Tag: Numerical Modeling and Data Assimilation

Figure: Vertical profiles of co-located LEO AMVs and RAY (red) and MIE (blue) winds. The top row shows the Arctic (north of 60° N), (a) mean AMV HLOSV (solid lines), Aeolus HLOSV (long dashed lines; m s−1), and mean AMV wind speed (short dashed lines; m s−1), (b) MCDs (solid), SDCDs (short dashed), and AMV HLOSV error, as represented by SDCD–Aeolus L2B uncertainty (long dashed; m s−1), and (c) co-location counts. Panels (d–f) are as in panels (a–c) but for the Antarctic (south of 60° S). Colored open circles indicate levels where MCDs are statistically significant at the 95 % level (p value < 0.05), using the paired Student’s t test. Vertical zero lines are displayed in the center panels in black. Levels with observation counts > 25 are plotted.

Atmospheric Motion Vector Bias and Uncertainty

May 23, 2022

ESSIC/CISESS Scientists Katherine Lukens (a former CISESS grad student), Kayo Ide, Hui Liu, and Ross Hoffman have a new article in the journal Atmospheric Measurement Techniques about their work with the NOAA/NESDIS Office of Projects, Planning, and Acquisition (OPPA) Technology Maturation Program (TMP). The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty.

A New NOAA Blended Soil Moisture Product that Does Not Rely on Model Climatology

May 3, 2022

ESSIC/CISESS Scientists Jifu Yin, Jicheng Liu and Ralph Ferraro published a new article last month that discussed their work with NOAA’s Soil Moisture Operational Product System (SMOPS). SMOPS is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real time blended soil moisture (SM) for Numeric Weather Prediction and National Water Model applications.

Giant miscanthus can grow up to 3-4 meters.

Growing This Plant Could Mitigate Climate Change in the Midwest

March 25, 2022

A new study including ESSIC scientist Xin-Zhong Liang has discovered that growing Miscanthus + giganteus, a type of perennial biomass crop, has a strong likelihood of significantly lowering regional summer temperatures and the vapor-pressure deficit, while increasing rainfall and overall crop productivity. This work was published in Global Change Biology-Bioenergy with Liang serving as Lead Principal Investigator. The first author, Yufeng He, is a former ESSIC Postdoctoral Associate.

Road salt after winter storm. The salinization from winter road salt combined with other pollutants creates “chemical cocktails” that can jeopardize the ecological balance of waterways. Credit: Sujay Kaushal.

New Scoring Scale Tracks the Harmful Effects of Salt Pollution in Freshwater Streams and Rivers

March 16, 2022

The road salt that makes your drive to work easier on snowy days may be damaging the water quality in local rivers and streams long after winter is over.

Leveraging Machine Learning to Improve Sea Surface Temperature Retrievals

February 28, 2022

The Microwave Integrated Retrieval System (MiRS) Science Team has published a paper in IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) titled “Improvement of MiRS Sea Surface Temperature Retrievals Using a Machine Learning Approach”.

Figure 1. (a) Horizontal distribution of MiRS NOAA-20 ATMS TPW for all of 2019, (b) meridional distribution of statistics for MiRS NOAA-20 ATMS TPW versus ECMWF (black) and GDAS (red) (dots are bias (mm) and lines are standard deviation (mm)): number of pixels are more than 1.2 million for each latitude between 80°S and 80°N. Beyond this area the number of pixels decreases significantly. Global distribution of bias (mm) of (c) MiRS NOAA-20 ATMS TPW – ECMWF TPW, (d) MiRS NOAA-20 ATMS TPW – GDAS TPW, standard deviation (mm) of MiRS NOAA-20 ATMS TPW versus (e) ECMWF TPW and (f) GDAS TPW. All results are for combined ascending and descending orbits in 2019. The red box (120°W ∼ 150°W & 8°N ∼ 12°N) in each plot indicates an area typically characterized by strong convection (CONV area) and the black box (100°W ∼ 120°W & 5°S ∼ 12°S) indicates an area typically dominated by subsidence (SUBS area).

In-Depth Evaluation of MiRS Total Precipitable Water From NOAA-20 ATMS

February 16, 2022

The MiRS Science Team, composed of ESSIC/CISESS scientists Yong-Keun Lee and Christopher Grassotti, as well as NOAA STAR scientist Mark Liu, published a paper this week titled “In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets” in Earth and Space Science. Lee was the first author of the study.

ESSIC/CISESS at AMS Conferences

February 9, 2022

The American Meteorological Society (AMS) Annual Meeting was held virtually this year from January 23 to 27. Simultaneously, AMS held a number of specialized conferences and symposiums, focusing on topics including hydrology, climate variability and change, and atmospheric chemistry. ESSIC/CISESS scientists contributed a large number of talks and posters at the event. Talks included:

Isaac Moradi smiles for the camera, wearing a red gridded button-up and a red tie

Congratulations to Isaac Moradi, Newly Elected Member of the University State Senate

February 9, 2022

ESSIC/CISESS Research Scientist Isaac Moradi has been elected to the University State Senate as one of the Professional-Track Faculty members. The University Senate, composed of faculty, staff, students, and administrators, is one of the largest and most influential governing bodies at the University of Maryland. As a member of the senate, Moradi will help advise the University President on campus policy matters and concerns, including education, budget, personnel, campus-community, long range plans, facilities, and faculty, staff and student affairs.

Using Machine Learning to Improve Snow Cover Fraction Observations

January 25, 2022

ESSIC/CISESS scientists Soni Yatheendradas and Sujay Kumar are co-authors on a paper in Journal of Hydrometeorology titled, “A novel Machine Learning-based gap-filling of fine-resolution remotely sensed snow cover fraction data by combining downscaling and regression”.

Figure 1 | Flooding in July 2021 caused mass evacuation in Weihui, Henan province, in China.

Extreme Rainfall Slows the Global Economy

January 13, 2022

Xin-Zhong Liang was recently published in Nature’s “News and Views” section giving his insight on new research that reported a comprehensive assessment of changes in gross regional product (GRP) relating to excessive precipitation. The study concluded that increases in the numbers of wet days and in extreme daily rainfall dramatically reduces worldwide macroeconomic growth rates.