Recent developments indicate that moisture in the lower free troposphere (LFT) strongly affects deep convection. This basic process introduces observational constraints, due challenges in obtaining high-vertical resolution profiles of moisture and temperature in and near convective weather. In addition to identifying layers of influence, such observations would serve as diagnostics for the parameterization of convection in climate and weather forecast models by characterizing the dependence of convection on the humidity-temperature environment.

To address these constraints, the use of Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) measurements is explained.  A closely-spaced sequence of PRO satellite observations would capture independent profiles of coordinated temperature, moisture and precipitation within convection and its environment. Similar to the principle underlying polarimetric weather radar, PRO relies upon hydrometeor shape asymmetry, such that a differential phase time delay is induced between the horizontal- and vertically-polarized radio signals propagating through a precipitation medium. The PRO concept has been demonstrated by the Radio Occultations and Heavy Precipitation (ROHP) experimental receiver orbiting onboard the Spanish PAZ satellite since May 2018. Highlights from the past four years of ROHP analysis will be presented, as well as applications for these data in weather modeling.

Dr. Shuman, glaciologist and senior research scientist with GESTAR II, shared this Earth Observatory feature, which he contributed to and which uses Landsat data from the USGS, images that are 50 years apart and provide valuable insight to changes in Greenland's peripheral glaciers: "A Half-Century of Loss in Northwest Greenland." He also provided his study, "50 Years of Landsat - Ice Cover Changes, East Greenland, from 1972 to 2022" (see below).

Ms. Casasanto, ICESat-2 mission outreach lead and JCET member, shared that she is "definitely inspired by her as a female from a time when the field was predominantly male, and who was doing something absolutely amazing for the world." She frequently uses Landsat data as it complements the ICESat-2 elevation data. Additionally, for a 2020 article about Landsat working with Jacques Cousteau, she translated the interview from French with the chief diver.

We hope you will take the time to read this piece from NPR, to visit the Earth Observatory site, where Landsat images are shared from all over the world, and to appreciate the beauty of our planet and Ms. Norwood's legacy.

Don Engel, Assoc. VP of Research Development and Director of CSST (among other roles), is one of the creators of CIDER. He explains, "People with their primary appointments in centers or institutes ... are eligible to be PIs on CIDER proposals. ... CIDER was designed specifically with them in mind. It does require that they have a secondary collaborator (a co-I) who is in a degree-granting department, as an additional purpose of CIDER is to support building bridges between centers/institutes and the rest of the university."

This initial group of awardees included four research teams who were each awarded a $50,000 CIDER grant for 18 months to pursue their studies. Among the awardees is the proposal "Model Development for Polarimetric Remote Sensing of Clouds in the Thermal Infrared," led by GESTAR II's Xiaoguang Xu (616/UMBC), with co-investigator Vanderlei Martins, professor of physics and GESTAR II member (613/UMBC), and Jie Gong, NASA. 

Additionally, funding was awarded for the proposal "Harnessing the Power of Machine Learning to Discover What Powers Distant Galaxies" led by Antara Basu-Zych (662/UMBC/CSST II), with co-investigator Sanjay Purushotham, assistant professor of information systems, and Kristen Garofali, NASA. (Both GESTAR II and CSST II are managed by ESRA (Earth & Space Research Administration), part of UMBC's Office of Research and Creative Achievement.)

Tokay, A., C. N. Helms, K. Kim, P. N. Gatlin, and D. B. Wolff (2023). Evaluation of SWER(Ze) relationships by Precipitation Imaging Package (PIP) during ICE POP 2018. J. Hydrometeor., https://doi.org/10.1175/JHM-D-22-0101.1.

Tokay, A., L. Liao, R. Meneghini, C. N. Helms, S. J. Munchak, D. B. Wolff, and P. N. Gatlin (2023). Retrieval of normalized gamma size distribution parameters using Precipitation Imaging Package (PIP) snowfall observations during ICE POP 2018.  J. Appl. Meteor. Climatol., https://doi.org/10.1175/JAMC-D-21-0266.1.

