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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

SBUV version 8.6 Retrieval Algorithm: Error Analysis and Validation Technique

SBUV version 8.6 algorithm was used to reprocess data from the Back Scattered Ultra Violet (BUV), the Solar Back Scattered Ultra Violet (SBUV) and a number of SBUV/2 instruments, which 'span a 41-year period from 1970 to 2011 (except a 5-year gap in the 1970s)[see Bhartia et al, 2012]. In the new version Daumont et al. [1992] ozone cross section were used, and new ozone [McPeters et ai, 2007] and cloud climatologies Doiner and Bhartia, 1995] were implemented. The algorithm uses the Optimum Estimation technique [Rodgers, 2000] to retrieve ozone profiles as ozone layer (partial column, DU) on 21 pressure layers. The corresponding total ozone values are calculated by summing ozone columns at individual layers. The algorithm is optimized to accurately retrieve monthly zonal mean (mzm) profiles rather than an individual profile, since it uses monthly zonal mean ozone climatology as the A Priori. Thus, the SBUV version 8.6 ozone dataset is better suited for long-term trend analysis and monitoring ozone changes rather than for studying short-term ozone variability. Here we discuss some characteristics of the SBUV algorithm and sources of error in the SBUV profile and total ozone retrievals. For the first time the Averaging Kernels, smoothing errors and weighting functions (or Jacobians) are included in the SBUV metadata. The Averaging Kernels (AK) represent the sensitivity of the retrieved profile to the true state and contain valuable information about the retrieval algorithm, such as Vertical Resolution, Degrees of Freedom for Signals (DFS) and Retrieval Efficiency [Rodgers, 2000]. Analysis of AK for mzm ozone profiles shows that the total number of DFS for ozone profiles varies from 4.4 to 5.5 out of 6-9 wavelengths used for retrieval. The number of wavelengths in turn depends on solar zenith angles. Between 25 and 0.5 hPa, where SBUV vertical resolution is the highest, DFS for individual layers are about 0.5.

Kramarova, N. A.↗
NASA NTRS2016

Effects of Polar Stratospheric Clouds in the Nimbus 7 LIMS Version 6 Data Set

The historic Limb Infrared Monitor of the Stratosphere (LIMS) measurements of 1978-1979 from the Nimbus 7 satellite were re-processed with Version 6 (V6) algorithms and archived in 2002. The V6 data set employs updated radiance registration methods, improved spectroscopic line parameters, and a common vertical resolution for all retrieved parameters. Retrieved profiles are spaced about every 1.6 of latitude along orbits and include the additional parameter of geopotential height. Profiles of O3 are sensitive to perturbations from emissions of polar stratospheric clouds (PSCs). This work presents results of implementing a first-order screening for effects of PSCs using simple algorithms based on vertical gradients of the O3 mixing ratio. Their occurrences are compared with the co-located, retrieved temperatures and related to the temperature thresholds needed for saturation of H2O and/or HNO3 vapor onto PSC particles. Observed daily locations where the major PSC screening criteria are satisfied are validated against PSCs observed with the Stratospheric Aerosol Monitor (SAM) II experiment also on Nimbus 7. Remnants of emissions from PSCs are characterized for O3 and HNO3 following the screening. PSCs may also impart a warm bias in the co-located LIMS temperatures, but by no more than 1-2K at the altitudes of where effects of PSCs are a maximum in the ozone; thus, no PSC screening was applied to the V6 temperatures. Minimum temperatures vary between 187 and 194K and often occur 1 to 2 km above where PSC effects are first identified in the ozone (most often between about 21 and 28 hPa). Those temperature-pressure values are consistent with conditions for the existence of nitric acid trihydrate (NAT) mixtures and to a lesser extent of super-cooled ternary solution (STS) droplets. A local, temporary uptake of HNO3 vapor of order 1-3 ppbv is indicated during mid-January for the 550K surface. Seven-month time series of the distributions of LIMS O3 and HNO3 are shown based on their gridded Level 3 data following the PSC screening. Zonal coefficients of both species are essentially free of effects from PSCs on the 550K surface, based on their average values along PV contours and in terms of equivalent latitude. Remnants of PSCs are still present in O3 on the 450K surface during mid- January. It is judged that the LIMS Level 3 data are of good quality for analyzing the larger-scale, stratospheric chemistry and transport processes during the Arctic winter of 1978- 1979.

Remsberg, Ellis↗
NASA NTRS2015

A Linearized Prognostic Cloud Scheme in NASAs Goddard Earth Observing System Data Assimilation Tools

A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA's Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.

Precipitation↗
NASA NTRS2006

The Role of Aerosols on Precipitation Processes

Cloud physics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distribution below the clouds. Therefore, the size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral--bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral-bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e., pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions.

