Engineering topics

Nayeong Cho

Publications and source records attributed to Nayeong Cho.

12 records

NASA NTRS2025

Describing Seasonal Mixtures of Cloud Regimes Via “Regimes of Regimes”

We propose a new type of cloud classification, relevant to monthly or longer time scales, but which inherently still encompasses cloud subgrid variability information at ~100 km scales. Our proposed classification partitions frequencies of occurrence over these scales of previously defined cloud regimes (CRs). We call the resulting distinct cloud entities regimes of regimes (RORs). While the CRs have been previously shown to successfully classify daily mesoscale subgrid variability via distributions of cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness, the RORs essentially represent the prevalent seasonal mixtures of these CRs. RORs thus embody the seasonal cloudiness of a mesoscale region. We show that each ROR can still be associated with more traditional cloud classifications via composites of coincident active (lidar and cloud radar) cloud views. In a first application that gauges the potential utility of RORs, we pair them with CERES EBAF radiative fluxes to gain insight into recent trends of the cloud radiative effect. The ROR corresponding to an environment of shallow convection stands out in this analysis largely because of its declining population. Our study demonstrates the potential of RORs to categorize globally mesoscale cloudiness at monthly/seasonal scales and to serve as proxies of different atmospheric states at these scales.

Nayeong Cho↗

NASA NTRS2024

Regimes of Cloud Vertical Structure From Active Observations

We introduce a new type of cloud class, which we call “active cloud regime” (ACR), owing to its provenance from active (lidar and cloud radar) spaceborne cloud observations. ACRs intend to provide a climatological description based on cloud vertical structure (CVS) of the most prevalent monthly CVS mixtures encountered at large spatial scales of ~400 km. ACRs are thus a way to create a gridded dataset of a vertically-resolved cloud mask that can facilitate joint analysis with other gridded datasets. The detailed 2D cloud mask comes from the 2B-CLDCLASS-LIDAR CloudSat dataset fusing CALIPSO (lidar) and CloudSat (cloud radar) cloud detections. We show that the global classification of cloudiness under the ACR framework provides valuable insights on how the world’s dominant cloud systems regulate the two major components of atmospheric energetics, precipitation and radiative cooling. NASA’s GEOS model allows us to demonstrate the feasibility of applying the ACR concept in Earth System Models that have the capability to produce subgrid cloudiness obeying pre-specified vertical overlap rules. Comparison of observed and simulated ACRs provides thus another means to assess the realism of modeled clouds.

cloud vertical structure↗

NASA NTRS2024

Substantial Cooling Effect From Aerosol-Induced Increase in Tropical Marine Cloud Cover

With global warming currently standing at approximately +1.2 °C since preindustrial times, climate change is a pressing global issue. Marine cloud brightening is one proposed method to tackle warming through injecting aerosols into marine clouds to enhance their reflectivity and thereby planetary albedo. However, because it is unclear how aerosols influence clouds, especially cloud cover, both climate projections and the effectiveness of marine cloud brightening remain uncertain. Here, we use satellite observations of volcanic eruptions in Hawaii to quantify the aerosol fingerprint on tropical marine clouds. We observe a large enhancement in reflected sunlight, mainly due to an aerosol-induced increase in cloud cover. This observed strong negative aerosol forcing suggests that the current level of global warming is driven by a weaker net radiative forcing than previously thought, arising from the competing effects of greenhouse gases and aerosols. This implies a greater sensitivity of Earth’s climate to radiative forcing and therefore a larger warming response to both rising greenhouse gas concentrations and reductions in atmospheric aerosols due to air quality measures. However, our findings also indicate that mitigation of global warming via marine cloud brightening is plausible and is most effective in humid and stable conditions in the tropics where solar radiation is strong.

Atmospheric science↗

NASA NTRS2022

Assessment of Two Stochastic Cloud Subcolumn Generators Using Observed Fields of Vertically Resolved Cloud Extinction

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed phase clouds when blended at ~200 m vertical and ~ 2 km horizontal scales. Upon segmenting the dataset to individual “scenes”, mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud top pressure –cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 Wm-2 for the cloud radiative effect in the shortwave part of the spectrum.

cloud variability↗

NASA NTRS2022

Machine-Learning Reveals Climate Forcing From Aerosols is Dominated by Increased Cloud Cover

Aerosol-cloud interactions have a potentially large impact on climate, but are poorly quantified and thus contribute a significant and long-standing uncertainty in climate projections. The impacts derived from climate models are poorly constrained by observations, because retrieving robust large-scale signals of aerosol-cloud interactions are frequently hampered by the considerable noise associated with meteorological co-variability. The Iceland-Holuhraun effusive eruption in 2014 resulted in a massive aerosol plume in an otherwise near-pristine environment and thus provided an ideal natural experiment to quantify cloud responses to aerosol perturbations. Here we disentangle significant signals from the noise of meteorological co-variability using a satellite-based machine-learning approach. Our analysis shows that aerosols from the eruption increased cloud cover by approximately 10%, and this appears to be the leading cause of climate forcing, rather than cloud brightening as previously thought. We find that volcanic aerosols do brighten clouds by reducing droplet size, but this has a significantly smaller radiative impact than changes in cloud fraction. These results add substantial observational constraints on the cooling impact of aerosols. Such constraints are critical for improving climate models, which still inadequately represent the complex macro-physical and micro-physical impacts of aerosol-cloud interactions.

