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An overview of GEOS Forecasting capabilities for Air Quality, showing the evolution from GEOS with GOCART to GEOS with GEOS-Chem. Ways to access GEOS for research scientists and engaged community members will be given, as well as examples on how the GEOS-CF forecasts can be bias-corrected and downscaled for decision making processes.
The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.
An overview of GEOS Forecasting capabilities for Air Quality, showing the evolution from GEOS with GOCART to GEOS with GEOS-Chem. Ways to access GEOS for research scientists and engaged community members will be given, as well as examples on how the GEOS-CF forecasts can be bias-corrected and downscaled for decision making processes.
The purpose of this document is to provide a forecast of major meteor shower activity on the lunar surface. While the predictions in this document are for the surface, spacecraft orbiting the Moon at low altitudes will encounter meteoroids at similar rates. Most annual showers are expected to display typical activity, but the eta Aquariids (ETA) are once again expected to exhibit more than double their typical activity level. A complete discussion of expected meteor shower activity in 2024 is available in the meteor shower activity forecast for low Earth orbit.
The NASA GEOS Composition Forecast (GEOS-CF), provides global near real-time analysis and 5-day forecasts of atmospheric composition, at a horizontal resolution of 25 km extending from the surface to 80 km altitude.
This work is focused on optimizing the assimilation of hyperspectral infrared (IR) radiances from the Cross-track Infrared Sounder (CrIS) with the goal of improving the representation of tropical cyclones (TCs) in global analyses and forecasts. Current operational assimilation systems rely on subsampling IR radiances on a regular thinning grid. A new and improved adaptive methodology based on machine learning (ML) recognizes TCs from geostationary satellite imagery and is implemented in the Goddard Earth Observing System (GEOS) model and data assimilation framework. The ML methodology is extensively trained on existing TC data sets and creates for each TC a dynamic mask, based on the evolving shape and life cycle of that specific event. Once a TC mask is created, a switch is then activated in the data assimilation system to alter the thinning, ingesting more CrIS radiances within the moving mask, thus increasing the TC sampling. After the TC dissipates, the assimilation of CrIS radiances reverts to normal data density. Results of TC segmentation provided by a state-of-the-art generative machine learning model known as the Denoising Diffusion Probabilistic Model (DDPM) are compared to the previously used U-Net model. The new approach surpasses the performance of the previously developed one. The methodology is applied to both clear-sky and cloud-cleared radiances. Benefits from the latter methodology, particularly in improving the structure of TCs and the intensity forecasts, are presented.
Weather forecasting and the raisin industry
Utility of weather forecasts to the raisin industry
Just 2 years ago the first Tiros weather satellite gave meteorologists powerful new eyes with which to view their atmospheric domain. Since that time developments have been rapid. Techniques have been developed for using the satellite observations in day-to-day weather analysis and forecasting. Communications have been improved to increase the availability of the satellite observations throughout the world. Research using the Tiros data is increasing our understanding of the atmosphere and will lead to improved weather observing satellite
Synoptic weather forecasting
Numerical analysis in weather forecasting
Mesospheric meteorology and possibilities of forecasting local weather
This paper provides a detailed illustration that it can be much more beneficial for ENSO forecasting to use data to improve the model parameterizations rather than to modify the initial conditions to gain in consistency with the simulated coupled system.
The Telecom Forecaster Predictor (TFP) fulfills the need for a powerful, easily adaptable, multi-mission telecommunications analysis tool.
Planners for the Deep Space Network at JPL require long-term forecasts of ground station view periods.
The paper introduces the concept of the long range forecasting function, which is then used to identify periods of peak oversubscription of resources years before they actually happen, so that proper corrective actions (construction of additional antennas, reduction in support committment to the impacted projects) can be implemented.
In this paper, an analytical method for comparing the performance of an ideal forecasting algorithm to the best statistical methods in terms of average data return is derived.
This paper presents a proposed approach for Ka-band link management for deep space applications using daily weather forecasts and discusses the tools that will be employed for operations. Performance metrics are also presented. The proposed approach will be tested in a two-year experiment campaign.