevation. The instrument is suited to be operated in rainy conditions and is intended for retrieving simultaneously water vapor, rain, and cloud liquid water paths. To this goal the authors implemented a Bayesian retrieval scheme based on many state realizations simulated by the Goddard Cumulus Ensemble model that build up a prior probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations, which account for the presence of nonspherical particles in preferential orientation, simulate the downwelling brightness temperatures and establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. Particular attention is devoted to the sensitivity of the ADMIRARI signal to 3D effects, raindrop size distribution, and axial ratio parameterizations. The polarization and multifrequency signals represent key information to separate the effects introduced by non-Rayleigh scatterers and to separate rainwater (r-LWP) from the cloud water component (c-LWP). Long-term observations demonstrate that observed brightness temperatures and polarization differences can be well interpreted and reproduced by the simulated ones for all three channels simultaneously. Rough estimates of r-LWP derived from collocated observations with a micro rain radar confirm the rain/no rain separation and the variability trend of r-LWP provided by the radiometer-based retrieval algorithm. With this work the authors demonstrate the potential of ADMIRARI to retrieve information about the rain/cloud partitioning for midlatitude precipitation systems; future studies with this instrument will provide crucial information on rain efficiency of clouds for cloud modelers that might lead toward a better characterization of rain processes.

Improving a spectral bin microphysical scheme using TRMM satellite observations

AbstractTRMM‐observed mature‐stage squall lines during late spring and early summer in the central USA over a 9‐year period are compiled and compared with a case simulation by the Goddard Cumulus Ensemble (GCE) model with a spectral bin microphysical scheme. During the quasi‐steady state of the simulation, a forward radiative transfer model calculates TRMM Precipitation Radar (PR) reflectivity and 85 GHz brightness temperatures from simulated particle size distributions. Comparisons between model and TRMM observations using radar Contoured Frequency with Altitude Diagrams (CFADs) and 85 GHz brightness temperature probability density distributions are performed, in addition to CFADs from a surface C‐band radar for the same case. Radar CFADs comparisons reveal that the model overestimates sizes of snow/aggregates in the stratiform region.Three sets of sensitivity tests are carried out in order to improve the simulated radar reflectivity profiles: increase of aggregates' density and terminal fall velocity; changing temperature dependency of collection efficiency between ice‐phase particles, particularly those of the plate‐type; and adding a break‐up scheme for large aggregates. While all three approaches mitigate the discrepancies, changing collection efficiency produces the best match in magnitudes and characteristics of radar CFADs. In addition, interactions between ice‐ and water‐phase particles also need to be adjusted in order to have good comparisons in both radar CFADs and 85 GHz brightness temperature distributions. This study shows that long‐term satellite observations, especially those with multiple sensors, can be very useful in constraining model microphysics. Copyright © 2010 Royal Meteorological Society

Identifying convective transport of carbon monoxide by comparing remotely sensed observations from TES with cloud modeling simulations

Abstract. Understanding the mechanisms that transport pollutants from the surface to the free atmosphere is important for determining the atmosphere's chemical composition. This study quantifies the vertical transport of tropospheric carbon monoxide (CO) by deep mesoscale convective systems and assesses the ability of the satellite-borne Tropospheric Emission Spectrometer (TES) to detect the resulting enhanced CO in the upper atmosphere. A squall line that is similar to one occurring during NASA's INTEX-B mission is simulated using a typical environmental wind shear profile and the 2-D Goddard Cumulus Ensemble model. The simulation provides post-convection CO profiles. The structure of the simulated squall line is examined, and its vertical transport of CO is quantified. Then, TES' ability to resolve the convectively modified CO distribution is documented using a "clear-sky" retrieval scheme. Results show that the simulated squall line transports the greatest mass of CO in the upper levels, with a value of 96 t upward and 67 t downward at 300 hPa. Results indicate that TES has sufficient sensitivity to resolve convectively lofted CO, as long as the retrieval scene is cloud-free. TES swaths located immediately downwind of squall lines have the greatest chance of sensing convective transport because the impact of clouds on retrieval quality becomes less. A note of caution is to always analyze TES-derived CO data (or data from any satellite sensor) together with the retrieval averaging kernels that describe the information content of the retrieval.

