Search NASA⌕ Search

SEARCH · Search NASA

Results for “Forecast Models”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

Mathematical algorithms to maximize performance in numerical weather prediction

Numerical weather prediction models, which involve the solution of non-linear partial differential equations at points on an extensive three dimensional grid, are ideally suited for processing on vector machines. It was logical therefore that the new global forecast model to be implemented at the Meteorological Office should be written in vector code for the CYBER 205. In order to achieve full efficiency and to reduce storage requirements the model used 32-bit arithmetic which was found to provide high enough precision. Unfortunately, however, the trigonometrical and logarithmic functions provided by CDC could only handle 64-bit vectors and, although written in efficient scalar code, did not take advantage of the special facilities of a vector processor. It was therefore necessary to rewrite the functions in vector code to handle both 32 and 64-bit vectors. There was also no half-precision compiler available for the Cyber 205 at that time and so the functions, like the model, had to make extensive use of the special call syntax. This made the code more difficult to write but it allowed much greater flexibility in that it became possible to access the exponent of a floating-point number independently of its coefficient. A description is given of the technique and the results which were achieved are summarized.

Foreman, A.↗

Early Comparison of Ocean Analyses Using JEDI/SOCA 3 DVar vs. GEOS-S2S- 3 ODAS LETKF

At NASA’s Global Modeling and Assimilation Office (GMAO), data assimilation (DA) for the next-generation Goddard Earth Observing System Subseasonal-to-Seasonal (GEOS-S 2 S) coupled-model forecast system will transition to the Joint Effort for DA Integration (JEDI) system, which includes the marine DA component SOCA (Sea ice, Ocean, and Coupled Assimilation). It is envisioned that incorporating SOCA into GMAO Earth System Modeling will allow a more systematic approach to assimilating new data types (e.g., SWOT KaRIn), increasing resolution (e.g., 1/12°), and facilitating weakly (and eventually strongly) coupled DA (air-sea-ice, etc.). To prepare for this transition, testing is underway to compare SOCA results at high ocean resolution against the current GEOS-S 2 S Version 3 Ocean DA System (ODAS) 1 using similar initial conditions and observations.

David Russell↗

Approach to Integrate Global-Sun Models of Magnetic Flux Emergence and Transport for Space Weather Studies

The Sun lies at the center of space weather and is the source of its variability. The primary input to coronal and solar wind models is the activity of the magnetic field in the solar photosphere. Recent advancements in solar observations and numerical simulations provide a basis for developing physics-based models for the dynamics of the magnetic field from the deep convection zone of the Sun to the corona with the goal of providing robust near real-time boundary conditions at the base of space weather forecast models. The goal is to develop new strategic capabilities that enable characterization and prediction of the magnetic field structure and flow dynamics of the Sun by assimilating data from helioseismology and magnetic field observations into physics-based realistic magnetohydrodynamics (MHD) simulations. The integration of first-principle modeling of solar magnetism and flow dynamics with real-time observational data via advanced data assimilation methods is a new, transformative step in space weather research and prediction. This approach will substantially enhance an existing model of magnetic flux distribution and transport developed by the Air Force Research Lab. The development plan is to use the Space Weather Modeling Framework (SWMF) to develop Coupled Models for Emerging flux Simulations (CMES) that couples three existing models: (1) an MHD formulation with the anelastic approximation to simulate the deep convection zone (FSAM code), (2) an MHD formulation with full compressible Navier-Stokes equations and a detailed description of radiative transfer and thermodynamics to simulate near-surface convection and the photosphere (Stagger code), and (3) an MHD formulation with full, compressible Navier-Stokes equations and an approximate description of radiative transfer and heating to simulate the corona (Module in BATS-R-US). CMES will enable simulations of the emergence of magnetic structures from the deep convection zone to the corona. Finally, a plan will be summarized on the development of a Flux Emergence Prediction Tool (FEPT) in which helioseismology-derived data and vector magnetic maps are assimilated into CMES that couples the dynamics of magnetic flux from the deep interior to the corona.

