Steve Albers' Research Projects

Aerosols and Clouds (HAALE - MURI and CAMP2Ex)

SAIL

ELAPS

Building upon the Local Analysis and Prediction System (LAPS) developed by NOAA, CIRA, and CIRES between 1988 and 2014, I developed an Enhanced LAPS (ELAPS) during the past several years. The enhancements run throughout the system, with an emphasis on a variational satellite radiance driven cloud / aerosol analysis, and 3-D radiative transfer of model fields relating to sky and land for visualization. This analysis includes a capability to use GOES-16 or other geosynchronous satellite radiance data (clean IR window and visible channels) and produce 500m resolution 5-minute update cloud fields that are consistent with the satellite radiances, radar, METARs and other observational data. When LAPS analyses are paired with the hot-start forecast model initialization, the radar reflectivity forecasts from 0-2 hours show a more realistic skill score evolution with a peak at 0 hours and generally trailing off from there, following the theoretical predictability vs time relationships fairly closely. My "personal" real-time ELAPS run is displayed below in the form of plan views and Simulated Weather Imagery (SWIm) 3D visualizations.

The Simulated Weather Imagery (SWIm) package can be run to yield interesting (often remarkably close) comparisons between these analyses and simultaneous ground-based all-sky camera images. Both the cloud analysis and SWIm package are housed in the LAPS software distribution, though are modular enough to be used in other modeling systems.

SWIm Visualizations (real-time):

Aerosols and Visibility:

Hurricane Hunter Camera System


Aircraft camera mosaic video
. This is an animation of about 2000 Hurricane Hunter aircraft frames of Hurricane Lisa. Each frame is a mosaic from 4 cameras projected into a 360 cylindrical projection. Here are several flight segments from the Nov 2nd morning flight for Lisa.

Geoengineering Sky Simulations

SWIm can be used to simulate the appearance of the sky with stratospheric aerosols potentially being introduced to compensate for greenhouse warming, referred to as as geoengineering. This is similar to what is seen following major volcanic eruptions such as El Chichon in the 1980s and Pinatubo in 1991.

Variational Cloud Analysis

There are plans to further improve the cloud analysis in a variational context, allowing Numerical Weather Prediciton (NWP) models to more accurately match high resolution features seen in reality. This would entail a 4D variational analysis within an LES cloud model that directly uses visible and IR radiance information (e.g. with sub-kilometer resolution at a 1-minute cadence) solving for evolving 3-D cloud (and wind) fields. Observations from ground based cameras, also described in this paper, can be used along with satellites, and radars to variationally constrain clouds by looking at multiply scattered light from different vantage points. Since the light scatters throughout the interior of the clouds, the observed radiance provides information on the cloud optical and microphysical charasterics, such as optical thickness and liquid water content. A video explaining these concepts can be found on YouTube or in MP4 format (courtesy Israel Institute of Technology). A python software package performing this type of tomographic analysis based on SHDOM can be found here, described in this research article using airborne cameras. Here is a neural network version of the tomographic 3-D cloud retrieval. This related satellite data assimilation discussion helps to summarize things.

A preliminary result is shown above for a 4D-Var cloud analysis with observed visible "VIS" satellite (upper left), simulated VIS from regular LAPS cloud analysis (upper right), and 4D-Var cloud analysis fitted with an observation window from 2115 UTC to 2120 UTC (bottom). Both bottom panels are the same data. A subsequent 4D-Var forecast continues at 2125 UTC. Thus the 4D-Var model sequence is constrained by the data at 0000 and 0005 minutes, and extended as a free forecast out to 0010 minutes.

Cloud Downscaling

In the comparison images below, the 500m horizontal resolution gridded Cloud Analysis is downscaled to produce 3D fields of hydrometeors on a finer 12.5m grid. With SWIm we then simulate sky images to compare the two cloud fields. This case is from September 7, 2025 at 1735UTC. I hope to run this in real-time on a 50-100m grid.

Original SWIm Sky Simulation
500m resolution Cloud Analysis

Enhanced SWIm Simulation
Clouds downscaled to 12.5m resolution

All-Sky Camera Reference
University of Colorado, Boulder

References:

  • A. Aides, A. Levis, V. Holodovsky, Y. Y. Schechner, D. Althausen and A. Vainiger, Distributed Sky Imaging Radiometry and Tomography, 2020 IEEE International Conference on Computational Photography (ICCP), 2020, pp. 1-12, doi: 10.1109/ICCP48838.2020.9105241.

  • [1] Levis, A.; Schechner, Y.Y.; Davis, A.B.; Loveridge, J. Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size. Remote Sens. 2020, 12, 2831. https://doi.org/10.3390/rs12172831

  • Kim, Jin-Young, Steve Albers, Purnendranath Sen, Hyun-Goo Kim, Keunhoon Kim, and Su-Jin Hwang. 2022. The Impact of Assimilating Winds Observed during a Tropical Cyclone on a Forecasting Model Atmosphere 13, no. 8: 1302