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The presentation slides include: The SPoRT Center, History and Future of SPoRT, Great Lakes Applications, Great Lakes Forecasting Issues, Applications to the WRF-EMS, Precipitation Science, Lake Effect Precipitation, Sensitivity to Microphysics, Exploring New Schemes, Opportunities for Collaboration, and SPoRT Research and Development.
The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.
For the last several years, the NASA Short-term Prediction Research and Transition (SPoRT) project at has been working with the various algorithm working groups and science teams to demonstrate the utility of future operational sensors for GOES-R and the suite of instruments for the JPSS observing platforms. For GOES-R, imagery and products have been developed from polar-orbiting sensors such as MODIS and geostationary observations from SEVIRI, simulated imagery, enhanced products derived from existing GOES satellites, and data from ground-based observing systems to generate pseudo or proxy products for the ABI and GLM instruments. The suite of products include GOES-POES basic and RGB hybrid imagery, total lightning flash products, quantitative precipitation estimates, and convective initiation products. SPoRT is using imagery and products from VIIRS, CrIS, ATMS, and OMPS to show the utility of data and products from their operational counterparts on JPSS. The products include VIIRS imagery in swath form, the GOES-POES hybrid, a suite of RGB products including the air mass RGB using water vapor and ozone channels from CrIS, and several DNB products. Over a dozen SPoRT collaborative WFOs and several National Centers are involved in an intensive evaluation of the operational utility of these products.
SPoRT (NASA's Short-term Prediction Research and Transition Center) has collaboratively worked with Alaska WFOs (Weather Forecast Offices) to introduce RGB (Red/Green/Blue false color image) imagery to prepare for NOAA-20 (National Oceanic and Atmospheric Administration, JPSS (Joint Polar Satellite System) series-20 satellite) VIIRS (Visible Infrared Imaging Radiometer Suite) and improve forecasting aviation-related hazards. Last R2O/O2R (Research-to-Operations/Operations-to-Research) steps include incorporating NOAA-20 VIIRS in RGB suite and fully transitioning client-side RGB processing to GINA (Geographic Information Network of Alaska) and Alaska Region. Alaska Region WFOs have been part of the successful R2O/O2R story to assess the use of NESDIS (National Environmental Satellite, Data, and Information Service) Snowfall Rate product in operations. SPoRT introduced passive microwave rain rate and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) (IMERG) to Alaska WFOs for use in radar-void areas and assessing flooding potential. SPoRT has been part of the multi-organization collaborative effort to introduce Gridded NUCAPS (NOAA Unique CrIS/ATMS (Crosstrack Infrared Sounder/Advanced Technology Microwave Sounder) Processing System) to the Anchorage CWSU (Center Weather Service Unit) to assess Cold Air Aloft events, [and as part of NOAA's PG (Product Generation) effort].
- Near real-time identification of airborne dust in satellite imagery is important for mitigating the adverse effects of dust storms on human activities. - False color Red-Green-Blue (RGB) imagery has been used for dust detection, but it has limitations and can be difficult to interpret. - The NASA Short-term Research and Transition (SPoRT) center has developed a night-time dust detection random forest (NT-DustTracker-AI, Berndt et al. 2021) model using NASA/NOAA Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery as inputs. - The SPoRT center has partnered with the NOAA National Weather Service to evaluate the model for use in weather forecasting operations, and preliminary results have been positive. - The SPoRT center has expanded the model to cover both day and night, continuing to use infrared imagery as inputs. This new model, known as DustTracker-AI, has shown good agreement with available dust observations
The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.
The NASA Short-term Prediction Research and Transition (SPoRT) Center has begun to develop products to address operational challenges and enhance safety during the lifecycle of lightning activity, and conduct evaluations in concentrated R2O/O2R efforts. The product evaluated for this study is Lightning Artificial Intelligence (A.I.), which predicts the probability of lightning out to 15 minutes in advance and spatially maps it using filled color contours. Lightning A.I. uses reflectivity, differential reflectivity, and correlation coefficient data from a subset of radars within the NEXRAD network to generate lightning probabilities at approximately 2 km resolution. The domains, spanning 145 x 145 km are centered over NASA-affiliated centers across the CONUS and use radar data which are closest in proximity. Lightning A.I. was developed with intended use by emergency managers at NASA centers. However, National Weather Service (NWS) offices are also often tasked with monitoring and forecasting the threat for lightning within their County Warning Areas for various impact-based decision support services. These forecasts are typically provided for aviation operations and large-scale, outdoor events, which may have varying safety requirements for decision-making based on lightning proximity and recency. The NASA SPoRT center conducted an assessment of the operational uses of Lightning A.I by various collaborative NWS Offices from late July into early September. This assessment included feedback from participants on both the product itself as well as its accessibility within the new, interactive NASA SPoRT Lightning Viewer. This presentation will include background information about Lightning A.I. and highlight results from this assessment. The feedback from the assessment will be used to inform research on any necessary modifications to this and future lightning products to assist end users.