Mansour, Nagi N.↗

Emissions Characterization and Smoke Transport of A Prescribed Fire

Under the background of climate change, vast regions of the world will face a hotter and probably drier future that favors ignition and spread of wildfires. This poses additional challenges to the communities who are fighting the ever-larger wildfires. Fire and smoke models are valuable tools to fire managers and decision-makers to mitigate the impact of fires. Existing fire modeling systems bear large uncertainties due to the difficulty in collecting data to characterize fire behavior and smoke transport. More observations are urgently needed for improvement of fire modeling and reduction in model uncertainty. The coordinated prescribed burn experiment, such as the Fire and Smoke Model and Measurement Evaluation Experiment (FSMMEE), will allow the concurrent collection of critical measurements of fuel, fire behavior, smoke, and meteorology to better understand and model fires. In this preliminary investigation, we employed the data from the prescribed burn experiment at Langdon Mountain, Utah, in November 2019, to reconstruct the fire emissions and characterize the smoke transport. The NASA Unified Weather Research and Forecasting Model (NU-WRF) was utilized to assist in identifying the potential pre-fire remote sensing capabilities, such as fuel load, moisture, and fire radiative power, which are helpful to model development and advancement. Along the way, two sets of NU-WRF experiments would be done by applying either a default fire emissions inventory (i.e., NASA’s Quick Fire Emissions Dataset, QFED) or reconstructed fire emissions using the data collected from the prescribed fire. The results would help answer the questions such as “How do reconstructed emissions compare to QFED emissions and their impact on plume transport”.

NU-WRF↗

GMAO OSSE Framework in Support of PBL Mission Science

The planetary boundary layer is the bottom layer of the troposphere where most of human activities take place. Atmospheric pollutants are capped by the temperature inversion layer in PBL. The thickness of PBL, ranging from tens of meter to several kilometer, affects pollutant dispersion and air quality. The thickness of PBL typically shows a strong diurnal cycle but it is difficult to model and predict its change because complicated dynamic and thermodynamic processes involved with air-surface exchange of temperature and moisture, convective mixing, surface friction, topography, advection and radiation heating and cooling influence the PBL height. Various parameterization schemes for PBL were developed but the underlying processes of PBL are neither clearly understood nor represented in NWP models and adding large uncertainty into weather and climate predictions. There are no systematic global observations to provide information on thermodynamic structure of PBL and efforts to explore new spaceborne instruments and measurement techniques are growing. This study aims to develop a PBL OSSE framework leveraging existing GMAO’s OSSE system that utilizes GEOS data assimilation and global forecast model (1) to evaluate existing and potential new observation types for the PBL structure analysis and prediction and (2) to test sensitivity of PBL parameterization schemes to PBL forecasts.

Min-Jeong Kim↗

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↗

Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories

High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear – correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For unbiased measurements of cosmic shear, all other sources of correlated image distortions must be modeled or removed. One such source is the correlated blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. In this work, we leverage data from weather forecasting models to produce wind speeds and directions, and turbulence parameters, that are realistically correlated with altitude. To study the resulting correlations in the size and shape of the PSF, we generate simulated images of the PSF across a ~10 square-degree field of view – the size of the camera focal plane for the Vera C. Rubin Observatory in Chile – using weather data and historical seeing for a geographic location near the Observatory. We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. We observe a strong anisotropy in the two-dimensional 2PCF, which is expected based on observations in real images, and study the dependence of the orientation of the anisotropy on dominant wind directions near the ground and at higher altitudes.

