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At least 361 records · Page 20

Detecting Super-Thin Clouds with Polarized Sunlight

We report a novel method for detecting cloud particles in the atmosphere. Solar radiation backscattered from clouds is studied with both satellite data and a radiative transfer model. A distinct feature is found in the angle of linear polarization of solar radiation that is backscattered from clouds. The dominant backscattered electric field from the clear-sky Earth-atmosphere system is nearly parallel to the Earth surface. However, when clouds are present, this electric field can rotate significantly away from the parallel direction. Model results demonstrate that this polarization feature can be used to detect super-thin cirrus clouds having an optical depth of only ~0.06 and super-thin liquid water clouds having an optical depth of only ~0.01. Such clouds are too thin to be sensed using any current passive satellite instruments.

Aerosols and particles↗

Improving High-Resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with an Updated Kain-Fritsch Scheme

Efforts to improve the prediction accuracy of high resolution (1-10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale-awareness for improving high-resolution short-term precipitation forecasts in the WRF model. To address these issues, we introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain-Fritsch (KF) convective parameterization scheme in the WRF model. These changes include subgrid-scale cloud-radiation interactions, a dynamic adjustment timescale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level-based entrainment methodology that includes scale dependency. A series of 48-hour retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that (1) the source of initial conditions play a key role in high-resolution precipitation forecasting, and (2) our updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, we found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

convective parameterization↗

WRF-Chem Simulations of Lightning-NOx Production and Transport in Oklahoma and Colorado Thunderstorms Observed During DC3

The focus of this analysis is on lightning-generated nitrogen oxides (LNOx) and their distribution for two thunderstorms observed during the Deep Convective Clouds and Chemistry (DC3) field campaign in May-June 2012. The Weather Research and Forecasting Chemistry (WRF-Chem) model is used to perform cloud-resolved simulations for the May 29-30 Oklahoma severe convection, which contained one supercell, and the June 6-7 Colorado squall line. Aircraft and ground-based observations (e.g., trace gases, lightning and radar) collected during DC3 are used in comparisons against the model-simulated lightning flashes generated by the flash rate parameterization schemes (FRPSs) incorporated into the model, as well as the model-simulated LNOx predicted in the anvil outflow. Newly generated FRPSs based on DC3 radar observations and Lightning Mapping Array data are implemented in the model, along with previously developed schemes from the literature. The results of these analyses will also be compared between storms to investigate which FRPSs were most appropriate for the two types of convection and to examine the variation in the LNOx production. The simulated LNOx results from WRF-Chem will also be compared against other previously studied mid-latitude thunderstorms.

Lightning↗

Atmospheric Diabatic Heating in Different Weather States and the General Circulation

Analysis of multiple global satellite products identifies distinctive weather states of the atmosphere from the mesoscale pattern of cloud properties and quantifies the associated diabatic heating/cooling by radiative flux divergence, precipitation, and surface sensible heat flux. The results show that the forcing for the atmospheric general circulation is a very dynamic process, varying strongly at weather space-time scales, comprising relatively infrequent, strong heating events by ''stormy'' weather and more nearly continuous, weak cooling by ''fair'' weather. Such behavior undercuts the value of analyses of time-averaged energy exchanges in observations or numerical models. It is proposed that an analysis of the joint time-related variations of the global weather states and the general circulation on weather space-time scales might be used to establish useful ''feedback like'' relationships between cloud processes and the large-scale circulation.

Cooling↗

RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, kappa, are derived from observations to be approximately 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

Boundary layers↗

A CloudSat-CALIPSO View of Cloud and Precipitation Properties Across Cold Fronts over the Global Oceans

The distribution of cloud and precipitation properties across oceanic extratropical cyclone cold fronts is examined using four years of combined CloudSat radar and CALIPSO lidar retrievals. The global annual mean cloud and precipitation distributions show that low-level clouds are ubiquitous in the post frontal zone while higher-level cloud frequency and precipitation peak in the warm sector along the surface front. Increases in temperature and moisture within the cold front region are associated with larger high-level but lower mid-/low level cloud frequencies and precipitation decreases in the cold sector. This behavior seems to be related to a shift from stratiform to convective clouds and precipitation. Stronger ascent in the warm conveyor belt tends to enhance cloudiness and precipitation across the cold front. A strong temperature contrast between the warm and cold sectors also encourages greater post-cold-frontal cloud occurrence. While the seasonal contrasts in environmental temperature, moisture, and ascent strength are enough to explain most of the variations in cloud and precipitation across cold fronts in both hemispheres, they do not fully explain the differences between Northern and Southern Hemisphere cold fronts. These differences are better explained when the impact of the contrast in temperature across the cold front is also considered. In addition, these large-scale parameters do not explain the relatively large frequency in springtime post frontal precipitation.