Tao, Wei-Kuo↗
NASA NTRS1993

Analysis of the GOES 6.7 micrometer channel observations during FIRE 2

Clouds form in moist environments. FIRE Phase II Cirrus Implementation Plan (August, 1990) noted the need for mesoscale measurements of upper tropospheric water vapor content. These measurements are needed for initializing and verifying numerical weather prediction models and for describing the environment in which cirrus clouds develop and dissipate. Various instruments where deployed to measure the water vapor amounts of the upper troposphere during FIRE II (e.g. Raman lidar, CLASS sonds and new cryogenic frost hygrometer on-board aircraft). The formation, maintenance and dissipation of cirrus clouds involve the time variation of the water budget of the upper troposphere. The GOES 6.7 mu m radiance observations are sensitive to the upper tropospheric relative humidity, and therefore proved extremely valuable in planning aircraft missions during the field phase of FIRE II. Warm 6.7 mu m equivalent black body temperatures indicate a relatively dry upper troposphere and were associated with regions generally free of cirrus clouds. Regions that were colder, implying more moisture was available may or may not have had cirrus clouds present. Animation of a time sequence of 6.7 mu m images was particularly useful in planning various FIRE missions. The 6.7 mu m observations can also be very valuable in the verification of model simulations and describing the upper tropospheric synoptic conditions. A quantitative analysis of the 6.7 mu m measurement is required to successfully incorporate these satellite observations into describing the upper tropospheric water vapor budget. Recently, Soden and Bretherton (1993) have proposed a method of deriving an upper tropospheric humidity based on observations from the GOES 6.7 mu m observations. The method is summarized in the next section. In their paper they compare their retrieval method to radiance simulations. Observations were also compared to ECMWF model output to assess the model performance. The FIRE experiment provides a unique opportunity to further verify the GOES upper tropospheric relative humidity retrieval scheme by providing (1) aircraft observations to cross-validate the calibration of the GOES 6.7 mu m channel, (2) accurate upper tropospheric water vapor concentrations for verification, and (3) veritical variability of upper tropospheric water vapor.

Soden, B. J.↗
NASA NTRS2007

Multi-Layer Arctic Mixed-Phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity Experiments

A cloud-resolving model (CRM) is used to simulate the multiple-layer mixed-phase stratiform (MPS) clouds that occurred during a three-and-a-half day subperiod of the Department of Energy-Atmospheric Radiation Measurement Program s Mixed-Phase Arctic Cloud Experiment (M-PACE). The CRM is implemented with an advanced two-moment microphysics scheme, a state-of-the-art radiative transfer scheme, and a complicated third-order turbulence closure. Concurrent meteorological, aerosol, and ice nucleus measurements are used to initialize the CRM. The CRM is prescribed by time-varying large-scale advective tendencies of temperature and moisture and surface turbulent fluxes of sensible and latent heat. The CRM reproduces the occurrences of the single- and double-layer MPS clouds as revealed by the M-PACE observations. However, the simulated first cloud layer is lower and the second cloud layer thicker compared to observations. The magnitude of the simulated liquid water path agrees with that observed, but its temporal variation is more pronounced than that observed. As in an earlier study of single-layer cloud, the CRM also captures the major characteristics in the vertical distributions and temporal variations of liquid water content (LWC), total ice water content (IWC), droplet number concentration and ice crystal number concentration (nis) as suggested by the aircraft observations. However, the simulated mean values differ significantly from the observed. The magnitude of nis is especially underestimated by one order of magnitude. Sensitivity experiments suggest that the lower cloud layer is closely related to the surface fluxes of sensible and latent heat; the upper cloud layer is probably initialized by the large-scale advective cooling/moistening and maintained through the strong longwave (LW) radiative cooling near the cloud top which enhances the dynamical circulation; artificially turning off all ice-phase microphysical processes results in an increase in LWP by a factor of 3 due to interactions between the excessive LW radiative cooling and extra cloud water; heating caused by phase change of hydrometeors could affect the LWC and cloud top height by partially canceling out the LW radiative cooling. It is further shown that the resolved dynamical circulation appears to contribute more greatly to the evolution of the MPS cloud layers than the parameterized subgrid-scale circulation.

Luo, Yali↗
NASA NTRS2016

Regime-Based Evaluation of Cloudiness in CMIP5 Models

The concept of Cloud Regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating for each gridcell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product (long-term average total cloud amount [TCA]), cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our findings support previous studies showing that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite their shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer (MODIS) cloud observations evaluated against ISCCP as if they were another model output. Lastly, cloud simulation performance is contrasted with each model's equilibrium climate sensitivity (ECS) in order to gain insight on whether good cloud simulation pairs with particular values of this parameter.

cloud climatology↗
NASA NTRS2013

Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II

Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.