The NASA Short-term Prediction Research and Transition Center (SPoRT) has been running a near-real-time deep learning model, Streamflow-AI, that predicts stream heights at over 250 locations across the Eastern United States out to 7 days for several years. This project was born out of a research-to-operations/operation-to-research (R2O/O2R) paradigm within SPoRT through extensive collaboration with National Weather Service (NWS) and River Forecast Center (RFC) forecasters. The original intention of Streamflow-AI was to provide accurate medium range (>2 days) forecasts of river flooding events that are particularly challenging for NWS forecasters, because routine streamflow forecasts may not incorporate quantitative precipitation forecast (QPF) data beyond one or two days. Therefore, Streamflow-AI was designed to help fill the operational gap, producing routine 7 day river level forecasts using a suite of QPFs. Throughout the evolution of the project, research efforts have been tailored to meet the needs of the end users. This presentation will provide a brief overview of NASA SPoRT’s Streamflow-AI product, highlighting O2R/R2O concepts with a focus on recent developments including a 1-hr rapid update model and 15-min burn scar flooding modeling efforts.
SPORT is a science mission using a 6U CubeSat and integrated ground network that will (1) advance understanding and (2) enable improved predictions of scintillation occurrence that impact GPS signals and radio communications. This is the science of Space Weather. SPORT is an international partnership with NASA, U.S. institutions, the Brazilian National Institute for Space Research (INPE), and the Technical Aeronautics Institute under the Brazilian Air Force Command Department (DCTA/ITA).
The NASA Short-term Prediction Research and Transition Center (SPoRT)'s new "Weather in a Box" resources will provide weather research and forecast modeling capabilities for real-time application. Model output will provide additional forecast guidance and research into the impacts of new NASA satellite data sets and software capabilities. By combining several research tools and satellite products, SPoRT can generate model guidance that is strongly influenced by unique NASA contributions.
NASA Short-term Prediction Research and Transition (SPoRT) Center collaborates with operational partners of different sizes and operational goals to improve forecasts using targeted projects and data sets. Modeling and DA activities focus on demonstrating utility of NASA data sets and capabilities within operational systems. SPoRT has successfully assimilated the Atmospheric Infrared Sounder (AIRS) radiance and profile data. A collaborative project is underway with the Joint Center for Satellite Data Assimilation (JCSDA) to use AIRS profiles to better understand the impact of AIRS radiances assimilated within Gridpoint Statistical Interpolation (GSI) in hopes of engaging the operational DA community in a reassessment of assimilation methodologies to more effectively assimilate hyperspectral radiances.
A joint collaborative modeling effort among the NWS offices in Mobile, AL, and Houston, TX, and NASA Short-term Prediction Research and Transition (SPoRT) Center began during the 2011-2012 cold season, and continued into the 2012 warm season. The focus was on two frequent U.S. Deep South forecast challenges: the initiation of deep convection during the warm season; and heavy precipitation during the cold season. We wanted to examine the impact of certain NASA produced products on the Weather Research and Forecasting Environmental Modeling System in improving the model representation of mesoscale boundaries such as the local sea-, bay- and land-breezes (which often leads to warm season convective initiation); and improving the model representation of slow moving, or quasi-stationary frontal boundaries (which focus cold season storm cell training and heavy precipitation). The NASA products were: the 4-km Land Information System, a 1-km sea surface temperature analysis, and a 4-km greenness vegetation fraction analysis. Similar domains were established over the southeast Texas and Alabama coastlines, each with an outer grid with a 9 km spacing and an inner nest with a 3 km grid spacing. The model was run at each NWS office once per day out to 24 hours from 0600 UTC, using the NCEP Global Forecast System for initial and boundary conditions. Control runs without the NASA products were made at the NASA SPoRT Center. The NCAR Model Evaluation Tools verification package was used to evaluate both the positive and negative impacts of the NASA products on the model forecasts. Select case studies will be presented to highlight the influence of the products.
