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NASA NTRS2020

High Ice Water Content in Tropical Cyclones during NASA/FAA Radar Flight Campaigns with Comparison to Numerical Simulations

Results from two recent flight campaigns into High Ice Water Content (HIWC) are summarized. HIWC was detected with airborne radar and measured via microphysical probes mounted on NASA’s DC-8. Results from the campaign demonstrate detectability of HIWC using airborne radar. In the cases examined, tropical storms are more proficient at producing large areas of HIWC than hurricanes. Three-dimensional, numerical simulations of tropical cyclones encountered during the campaign are also presented and are compared to measurements in order to understand conditions associated with HIWC. Within Hurricane Lane (2018), the highest ice water contents were found in regenerating thermal plumes located in the eye wall and in feeder bands. Within Tropical Storm Danny (2015), large areas of ice water content greater than 1 g m-3 were fed by broad-areas of convection occurring downwind from the center of circulation.

Aviation hazards↗

NASA NTRS1998

The NASA-Langley Wake Vortex Modelling Effort in Support of an Operational Aircraft Spacing System

Two numerical modelling efforts, one using a large eddy simulation model and the other a numerical weather prediction model, are underway in support of NASA's Terminal Area Productivity program. The large-eddy simulation model (LES) has a meteorological framework and permits the interaction of wake vortices with environments characterized by crosswind shear, stratification, humidity, and atmospheric turbulence. Results from the numerical simulations are being used to assist in the development of algorithms for an operational wake-vortex aircraft spacing system. A mesoscale weather forecast model is being adapted for providing operational forecast of winds, temperature, and turbulence parameters to be used in the terminal area. This paper describes the goals and modelling approach, as well as achievements obtained to date. Simulation results will be presented from the LES model for both two and three dimensions. The 2-D model is found to be generally valid for studying wake vortex transport, while the 3-D approach is necessary for realistic treatment of decay via interaction of wake vortices and atmospheric boundary layer turbulence. Meteorology is shown to have an important affect on vortex transport and decay. Presented are results showing that wake vortex transport is unaffected by uniform fog or rain, but wake vortex transport can be strongly affected by nonlinear vertical change in the ambient crosswind. Both simulation and observations show that atmospheric vortices decay from the outside with minimal expansion of the core. Vortex decay and the onset three-dimensional instabilities are found to be enhanced by the presence of ambient turbulence.

Proctor, Fred H.↗

NASA NTRS1985

Utilization of satellite data and regional scale numerical models in short range weather forecasting

Overwhelming evidence was developed in a number of studies of satellite data impact on numerical weather prediction that it is unrealistic to expect satellite temperature soundings to improve detailed regional numerical weather prediction. It is likely that satellite data over the United States would substantially impact mesoscale dynamical predictions if the effort were made to develop a composite moisture analysis system. The horizontal variability of moisture, most clearly depicited in images from satellite water vapor channels, would not be determined from conventional rawinsondes even if that network were increased by a doubling of both the number of sites and the time frequency.

Kreitzberg, C. W.↗

NASA NTRS1983

Predictability of planetary waves - Interhemispheric differences and the effects of stationary forcing

An attempt is made to determine the way in which errors appear in the ultralong waves of numerical weather prediction models, as well as the means by which such errors may be alleviated. Such efforts to improve the numerical forecasting of planetary waves derive their importance from the fact that energetic large scale phenomena are the basis for prospective improvements in extended range forecasting. A simple, linear, quasi-geostrophic and barotropic model is presently used in real data forecast simulations to explore, in an idealized context, several effects that may clarify discrepancies betweeen predictability theory and the performance of current numerical weather prediction models. Attention is given to the degree to which discrepancies may be accounted for by linear theory.

Roads, J. O.↗

NASA NTRS1983

A numerical investigation of the effects of cloudiness on mesoscale atmospheric circulation

The development of a comprehensive scheme for computing radiative transfer in a cloudy, stratified atmosphere is described, and the interactions of radiation, clouds, surface heating, and frictional processes are investigated utilizing a three-dimensional numerical weather prediction model. This model is described along with the design of a reliable radiative transfer scheme which allows any vertical moisture and temperature stratifications occurring within a numerical weather prediction model to be taken into account. Some preliminary results of sensitivity tests for three 24-hour simulations are depicted in order to identify the forecast fields which respond to change in atmospheric radiation induced by cloudiness.