Monte Carlo Bayesian inference↗
NASA NTRS2012

The Role of Aerosols on Precipitation Processes: Cloud Resolving Model Simulations

Cloud microphysics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e. pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions. The model is tested by studying the evolution of deep cloud systems in the west Pacific warm pool region, the sub-tropics (Florida) and midlatitudes using identical thermodynamic conditions but with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. Results indicate that the low CCN concentration case produces rainfall at the surface sooner than the high CeN case but has less cloud water mass aloft. Because the spectral-bin model explicitly calculates and allows for the examination of both the mass and number concentration of species in each size category, a detailed analysis of the instantaneous size spectrum can be obtained for these cases. It is shown that since the low (CN case produces fewer droplets, larger sizes develop due to greater condensational and collection growth, leading to a broader size spectrum in comparison to the high CCN case. Sensitivity tests were performed to identify the impact of ice processes, radiation and large-scale influence on cloud-aerosol interactive processes, especially regarding surface rainfall amounts and characteristics (i.e., heavy or convective versus light or stratiform types). In addition, an inert tracer was included to follow the vertical redistribution of aerosols by cloud processes. We will also give a brief review from observational evidence on the role of aerosol on precipitation processes.

Tao, Wei-Kuo↗
NASA NTRS2017

Improvements in the On-Orbit Response Versus Scan Angle Characterization of the Aqua MODIS Reflective Solar Bands

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) has been chosen by the Global Space-based Inter-Calibration System operational community as the reference sensor in cross-sensor calibration. A number of geostationary orbit and low-earth orbit sensors use the 0.64-μm band from Aqua MODIS as a calibration reference. After over 15 years on-orbit, the performance characteristics of the MODIS instrument have changed, with effects evident at short wavelengths. MODIS employs a reflectance-based calibration using the solar diffuser measurements with the monthly lunar observations facilitating a response versus scan angle characterization on-orbit. As the instrument continues to operate beyond its design lifetime of 6 years, the on-board calibrators alone are insufficient to accurately characterize the instrument's response at all scan angles. This results in a long-term reflectance drift, particularly observed in the 0.64- and 0.85-μm bands, while observing the temporally invariant desert sites. Long-term reflectance drifts of up to 2% and 3% are observed at the beginning of scan for the 0.64- and 0.85-μm bands, respectively. An approach using earth-view response to supplement the on-board calibrator measurements has been shown to overcome these inadequacies and is now implemented for the 0.64-, 0.85-, 0.46-, and 0.55-μm land bands of Aqua MODIS. This paper presents the details related to the algorithm implementation and an independent evaluation using the Dome Concordia site and deep-convective clouds. This approach has been reviewed, tested, and approved by the MODIS science team and has been implemented in the forward production of the MODIS L1B Collection 6 starting July 9, 2016. This enhanced approach has also been adopted in the MODIS L1B Collection 6.1 reprocess for the entire mission to facilitate an improved quality of downstream science products. The results with the enhanced approach reduce the reflectance drifts to within 0.5% for most cases.

Angal, Amit↗
NASA NTRS2021

An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin

Southern Africa produces almost a third of the Earth’s biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ~ 350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ~ 100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.

Jens Redemann↗
NASA NTRS2005

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Cloud microphysics are inevitable affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds, Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effect of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral-bin microphysical scheme was implemented into the the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral-bim microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e., pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions.

Tao, Wei-Kuo↗
NASA NTRS2008

Reconfigurable Processing for Satellite On-Board Automatic Cloud Cover Assessment (ACCA)

Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations.

El-Araby, Esam↗
DOE OSTI2025

SHADOW4: the popular ray tracing revived for evolving synchrotron sources in fourth-generation storage rings

We present SHADOW4, a new version of the popular ray tracing code. The SHADOW kernel has been completely rewritten in Python applying modern concepts of software engineering. A new user interface is available in the OASYS ecosystem. The new tool has been designed and implemented preparing the future needs both in computing (cloud computing, AI integration) and in the transit to fourth generation sources and beyond.

Sanchez del Rio, Manuel↗
DOE OSTI2024

Feedback-based quantum algorithm inspired by counterdiabatic driving

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Zooming in: SCREAM at 100 m using regional refinement over the San Francisco Bay Area

Pushing global climate models to large-eddy simulation (LES) scales over complex terrain has remained a major challenge. This study presents the first known implementation of a global model – SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) – at 100 m horizontal resolution using a regionally refined mesh (RRM) over the San Francisco Bay Area. Two hindcast simulations were conducted to test performance under both strong synoptic forcing and weak, boundary-layer-driven conditions. We demonstrate that SCREAM can stably run at LES scales while realistically capturing topography, surface heterogeneity, and coastal processes. The 100 m SCREAM-RRM substantially improves near-surface wind speed, temperature, humidity, and pressure biases compared to the baseline 3.25 km simulation, and better reproduces fine-scale wind oscillations and boundary-layer structures. These advances leverage SCREAM's scale-aware SHOC turbulence parameterization, which transitions smoothly across scales without tuning. Performance tests show that while CPU-only simulations remain costly, GPU acceleration with SCREAMv1 on NERSC's Perlmutter system enables two-day hindcasts to complete in under two wall-clock days. Our results open the door to LES-scale studies of orographic flows, boundary-layer turbulence, and coastal clouds within a fully comprehensive global modeling framework.

Geosciences↗