79 ASTRONOMY AND ASTROPHYSICS↗

Impact Assessment of All-Sky TROPICS Microwave Observations on the NASA GEOS Analyses and Forecasts and Progress to Use the Data in the JEDI-GEOS Analysis System

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a constellation of small satellites carrying state-of-art microwave temperature and humidity sounders with 12 channels between 91 GHz and 205 GHz frequency. Including TROPICS-pathfinder, launched on 30 June 2021, five TROPICS CubeSats operate and provide temperature and humidity data to NWP and atmospheric retrieval communities. This study is dedicated to evaluating the impact of the TROPICS satellite constellation microwave observations in numerical weather prediction (NWP) using the NASA Goddard Earth Observing System (GEOS). The TROPICS-01 (TROPICS-Pathfinder), TROPICS-03, TROPICS-05, and TROPICS-06 data in all-sky conditions over the ocean during the period of 25 July 2023 and 6 September 2023 are used for assessing forecast impacts on global NWP analysis and five-day forecasts. A series of experiments are carried out to measure the benefits of assimilating observations from only temperature sounders, water vapor sounders, and both sounders. Statistical analysis of the Observing System Experiments (OSEs) results has shown incremental improvements in global model forecast skills for critical geophysical parameters, including temperature, winds, and geopotential heights. The results demonstrate the potential of the TROPICS-like data to positively impact NWP by adding new information to the current observation and forecast system. In another set of experiments, the TROPICS-03, TROPICS-05, and TROPICS-06 data sets are added to the TROPICS-01 one by one to evaluate the impacts of increasing the revisit rate of TROPICS satellite measurements on NWP analysis for a tropical cyclone’s dynamical and microphysical structures. This study offers important insights into the capabilities of a new generation of small satellite microwave radiometers based on emerging technologies, including their unique measurements at 118 GHz and 205 GHz that are not available in traditional operational microwave sounders. Finally, the efforts to implement these new developments for TROPICS in the JEDI-GEOS atmospheric data assimilation system are in progress, and preliminary results from cycled JEDI-GEOS data assimilation experiments are presented.

Min-Jeong Kim↗

Continuously Improving Parametric Modeling with Historical Data on the ICESat-2 Mission

This paper delves into the details of the Joint Confidence Level (JCL) process performed for the Ice, Cloud, and Land Elevation Satellite (ICESat)-2 mission and how past performance was incorporated into subsequent JCL models to enable the project to continuously analyze potential slips to their launch readiness date (LRD). One year prior to the mission Preliminary Review (mPDR), the JCL model development process began. The first model was well received at the mPDR, held on October 10, 2012, and the input received by the Standing Review Board was incorporated into the model for the official data drop for key decision point (KDP)-C. The 70% JCL results of the October 2012 mPDR model forecast an LRD of February 2017 and associated cost of $830M. This result in 2012 immediately highlighted potential challenges with the project-planned LRD of July 2016. The year following the mPDR, the project had sustained a oneyear slip in the LRD due to problematic systems engineering requirement issues which impacted all project subsystems. This slip moved the project planned LRD from July 2016 to July 2017, an additional 5 months beyond the 2012 model’s 70% JCL result for the LRD of February 2017. As the project was quickly approaching the mission Critical Design Review (mCDR), the need for reliable JCL results increased significantly. The project held discussions on the JCL modeling process and focused on the input uncertainty distributions. Specifically, to identify the uncertainty distributions that the 2012 mPDR model would have needed to produce a 70% LRD result of July 2017. This led the project to compare multiple uncertainty distributions, and ultimately spurred the project to utilize uncertainty distributions that incorporated project past performance and historical data to forecast potential LRD slips. The revised results, created in 2014 and utilizing the new uncertainty distributions, showed that with 70% confidence, the ICESat-2 mission would launch in August 2018 with a cost of $1,044M. Today, ICESat-2 is scheduled to launch on September 15, 2018 with a project management (PM) agreement value of $1,056M. This illustrates how a JCL model can be continuously improved to produce valuable results for a project, even in cases of LRD delays. The primary reason for the ICESat-2 LRD delay is due to a laser failure on the primary instrument. Laser failure was one of the highest risk and uncertainty drivers within the JCL model. The project placed the most risk in this area of the model, and the model further identified the laser as the top risk driver and contributor to the LRD result. This further illustrates how a JCL can be used to predict and quantify possible issues on new technology missions.

