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NASA NTRS2000

GCSS Idealized Cirrus Model Comparison Project

The GCSS Working Group on Cirrus Cloud Systems (WG2) is conducting a systematic comparison and evaluation of cirrus cloud models. This fundamental activity seeks to support the improvement of models used for climate simulation and numerical weather prediction through assessment and improvement of the "process" models underlying parametric treatments of cirrus cloud processes in large-scale models. The WG2 Idealized Cirrus Model Comparison Project is an initial comparison of cirrus cloud simulations by a variety of cloud models for a series of idealized situations with relatively simple initial conditions and forcing. The models (16) represent the state-of-the-art and include 3-dimensional large eddy simulation (LES) models, two-dimensional cloud resolving models (CRMs), and single column model (SCM) versions of GCMs. The model microphysical components are similarly varied, ranging from single-moment bulk (relative humidity) schemes to fully size-resolved (bin) treatments where ice crystal growth is explicitly calculated. Radiative processes are included in the physics package of each model. The baseline simulations include "warm" and "cold" cirrus cases where cloud top initially occurs at about -47C and -66C, respectively. All simulations are for nighttime conditions (no solar radiation) where the cloud is generated in an ice supersaturated layer, about 1 km in depth, with an ice pseudoadiabatic thermal stratification (neutral). Continuing cloud formation is forced via an imposed diabatic cooling representing a 3 cm/s uplift over a 4-hour time span followed by a 2-hour dissipation stage with no cooling. Variations of these baseline cases include no-radiation and stable-thermal-stratification cases. Preliminary results indicated the great importance of ice crystal fallout in determining even the gross cloud characteristics, such as average vertically-integrated ice water path (IWP). Significant inter-model differences were found. Ice water fall speed is directly related to the shape of the particle size distribution and the habits of the ice crystal population, whether assumed or explicitly calculated. In order to isolate the fall speed effect from that of the associated ice crystal population, simulations were also performed where ice water fall speed was set to the same constant value everywhere in each model. Values of 20 and 60 cm/s were assumed. Current results of the project will be described and implications will be drawn. In particular, this exercise is found to strongly focus the definition of issues resulting in observed inter-model differences and to suggest possible strategies for observational validation of the models. The next step in this project is to perform similar comparisons for well observed case studies with sufficient high quality data to adequately define model initiation and forcing specifications and to support quantitative validation of the results.

Starr, David OC.↗

NASA NTRS1983

Potential impact of remote sensing data on sea-state analysis and prediction

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

Cardone, V. J.↗

NASA NTRS2002

Understanding the Global Water and Energy Cycle Through Assimilation of Precipitation-Related Observations: Lessons from TRMM and Prospects for GPM

Understanding the Earth's climate and how it responds to climate perturbations relies on what we know about how atmospheric moisture, clouds, latent heating, and the large-scale circulation vary with changing climatic conditions. The physical process that links these key climate elements is precipitation. Improving the fidelity of precipitation-related fields in global analyses is essential for gaining a better understanding of the global water and energy cycle. In recent years, research and operational use of precipitation observations derived from microwave sensors such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) have shown the tremendous potential of using these data to improve global modeling, data assimilation, and numerical weather prediction. We will give an overview of the benefits of assimilating TRMM and SSM/I rain rates and discuss developmental strategies for using space-based rainfall and rainfall-related observations to improve forecast models and climate datasets in preparation for the proposed multi-national Global Precipitation Mission (GPM).

Hou, Arthur↗

DOE OSTI2022

Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar

Model parameters are a major source of uncertainty in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the most sensitive parameters has the potential to improve solar irradiance predictions. Here, we adopted a multiobjective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California, in order to focus on parameter calibration under cloudy conditions with different aerosol loading. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE. Our investigation reveals, however, that optimizing TE14 has a limited impact on irradiance simulations under less-polluted conditions in the SGP.

