Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cloud parameterizations”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

New Challenges of Cloud Remote Sensing from Space

Clouds are recognized as a major source of uncertainties in predicting climate change. Lack of observational constraints on cloud processes has hindered the development of more reliable climate and weather models. With the advanced NASA EOS and A-Train sensors, we now have better knowledge about vertical distribution, water content, and occurrence frequency of global cloudiness. New ice water content (IWC) measurements from CloudSat and MLS have led to several improvements in model physics and parameterization schemes. MISR and GPS high-resolution data start to reveal deep insights on cloud processes and dynamicS in the planetary boundary layer (PBL). However, Earth sciences are still facing urgent needs to measure cloud microphysical properties, interactions between clouds and aerosol/precipitation, and processes that are coupled in 3-D space but not adequately sampled by the A-Train curtains or by the 2-D imageries. Submillimeter- wave, multi-angle imaging, and GPS radio occultation have emerged as promising remote sensing techniques to meet the challenges and enable new sciences for mid-tropospheric and PBL clouds.

Wu, Dong L.↗
DOE OSTI2022

Linking Large-Scale Double-ITCZ Bias to Local-Scale Drizzling Bias in Climate Models

Tropical precipitation in climate models presents significant biases in both the large-scale pattern (i.e., double intertropical convergence zone bias) and local-scale characteristics (i.e., drizzling bias with too frequent drizzle/convection and reduced occurrences of no and heavy precipitation). By untangling the coupled system and analyzing the biases in precipitation, cloud, and radiation, this study shows that local-scale drizzling bias in atmospheric models can lead to large-scale double-ITCZ bias in coupled models by inducing convective-regime-dependent biases in precipitation and cloud radiative effects (CRE). The double-ITCZ bias consists of a hemispherically asymmetric component that arises from the asymmetric SST bias and a nearly symmetric component that exists in atmospheric models without the SST bias. By increasing light rain but reducing heavy rain, local-scale drizzling bias induces positive (negative) precipitation bias in the moderate (strong) convective regime, leading to the nearly symmetric wet bias in atmospheric models. By affecting the cloud profile, local-scale drizzling bias induces positive (negative) CRE bias in the stratocumulus (convective) regime in atmospheric models. Because the stratocumulus (convective) region is climatologically more pronounced in the southern (northern) tropics, the CRE bias is deemed to be hemispherically asymmetric and drives warm and wet (cold and dry) biases in the southern (northern) tropics when coupled to ocean. Our results suggest that correcting local-scale drizzling bias is critical for fixing large-scale double-ITCZ bias. Finally, the drizzling and double-ITCZ biases are not alleviated in models with mesoscale (0.25°–0.5°) or even storm-resolving (~3 km) resolution, implying that either large-eddy simulation or fundamental improvement in small-scale subgrid parameterizations is needed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Role of Satellite Rainfall Information in Improving Understanding of the Dynamical Link Between the Tropics and Extratropics Prospects of Improved Forecasts of Weather and Short-Term Climate Variability on Sub-Seasonal Time Scales

The tropics and extratropics are two dynamically distinct regimes. The coupling between these two regimes often defies simple analytical treatment. Progress in understanding of the dynamical interaction between the tropics and extratropics relies on better observational descriptions to guide theoretical development. However, global analyses currently contain significant errors in primary hydrological variables such as precipitation, evaporation, moisture, and clouds, especially in the tropics. Tropical analyses have been shown to be sensitive to parameterized precipitation processes, which are less than perfect, leading to order-one discrepancies between estimates produced by different data assimilation systems. One strategy for improvement is to assimilate rainfall observations to constrain the analysis and reduce uncertainties in variables physically linked to precipitation. At the Data Assimilation Office at the NASA Goddard Space Flight Center, we have been exploring the use of tropical rain rates derived from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments in global data assimilation. Results show that assimilating these data improves not only rainfall and moisture fields but also related climate parameters such as clouds and radiation, as well as the large-scale circulation and short-range forecasts. These studies suggest that assimilation of microwave rainfall observations from space has the potential to significantly improve the quality of 4-D assimilated datasets for climate investigations (Hou et al. 2001). In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission in 2007. Continued improvements in assimilation methodology, rainfall error estimates, and model parameterizations are needed to ensure that we derive maximum benefits from these observations.