Krygiel, Joseph↗

Applications of estimation theory to numerical weather prediction

Numerical weather prediction (NWP) is an initial value problem for a system of nonlinear partial differential equations in which the initial values are known only incompletely and inaccurately. Data at initial time can be supplemented, however, by observations of the system distributed over a time interval preceding it. Estimation theory was successful in approaching such problems for models governed by systems of ordinary differential equations and of linear PDEs. Estimation-theoretic methods for NWP are developed. A model exhibiting many features of large scale atmospheric flow important in NWP is the one governed by the shallow fluid equations. The estimation problem for a linearized formulation of these equations is studied. A finite difference version of the equations is used as a forecast model to simulate the numerical models used in NWP.

Cohn, S.↗

Integrating Low-Cost Sensor Systems and Networks to Enhance Air Quality Applications

Recent advances in low-cost air quality sensor systems are rapidly increasing the accessibility of air quality information around the world. At the same time, there are many technical challenges to appropriately and effectively using of the information they provide. A key opportunity to increase the applicability and actionability of low-cost sensor data is to use these devices at network scale and combine their information with insights from other air quality data sources such as numerical models and satellite remote sensing. When the limitations of low-cost sensors are understood and acknowledged, and appropriate complementary data are used to overcome these limitations, low cost sensors can support a variety of applications such as improving location-specific air quality forecasting and estimation, quantifying local source impacts, identifying air quality disparities, assessing the benefits of mitigation actions, and promoting community engagement with air quality issues. This presentation will first provide an overview and summarize key findings from a recently released World Meteorological Organization report discussing how networks of low-cost air quality sensor systems can be integrated to effectively support such applications. The presentation will also give a brief overview of an ongoing NASA-funded effort to create accessible tools for integrating multiple sources of air quality information, including global forecasting models, satellite remote sensing data, and in-situ information from both regulatory and low-cost monitors.

Carl Malings↗

Evaluation of Skin Temperatures Retrieved from GOES-8

Skin temperatures derived from geostationary satellites have the potential of providing the temporal and spatial resolution needed for model assimilation. To adequately assess the potential improvements in numerical model forecasts that can be made by assimilating satellite data, an estimate of the accuracy of the skin temperature product is necessary. A particular skin temperature algorithm, the Physical Split Window Technique, that uses the longwave infrared channels of the GOES Imager has shown promise in recent model assimilation studies to provide land surface temperatures with reasonable accuracy. A comparison of retrieved GOES-8 skin temperatures from this algorithm with in situ measurements is presented. Various retrieval algorithm issues are addressed including surface emissivity

Suggs, Ronnie, J.↗

The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observations

Salinty↗

Observation impacts in the lower troposphere and challenges of Planetary Boundary Layer data assimilation

The Goddard Earth Observing System (GEOS) developed by the NASA Global Modeling and Assimilation Office assimilates a wide range of observations to support various NASA Earth Science missions. To set the stage for follow-on Planetary Boundary Layer (PBL) science and prepare for future observing systems of the next decade, we have assessed the effectiveness of the use of existing observing systems in the lower troposphere in GEOS, and analyzed model responses to the incremental analysis update (IAU) forcing. With a better understanding of the GEOS data assimilation algorithms in the PBL, we have developed strategies for improved PBL data assimilation in GEOS. The strategies to enhance data usages in both the data assimilation system and forecast model will be presented, and the utilization of PBL height data from multiple observing systems will be discussed as well.