Wong, V. C.↗

NASA NTRS2020

Robustness and Behavior of Adjoint Calculations of Observation Impacts in Numerical Weather Prediction

Adjoint models are powerful tools that can be used to estimate the impact of observations on a chosen norm for numerical weather prediction forecasts. In this study, the Global Modeling and Assimilation Office (NASA/GMAO) Observing System Simulation Experiment framework is employed to investigate the behavior of the adjoint tool in an environment where the 'true' state of the atmosphere is fully known. This allows for the calculation of adjoint estimates of observation impact for very short forecast times including the zero-hour analysis state. The adjoint calculations using self-analysis verification can also be compared to adjoint calculations using the 'truth' as verification in order to characterize the robustness of adjoint estimations in the operational setting. Results from a experiments exploring various aspects of performance of the adjoint tool will be presented.

Prive, Nikki C.↗

NASA NTRS1992

Assimilation of ERBE data with a nonlinear programming technique to improve cloud-cover diagnosis

A method is developed to assimilate satellite data for the purpose of improving the diagnosis of fractional cloud cover within a numerical weather prediction model. The method makes use of a nonlinear programming technique to find a set of parameters for the cloud diagnosis that minimizes the difference between the observed and model-produced outgoing longwave radiation (OLR). The algorithm and theoretical basis of the method are presented. The method has been applied in two forecast experiments using a numerical weather prediction model. The results from a winter case demonstrate that the root-mean-square (rms) difference between the observed and forecasted OLR can be reduced by 50 percent when the optimized cloud diagnosis is used, with the remaining rms difference within the background noise.

Wu, Xiangqian↗

NASA NTRS2021

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗

NASA NTRS2007

New, Improved Bulk-microphysical Schemes for Studying Precipitation Processes in WRF: Comparisons with Other Schemes - Part 1

Advances in computing power allow atmospheric prediction models to be mn at progressively finer scales of resolution, using increasingly more sophisticated physical parameterizations and numerical methods. The representation of cloud microphysical processes is a key component of these models, over the past decade both research and operational numerical weather prediction models have started using more complex microphysical schemes that were originally developed for high-resolution cloud-resolving models (CRMs). A recent report to the United States Weather Research Program (USWRP) Science Steering Committee specifically calls for the replacement of implicit cumulus parameterization schemes with explicit bulk schemes in numerical weather prediction (NWP) as part of a community effort to improve quantitative precipitation forecasts (QPF). An improved Goddard bulk microphysical parameterization is implemented into a state-of the-art of next generation of Weather Research and Forecasting (WRF) model. High-resolution model simulations are conducted to examine the impact of microphysical schemes on two different weather events (a midlatitude linear convective system and an Atllan"ic hurricane). The results suggest that microphysics has a major impact on the organization and precipitation processes associated with a summer midlatitude convective line system. The 31CE scheme with a cloud ice-snow-hail configuration led to a better agreement with observation in terms of simulated narrow convective line and rainfall intensity. This is because the 3ICE-hail scheme includes dense ice precipitating (hail) particle with very fast fall speed (over 10 m/s). For an Atlantic hurricane case, varying the microphysical schemes had no significant impact on the track forecast but did affect the intensity (important for air-sea interaction)

Tao, W.-K.↗

NASA NTRS2005

Contrail Tracking and ARM Data Product Development

A contrail tracking system was developed to help in the assessment of the effect of commercial jet contrails on the Earth's radiative budget. The tracking system was built by combining meteorological data from the Rapid Update Cycle (RUC) numerical weather prediction model with commercial air traffic flight track data and satellite imagery. A statistical contrail-forecasting model was created a combination of surface-based contrail observations and numerical weather analyses and forecasts. This model allows predictions of widespread contrail occurrences for contrail research on either a real-time basis or for long-term time scales. Satellite-derived cirrus cloud properties in polluted and unpolluted regions were compared to determine the impact of air traffic on cirrus.