Hou, Arthur Y.↗
DOE OSTI2018

A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer

Abstract In convective flows, vertical turbulent fluxes, covariances between vertical velocity and scalar thermodynamic variables, include contributions from local mixing and large-scale coherent motions, such as updrafts and downdrafts. The relative contribution of these motions to the covariance is important in turbulence parameterizations. However, the flux partition is challenging, especially in regions without convective cloud. A method to decompose the vertical flux based on the corresponding joint probability density function (JPD) is introduced. The JPD-based method partitions the full JPD into a joint Gaussian part and the complement, which represent the local mixing and the large-scale coherent motions, respectively. The coherent part can be further divided into updraft and downdraft parts based on the sign of vertical velocity. The flow decomposition is independent of water condensate (cloud) and can be applied in cloud-free convection, the subcloud layer, and stratiform cloud regions. The method is applied to large-eddy simulation model data of three boundary layers. The results are compared with traditional cloud and cloud-core decompositions and a decaying scalar conditional sampling method. The JPD-based method includes a single free parameter and sensitivity tests show weak dependence on the parameter values. The results of the JPD-based method are somewhat similar to the cloud-core and conditional sampling methods. However, differences in the relative magnitude of the flux decomposition terms suggest that an objective definition of the flow regions is subtle and diagnosed flow properties like updraft characteristics depend on the sampling method. Moreover, the flux decomposition depends on the thermodynamic variable and convection characteristics.

Chinita, Maria J.↗
DOE OSTI2019

Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0)

Abstract. Herein, we present a description of the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0). This iron processing module was developed for use within Earth system models and has been updated within a modal aerosol framework from the original implementation in a bulk aerosol model. MIMI simulates the emission and atmospheric processing of two main sources of iron in aerosol prior to deposition: mineral dust and combustion processes. Atmospheric dissolution of insoluble to soluble iron is parameterized by an acidic interstitial aerosol reaction and a separate in-cloud aerosol reaction scheme based on observations of enhanced aerosol iron solubility in the presence of oxalate. Updates include a more comprehensive treatment of combustion iron emissions, improvements to the iron dissolution scheme, and an improved physical dust mobilization scheme. An extensive dataset consisting predominantly of cruise-based observations was compiled to compare to the model. The annual mean modelled concentration of surface-level total iron compared well with observations but less so in the soluble fraction (iron solubility) for which observations are much more variable in space and time. Comparing model and observational data is sensitive to the definition of the average as well as the temporal and spatial range over which it is calculated. Through statistical analysis and examples, we show that a median or log-normal distribution is preferred when comparing with soluble iron observations. The iron solubility calculated at each model time step versus that calculated based on a ratio of the monthly mean values, which is routinely presented in aerosol studies and used in ocean biogeochemistry models, is on average globally one-third (34 %) higher. We redefined ocean deposition regions based on dominant iron emission sources and found that the daily variability in soluble iron simulated by MIMI was larger than that of previous model simulations. MIMI simulated a general increase in soluble iron deposition to Southern Hemisphere oceans by a factor of 2 to 4 compared with the previous version, which has implications for our understanding of the ocean biogeochemistry of these predominantly iron-limited ocean regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2

In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1993

Session on modeling of radiative transfer processes

The session on modeling of radiative transfer processes is reviewed. Six critical issues surfaced in the discussion concerning scale-interactive radiative processes relevent to the mesoscale convective systems (MCS's). These issues are the need to expand basic knowledge of how MCS's influence climate through extensive cloud shields and increased humidity in the upper troposphere; to improve radiation parameterizations used in mesoscale and General Circulation Model (GCM) models; to improve our basic understanding of the influence of radiation on MCS dynamics due to diabatic heating, production of condensate, and vertical and horizontal heat fluxes; to quantify our understanding of radiative impacts of MCS's on the surface and free atmosphere energy budgets; to quantify and identify radiative and microphysical processes important in the evolution of MCS's; and to improve the capability to remotely sense MCS radiative properties from space and ground-based systems.

