- Volcanoes
-
Measure ash concentration in seconds to maximize aviation safety
In 2010 and 2011, volcanic eruptions in Iceland, Japan, and Chile spread volcanic ash across the globe. Commercial aviation ground to a halt as ash particles threatened aircraft engines. In 2010, lidar networks such as EARLINET were able to observe and characterize the volcanic ash—establishing lidar’s utility for determining when planes could safely return to the air.
Leading the industry
Sigma Space first demonstrated MPL’s volcanic ash detection capabilities in Japan following the Shinmoedake eruption in January 2011. MPL was again deployed in Argentina in January 2012, following the eruption of the Chilean volcano Puyehue—marking the world’s first operational use of real-time lidar ash data at an airport for aviation safety.
Effective ash detection technology
Clouds and fog contain spherical liquid droplets, which have a low depolarization ratio. Volcanic ash particles are asymmetrical, and have a high depolarization ratio, which is readily detected by MPL systems due their dual polarization channels.
CERTAINTY COUNTS
With so much riding on their decisions, airports need the best information science and technology can provide. Combining sensitive volcanic ash detection with continuous, worry-free operation, MPLs are the clear choice for determining when it’s safe to fly.
MPL data, Bariloche Airport, Argentina. Two MPL time sequence plots shown here. The top one shows the co-polarized backscatter and the bottom one shows the ratio of the cross- and co-polarized backscatter, known as the depolarization ratio. The large depolarization ratio indicates the presence of volcanic ash (asymmetrical particles)—and gives the vertical distribution of volcanic ash above the airport. These lidar measurements are proportional to the amount of volcanic ash at a given height.
- PBL & Aerosols
-
Effective aerosol tracking
With their accurate measurements of aerosol structure, MPL systems are versatile tools for air quality forecasting:
Track aerosol transport and mixing into surface layers of air.
Observe plumes from major events such as forest fires, dust storms, and volcanic eruptions.
Trace gas-phase pollutants via their associated aerosols.
Fast, reliable, high-resolution PBL measurements
Since aerosols accumulate in the PBL—the layer of the atmosphere closest to Earth’s surface—MPLs are ideally suited for PBL profiling across applications.
Get a clear forecast
Solar heating of the planet’s surface readily warms the air in the PBL—driving the weather. As a result, measuring PBL height and approximate volume enhances weather prediction models.
Improve emissions estimates
CO2 and other greenhouse gases (GHGs) from surface sources collect in the PBL, and PBL height is needed to calculate top-down emissions estimates. Climate scientists can therefore use PBL data to refine GHG inventories.
Optimize wind energy measurements
PBL height affects the vertical profile of near-surface wind speeds and the amount of energy available for wind turbines, making PBL measurements useful input to wind power meteorological forecasts for planning and management.
MiniMPL PBL data, NASA DISCOVER-AQ, Edgewood, MD.
This NRB time sequence of 30-second averaged profiles shows optically thick low clouds at night, indicated by high backscatter intensity near 0.5–1 kilometers between 0:00–04:00 hours UTC (local time is UTC minus 5). Then the cloud clears to reveal the prior day’s residual PBL height with advection and entrainment at PBL top (green signal level near 2 kilometers). Then at 11:00 hours UTC rising daytime PBL is observed as the rising yellow signal level on the right side of the plot. Also detected are turbulent mixing features in the lower morning PBL and dynamics along the rising PBL edge showcasing the MiniMPL’s excellent signal-to-noise ratio and sensitivity.
- Clouds
-
MPL systems collect the detailed, real-time cloud data needed for industry-leading forecasts, accurate weather models, and optimal air traffic support.
Determine cloud extent and structure
MPL’s vertically resolved, ground-based measurements complement satellite images by providing detailed information about cloud height, extent, and structure. These properties are key to forecasting because they are directly related to atmospheric processes below and within the clouds. What’s more, using cloud height (pressure level), optical thickness, and signal depolarization,
SigmaMPL software quickly and accurately classifies cloud types and structures, such as stratocumulus or cirrus.
Identify cloud phase
Using polarization data, MPL systems can determine whether clouds contain liquid, ice, or both. MPL software includes algorithms to mark other important features as well, including the planetary boundary layer, cloud base, cloud peaks, and top of the aerosol layer.
Simultaneously map multiple cloud layers
Together with boundary layer trends, this information can be used to forecast frontal passages.
MiniMPL aerosol profiles of clouds and time sequence data taken over 16 hours, Lanham, MD. The 30-second averaged profiles on the right correspond to the time indicated by the red markers on the time sequence plots. The profiles show a thin water cloud near 2 kilometers, and multiple layers of clouds containing water and ice between 9 and 10 kilometers. To the left, time sequence plots of 30-second averaged profiles illustrate cloud evolution. For example, over the first 8 hours of data collection, a layer of clouds descends from 6 kilometers and evolves into multiple layers between 2 and 5 kilometers.
- MPL Software video tutorial
-
This video walks through the basics of SigmaMPL, briefly discussing the main features of the software. After watching this tutorial, users should be able to read and manipulate the data that is produced by Sigma Space's MPL line of products (MPL and MiniMPL - Micro Pulse Lidar). This overview explains the functionality of the "Raw Data", "R2 Corrected", "NRB", "SNR", and "Housekeeping" screens.
- A walkthrough MPL data
-
This document explains what kind of data you can get from MPL/MiniMPL and why they are important to you. In the attachement below we will have a step by step walkthrough of MiniMPL data and explain what each one means to you. Click here to download the pdf file.