Duda, David P.↗

NASA NTRS2006

Introduction to MODIS Cloud Products

The Earth's radiative energy balance and hydrological cycle are fundamentally coupled with the distribution and properties of clouds. Therefore, the ability to remotely infer cloud properties and their variation in space and time is crucial for establishing climatologies as a reference for validation of present-day climate models and in assessing future climate change. Remote cloud observations also provide data sets useful for testing and improving cloud model physics, and for assimilation into numerical weather prediction models. The MODerate Resolution Imaging Spectroradiometer (MODIS) imagers on the Terra and Aqua Earth Observing System (EOS) platforms provide the capability for globally retrieving these properties using passive solar reflectance and infrared techniques. In addition to providing measurements similar to those offered on a suite of historical operational weather platforms such as the Advanced Very High Resolution Radiometer (AVHRR), the High-resolution Infrared Radiation Sounder (HIRS), and the Geostationary Operational Environmental Satellite (GOES), MODIS provides additional spectral and/or spatial resolution in key atmospheric bands, along with on-board calibration, to expand the capability for global cloud property retrievals. The core MODIS operational cloud products include cloud top pressure, thermodynamic phase, optical thickness, particle size, and water path, and are derived globally at spatial resolutions of either 1- or 5-km (referred to as Level-2 or pixel-level products). In addition, the MODIS atmosphere team (collectively providing cloud, aerosol, and clear sky products) produces a combined gridded product (referred to as Level-3) aggregated to a 1 equal-angle grid, available for daily, eight-day, and monthly time periods. The wealth of information available from these products provides critical information for climate studies as well as the continuation and improved understanding of existing satellite-based cloud climatologies derived from heritage instruments. This chapter provides an overview of the MODIS Level-2 and -3 operational cloud products.

Baum, Bryan A.↗

NASA NTRS1987

A survey of nested grid techniques and their potential for use within the MASS weather prediction model

A survey of various one- and two-way interactive nested grid techniques used in hydrostatic numerical weather prediction models is presented and the advantages and disadvantages of each method are discussed. The techniques for specifying the lateral boundary conditions for each nested grid scheme are described in detail. Averaging and interpolation techniques used when applying the coarse mesh grid (CMG) and fine mesh grid (FMG) interface conditions during two-way nesting are discussed separately. The survey shows that errors are commonly generated at the boundary between the CMG and FMG due to boundary formulation or specification discrepancies. Methods used to control this noise include application of smoothers, enhanced diffusion, or damping-type time integration schemes to model variables. The results from this survey provide the information needed to decide which one-way and two-way nested grid schemes merit future testing with the Mesoscale Atmospheric Simulation System (MASS) model. An analytically specified baroclinic wave will be used to conduct systematic tests of the chosen schemes since this will allow for objective determination of the interfacial noise in the kind of meteorological setting for which MASS is designed. Sample diagnostic plots from initial tests using the analytic wave are presented to illustrate how the model-generated noise is ascertained. These plots will be used to compare the accuracy of the various nesting schemes when incorporated into the MASS model.

Koch, Steven E.↗

NASA NTRS2015

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

OSSE↗

NASA NTRS1987

Use of simulated satellite radiances from a mesoscale numerical model to understand kinematic and dynamic processes

Techniques by which mesoscale numerical weather prediction model output and radiative transfer codes are combined to simulate the radiance fields that a given passive temperature/moisture satellite sensor would see if viewing the evolving model atmosphere are introduced. The goals are to diagnose the dynamical atmospheric processes responsible for recurring patterns in observed satellite radiance fields, and to develop techniques to anticipate the ability of satellite sensor systems to depict atmospheric structures and provide information useful for numerical weather prediction (NWP). The concept of linking radiative transfer and dynamical NWP codes is demonstrated with time sequences of simulated radiance imagery in the 24 TIROS vertical sounder channels derived from model integrations for March 6, 1982.