Flatau, Piotr↗
NASA NTRS2007

Simulations of Tropospheric NO2 by the Global Modeling Initiative (GMI) Model Utilizing Assimilated and Forecast Meteorological Fields: Comparison to Ozone Monitoring Instrument (OMI) Measurements

We present simulations of the tropospheric composition for the years 2004 and 2005, carried out by the GMI Combined Stratosphere-Troposphere (Combo) model, at a resolution of 2degx2.5deg. The model includes a new parameterization of lightning sources of NO(x) which is coupled to the cloud mass fluxes in the adopted meteorological fields. These simulations use two different sets of input meteorological fields: a)late-look assimilated fields from the Global Modeling and Assimilation Office (GMAO), GEOS-4 system and b) 12-hour forecast fields initialized with the assimilated data. Comparison of the forecast to the assimilated fields indicates that the forecast fields exhibit less vigorous convection, and yield tropical precipitation fields in better agreement with observations. Since these simulations include a complete representation of the stratosphere, they provide realistic stratosphere-tropospheric fluxes of O3 and NO(y). Furthermore, the stratospheric contribution to total columns of different troposheric species can be subtracted in a consistent fashion, and the lightning production of NO(y) will depend on the adopted meteorological field. We concentrate here on the simulated tropospheric columns of NO2, and compare them to observations by the OM1 instrument for the years 2004 and 2005. The comparison is used to address these questions: a) is there a significant difference in the agreement/disagreement between simulations for these two different meteorological fields, and if so, what causes these differences?; b) how do the simulations compare to OMI observations, and does this comparison indicate an improvement in simulations with the forecast fields? c) what are the implications of these simulations for our understanding of the NO2 emissions over continental polluted regions?

Rodriquez, J. M.↗
NASA NTRS2014

A Flexible Parameterization for Shortwave Optical Properties of Ice Crystals

A parameterization is presented that provides extinction cross section sigma (sub e), single-scattering albedo omega, and asymmetry parameter (g) of ice crystals for any combination of volume, projected area, aspect ratio, and crystal distortion at any wavelength in the shortwave. Similar to previous parameterizations, the scheme makes use of geometric optics approximations and the observation that optical properties of complex, aggregated ice crystals can be well approximated by those of single hexagonal crystals with varying size, aspect ratio, and distortion levels. In the standard geometric optics implementation used here, sigma (sub e) is always twice the particle projected area. It is shown that omega is largely determined by the newly defined absorption size parameter and the particle aspect ratio. These dependences are parameterized using a combination of exponential, lognormal, and polynomial functions. The variation of (g) with aspect ratio and crystal distortion is parameterized for one reference wavelength using a combination of several polynomials. The dependences of g on refractive index and omega are investigated and factors are determined to scale the parameterized (g) to provide values appropriate for other wavelengths. The parameterization scheme consists of only 88 coefficients. The scheme is tested for a large variety of hexagonal crystals in several wavelength bands from 0.2 to 4 micron, revealing absolute differences with reference calculations of omega and (g) that are both generally below 0.015. Over a large variety of cloud conditions, the resulting root-mean-squared differences with reference calculations of cloud reflectance, transmittance, and absorptance are 1.4%, 1.1%, and 3.4%, respectively. Some practical applications of the parameterization in atmospheric models are highlighted.

parameterization↗
NASA NTRS2014

A Wrf-Chem Flash Rate Parameterization Scheme and LNO(x) Analysis of the 29-30 May 2012 Convective Event in Oklahoma During DC3

The Deep Convective Clouds and Chemistry (DC3) field campaign in 2012 provided a plethora of aircraft and ground-based observations (e.g., trace gases, lightning and radar) to study deep convective storms, their convective transport of trace gases, and associated lightning occurrence and production of nitrogen oxides (NOx). Based on the measurements taken of the 29-30 May 2012 Oklahoma thunderstorm, an analysis against a Weather Research and Forecasting Chemistry (WRF-Chem) model simulation of the same event at 3-km horizontal resolution was performed. One of the main objectives was to include various flash rate parameterization schemes (FRPSs) in the model and identify which scheme(s) best captured the flash rates observed by the National Lightning Detection Network (NLDN) and Oklahoma Lightning Mapping Array (LMA). The comparison indicates how well the schemes predicted the timing, location, and number of lightning flashes. The FRPSs implemented in the model were based on the simulated thunderstorms physical features, such as maximum vertical velocity, cloud top height, and updraft volume. Adjustment factors were added to each FRPS to best capture the observed flash trend and a sensitivity study was performed to compare the range in model-simulated lightning-generated nitrogen oxides (LNOx) generated by each FRPS over the storms lifetime. Based on the best FRPS, model-simulated LNOx was compared against aircraft measured NOx. The trace gas analysis, along with the increased detail in the model specification of the vertical distribution of lightning flashes as suggested by the LMA data, provide guidance in determining the scenario of NO production per intracloud and cloud-to-ground flash that best matches the NOx mixing ratios observed by the aircraft.