14 SOLAR ENERGY↗

NASA NTRS2001

Use of Remotely-Sensed Rainfall Data in Global Modeling and Data Assimilation

Precipitation observations derived from microwave sensors available from the Tropical Rainfall Measuring Mission (TRMM) and the proposed Global Precipitation Mission (GPM) can provide crucial information needed for improving global modeling, data assimilation, and numerical weather prediction. New methodologies are being developed at NASA to make effective use of this new data type in these applications. Currently, global analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating 6-h averaged TRMM rainfall retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System. The improved analysis also leads to improved short-range forecasts in the tropics. The above results were obtained using a variational assimilation procedure that uses rainfall observations to derive moisture and temperature tendency corrections every 6 hours to compensate for errors arising from imperfect initial conditions and deficiencies in the model physics. We will describe a developmental path towards using space-borne rainfall data to empirically estimate and correct for state-dependent systematic errors in parameterized model physics. The study provides a demonstration of the potential of using remote-sensed rainfall data from microwave instruments to improve the 4-dimensional global datasets for climate analysis and numerical weather prediction.

Hou, Arthur↗

NASA NTRS1984

The effect of compact implicit differencing in a baroclinic primitive equations model

In an effort to improve forecast accuracy, the horizontal accuracy of gridpoint forecast models at a number of numerical weather prediction (NWP) centers was increased from second order to fourth order. The current GLAS model uses second-order explicit finite-difference formulas in the vertical and fourth-order explicit finite-difference formulas in the horizontal. Experiments are described which indicate the increased forecast accuracy gained by use of compact fourth-order finite differences in a simple baroclinic model. Tables show an increase in forecast accuracy by a factor of 100-150 by use of fourth-order instead of second-order vertical discretization.

Augenbaum, J. M.↗

NASA NTRS2015

Comparison of the Tangent Linear Properties of Tracer Transport Schemes Applied to Geophysical Problems.

A number of geophysical applications require the use of the linearized version of the full model. One such example is in numerical weather prediction, where the tangent linear and adjoint versions of the atmospheric model are required for the 4DVAR inverse problem. The part of the model that represents the resolved scale processes of the atmosphere is known as the dynamical core. Advection, or transport, is performed by the dynamical core. It is a central process in many geophysical applications and is a process that often has a quasi-linear underlying behavior. However, over the decades since the advent of numerical modelling, significant effort has gone into developing many flavors of high-order, shape preserving, nonoscillatory, positive definite advection schemes. These schemes are excellent in terms of transporting the quantities of interest in the dynamical core, but they introduce nonlinearity through the use of nonlinear limiters. The linearity of the transport schemes used in Goddard Earth Observing System version 5 (GEOS-5), as well as a number of other schemes, is analyzed using a simple 1D setup. The linearized version of GEOS-5 is then tested using a linear third order scheme in the tangent linear version.

4DVAR↗

DOE OSTI2022

The COMBLE campaign: a study of marine boundary-layer clouds in Arctic cold-air outbreaks

One of the most intense air mass transformations on Earth happens when cold air flows from frozen surfaces to much warmer open water in cold-air outbreaks (CAOs), a process captured beautifully in satellite imagery. Despite the ubiquity of the CAO cloud regime over high-latitude oceans, we have a rather poor understanding of its properties, its role in energy and water cycles, and its treatment in weather and climate models. The Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) was conducted to better understand this regime and its representation in models. COMBLE aimed to examine the relations between surface fluxes, boundary-layer structure, aerosol, cloud and precipitation properties, and mesoscale circulations in marine CAOs. Processes affecting these properties largely fall in a range of scales where boundary-layer processes, convection, and precipitation are tightly coupled, which makes accurate representation of the CAO cloud regime in numerical weather prediction and global climate models most challenging. COMBLE deployed an Atmospheric Radiation Measurement Mobile Facility at a coastal site in northern Scandinavia (69°N), with additional instruments on Bear Island (75°N), from December 2019 to May 2020. CAO conditions were experienced 19% (21%) of the time at the main site (on Bear Island). A comprehensive suite of continuous in situ and remote sensing observations of atmospheric conditions, clouds, precipitation, and aerosol were collected. Because of the clouds’ well-defined origin, their shallow depth, and the broad range of observed temperature and aerosol concentrations, the COMBLE dataset provides a powerful modeling test bed for improving the representation of mixed-phase cloud processes in large-eddy simulations and large-scale models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intra- hour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. Here, the framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% - 30% compared with state-of-the-art methods.

