The NASA Olympic Mountains Experiment (OLYMPEX) just finished its intensive observing period (IOP) after nearly two months of data collection. OLYMPEX was a ground validation field campaign designed to verify and validate satellite measurement of precipitation from the constellation of satellites known as the Global Precipitation Measurement (GPM). The primary goal of OLYMPEX is to validate rain and snow measurements in midlatitude frontal systems moving from ocean to coast to mountains and to determine how remotely sensed measurements of precipitation by GPM can be applied to a range of hydrologic, weather forecasting and climate data. OLYMPEX will have a wide variety of ground instrumentation, and several radars and aircraft monitoring oceanic storm systems as they approach and traverse the Peninsula and the Olympic Mountains. Prof. Nesbitt and group alumnus George Duffy participated in the execution of the campaign.
The Nesbitt research group was renewed as a principal investigator on the NASA Precipitation Measurement Missions science team, which is responsible for improving and using data from the Global Precipitation Measurement (GPM) mission satellite, which has been in orbit since February 2014. Our group will continue to use ground validation data collected during a series of international field campaigns to test and improve the algorithms.
The Department of Energy Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was selected for funding by the US Department of Energy Atmospheric Radiation Measurement program for 2018-19. This campaign will investigate the forcing, structure, and life cycle of orographic convection for a period of 8 months with the Atmospheric Radiation Measurement mobile facility 1 (AMF-1), CSAPR-2 dual polarization precipitation radar, a hydrometeorlogical network, and Mobile Aerosol Observing Facility (MAOS). A 1.5-month intensive observing period (IOP) during late 2018 will add the ARM Gulfstream-1 microphysics and aerosol sampling aircraft to study wet season convective development and upscale growth. The project description is below, also check out the DOE ARM CACTI web site. Our group will participate extensively in the planning and execution of this campaign.
General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage. Despite important impacts on the distribution of atmospheric heating, moistening, and momentum, nearly all climate models fail to represent convective organization, while system evolution is not represented at all. Improving representation of convective systems in models requires characterization of their predictability as a function of environmental conditions, and this characterization depends on observing many cases of convective initiation, non-initiation, organization, and non-organization. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) experiment in the Sierras de Córdoba mountain range of north-central Argentina is designed to improve understanding of cloud lifecycle and organization in relation to environmental conditions so that cumulus, microphysics, and aerosol parameterizations in multi-scale models can be improved. The Sierras de Córdoba range has a high frequency of orographic boundary layer clouds, many reaching congestus depths, many initiating into deep convection, and some organizing into mesoscale systems uniquely observable from a single fixed site. Some systems even grow upscale to become among the deepest, largest, and longest-lived in the world. These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry bias in climate models downstream of the Sierras de Córdoba range in a key agricultural region. Many environmental factors influence the convective lifecycle in this region including orographic, low level jet, and frontal circulations, surface fluxes, synoptic vertical motions influenced by the Andes, cloud detrainment, and aerosol properties. Local and long range transport of smoke resulting from biomass burning as well as blowing dust are common in the austral spring, while changes in land surface properties as the wet season progresses impact surface fluxes and boundary layer evolution on daily and seasonal time scales that feed back to cloud and rainfall generation. This range of environmental conditions and cloud properties coupled with a high frequency of events makes this an ideal location for improving our understanding of cloud-environment interactions. The following primary science questions will be addressed through coordinated ARM and guest instrumentation observations: 1. How are the properties and lifecycles of orographically generated cumulus humulis, mediocris, and congestus clouds affected by environmental kinematics, thermodynamics, aerosols, and surface properties? How do these cloud types alter these environmental conditions? 2. How do environmental kinematics, thermodynamics, and aerosols impact deep convective initiation, upscale growth, and mesoscale organization? How are soil moisture, surface fluxes, and aerosol properties altered by deep convective precipitation events and seasonal accumulation of precipitation?
As part of their role on NASA’s Precipitation Measurement Missions Science Team, Prof. Steve Nesbitt, Prof. Greg McFarquhar, researchers Dr. Brian Jewett, and Dr. Dan Harnos, and graduate students Kim Reed, Kirstin Harnos, and George Duffy were recently awarded the 2014 NASA Robert H. Goddard Award for Exceptional Achievement in Science. The award was given to the group as a member of the NASA Global Precipitation Measurement mission Ground Validation team, in recoginition of their efforts to further enhance Earth Science research.