Lionel Arteaga (610.1/UMBC) has published a lead author article today in the Nature journal Communications Biology titled "Impact of Pacific Ocean heatwaves on phytoplankton community composition" (doi:10.1038/s42003-023-04645-0). Dr. Arteaga provided this summary and corresponding slide (credit: L. Arteaga):

"Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anomalies are increased mortality of higher trophic organisms such as marine mammals and seabirds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we combine NASA's biogeochemical modelling and satellite imagery to reveal community level shifts in phytoplankton composition during northeastern and equatorial Pacific Ocean warming events of the last decade (2010s). In the Gulf of Alaska, the expansion of the marine heatwave known as the "Blob" in 2014 altered the transport of nutrients sustaining phytoplankton growth, causing a decline in large and silica-dependent diatoms in favor of smaller phytoplankton groups. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Nino resulted in a major decline of about 40% in surface chlorophyll, which was associated with a nearly total collapse in diatoms. These changes in community composition could signify an important reduction in the production and export of organic carbon that sustains marine ecosystems and regulates atmospheric carbon dioxide."

In the global monitoring of precipitation, extreme high values, even if relatively infrequent, are of particular interest to the research community, because of their strong hydrological and climatological impact and their impact on human activities. Moreover, the uncertainty regarding their evolution under a changing climate reinforces the necessity to have accurate measurements and records of precipitation extremes all around the globe. Global atmospheric models lack accuracy in reproducing precipitation extremes at scales relevant for decision making (sub-daily and sub-meso). The same applies to global observational precipitation products derived from gauges and/or satellite observations. Indeed, the information content of satellite-measured radiances and the limited spatio-temporal sampling allowed by the constellation of precipitation-relevant passive sensors does not always allow accurate deterministic mapping of extreme precipitation rates at resolution one degree and one day or finer. The global network of operational rain gauges also generally lacks the required density to produce accurate estimates of extreme precipitation amounts at these fine scales. Consequently, the many available global precipitation records are largely inconsistent with each other when it comes to precipitation extremes.

Moreover, the existing global precipitation estimates are generally designed to have minimal variance of their residual errors (minimum MSE estimates) or to produce the most likely estimate given the observations (maximum likelihood estimates). These types of estimators tend to produce smooth estimates and compress the dynamical range of the target variable, they don't preserve its statistical distribution and systematically underrepresent the occurrence of extremes. We discuss alternate techniques and optimization criteria for the statistical preservation of extremes in estimation products within a deep-learning framework. We also discuss the fact that the preservation of extremes must be assessed as a multiscale problem; indeed, preserving or correcting the statistical distribution of precipitation rates at the pixel level (i.e., at the native resolution of the estimate) does not guarantee that the distribution will be preserved at coarser aggregated scales. To preserve extremes across all scales one must consider the multiscale structure of precipitation fields and their spatial and temporal autocorrelation.

Dandridge, C., Stanley, T.A. (617/UMBC), Kirschbaum, D.B., and Lakshmi, V. (2023), Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog, Sustainability 2023, 15, https://doi.org/10.3390/su15043323. 

Salinas, C. C. J. H. (613/UMBC), Wu, D. L., Lee, J. N. (613/UMBC), Chang, L. C., Qian, L., and Liu, H. (2023), Aura/MLS observes and SD-WACCM-X simulates the seasonality, quasi-biennial oscillation and El Nino-Southern Oscillation of the migrating diurnal tide driving upper mesospheric CO primarily through vertical advection, Atmos. Chem. Phys., 23, https://doi.org/10.5194/acp-23-1705-2023.

Sayer, A. M. (616/UMBC), Lelli, L., Cairns, B., van Diedenhoven, B., Ibrahim, A., Knobelspiesse, K. D., Korkin, S. (613/UMBC), and Werdell, P. J. (2023), The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, Atmos. Meas. Tech., 16, https://doi.org/10.5194/amt-16-969-2023.

Wargan, K., Weir, B. (610.1/MSU), Manney, G. L., Cohn, S. E., Knowland, K. E.  (610.1/MSU), Wales, P. A. (610.1/MSU), and Livesey, N. J. (2023), M2-SCREAM: A stratospheric composition reanalysis of Aura MLS data with MERRA-2 transport, Earth Space Sci., https://doi.org/10.1029/2022EA002632.