42 ENGINEERING↗

NASA NTRS2022

Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC

The 2020 ‘Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere’ (EUREC4A-OA) and the ‘Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign’ (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

Kashawn Hall↗

DOE OSTI2026

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗

DOE OSTI2020

Radiative heating rate profiles over the southeast Atlantic Ocean during the 2016 and 2017 biomass burning seasons

Marine boundary layer clouds, including the transition from stratocumulus to cumulus, are poorly represented in numerical weather prediction and general circulation models. Further uncertainties in the cloud structure arise in the presence of biomass burning carbonaceous aerosol, as is the case over the southeast Atlantic Ocean, where biomass burning aerosol is transported from the African continent. As the aerosol plume progresses across the southeast Atlantic Ocean, radiative heating within the aerosol layer has the potential to alter the thermodynamic environment and therefore the cloud structure; however, limited work has been done to quantify this along the trajectory of the aerosol plume in the region. The deployment of the first Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1) in support of the Layered Atlantic Smoke Interactions with Clouds field campaign provided a unique opportunity to collect observations of cloud and aerosol properties during two consecutive biomass burning seasons during July through October of 2016 and 2017 over Ascension Island (7.96° S, 14.35° W). Using observed profiles of temperature, humidity, and clouds from the field campaign alongside aerosol optical properties from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM), profiles of the radiative heating rate due to aerosols and clouds were computed. Radiative heating is also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Idealized experiments using the RRTM with and without aerosols and a range of values for the single-scattering albedo (SSA) demonstrate that shortwave (SW) heating within the aerosol layer above Ascension Island can locally range between 2 and 8 K d -1 depending on the aerosol optical properties, though impacts of the aerosol can be felt elsewhere in the atmospheric column. When considered under clear conditions, the aerosol has a cooling effect at the TOA, and based on the observed cloud properties at Ascension Island, the cloud albedo is not large enough to overcome this. Shortwave radiative heating due to biomass burning aerosol is not balanced by additional longwave (LW) cooling, and the net radiative impact results in a stabilization of the lower troposphere. However, these results are extremely sensitive to the single-scattering albedo assumptions in models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

A simple atmospheric model on the sphere with 100% parallelism

A simple shallow water equations model used to test numerical weather prediction schemes is presented. The spherical geometry is transformed into a formally doubly periodic configuration which can be implemented on a massively parallel computer at 100% parallelism. A nonstaggered grid with all variables defined at the grid points (circles) is used. The parallel structure was simulated by performing step 1 in a FORTRAN array, on an Amdahl serial machine, changing the signs of the winds on the dark side.

Kalnay, E.↗

NASA NTRS1984

Orographic Effects in Atmospheric Circulation

The role of orography on the large-scale and regional atmospheric circulation, with special emphasis on lee cyclogenesis was studied. Effects observed in the actual atmosphere as well as numerical weather prediction and general circulation models are included.

Duffy, D. G.↗

NASA NTRS2004

Multi-Spectral Cloud Retrievals from Moderate Image Spectrometer (MODIS)

MODIS observations from the NASA EOS Terra spacecraft (1030 local time equatorial sun-synchronous crossing) launched in December 1999 have provided a unique set of Earth observation data. With the launch of the NASA EOS Aqua spacecraft (1330 local time crossing! in May 2002: two MODIS daytime (sunlit) and nighttime observations are now available in a 24-hour period allowing some measure of diurnal variability. A comprehensive set of remote sensing algorithms for cloud masking and the retrieval of cloud physical and optical properties has been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate modeling, climate change studies, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. An overview of the instrument and cloud algorithms will be presented along with various examples, including an initial analysis of several operational global gridded (Level-3) cloud products from the two platforms. Statistics of cloud optical and microphysical properties as a function of latitude for land and Ocean regions will be shown. Current algorithm research efforts will also be discussed.