Cloud physics↗
NASA NTRS2006

Cloud Radiative Forcing at the ARM Climate Research Facility: Technique, Validation, and Comparison to Satellite-derived Diagnostic Quantities - Part 1

It has been hypothesized that continuous ground-based remote sensing measurements from active and passive remote sensors combined with regular soundings of the atmospheric thermodynamic structure can be combined to describe the effects of clouds on the clear sky radiation fluxes. We critically test that hypothesis in this paper and a companion paper (Part II). Using data collected at the Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site sponsored by the U.S. Department of Energy, we explore an analysis methodology that results in the characterization of the physical state of the atmospheric profile at time resolutions of five minutes and vertical resolutions of 90 m. The description includes thermodynamics and water vapor profile information derived by merging radiosonde soundings with ground-based data, and continues through specification of the cloud layer occurrence and microphysical and radiative properties derived from retrieval algorithms and parameterizations. The description of the atmospheric physical state includes a calculation of the infrared and clear and cloudy sky solar flux profiles. Validation of the methodology is provided by comparing the calculated fluxes with top of atmosphere (TOA) and surface flux measurements and by comparing the total column optical depths to independently derived estimates. We find over a 1-year period of comparison in overcast uniform skies, that the calculations are strongly correlated to measurements with biases in the flux quantities at the surface and TOA of less than 10% and median fractional errors ranging from 20% to as low as 2%. In the optical depth comparison for uniform overcast skies during the year 2000 where the optical depth varies over 3 orders of magnitude we find a mean positive bias of 46% with a median bias of less than 10% and a 0.89 correlation coefficient. The slope of the linear regression line for the optical depth comparison is 0.86 with a normal deviation of 20% about this line. In addition to a case study where we examine the cloud radiative effects at the TOA, surface and atmosphere by a middle latitude synoptic-scale cyclone, we examine the cloud top pressure and optical depth retrievals of ISCCP and LBTM over a period of 1 year. Using overcast period from the year 2000, we find that the satellite algorithms tend to bias cloud tops into the middle troposphere and underestimate optical depth in high optical depth events (greater than 100) by as much as a factor of 2.

Mace, Gerald G.↗
NASA NTRSDate not supplied

A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign and the 1998 First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). We investigate two related approaches to prediction of GS cloud properties from SGS variability: the critical saturation ratio method, and the critical relative humidity method. We find significant correlation between GS cloud water and SGS supersaturation when the critical saturation ratio is set at 100%, as well as a notable relationship between GS cloud water and the width of the SGS total water distribution. Critical relative humidity also compares well with GS cloud water. However, we also find that the assumptions of a static critical saturation ratio of 100% to be unrealistic, as well as a fixed SGS distribution width. Empirical calculations from the ARISE data show a large sensitivity of these SGS variables to GS relative humidity, and so a SGS parameterization allowing them to vary according to GS thermodynamic properties may result in more realistic GS cloud water values.

J. Brant Dodson↗
NASA NTRSDate not supplied

A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). Multiple models/reanalyses use SGS supersaturated total water (relative to GS saturation) as a threshold for partitioning available water for condensation, and indeed we find significant correlation between GS cloud water and SGS supersaturation. However, we also find that the assumption of a static threshold of 100% saturation to be unrealistic. Empirical calculations from the ARISE data show a large sensitivity of this threshold to GS relative humidity, and so a microphysical parameterization allowing the threshold to vary according to GS thermodynamic properties may result in more realistic GS cloud water values. Finally, to determine how sensitive the ARISE-derived results are to that particular campaign, we include additional data from the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) conducted in 1998. The inclusion of the second dataset will help with demonstrating the robustness of the results and their utility in improving the representation of Arctic clouds in models and reanalyses.