Aerosols↗

NASA NTRS2022

Evaluation of GPROF V05 Precipitation Retrievals under Different Cloud Regimes

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

precipitation↗

NASA NTRS2021

Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes

We present an updated Cloud Regime (CR) dataset based on Moderate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud products, specifically joint histograms that partition cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness ranges. The paper focuses on an edition of the CR dataset derived from our own aggregation of MODIS pixel-level cloud retrievals on an equal area grid and pre-specified 3-hour UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1-degree daily aggregation provided by standard MODIS Level-3 data, as in previous versions of the MODIS CRs, for easier use with datasets mapped on equal angle grids. Both editions consist of 11 clusters whose centroids are nearly identical.We provide a physical interpretation of the new CRs and aspects of their climatology that have not been previously examined, such as seasonal and interannual variability of CR frequency of occurrence. We also examine the makeup and precipitation properties of the CRs assisted by independent datasets originating from active observations, and provide a first glimpse of how MODIS CRs relate to clouds as seen by ISCCP.

MODIS↗

NASA NTRS2019

The Role of Thermodynamic Phase Shifts in Cloud Optical Depth Variations with Temperature

We present a novel method that identifies the contributions of thermodynamic phase shifts and processes governing supercooled liquid and ice clouds to cloud optical depth variations with temperature using MODIS observations. Our findings suggest that thermodynamic phase shifts outweigh the net influence of processes governing supercooled liquid and ice clouds in causing increases in mid-latitudinal cold cloud optical depth with temperature. Cloud regime analysis suggests that dynamical conditions appear to have less influence on the contribution of thermodynamic phase shifts to cloud optical depth variations with temperature. Thermodynamic phase shifts also contribute more to increases in cloud optical depth during colder seasons due to the enhanced optical thickness contrast between liquid and ice clouds. The results of this study highlight the importance of thermodynamic phase shifts in explaining cold cloud optical depth increases with temperature in the current climate and may elucidate their role in the cloud optical depth feedback.

cloud optical depth↗

NASA NTRSDate not supplied

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering significant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

cloud↗

NASA NTRSDate not supplied

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering signi􀂦cant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

clouds↗

NASA NTRSDate not supplied

Cloud-Precipitation Hybrid Regimes and their Projection onto IMERG Precipitation Data

We extend and enhance the concept of the Cloud Regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), by adding precipitation information in order to better understand cloud-precipitation relationships. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, cloud-precipitation “hybrid” regimes are derived by implementing the k-means clustering algorithm with advanced initialization and objective measures to determine the most optimal clusters. By expressing precipitation rates within 1-degree grid cell as histograms and making choices on the relative weight of cloud and precipitation, we could obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part of the hybrid entroids resembles the centroid of cloud-only regimes, but still tightens the cloud-precipitation relationship by decreasing the precipitation variability of each regime. As precipitation weight progressively increases, the shape of the cloudy part of the hybrid centroids becomes blunter, while the precipitation part of the centroids sharpens. In the case where cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct features in the precipitation parts of the centroids, which allows us to project them onto the 30-minly IMERG domain. Such a projection can be used to overcome the temporal sparseness of MODIS cloud observations, which leads to great application potential for various convection-focused studies, including diurnal cycle analysis.

cloud-precipitation↗

NASA NTRSDate not supplied

Examining the Feasibility of Detailed Decomposition of Radiative Flux Anomalies to Cloud Changes

This presentation will explore the degree to which radiative flux anomalies in the last two decades seen by CERES can be interpreted as the result of changes in particular cloud types. A study of this kind is potentially enabled by the new CERES FlxByCldTyp (FBCT) product which provides combined Terra and Aqua daytime 1°-regional gridded daily and monthly top-of-the-atmosphere radiative fluxes and associated MODIS-derived cloud properties stratified by Cloud Top Pressure (CTP) and Cloud Optical Thickness (TAU), i.e., arrays of cloud properties and radiative fluxes resolved in CTP-TAU bins. It has been shown (Sun et al. 2022) that the FBCT time series of global flux anomalies tracks closely its SSF1deg and EBAF counterparts, indicating that these three products provide fluxes of consistent stability for interannual variability studies. We are thus in position to examine whether CERES regional or global radiative fluxes anomalies and trends are driven by changes in certain elements of FBCT flux arrays corresponding to particular cloud types. Anomalies in flux array elements will in turn be investigated in terms of cloud property variations within the CTP-TAU bins where the biggest flux anomalies are encountered. We will essentially seek to obtain the relative contributions to the anomalies of binned all-sky fluxes of changes in cloud fraction within cloud types and of changes in their overcast fluxes modulated by other cloud properties. Such an analysis holds great promise of unveiling the root causes of cloud-driven changes in the planetary radiation budget of the last two decades.

cloud↗