Kalb, Michael↗

NASA NTRS2013

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Model Error↗

NASA NTRS2014

Expansion of the Real-time Sport-land Information System for NOAA/National Weather Service Situational Awareness and Local Modeling Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center has been running a real-time version of the Land Information System (LIS) since summer 2010 (hereafter, SPoRTLIS). The real-time SPoRT-LIS runs the Noah land surface model (LSM) in an offline capacity apart from a numerical weather prediction model, using input atmospheric and precipitation analyses (i.e., "forcings") to drive the Noah LSM integration at 3-km resolution. Its objectives are to (1) produce local-scale information about the soil state for NOAA/National Weather Service (NWS) situational awareness applications such as drought monitoring and assessing flood potential, and (2) provide land surface initialization fields for local modeling initiatives. The current domain extent has been limited by the input atmospheric analyses that drive the Noah LSM integration within SPoRT-LIS, specifically the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analyses. Due to the nature of the geographical edges of the Stage IV precipitation grid and its limitations in the western U.S., the SPoRT-LIS was originally confined to a domain fully nested within the Stage IV grid, over the southeastern half of the Conterminous United States (CONUS). In order to expand the real-time SPoRT-LIS to a full CONUS domain, alternative precipitation forcing datasets were explored in year-long, offline comparison runs of the Noah LSM. Based on results of these comparison simulations, we chose to implement the radar/gauge-based precipitation analyses from the National Severe Storms Laboratory as a replacement to the Stage IV product. The Multi-Radar Multi-Sensor (MRMS; formerly known as the National Mosaic and multi-sensor Quantitative precipitation estimate) product has full CONUS coverage at higher-resolution, thereby providing better coverage and greater detail than that of the Stage IV product. This paper will describe the expanded/upgraded SPoRT-LIS, present comparisons between the original and upgraded SPoRT-LIS, and discuss the path forward for future collaboration opportunities with SPoRT partners in the NWS.

Case, Jonathan L.↗

NASA NTRS2017

JIGSAW-GEO (1.0): Locally Orthogonal Staggered Unstructured Grid Generation for General Circulation Modelling on the Sphere

An algorithm for the generation of non-uniform, locally orthogonal staggered unstructured spheroidal grids is described. This technique is designed to generate very high-quality staggered VoronoiDelaunay meshes appropriate for general circulation modelling on the sphere, including applications to atmospheric simulation, ocean-modelling and numerical weather prediction. Using a recently developed Frontal-Delaunay refinement technique, a method for the construction of high-quality unstructured spheroidal Delaunay triangulations is introduced. A locally orthogonal polygonal grid, derived from the associated Voronoi diagram, is computed as the staggered dual. It is shown that use of the Frontal-Delaunay refinement technique allows for the generation of very high-quality unstructured triangulations, satisfying a priori bounds on element size and shape. Grid quality is further improved through the application of hill-climbing-type optimisation techniques. Overall, the algorithm is shown to produce grids with very high element quality and smooth grading characteristics, while imposing relatively low computational expense. A selection of uniform and non-uniform spheroidal grids appropriate for high-resolution, multi-scale general circulation modelling are presented. These grids are shown to satisfy the geometric constraints associated with contemporary unstructured C-grid-type finite-volume models, including the Model for Prediction Across Scales (MPAS-O). The use of user-defined mesh-spacing functions to generate smoothly graded, non-uniform grids for multi-resolution-type studies is discussed in detail.

geophysical fluid dynamics↗

NASA NTRS1984

VAS data assimilation into a mesoscale model

In numerical weather prediction, one of the arising problems has always been related to insufficient data, especially in cases involving scales smaller than the synoptic rawinsonde network. The Visible Infrared Spin-Scan Radiometer (VISSR) Atmospheric Sounder (VAS) instrument on the Geostationary Operational Environmental Satellite (GOES) can provide mesoscale vertical profiles of temperature and moisture several times over a twelve hour period. However, as this data is only available over a relatively small area at any one time, the VAS data is more suited to data assimilation. A variational method has been developed to assimilate VAS data into a mesoscale model. In the present paper, the assimilation method and VAS data from one case are discussed. It is found that the greatest improvements to the forecast resulted when the VAS data resolved the mesoscale structure of the temperature and relative humidity fields.

Cram, J. M.↗