The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its NOAA/National Weather Service (NWS) partners that can initialize specific fields for local model runs within the NOAA/NWS Science and Training Resource Center Environmental Modeling System (EMS). The suite of SPoRT products for use in the EMS consists of a Sea Surface Temperature (SST) composite that includes a Lake Surface Temperature (LST) analysis over the Great Lakes, a Great Lakes sea-ice extent within the SST composite, a real-time Green Vegetation Fraction (GVF) composite, and NASA Land Information System (LIS) gridded output. This paper and companion poster describe each dataset and provide recent upgrades made to the SST, Great Lakes LST, GVF composites, and the real-time LIS runs.
The initiation of deep convection during the warm season is a forecast challenge in the relative high instability and low wind shear environment of the U.S. Deep South. Despite improved knowledge of the character of well known mesoscale features such as local sea-, bay- and land-breezes, observations show the evolution of these features fall well short in fully describing the location of first initiates. A joint collaborative modeling effort among the NWS offices in Mobile, AL, and Houston, TX, and NASA s Short-term Prediction Research and Transition (SPoRT) Center was undertaken during the 2012 warm season to examine the impact of certain NASA produced products on the Weather Research and Forecasting Environmental Modeling System. The NASA products were: a 4-km Land Information System data, a 1-km sea surface temperature analysis, and a 4-km greenness vegetation fraction analysis. Similar domains were established over the southeast Texas and Alabama coastlines, each with a 9 km outer grid spacing and a 3 km inner nest spacing. The model was run at each NWS office once per day out to 24 hours from 0600 UTC, using the NCEP Global Forecast System for initial and boundary conditions. Control runs without the NASA products were made at the NASA SPoRT Center. The NCAR Model Evaluation Tools verification package was used to evaluate both the forecast timing and location of the first initiates, with a focus on the impacts of the NASA products on the model forecasts. Select case studies will be presented to highlight the influence of the products.
The NWS Office in Huntsville is tasked with monitoring and predicting the threat for lightning within its County Warning Area. These impact-based decision support services (IDSS) are provided routinely for aviation operations, irregularly for large-scale, outdoor events, and can have varying safety requirements, such as proximity to location and duration. Lightning monitoring and prediction can require the rapid synthesis of a plethora of data, so products that make this process more efficient and effective are sought by the operational community. The Huntsville NWS Office benefits from close collaboration with the NASA SPoRT Center, which is developing several products to address these operational challenges in concentrated R2O/O2R efforts. One example of this is the StopLight product, which uses Geostationary Lightning Mapper (GLM) flash extent density (FED) to provide an easy-to-interpret visual aid of lightning occurring within the last 30 minutes. The StopLight product shows both the age and location of the last lightning flash within each GLM pixel, which can be particularly useful for IDSS with determining when to resume activities or operations that have been shut down due to lightning. This presentation highlights results of collaboration between NASA SPoRT and NWS Huntsville, including testing of the Stoplight product, and additional experimental products in development. A brief summary of the lightning products are provided while the main focus will discuss product evaluation and forecaster perspective during real-time weather watch activities to support aviation operations and events where IDSS was necessary.
National Weather Service (NWS) offices monitor and forecast the threat for lightning within their County Warning Areas for various impact-based decision support services (IDSS). These forecasts are provided routinely for aviation operations, but irregularly for large-scale, outdoor events, which typically have varying safety requirements with regards to lightning. Lightning monitoring and forecasting can require synthesizing a plethora of data, so products that make this process more efficient and effective are sought by the operational community. The NASA Short-term Prediction Research and Transition (SPoRT) Center has begun to develop several products to address these operational challenges during the lifecycle of lightning activity, and conduct evaluations in concentrated (Research-to-Operations/Operations-to-Research) efforts. The product evaluated for this study is the Geostationary Lightning Mapper (GLM) Stoplight product, which addresses lightning safety specifically with regards to lightning recency and cessation. The GLM Stoplight product displays the spatial extent of lightning and uses color-coded pixels to show the recency of lightning in 10-minute increments. This presentation highlights results from an assessment of the GLM Stoplight product by select NWS offices, as well as emergency managers, that took place over a 5-week period from May 1st to June 2nd. Feedback was collected on the GLM Stoplight product and on a new and interactive lightning viewer, which allowed participants to tailor visualization of the product based on their location and desired application. Results will help NASA SPoRT gain a better understanding of how forecasters and emergency managers use lightning data for safety applications. In addition, the assessment will inform research on any necessary modifications to this and future lightning products to assist end users.
Kinesthetic spatial discrimination in sports
Tests with cardioleader to control the physical, technical and tactical preparedness of athletes in cyclic types of sports are discussed. Ways of increasing the reliability and information content of the tests were studied.