The Evolution of Gridded NUCAPS: An Overview of Research to Operations Activities

The next-generation S-NPP and NOAA-20 Cross-track Infrared Sounder (CrIS) temperature and moisture profiles can provide valuable observations; (1) Where conventional radiosonde observations are sparse, (2) Between radiosonde launches. CrIS observations are combined with the Advanced Technology Microwave Sounder (ATMS) to produce high quality vertical soundings in clear and partly cloudy conditions. NUCAPS (NOAA Unique Combined Atmospheric Processing System) is the operational algorithm for processing combined hyperspectral infrared and microwave measurements. NUCAPS Soundings are operationally available in AWIPS as Skew-T plots. The capability to visualize the data in plan view or cross section would be valuable to maximize the benefits of NUCAPS data in AWIPS. A multi-organizational collaboration through the JPPS Soundings Applications Initiative developed the capability for plan view and cross section displays of NUCAPS in AWIPS (i.e., Gridded NUCAPS)

IN53D-04: Results from Evaluations of Gridded CrIS/ATMS Visualization for Operational Forecasting

No abstract availabl

Results from an Operational Demonstration of a Gridded CrIS/ATMS Product for Cold Air Aloft

No abstract availabl

Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture

<p>Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather that threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high-impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the mid- and upper-atmosphere.  emperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual UNet performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well-calibrated and can provide useful information to forecasters.</p><p>Funding provided by: National Oceanic and Atmospheric Administration<br>Crossref Funder Registry ID: https://ror.org/02z5nhe81<br>Award Number: NA19OAR4320073</p><p>This dataset was collected for the corresponding publication in <em>Artificial Intelligence for the Earth Systems</em>, and the processing methodology is outlined in that publication.</p&gt

Code Archive for Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture

<p>This code accompanies the publication:</p><p>Haynes, K., Stock, J., Dostalek, J., Anderson, C. & Ebert-Uphoff, I. (2023).  Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture.   <i>Artificial Intelligence for the Earth Systems.</i></p&gt

An Overview of Transition of Research to Operations Activities: Hyperspectral Infrared Sounder Retrievals in NOAA NWS Operations

No abstract availabl

Insulin Resistance, Ceramide Accumulation, and Endoplasmic Reticulum Stress in Human Chronic Alcohol-Related Liver Disease

Background. Chronic alcohol-related liver disease (ALD) is mediated by insulin resistance, mitochondrial dysfunction, inflammation, oxidative stress, and DNA damage. Recent studies suggest that dysregulated lipid metabolism with accumulation of ceramides, together with ER stress potentiate hepatic insulin resistance and may cause steatohepatitis to progress. Objective. We examined the degree to which hepatic insulin resistance in advanced human ALD is correlated with ER stress, dysregulated lipid metabolism, and ceramide accumulation. Methods. We assessed the integrity of insulin signaling through the Akt pathway and measured proceramide and ER stress gene expression, ER stress signaling proteins, and ceramide profiles in liver tissue. Results. Chronic ALD was associated with increased expression of insulin, IGF-1, and IGF-2 receptors, impaired signaling through IGF-1R and IRS1, increased expression of multiple proceramide and ER stress genes and proteins, and higher levels of the C14, C16, C18, and C20 ceramide species relative to control. Conclusions. In human chronic ALD, persistent hepatic insulin resistance is associated with dysregulated lipid metabolism, ceramide accumulation, and striking upregulation of multiple ER stress signaling molecules. Given the role of ceramides as mediators of ER stress and insulin resistance, treatment with ceramide enzyme inhibitors may help reverse or halt progression of chronic ALD