Our group, in collaboration with Profs. Jeff Trapp and Sonia Lasher-Trapp in the department shared in a 3 year project funded by the Department of Energy Atmospheric Radiation Measurement (ASR) Atmospheric Systems Research (ASR) program to study the parameterization of convection in climate models, using observations from the DOE site in the Southern Great Plains as observational testbed. Data from the comprehensive Midlatitude Continental Convective Cloud Experiment (MC3E), which was a joint field program involving NASA Global Precipitation Measurement Program and ARM investigators conducted in south-central Oklahoma during the April to May 2011 period, will provide the necessary observations for this study.
Our research group will be collaborating with Timothy Lang (NASA MSFC) and Themis Chronis (University of Alabama-Huntsville) on a 4-year NASA project to examine and use scatterometer winds to study low- and high-latitude precipitation systems, including the new RapidScat platform soon to be deployed on the International Space Station. More details at NASA JPL’s scatterometer wind site: http://winds.jpl.nasa.gov
We’ve added more python resources to our RADAR information page. Check them out!
Prof. Nesbitt will be participating in a webinar on 9/11/13 sponsored by PBS/NOVA and the National Earth Science Teachers Association. Click here for more information!
It is a common practice with SIGMET signal processors (and even from other radars such as research radars and operational data such as NEXRAD) to use the Doppler velocity spectrum in an attempt to filter ground clutter from other fields (which ideally has a near 0 velocity spectrum). Clutter (reflections off of objects, both stationary and moving targets such as mountains, aircraft, buildings, cars, etc.) is an annoyance because it contaminates the presentation of the radar images as well as causes errors in radar retrievals (velocity for severe weather interpretation, precipitation estimates, etc.). One idea is to remove the clutter by filtering the time series (pulse by pulse) by removing returns with near zero velocity. This is desirable because the remaining signal will be meteorological echo (which is usually moving towards or away from the radar). However, sometimes it’s not, and here are some examples where it can cause problems.
Here is an example from a NEXRAD PPI near Cleveland in widespread precipitation (snow in this case), showing the reduction in reflectivity factor near the radar where the velocity is near 0. You can see this effect nearly every time there is stratiform rain near a NEXRAD. The algorithm must have a range or altitude dependence, since the effect usually goes away after 15 km or so. However, if you’re trying to use reflectivity to estimate precipitation, the value of Z is missing some meteorological echo. It’s probably better than possibly including clutter (which will blow up your estimates of precipitation or mean particle size), but not ideal.
The problem is that this removes power from meteorological echo, and thus can bias Z and Zdr measurements in these regions. Here is an example of this in action: in this RHI scan a 0 isodop (an isodop is a surface of constant Doppler velocity) filter is able to remove low level clutter, but also removes valid data near the 0 isodop. When designing radar scanning strategies, the radar meteorologist must be aware of these settings for each sweep, and for research measurements it may be advisable to use polarimetric methods of QC rather than using the velocity spectra.
In this animation, you can see that in the “quality controlled” reflectivity (the image without the clutter and clear air echo aloft, there is power missing where the mean Doppler velocity is near 0 (especially below the freezing level in several broad horizontal regions), as shown via the black areas in the image below:
It also influences the differential reflectivity (Zdr), making the values negative since more power is preferentially removed from the horizontal reflectivity compared with the vertical reflectivity:
Online publication date: 1-Jul-2009.
Abstract . Full Text . PDF (2852 KB)
and there is even a patent on the technique outlined in the first paper! It is not known at this time what specific filtering technique was used on the data displayed here. The impact is the following: The “quality controlled” reflectivity field, and other fields are impacted with biases, that must be identified and removed from quantitative retrievals. In addition, this quality control process deleted data in many of the other fields, which along with biases in the measured fields hampers the use of dual-polarization variables for quality control (including correlation coefficient and standard deviation of differential propagation phase). We have the uncorrected reflectivity, but we can’t effectively use the polarimetric variables to correct the This cannot be undone in recorded data, so unless one is careful, this issue can cause issues with your dataset.
Carey, L. D., S. A. Rutledge, D. A. Ahijevych, and T. D. Keenan, 2000: Correcting propagation effects in C-band polarimetric radar observations of tropical convection using differential propagation phase. J. Appl. Meteor., 39, 1405–1433. [Abstract]
Clearly improving time series/spectral analysis is an active area of research, so stay tuned for improved algorithms. Note that dual-pol QC methods aren’t perfect either, but that topic will be saved for another post.
Software credit: ARM-PyART, Argonne National Lab
We’ve added a new page with a small but hopefully growing list of radar data resources. Stay tuned for updates!