Marine meteorology↗

Physical Characteristics of Unstructured Coronal Clouds

Active regions in the inner solar corona, when observed in X-ray emission, consist of bright, hot loops surrounded by unstructured clouds. The emission from the clouds extends to a height of≈4–5×104km at temperatures of ≈2–3 MK. These “hot clouds” are variable, but persist for many days and do not appear to connect directly to the active region streamers or other large-scale structures observed higher in the corona. We present an observational analysis of these diffuse structures to establish basic plasma parameters such as magnetic field strength, particle density, and temperature. The values ofβ, the ratio of the plasma pressure to the magnetic field pressure, were found to be generally less than unity, though often approaching unity in the upper portions of the active region, where the hot clouds are located. The magnetic field may therefore only partially confine these regions and inhibit flare-like instabilities that could otherwise be driven by gradients of plasma pressure and current density.

The Sun↗

Airborne Observations of Particle Density in High Ice Water Content (HWC) Cloud Systems

A series of airborne field campaigns has been conducted since 2014 to measure the microphysical properties of high ice-water content (HIWC) clouds in maritime mesoscale convective systems (MCS) and tropical storms. Two of these, the HIWC RADAR-2 campaign (2018) targeting cloud systems in the Gulf of Mexico and Pacific Ocean, and the HIWC-2022 campaign targeting cloud systems in the Gulf of Mexico, Caribbean Sea, and Atlantic Ocean, have been analyzed to investigate graupel encounters at typical jet aircraft cruise altitudes. In-situ cloud observations from these two campaigns confirmed results from the prior studies, indicating that high concentrations of small ice particles exist in areas of HIWC. However, in the 2022 campaign, 90% of the cloud regions with ice water content (IWC) above 2.0 g/m3 also contained significant amounts of graupel and/or frozen drops. Using a mass-size parameterization that accounts for both small ice and larger high-density particles, we show that the mass distributions in cloud regions above 2.0 g/m3 are often strongly bimodal, with the small particle mode containing roughly half of the collective cloud mass, and the graupel mode containing the other half. In contrast, HIWC cloud passes in a 2018 tropical cyclone contained less relative graupel mass than the MCS cloud systems of 2018 and 2022. We present two different methods of estimating particle mass for mixed populations of small-ice and graupel particles, one using a hybrid two-class approach, and another using a single-class approach that takes both particle size and shape metrics into account. Derived IWC using these methods are compared to bulk IWC measurements collected during the campaigns. We also show the relative occurrence of graupel at various IWC levels and discuss the potential application of these results in evaluating density-aware microphysical schemes in cloud and weather models.

Aircraft Icing↗

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data: Preprint

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data input to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

cloud properties↗

Performance of Two Cloud-Radiation Parameterization Schemes in the Finite Volume General Circulation Model for Anomalously Wet May and June 2003 Over the Continental United States and Amazonia

An objective assessment of the impact of a new cloud scheme, called Microphysics of Clouds with Relaxed Arakawa-Schubert Scheme (McRAS) (together with its radiation modules), on the finite volume general circulation model (fvGCM) was made with a set of ensemble forecasts that invoke performance evaluation over both weather and climate timescales. The performance of McRAS (and its radiation modules) was compared with that of the National Center for Atmospheric Research Community Climate Model (NCAR CCM3) cloud scheme (with its NCAR physics radiation). We specifically chose the boreal summer months of May and June 2003, which were characterized by an anomalously wet eastern half of the continental United States as well as northern regions of Amazonia. The evaluation employed an ensemble of 70 daily 10-day forecasts covering the 61 days of the study period. Each forecast was started from the analyzed initial state of the atmosphere and spun-up soil moisture from the first-day forecasts with the model. Monthly statistics of these forecasts with up to 10-day lead time provided a robust estimate of the behavior of the simulated monthly rainfall anomalies. Patterns of simulated versus observed rainfall, 500-hPa heights, and top-of-the-atmosphere net radiation were recast into regional anomaly correlations. The correlations were compared among the simulations with each of the schemes. The results show that fvGCM with McRAS and its radiation package performed discernibly better than the original fvGCM with CCM3 cloud physics plus its radiation package. The McRAS cloud scheme also showed a reasonably positive response to the observed sea surface temperature on mean monthly rainfall fields at different time leads. This analysis represents a method for helpful systematic evaluation prior to selection of a new scheme in a global model.

