Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

Machine Learning for Targeted Assimilation of Satellite Data

Abstract: Optimizing the utilization of huge data sets is a challenging problem for weather prediction. To a significant degree, prediction accuracy is determined by the data used in model initialization, assimilated from a variety of observational platforms. At present, the volume of weather data collected in a given day greatly exceeds the ability of assimilation systems to make use of it. Typically, data is ingested uniformly at the highest fixed resolution that enables the numerical weather prediction (NWP) model to deliver its prediction in a timely fashion. In order to make more efficient use of newly available high-resolution data sources, we seek to identify regions of interest (ROI) where increased data quality or volume is likely to significantly enhance weather prediction accuracy. In particular, we wish to improve the utilization of data from the recently launched Geostationary Operation Environmental Satellite (GOES)-16, which provides orders of magnitude more data than its predecessors. To achieve this, we demonstrate a method for locating tropical cyclones using only observations of precipitable water, which is evaluated using the Global Forecast System (GFS) weather prediction model. Most state of the art hurricane detection techniques rely on multiple feature sets, including wind speed, wind direction, temperature, and IR emissions, potentially from multiple data sources. In contrast, we demonstrate that this model is able to achieve comparable performance on historical tropical cyclone data sets, using only observations of precipitable water.

Effects of Diurnal Cycle on a Simulated Asian Summer Monsoon

Abstract: This study investigates the effects of the diurnal cycle on monsoonal circulations over Asia in summer with a focus on precipitation. To this end, two sets of experiments are designed in a regional climate modeling framework forced by reanalysis data. The control experiment is a normal integration in which radiation is computed hourly, whereas the no-diurnal experiment is an experimental integration in which the daily averaged solar flux is computed once a day. Analysis of the results from the two experiments reveals that the diurnal cycle enhances the daily averaged sensible heat flux over land and the latent flux over oceans, which means that daytime net solar heating exceeds nighttime cooling in terms of the effects in surface climate and monsoonal circulations. Seasonal precipitation increased by about 3% over land and 11% over oceans. The surface hydroclimate over land is strongly influenced by the interaction between land and the atmosphere, and results in cooler surface temperatures except ...

Hygroscopic organic aerosols during BRAVO

Abstract: The hygroscopic properties of the organic fraction of aerosols are poorly understood. The ability of organic aerosols to absorb water as a function of relative humidity (RH) was examined using data collected during the 1999 Big Bend Regional Aerosol and Visibility Observational Study (BRAVO). (On average, organics accounted for 22% of fine particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5) mass). Hourly RH exceeded 80% only 3.5% of the time and averaged 44%. BRAVO aerosol chemical composition and dry particle size distributions were used to estimate PM2.5 light scattering (Bsp) at low and high ambient RH. Liquid water growth associated with inorganic species was sufficient to account for measured Bsp for RH between 70 and 95%.

A Random-Forest Model to Assess Predictor Importance and Nowcast Severe Storms Using High-Resolution Radar–GOES Satellite–Lightning Observations

Abstract: Few studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s–1, tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.