J Brant Dodson↗
NASA NTRS2008

Optical-Microphysical Cirrus Model

A model is presented that permits the simulation of the optical properties of cirrus clouds as measured with depolarization Raman lidars. It comprises a one-dimensional cirrus model with explicit microphysics and an optical module that transforms the microphysical model output to cloud and particle optical properties. The optical model takes into account scattering by randomly oriented or horizontally aligned planar and columnar monocrystals and polycrystals. Key cloud properties such as the fraction of plate-like particles and the number of basic crystals per polycrystal are parameterized in terms of the ambient temperature, the nucleation temperature, or the mass of the particles. The optical-microphysical model is used to simulate the lidar measurement of a synoptically forced cirrostratus in a first case study. It turns out that a cirrus cloud consisting of only monocrystals in random orientation is too simple a model scenario to explain the observations. However, good agreement between simulation and observation is reached when the formation of polycrystals or the horizontal alignment of monocrystals is permitted. Moreover, the model results show that plate fraction and morphological complexity are best parameterized in terms of particle mass, or ambient temperature which indicates that the ambient conditions affect cirrus optical properties more than those during particle formation. Furthermore, the modeled profiles of particle shape and size are in excellent agreement with in situ and laboratory studies, i.e., (partly oriented) polycrystalline particles with mainly planar basic crystals in the cloud bottom layer, and monocrystals above, with the fraction of columns increasing and the shape and size of the particles changing from large thin plates and long columns to small, more isometric crystals from cloud center to top. The findings of this case study corroborate the microphysical interpretation of cirrus measurements with lidar as suggested previously.

Reichardt, J.↗
DOE OSTI2023

Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach

Abstract. Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the turbulent-kinetic-energy-based (TKE-based) simplified higher-order closure (SHOC) – a simplified version of the assumed-double-Gaussian-PDF (probability density function) higher-order-closure method. In this paper, we implement a stochastic-multiplume mass-flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian-PDF closures and couple it to SHOC (SHOC+MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference data from large-eddy simulations, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC+MF parameterization shows weak sensitivity to the vertical grid resolution and model time step.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of a New Approach for Entrainment and Detrainment Rate Estimation

Entrainment and detrainment rates (ε and δ) constitute the most critical free parameters in mass flux schemes commonly employed for cumulus parameterizations. Recently, Zhu et al. (2021) introduced a new approach that utilizes aircraft observations to simultaneously estimate ε and δ for cumulus clouds, overcoming the limitation of other observation-based approaches that solely yield ε without offering insights into δ. This study aims to comprehensively evaluate the reliability of this new approach. First, evaluation using an Explicit Mixing Parcel Model demonstrates the capability of the new approach to back-calculate predetermined ε and δ based on the physical properties before and after the entrainment mixing. Second, evaluation using large-eddy simulations illustrates that the new approach yields consistent ε and δ profiles compared to the traditional approach. Sensitivity tests indicate a weak sensitivity of the estimated δ with the new approach to the entrained air source. A decrease in the proportion of cloudy air in the assumed detrained air leads to a reduction in the estimated δ, while ε remains unaffected. Finally, the most appropriate assumptions for entrained and detrained air are discussed. Estimating ε for cumulus parameterizations involves acquiring ambient air more than 500 m away from the cloud edge as entrained air. Due to implicit mean field approximations in the traditional approach, determining the optimal assumption for detrained air properties proves challenging. Finally, this study confirms the reliability of the new approach in estimating ε and δ, providing confidence in its application to extensive observational data and advancement in parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Using High Frequency Passive Microwave, A-train, and TRMM Data to Evaluate Hydrometer Structure in the NASA GEOS-5 Data Assimilation System

Validating water vapor and prognostic condensate in global models remains a challenging research task. Model parameterizations are still subject to a large number of tunable parameters; furthermore, accurate and representative in situ observations are very sparse, and satellite observations historically have significant quantitative uncertainties. Progress on improving cloud / hydrometeor fields in models stands to benefit greatly from the growing inventory ofA-Train data sets. ill the present study we are using a variety of complementary satellite retrievals of hydrometeors to examine condensate produced by the emerging NASA Modem Era Retrospective Analysis for Research and Applications, MERRA, and its associated atmospheric general circulation model GEOS5. Cloud and precipitation are generated by both grid-scale prognostic equations and by the Relaxed Arakawa-Schubert (RAS) diagnostic convective parameterization. The high frequency channels (89 to 183.3 GHz) from AMSU-B and MRS on NOAA polar orbiting satellites are being used to evaluate the climatology and variability of precipitating ice from tropical convective anvils. Vertical hydrometeor structure from the Tropical Rainfall Measuring Mission (TRMM) and CloudSat radars are used to develop statistics on vertical hydrometeor structure in order to better interpret the extensive high frequency passive microwave climatology. Cloud liquid and ice water path data retrieved from the Moderate Resolution Imaging Spectroradiometer, MODIS, are used to investigate relationships between upper level cloudiness and tropical deep convective anvils. Together these data are used to evaluate cloud / ice water path, gross aspects of vertical hydrometeor structure, and the relationship between cloud extent and surface precipitation that the MERRA reanalysis must capture.

Robertson, Franklin↗