Yanqiu Zhu↗

Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California

Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

17 WIND ENERGY↗

Overview of the NASA Earth Action Strategies Wildland Fire Initiative

As part of NASA’s new Earth Action strategy, the Wildland Fire initiative was established, which includes both the NASA Wildland Fire Program (WFP) and the FireSense project. NASA has over 50 years of experience generating data and technology to enhance fire science and operational management. The WFP’s mission is threefold: 1) assemble communities of practice through collaborative efforts with government, academia, and the private sector; 2) co-develop knowledge and applications with relevant partners and stakeholders in the wildfire community; and 3) improve wildland fire management through the transitioning of NASA data, technology, tools, and science to stakeholder organizations. The WFP is focusing on supporting proactive fire management, including situational awareness, preparedness, and risk mitigation. This will be accomplished through selected projects that identify management challenges, relevant to partners and end users, and the NASA data that will be utilized to deliver innovative solutions to enhance the management of wildland fires. Examples include: i) investigation of evaporative stress from OpenET to help predict the risk of wildfire occurrence in watersheds; ii) incorporation of space based LiDAR for the generation of 3-dimensional forest fuel metrics, used to improve wildfire risk and behavior models; iii) integration of global, multi-platform geostationary active-fire data in near-real-time into NASA’s Fire Information for Resource Management System (FIRMS); and iv) identification of post-fire ecohydrological conditions using thermal, multispectral, synthetic aperture radar (SAR), and hyperspectral remotely-sensed data to improve flood hazard forecast models. The FireSense project is a US-focused 5-year project that will focus on delivering NASA’s unique Earth science and technological capabilities to operational agencies, striving towards enhancing fire fighting and air quality management. The project will include airborne campaigns and new technology that will likely have global implications. Initial stakeholder engagement led FireSense to focus on four use-cases focused on the characterization and measurement of: (i) pre-fire fuels conditions, (ii) active fire-dynamics; (iii) post-fire impact and threats; and iv) air quality impacts and forecasting, each-developed with identified stakeholders.

Wildland Fire program↗

Use of an adjoint model for finding triggers for Alpine lee cyclogenesis

The authors propose a new procedure, designated the adjoint-based genesis diagnostic (AGD) procedure, for studying triggering mechanisms and the subsequent genesis of the synoptic phenomena of interest. This procedure makes use of a numerical model sensitivity to initial conditions and the nonlinear evolution of the initial perturbations that are designed using this sensitivity. The model sensitivity is evaluated using the associated adjoint model. This study uses the dry version of the National Center for Atmospheric Research Mesoscale Adjoint Modeling System (MAMS) for the numerical experiments. The authors apply the AGD procedure to two cases of Alpine lee cyclogenesis that were observed during the Alpine Experiment special observations period. The results show that the sensitivity fields that are produced by the adjoint model and the associated initial perturbations are readily related to the probable triggering mechanisms for these cyclones. Additionally, the nonlinear evolution of these initial perturbations points toward the physical processes involved in the lee cyclone formation. The AGD experiments for a weak cyclone case indicate that the MAMS forecast model has an underrepresented topographic forcing due to the sigma vertical coordinate and that this model error can be compensated by adjustments in the initial conditions that are related to the triggering mechanisms, which is not associated with the topographic blocking mechanism.

Vukicevic, Tomislava↗

Real-Time Dose Prediction for Artemis Missions

As large solar energetic particle (SEP) events can add significant radiation dose to astronauts in a short period of time and even induce acute clinical responses during missions, they present a concern for manned space flight operation. To assist the operations team in modeling and monitoring organ doses and any possible acute radiation-induced risks to astronauts during SEP events in real time, ARRT (Acute Radiation Risks Tool) 1.0 has been developed and successfully tested for Artemis I mission. The ARRT 2.0 described in this work integrates an established SEP forecasting model – UMASEP-100, further enabling real-time dose prediction for the upcoming Artemis II and following missions. With the new module linking with UMASEP-100 outputs in real time, the total BFO doses of most significant events can be communicated at the time of onset and hours before the peak. This is based on a flux-dose formula identified from comparing UMASEP-100 results with transport calculation for the events during 1994-2013 and validated with events outside that period. ARRT 2.0 also shows capability to distinguish minor events from significant ones to screen false alarms that will cause disruptions for space activities. This improvement provides additional information for operational teams to make timely decisions in contingent scenarios of severe SEP events to mitigate radiation exposure.

S Hu↗