Evaluation of Long-Term Cloud-Resolving Model Simulations Using Satellite Radiance Observations and Multifrequency Satellite Simulators

Abstract This paper proposes a methodology known as the Tropical Rainfall Measuring Mission (TRMM) Triple-Sensor Three-Step Evaluation Framework (T3EF) for the systematic evaluation of precipitating cloud types and microphysics in a cloud-resolving model (CRM). T3EF utilizes multisensor satellite simulators and novel statistics of multisensor radiance and backscattering signals observed from the TRMM satellite. Specifically, T3EF compares CRM and satellite observations in the form of combined probability distributions of precipitation radar (PR) reflectivity, polarization-corrected microwave brightness temperature (Tb), and infrared Tb to evaluate the candidate CRM. T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases involving the South China Sea Monsoon Experiment (SCSMEX) and the Kwajalein Experiment (KWAJEX). This evaluation reveals that the GCE properly captures the satellite-measured frequencies of different precipitating cloud types in the SCSMEX case but overestimates the frequencies of cumulus congestus in the KWAJEX case. Moreover, the GCE tends to simulate excessively large and abundant frozen condensates in deep precipitating clouds as inferred from the overestimated GCE-simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed errors in the GCE’s performance provides the better direction for model improvements.

A Multiscale Modeling System: Developments, Applications, and Critical Issues

A multi-scale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The GCM allows global coverage and the CRM allows explicit simulation of cloud processes and their interactions with radiation and surface processes. A new MMF has been developed that is based the Goddard finite volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved .surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger, propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using conventional cloud parameterizations. Both MMFs also produce a precipitation bias in the western Pacific during Northern Hemisphere summer. However, there are also notable differences between two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF overestimates global rainfall because of its oceanic component. Some critical issues associated with the Goddard MMF are presented in this paper.

Mechanisms of diurnal precipitation over the US Great Plains: a cloud resolving model perspective

The mechanisms of summertime diurnal precipitation in the US Great Plains were examined with the two-dimensional (2D) Goddard Cumulus Ensemble (GCE) cloud-resolving model (CRM). The model was constrained by the observed large-scale background state and surface flux derived from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program’s Intensive Observing Period (IOP) data at the Southern Great Plains (SGP). The model, when continuously-forced by realistic surface flux and large-scale advection, simulates reasonably well the temporal evolution of the observed rainfall episodes, particularly for the strongly forced precipitation events. However, the model exhibits a deficiency for the weakly forced events driven by diurnal convection. Additional tests were run with the GCE model in order to discriminate between the mechanisms that determine daytime and nighttime convection. In these tests, the model was constrained with the same repeating diurnal variation in the large-scale advection and/or surface flux. The results indicate that it is primarily the surface heat and moisture flux that is responsible for the development of deep convection in the afternoon, whereas the large-scale upward motion and associated moisture advection play an important role in preconditioning nocturnal convection. In the nighttime, high clouds are continuously built up through their interaction and feedback with long-wave radiation, eventually initiating deep convection from the boundary layer. Without these upper-level destabilization processes, the model tends to produce only daytime convection in response to boundary layer heating. This study suggests that the correct simulation of the diurnal variation in precipitation requires that the free-atmospheric destabilization mechanisms resolved in the CRM simulation must be adequately parameterized in current general circulation models (GCMs) many of which are overly sensitive to the parameterized boundary layer heating.

Increase of cloud droplet size with aerosol optical depth: An observation and modeling study

Cloud droplet effective radius (DER) is generally negatively correlated with aerosol optical depth (AOD) as a proxy of cloud condensation nuclei. In this study, cases of positive correlation were found over certain portions of the world by analyzing the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products, together with a general finding that DER may increase or decrease with aerosol loading depending on environmental conditions. The slope of the correlation between DER and AOD is driven primarily by water vapor amount, which explains 70% of the variance in our study. Various potential artifacts that may cause the positive relation are investigated including the effects of aerosol swelling, partially cloudy, atmospheric dynamics, cloud three‐dimensional (3‐D) and surface influence effects. None seems to be the primary cause for the observed phenomenon, although a certain degree of influence exists for some of the factors. Analyses are conducted over seven regions around the world representing different types of aerosols and clouds. Only two regions show positive dependence of DER on AOD, near coasts of the Gulf of Mexico and South China Sea, which implies physical processes may at work. Using a 2‐D Goddard Cumulus Ensemble model (GCE) with spectral‐bin microphysics which incorporated a reformulation of the Köhler theory, two possible physical mechanisms are hypothesized. They are related to the effects of slightly soluble organics (SSO) particles and giant cloud condensation nuclei (CCN). Model simulations show a positive correlation between DER and AOD, due to a decrease in activated aerosols with an increasing SSO content. Addition of a few giant CCNs also increases the DER. Further investigations are needed to fully understand and clarify the observed phenomenon.