Sud, Y. C.↗

Fractal dimension of optical cirrus in Stripe82

ABSTRACT The geometric characteristics of dust clouds provide important information on the physical processes that structure such clouds. One of such characteristics is the 2D fractal dimension D of a cloud projected on to the sky plane. In previous studies, which were mostly based on infrared (IR) data, the fractal dimension of individual clouds was found to be in a range from 1.1 to 1.7 with a preferred value of 1.2–1.4. In this work, we use data from Stripe82 of the Sloan Digital Sky Survey to measure the fractal dimension of the cirrus clouds. This is done here for the first time for optical data with significantly better resolution as compared to IR data. To determine the fractal dimension, the perimeter-area method is employed. We also consider IR (IRAS and Herschel) counterparts of the corresponding optical fields to compare the results between the optical and IR. We find that the averaged fractal dimension across all clouds in the optical is $\langle D \rangle =1.69^{+0.05}_{-0.05}$ which is significantly larger than the fractal dimension of its IR counterparts $\langle D\rangle =1.38^{+0.07}_{-0.06}$. We examine several reasons for this discrepancy (choice of masking and minimal contour level, image and angular resolution, etc.) and find that for approximately half of our fields the different angular resolution (point spread function) of the optical and IR data can explain the difference between the corresponding fractal dimensions. For the other half of the fields, the fractal dimensions of the IR and visual data remain inconsistent, which can be associated with physical properties of the clouds, but further physical simulations are required to prove it.

79 ASTRONOMY AND ASTROPHYSICS↗

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We report we have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combined GOES-16–GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI–GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

54 ENVIRONMENTAL SCIENCES↗

Testing the Two-Layer Model for Correcting Clear Sky Reflectance near Clouds

A two-layer model (2LM) was developed in our earlier studies to estimate the clear sky reflectance enhancement due to cloud-molecular radiative interaction at MODIS at 0.47 micrometers. Recently, we extended the model to include cloud-surface and cloud-aerosol radiative interactions. We use the LES/SHDOM simulated 3D true radiation fields to test the 2LM for reflectance enhancement at 0.47 micrometers. We find: The simple model captures the viewing angle dependence of the reflectance enhancement near cloud, suggesting the physics of this model is correct; the cloud-molecular interaction alone accounts for 70 percent of the enhancement; the cloud-surface interaction accounts for 16 percent of the enhancement; the cloud-aerosol interaction accounts for an additional 13 percent of the enhancement. We conclude that the 2LM is simple to apply and unbiased.

MODIS↗

Cirrus cloud properties derived from coincident GOES and lidar data during the 1986 FIRE Cirrus Intensive Field Observations (IFO)

One of the main difficulties in detecting cirrus clouds and determining their correct altitude using satellite measurements is their nonblackness. In the present algorithm (Rossow et al., 1985) used by the International Satellite Cloud Climatology Project (ISCCP), the cirrus cloud emissivity is estimated from the derived cloud reflectance using a theoretical model relating visible (VIS, 0.65 micron) optical depth to infrared (IR, 10.5 micron) emissivity. At this time, it is unknown how accurate this approach is or how the derived cloud altitude relates to the physical properties of the cloud. The First ISCCP Regional Experiment (FIRE) presents opportunities for determining how the observed radiances depend on the cloud properties. During the FIRE Cirrus Intensive Field Observations (IFO, see Starr, 1987), time series of cloud thickness, height, and relative optical densities were measured from several surface-based lidars. Cloud microphysics and radiances at various wavelengths were also measured simultaneously over these sites from aircraft at specific times during the IFO (October 19 to November 2, 1986). Satellite-observed radiances taken simultaneously can be matched with these data to determine their relationships to the cirrus characteristics. The first step is taken toward relating all of these variables to the satellite observations. Lidar-derived cloud heights are used to determine cloud temperatures which are used to estimate cloud emissivities from the satellite IR radiances. These results are then correlated to the observed VIS reflectances for various solar zenith angles.

Minnis, Patrick↗

Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF’s summit supercomputer

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.

58 GEOSCIENCES↗

Climatic Implications of the Observed Temperature Dependence of the Liquid Water Path of Low Clouds

The uncertainty in the global climate sensitivity to an equilibrium doubling of carbon dioxide is often stated to be 1.5-4.5 K, largely due to uncertainties in cloud feedbacks. The lower end of this range is based on the assumption or prediction in some GCMs that cloud liquid water behaves adiabatically, thus implying that cloud optical thickness will increase in a warming climate if the physical thickness of clouds is invariant. Satellite observations of low-level cloud optical thickness and liquid water path have challenged this assumption, however, at low and middle latitudes. We attempt to explain the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurements (ARM) Cloud And Radiation Testbed (CART) site in the Southern Great Plains. We find that low cloud liquid water path is insensitive to temperature in winter but strongly decreases with temperature in summer. The latter occurs because surface relative humidity decreases with warming, causing cloud base to rise and clouds to geometrically thin. Meanwhile, inferred liquid water contents hardly vary with temperature, suggesting entrainment depletion. Physically, the temperature dependence appears to represent a transition from higher probabilities of stratified boundary layers at cold temperatures to a higher incidence of convective boundary layers at warm temperatures. The combination of our results and the earlier satellite findings imply that the minimum climate sensitivity should be revised upward from 1.5 K.

DelGenio, Anthony↗