The NASA Short-Term Prediction Research and Transition Center and a Paradigm for Engaging Stakeholders
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Third National Aeronautics and Space Administration Weather and climate program science review
Research results of developing experimental and prototype operational systems, sensors, and space facilities for monitoring, and understanding the atmosphere are reported. Major aspects include: (1) detection, monitoring, and prediction of severe storms; (2) improvement of global forecasting; and (3) monitoring and prediction of climate change.
Kreins, E. R.↗
A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation
Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. Here, the results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.
54 ENVIRONMENTAL SCIENCES↗
Displaying Composite and Archived Soundings in the Advanced Weather Interactive Processing System
In a previous task, the Applied Meteorology Unit (AMU) developed spatial and temporal climatologies of lightning occurrence based on eight atmospheric flow regimes. The AMU created climatological, or composite, soundings of wind speed and direction, temperature, and dew point temperature at four rawinsonde observation stations at Jacksonville, Tampa, Miami, and Cape Canaveral Air Force Station, for each of the eight flow regimes. The composite soundings were delivered to the National Weather Service (NWS) Melbourne (MLB) office for display using the National version of the Skew-T Hodograph analysis and Research Program (NSHARP) software program. The NWS MLB requested the AMU make the composite soundings available for display in the Advanced Weather Interactive Processing System (AWIPS), so they could be overlaid on current observed soundings. This will allow the forecasters to compare the current state of the atmosphere with climatology. This presentation describes how the AMU converted the composite soundings from NSHARP Archive format to Network Common Data Form (NetCDF) format, so that the soundings could be displayed in AWl PS. The NetCDF is a set of data formats, programming interfaces, and software libraries used to read and write scientific data files. In AWIPS, each meteorological data type, such as soundings or surface observations, has a unique NetCDF format. Each format is described by a NetCDF template file. Although NetCDF files are in binary format, they can be converted to a text format called network Common data form Description Language (CDL). A software utility called ncgen is used to create a NetCDF file from a CDL file, while the ncdump utility is used to create a CDL file from a NetCDF file. An AWIPS receives soundings in Binary Universal Form for the Representation of Meteorological data (BUFR) format (http://dss.ucar.edu/docs/formats/bufr/), and then decodes them into NetCDF format. Only two sounding files are generated in AWIPS per day. One file contains all of the soundings received worldwide between 0000 UTC and 1200 UTC, and the other includes all soundings between 1200 UTC and 0000 UTC. In order to add the composite soundings into AWIPS, a procedure was created to configure, or localize, AWIPS. This involved modifying and creating several configuration text files. A unique fourcharacter site identifier was created for each of the 32 soundings so each could be viewed separately. The first three characters were based on the site identifier of the observed sounding, while the last character was based on the flow regime. While researching the localization process for soundings, the AMU discovered a method of archiving soundings so old soundings would not get purged automatically by AWl PS. This method could provide an alternative way of localizing AWl PS for composite soundings. In addition, this would allow forecasters to use archived soundings in AWIPS for case studies. A test sounding file in NetCDF format was written in order to verify the correct format for soundings in AWIPS. After the file was viewed successfully in AWIPS, the AMU wrote a software program in the Tool Command Language/Tool Kit (Tcl/Tk) language to convert the 32 composite soundings from NSHARP Archive to CDL format. The ncgen utility was then used to convert the CDL file to a NetCDF file. The NetCDF file could then be read and displayed in AWIPS.
Barrett, Joe H., III↗
TPSAS-NF1676L-12441-DND
The increasing presence of airport surface surveillance technologies has spawned a strong interest in airport surface traffic management research; the vast majority has assumed that the airport’s runway configuration is known and constant. Tactical Runway Configuration Management (TRCM), one component of NASA’s System Oriented Runway Management concept, plans the airport configuration to optimize traffic efficiency for the forecast weather, traffic, and other factors. While planning runway configuration can provide benefit and is required by other automation concepts, larger benefits are possible by planning other airport configuration decisions, such as runway assignment policies, that are currently made manually by controllers. A laboratory prototype of the TRCM algorithm which selects optimal airport configuration schedules has been implemented and studied within a simulation environment. The presentation will describe simulation results for several airports, under various weather and traffic conditions. The sequence of airport configurations recommended by TRCM result in significantly less delay than the airport configurations that were actually used by controllers, illustrating both the potential benefit from airport configuration optimization and the ability of TRCM to provide effective airport configuration schedules. The application to several, distinct airports demonstrates the approach is capable of handling the differences between airports with a common algorithm, while providing decision support that respects the current procedures at those airports. Consequently, TRCM is able to be deployed to any airport within the current National Airspace System. The TRCM algorithmic approach is also extensible to future operational scenarios. NASA is in the process of transferring the initial TRCM technology to the FAA. Future research will extend the concept and algorithm to provide coordinated plans for metroplex airports and study NextGen applications.
