nextGEMS | next Generation Earth Modelling Systems’ Post

Using #ARMdata collected in Colorado’s #RockyMountains during the Surface Atmosphere Integrated Field Laboratory (#ARMSAIL) field campaign, SAIL lead investigator Daniel Feldman and his fellow Berkeley Lab colleagues have discovered a surprising finding: a lot of existing weather models suggest that winter temperatures in mountains are 1-5 °C colder than what actual measurements have revealed. Recently published in BAMS, an American Meteorological Society publication, their study shows that these temperature differences can be caused by a mix of factors like large-scale weather patterns, land surface heterogeneity, ground temperature, and heat emissions. Mountains serve as Earth's natural water reservoirs, and changes in snowfall caused by these warmer temperatures could put our💧resources at risk. This new study illustrates the importance of increased observations and better model development to help fix this common problem. Discover more about this recent study at https://bit.ly/4auhuMa 📷 Nathan Bilow U.S. Department of Energy (DOE) #DOEClimateScience #ScienceNeverSleeps

I am happy to share my article on cyclone #snsinstitutions #snsdesignthinkers #snsdesignthinking Meteorology is a branch of the atmospheric sciences (which include atmospheric chemistry and physics) with a major focus on weather forecasting. The study of meteorology dates back millennia, though significant progress in meteorology did not begin until the 18th century. The 19th century saw modest progress in the field after weather observation networks were formed across broad regions. Prior attempts at prediction of weather depended on historical data. It was not until after the elucidation of the laws of physics, and more particularly in the latter half of the 20th century, the development of the computer (allowing for the automated solution of a great many modelling equations) that significant breakthroughs in weather forecasting were achieved. An important branch of weather forecasting is marine weather forecasting as it relates to maritime and coastal safety, in which weather effects also include atmospheric interactions with large bodies of water.

Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches - Frontiers in Marine Science: Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of in situ salinity measurements in the Russian Arctic seas collected during multiple field surveys is applied to train and validate the machine learning models. The error in SSS retrieval of the developed algorithm compared to the standard algorithm reduced from 3.15 to 2.15 psu, and the correlation with in situ data increased from 0.82 to 0.90. The obtained daily SSS fields are important to improve accurate assessment of spatial and temporal variability of large river plumes in the Arctic Ocean. https://lnkd.in/eD95YJTV

DATA: £5.7m investment will triple computing capacity for atmospheric #DataAnalysis UK Research and Innovation has invested £5.7m (US$7.15m) in a #compute cluster that will reportedly triple the processing power for analyzing atmospheric science data. The cluster will be available for scientists to use by the summer of 2024. Known as LOTUS, the expanded parallel compute cluster will enable a much greater volume of computing tasks and multiple streams of data analysis to be done at the same time. LOTUS is part of JASMIN, a data-intensive supercomputer used by researchers at the National Centre for Atmospheric Science with support from the Centre for Environmental Data Analysis. Read more here: https://lnkd.in/eVA7adMM #Meteorology #Climate #ClimateChange #Science #Weather #Data #Forecasts #Environment #Technology #MetTechExpo #MetTechExpoNA

How does drought affect forests and the terrestrial carbon cycle and how can satellite dataset help in this assessment? This is discussed in our new paper "Impact of drought-induced forest mortality on terrestrial carbon cycle from remote sensing perspective" as an invited opinion published in The Innovation Geoscience, pp.100057-1. https://lnkd.in/gPCcMDs6 The figure summarizes the three relevant, primary mechanisms for this issue: hydraulic failure, carbon starvation, and biological invasion, due to severe droughts. We list three challenges that need to be urgently addressed: (i) Data fusion from different sensors; (ii) Scale variance and data validation- including enhancing the reporting of impacts; (iiii) (Physics guided) model - data integrations especially as studies seek to understand the feedbacks. #drought #DataModelFusion #SatelliteData

Congratulations to IPE-affiliated faculty member Dr. Ben Kravitz, Department of Earth and Atmospheric Sciences, on his appointment to the International Commission on Climate (ICCL)! Membership in the ICCL is limited to fewer than 20 current scientists; Dr. Kravitz will be the sole American member on the expert commission. The commission is part of the International Union of Geodesy and Geophysics' International Association of Meteorology and Atmospheric Sciences. Kravitz’s main area of research is climate engineering, which involves major interventions to counter climate change. “We’re not doing enough quickly enough to prevent some of the awful effects of climate change that we’re experiencing right now,” Kravitz said. “There are things we could do temporarily in the meantime, while we get our act together, and these fall under the umbrella of climate engineering." At IU, Kravitz’s research group uses climate models to better understand how the Earth has changed in the past, and how it might change going forward. Such research can empower policymakers and the public to make better decisions about their futures in a warming world. "(W)e really need to understand how climate engineering work to know whether it could and should be part of a portfolio of responses—we only have one planet, after all. Therefore, a lot of the work that I do involves plugging climate engineering into climate models to see what might happen.” Kravitz’s lab specializes in combining global climate, regional climate, and process model simulations on such questions as: 🌏 How does the Earth system respond to climate engineering? ⚡ What are the risks and side effects? 👩🔬 How can scientists combine models across scales to quantify how climate change will affect people's daily lives? #geoengineering #ClimateEngineering #ClimateChange #EarthAndAtmosphericSciences

📚 WEEKEND READING 📚 Read an interesting article about the fascinating world of ocean waves! 🌊 As an #ocean wave laps up against a beach, it contains innumerable #swirls and eddies. The seawater forms complex patterns at each level, from the waves that surfers catch to ripples too small and fast for the human eye to notice. Each motion sets off another set of motions, cascading through layers of water. What's merely scenic at a beach is essential for scientists to understand. Describing more accurately how heat moves through the #ocean could help scientists develop better, more precise computer models of Earth's #climate. Understanding #turbulence - the irregular movement of fluids - in the ocean would help researchers solve this issue. Scientists at the University of Cambridge and the University of Massachusetts Amherst used the #Summit #supercomputer at the Department of Energy's Oak Ridge Leadership Computing Facility (OLCF) to run a new model of ocean turbulence. (The OLCF is a DOE Office of Science user facility.) The work is published in the Journal of Turbulence. Read more about how turbulence affects heat in the oceans and how it affects life on Earth. https://lnkd.in/g7Xyrn7G

Snow cover detection is particularly important in various fields such as climatology, hydrology, and meteorology. To that end, it is considered as one of the significant tasks of remote sensing. Moreover, detecting the snow cover in mountainous areas prone to snow accumulation and avalanches can effectively reduce the risk and prevent crises. Snow cover mapping using optical remote sensing images has its challenges, the biggest of which is the coexistence of snow and clouds in the scene. Having almost the same texture and color distribution of snow and cloud causes problems in optical satellite image classification algorithms. In order to solve the challenge, we (Ghazale Babapour and Soheil Majidi) could improve the classification results by applying a deep learning network to generate snow cover maps, including three classes: snow, cloud, and background.

Here's an article I wrote, published by BCS, The Chartered Institute for IT on #weather and #climate modelling. I discuss how #machinelearning is used with weather modelling now and in the future. Did you know #Fortran, originally developed by #IBM in the 1950's is still widely used in atmospheric models?