Platnick, Steven↗

NASA NTRS2018

Characterizing Differences in the Aerosol Plume and Cloud Structure over Ascension Island During the 2016 and 2017 Biomass Burning Seasons

Marine boundary layer clouds, including the transition from stratocumulus to cumulus, are poorly represented in numerical weather prediction and general circulation models. In many cases, the complex physical relationships between marine boundary cloud morphology and the environmental conditions in which the clouds exist are not well understood. Such uncertainties arise in the presence of biomass burning carbonaceous aerosol, as is the case over the southeast Atlantic Ocean. It is likely that the absorbing and heating properties of these aerosols influence the microphysical composition and macrophysical arrangement of marine stratocumulus and trade cumulus in this region; however, this has yet to be quantified. The deployment of the Atmospheric Radiation Measurement Mobile Facility #1 (AMF1) in support of LASIC (Layered Atlantic Smoke Interactions with Clouds) provided a unique opportunity to collect observations of cloud and aerosol properties during two consecutive biomass burning seasons during July through October of 2016 and 2017 over Ascension Island (7.96 S, 14.35 W). Through the use of AMF1 observations, the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and back trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), it will be demonstrated that differences in the atmospheric circulation during the two years result in varying aerosol conditions over Ascension Island. When the aerosol plume is overhead, the aerosol loading is higher during the 2016 season as a result of a weaker subtropical high-pressure system. Furthermore, the aerosol plume originates from central Africa in 2016, but further south in 2017. Contrasts in the season-to-season and day-to-day aerosol loading are used to categorize boundary layer cloud and sub-cloud turbulence measurements above Ascension Island using the AMF1 Doppler lidar and cloud radar.

Collow, Allison B. Marquardt↗

NASA NTRS2018

Radiative Heating Profiles over Ascension Island during the 2016 and 2017 Biomass Burning Seasons

Marine boundary layer clouds, including the transition from stratocumulus to cumulus, are poorly represented in numerical weather prediction and general circulation models. In many cases, the complex physical relationships between cloud morphology and the environmental conditions in which marine boundary layer clouds exist are not well understood. Such uncertainties arise in the presence of biomass burning carbonaceous aerosol, as is the case over the southeast Atlantic Ocean, where it is likely that the absorbing and heating properties of these aerosols modify the microphysical composition and macrophysical arrangement of marine stratocumulus and trade cumulus. The deployment of the Atmospheric Radiation Measurement Mobile Facility #1 (AMF1) in support of LASIC (Layered Atlantic Smoke Interactions with Clouds) provided a unique opportunity to observe thermodynamic, cloud and aerosol properties during two consecutive biomass burning seasons from July through October of 2016 and 2017 over Ascension Island (7.96 S, 14.35 W). These observations in conjunction with radiation transfer modeling were used to assess the impact of biomass burning carbonaceous aerosol plumes as they passed over the site.Thermodynamic profiles were generated using a combination of radiosonde data and thermodynamic profilers to provide high temporal resolution profiles of quantities that are important for cloud development, such as CAPE and CIN. Coincident Ka-band radar and lidar profiles were used to characterize the cloud and sub-cloud structure. The resulting thermodynamic and cloud profiles are used as input forcing for the Rapid Radiative Transfer Model (RRTM) to compute radiative heating profiles over the observation site. Idealized experiments using RRTM, with and without aerosols present, are used to assess the impacts of the absorbing aerosol on the heating rate profiles.

Cumulus↗

NASA NTRS2019

NASA Disasters Program: A Partner for Coastal Disaster Response, Resiliency, and Risk Reduction

Summary: The NASA Applied Sciences: Disasters Program has kicked off a pilot project as “Mid-Atlantic Resiliency Demonstration Study, Communities at Intensive Risk” in FY17 and FY18; Efforts focus on a holistic, interdisciplinary approach to integrate a full suite of capabilities from numerical weather prediction, tide and surge modeling at high spatial resolution and urban/neighborhood scales, and remote sensing capabilities for water detection and depth estimation; Future efforts - Partner with Federal Agencies, academia professional societies to complement and augment ongoing work using Earth observations unique to NASA - Translate hazards to risk in order to understand local and regional impacts and minimize vulnerability.

Bell, Jordan↗