Stephen Atkins↗
TPSAS-NF1676L-12263-DND
The increasing presence of airport surface surveillance technologies has spawned a strong interest in airport surface traffic management research; the vast majority has assumed that the airport’s runway configuration is known and constant. Tactical Runway Configuration Management (TRCM), one component of NASA’s System Oriented Runway Management concept, plans the airport configuration to optimize traffic efficiency for the forecast weather, traffic, and other factors. While planning runway configuration can provide benefit and is required by other automation concepts, larger benefits are possible by planning other airport configuration decisions, such as runway assignment policies, that are currently made manually by controllers. A laboratory prototype of the TRCM algorithm which selects optimal airport configuration schedules has been implemented and studied within a simulation environment. The presentation will describe simulation results for several airports, under various weather and traffic conditions. The sequence of airport configurations recommended by TRCM result in significantly less delay than the airport configurations that were actually used by controllers, illustrating both the potential benefit from airport configuration optimization and the ability of TRCM to provide effective airport configuration schedules. The application to several, distinct airports demonstrates the approach is capable of handling the differences between airports with a common algorithm, while providing decision support that respects the current procedures at those airports. Consequently, TRCM is able to be deployed to any airport within the current National Airspace System. The TRCM algorithmic approach is also extensible to future operational scenarios. NASA is in the process of transferring the initial TRCM technology to the FAA. Future research will extend the concept and algorithm to provide coordinated plans for metroplex airports and study NextGen applications.
Stephen Atkins↗
Simulation of the modern arctic climate by the NCAR CCM1
The National Center of Atmospheric Research (NCAR) Community Climate Model Version 1 (CCM1's) simulation of the modern arctic climate is evaluated by comparing a five-year seasonal cycle simulation with the European Center for Medium-Range Weather Forecasts (ECMWF) global analyses. The sea level pressure (SLP), storm tracks, vertical cross section of height, 500-hPa height, total energy budget, and moisture budget are analyzed to investigate the biases in the simulated arctic climate. The results show that the model simulates anomalously low SLP, too much storm activity, and anomalously strong baroclinicity to the west of Greenland and vice versa to the east of Greenland. This bias is mainly attributed to the model's topographic representation of Greenland. First, the broadened Greenland topography in the model distorts the path of cyclone waves over the North Atlantic Ocean. Second, the model oversimulates the ridge over Greenland, which intensifies its blocking effect and steers the cyclone waves clockwise around it and hence produces an artificial circum-Greenland trough. These biases are significantly alleviated when the horizontal resolution increases to T42. Over the Arctic basin, the model simulates large amounts of low-level (stratus) clouds in winter and almost no stratus in summer, which is opposite to the observations. This bias is mainly due to the location of the simulated SLP features and the negative anomaly of storm activity, which prevent the transport of moisture into this region during summer but favor this transport in winter. The moisture budget analysis shows that the model's net annual precipitation (P-E) between 70 deg N and the North Pole is 6.6 times larger than the observations and the model transports six times more moisture into this region. The bias in the advection term is attributed to the positive moisture fixer scheme and the distorted flow pattern. However, the excessive moisture transport into the Arctic basin does not solely result from the advection term. The contribution by the moisture fixer is as large as from advection. By contrast, the semi-Lagrangian transport scheme used in the CCM2 significantly improves the moisture simulation for this region; however, globally the error is as serious as for the positive moisture fixer scheme. Finally, because the model has such serious problems in simulating the present arctic climate, its simulations of past and future climate change for this region are questionable.
Bromwich, David H.↗
Cloud identification using genetic algorithms and massively parallel computation
As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced.
Buckles, Bill P.↗
The THOR Project-Reducing the Impact of Thunderstorms on Aviation and the General Public Through a Multi-Agency Effect
Thunderstorms are high impact weather phenomena. They also pose an extremely challenging forecast problem. The National Oceanic and Atmospheric Administration (NOAA), the Federal Aviation Administration (FAA), the National Aeronautic and Space Administration (NASA), and the Air Force Weather Agency (AFWA), have decided to pool technology and scientific expertise into an unprecedented effort to better observe, diagnose, and forecast thunderstorms. This paper describes plans for an operational field test called the THunderstorm Operational Research (THOR) Project beginning in 2002, the primary goals of which are to: 1) Reduce the number of Thunderstorm-related Air Traffic Delays with in the National Airspace System (NAS) and, 2) Improve severe thunderstorm, tornado and airport thunderstorm warning accuracy and lead time. Aviation field operations will be focused on the prime air traffic bottleneck in the NAS, the airspace bounded roughly by Chicago, New York City and Washington D.C., sometimes called the Northeast Corridor. A variety of new automated thunderstorm forecasting applications will be tested here that, when implemented into FAA-NWS operations, will allow for better tactical decision making and NAS management during thunderstorm days. Severe thunderstorm operations will be centered on Northern Alabama. NWS meteorologists from the forecast office in Birmingham will test the utility of experimental lightning, radar, and profiler data from a mesoscale observing network being established by NASA's Marshall Space Flight Center. In addition, new tornado detection and thunderstorm nowcasting algorithms will be examined for their potential for improving warning accuracy. The Alabama THOR site will also serve as a test bed for new gridded, digital thunderstorm and flash flood warning products.
Smith, Stephan B.↗
Traffic Flow Management Wrap-Up
Traffic Flow Management involves the scheduling and routing of air traffic subject to airport and airspace capacity constraints, and the efficient use of available airspace. Significant challenges in this area include: (1) weather integration and forecasting, (2) accounting for user preferences in the Traffic Flow Management decision making process, and (3) understanding and mitigating the environmental impacts of air traffic on the environment. To address these challenges, researchers in the Traffic Flow Management area are developing modeling, simulation and optimization techniques to route and schedule air traffic flights and flows while accommodating user preferences, accounting for system uncertainties and considering the environmental impacts of aviation. This presentation will highlight some of the major challenges facing researchers in this domain, while also showcasing recent innovations designed to address these challenges.
Grabbe, Shon↗
Simulation of the Impact of New Aircraft- and Satellite-Based Ocean Surface Wind Measurements on H*Wind Analyses and Numerical Forecasts
The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for hurricane observations that is currently under development by NASA Marshall Space Flight Center, NOAA Hurricane Research Division, the University of Central Florida and the University of Michigan. HIRAD is being designed to enhance the realtime airborne ocean surface winds observation capabilities of NOAA and USAF Weather Squadron hurricane hunter aircraft using the operational airborne Stepped Frequency Microwave Radiometer (SFMR). Unlike SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath (~ 3 x the aircraft altitude). The present paper describes a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing instruments (air, surface, and space-based) are simulated from the output of a detailed numerical model, and those results are used to construct H*Wind analyses. The H*Wind analysis, a product of the Hurricane Research Division of NOAA s Atlantic Oceanographic and Meteorological Laboratory, brings together wind measurements from a variety of observation platforms into an objective analysis of the distribution of wind speeds in a tropical cyclone. This product is designed to improve understanding of the extent and strength of the wind field, and to improve the assessment of hurricane intensity. See http://www.aoml.noaa.gov/hrd/data_sub/wind.html. Evaluations will be presented on the impact of the HIRAD instrument on H*Wind analyses, both in terms of adding it to the full suite of current measurements, as well as using it to replace instrument(s) that may not be functioning at the future time the HIRAD instrument is implemented. Also shown will be preliminary results of numerical weather prediction OSSEs in which the impact of the addition of HIRAD observations to the initial state on numerical forecasts of the hurricane intensity and structure is assessed.
Miller, Timothy↗
Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating an ensemble of forecasts from physics-based simulations to estimate the uncertainty. However, it is computationally expensive to generate many forecasts to predict real-time extreme weather events. Evidential Deep Learning (EDL) is an uncertainty-aware deep learning approach designed to provide confidence about its predictions using only one forecast. It treats learning as an evidence acquisition process where more evidence is interpreted as increased predictive confidence. We apply EDL to storm forecasting using real-world weather datasets and compare its performance with traditional methods. Our findings indicate that EDL not only reduces computational overhead but also enhances predictive uncertainty. This method opens up novel opportunities in research areas such as climate risk assessment, where quantifying the uncertainty about future climate is crucial.
97 MATHEMATICS AND COMPUTING↗
The Applied Meteorology Unit: Nineteen Years Successfully Transitioning Research Into Operations for America's Space Program
The Applied Meteorology Unit (AMU) provides technology development and transition services to improve operational weather support to America's space program . The AMU was founded in 1991 and operates under a triagency Memorandum of Understanding (MOU) between the National Aeronautics and Space Administration (NASA), the United States Air Force (USAF) and the National Weather Service (NWS) (Ernst and Merceret, 1995). It is colocated with the 45th Weather Squadron (45WS) at Cape Canaveral Air Force Station (CCAFS) and funded by the Space Shuttle Program . Its primary customers are the 45WS, the Spaceflight Meteorology Group (SMG) operated for NASA by the NWS at the Johnson Space Center (JSC) in Houston, TX, and the NWS forecast office in Melbourne, FL (MLB). The gap between research and operations is well known. All too frequently, the process of transitioning research to operations fails for various reasons. The mission of the AMU is in essence to bridge this gap for America's space program.
Madura, John T.↗
Advances in solar forecasting: Computer vision with deep learning
Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.
14 SOLAR ENERGY↗
NOAA-L
The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have jointly developed a valuable series of polar-orbiting Earth environmental observation satellites since 1978. These satellites provide global data to NOAA's short- and long-range weather forecasting systems. The system consists of two polar-orbiting satellites known as the Advanced Television Infrared Observation Satellites (TIROS-N) (ATN). Operating as a pair, these satellites ensure that environmental data, for any region of the Earth, is no more than six hours old. These polar-orbiting satellites have not only provided cost-effective data for very immediate and real needs but also for extensive climate and research programs. The weather data (including images seen on television news programs) has afforded both convenience and safety to viewers throughout the world. The satellites also support the SARSAT (Search and Rescue Satellite Aided Tracking) part of the COSPAS-SARSAT constellation. Russia provides the COSPAS (Russian for Space Systems for the Search of Vessels in Distress) satellites. The international COSPAS-SARSAT system provides for the detection and location of emergency beacons for ships, aircraft, and people in distress and has contributed to the saving of more than 10,000 lives since its inception in 1982.
McCain, Harry G.↗
Forecasting for the Weather Driven Energy System - A New Task under IEA Wind
The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast that has been the focus of considerable recent attention. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy has initiated a new IEA Wind Task with a much broader perspective, which includes prospective interaction with other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. In the new IEA Wind Task 51 (entitled "Foreacsting for the Weather Drive Energy System") the existing Work Packages (WPs) are complemented by work streams in a matrix structure. The Task is divided in three WPs according to the stakeholders: WP1 is mainly aimed at meteorologists, providing the weather forecast basis for the power forecasts. In WP2, the forecast service vendors are the main stakeholders, while the end users populate WP3. The new Task 51 started in January 2022. Planned activities include 4 workshops. The first will focus on the state of the art in forecasting for the energy system plus related research issues and be held during September 2022 in Dublin. The other three workshops will be held later during the 4-year Task period and address (1) seasonal forecasting with emphasis on Dunkelflaute, storage and hydro, (2) minute-scale forecasting, and (3) extreme power system events. The issues and conclusions of each of the workshops will be documented by a published paper. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective.
geophysics computing↗
Identifying Stratospheric Air Intrusions and Associated Hurricane-Force Wind Events over the North Pacific Ocean
Motivation: Ocean data is sparse: reliance on satellite imagery for marine forecasting; Ocean Prediction Center (OPC) –“mariner’s weather lifeline”. Responsible for: Pacific, Atlantic, Pacific Alaska surface analyses –24, 48, 96 hrs.; Wind & wave analyses –24, 48, 96 hrs.; Issue warnings, make decisions, Geostationary Operational Environmental Satellite –R Series (now GOES-16), Compared to the old GOES: 3 times spectral resolution, 4 times spatial resolution, 5 times faster coverage; Comparable to Japanese Meteorological Agency’s Himawari-8, used a lot throughout this research. Research Question: How can integrating satellite data imagery and derived products help forecasters improve prognosis of rapid cyclogenesis and hurricane-force wind events? Phase I –Identifying stratospheric air intrusions: Water Vapor –6.2, 6.9, 7.3 micron channels; Airmass RGB Product; AIRS, IASI, NUCAPS total column ozone and ozone anomaly; ASCAT (A/B) and AMSR-2 wind data.
Ozone↗
Improved Space Weather Observations and Modeling for Aviation Radiation
In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.
space weather↗