Mountain Rain or Snow

Mountain Rain or Snow

Jun 9, 2020

Mountain Rain or Snow

Mountain Rain or Shine Logo

Project Description

Tahoe Rain or Snow is growing, and we’ve taken on a new name: Mountain Rain or Snow.  You can help us by sending observations of precipitation during winter storms so that scientists can improve estimates of winter precipitation phase.

To get started, text WINTER to 855-909-0798.

Thanks to all current and past volunteers for helping advance this important work! Read below to discover the impacts of the reports made by volunteers during the past two winter seasons.

 

DRI scientist Monica Arienzo collects data for the Tahoe Rain or Snow project with Lake Tahoe in the distance.

2021 Report-back to community members

Read about the results of our 2020-2021 season in our new storymap. To view the storymap, click on the picture above.  

Learn More

2020 Report-back to community members

Tahoe Rain or Snow: 2020 Report to Community Members

If you’re new to Tahoe Rain or Snow, skip to the bottom to see an overview of the project first.

Community observations sent to Tahoe Rain and Snow in 2020

Community observations sent to Tahoe Rain or Snow in 2020

If you are reading this, you may be one of the many people who contributed to submitting 1040 observations this season of rain, snow, and mixed precipitation. This graph shows a timeline of the number of observations as they were submitted in the winter and spring. The different colored points depict your answers to “What is falling from the sky right now?”  A keen eye will notice that there are a few less than 1040 observations portrayed here; that is because some observations were submitted without a geo-tag (location services were off) and other similar issues. 

Growth of the Tahoe Rain or Snow community (sign-ups) over time

graph of number of observers in tahoe rain or snow project

The Tahoe Rain or Snow community is made up of over 200 observers who receive text message alerts during storm events. This graph shows the growth of our community through the season. Wondering why the growth curve  grew in a step-wise manner? Several organizations helped us spread the word (e.g., NWS-Reno and CoCoRaHS) and we had a lot of new sign-ups as a result.

To sign up, text WINTER to 855-909-0798.

Map of Tahoe Rain or Snow observations in 2020

Map of Tahoe Rain or Snow observations in 2020

Answers to the call for weather data came mainly from across the Tahoe, Sierra, and Truckee Meadows regions. We also had observations from the San Francisco Bay Area, the High Sierra, Fallon, Nevada, and even Portland, Oregon. Notice how the points cluster where people live. Next season, we hope to capture more observations from backcountry mountain areas where people are recreating or doing remote work.

A look at 2020 observations across the elevation gradient

graph of 2020 observations across the elevation gradient

As you look at these figures of the number of observations submitted (horizontal axis) at different elevations (vertical axis), notice how they were most often submitted from below 5200 feet (e.g., Reno/Washoe Valley) or above 5900 feet (e.g., Tahoe Basin).  Look at the next plot to see how the proportion of precipitation types of changed with elevation.

Proportion of 2020 observations at different elevation ranges

Proportion of 2020 observations at different elevation bands

Here, we divide elevation into ‘bins’ (an extra sciency way of saying elevation ranges), so you can see the number of observations submitted at each level. It won’t surprise you to see that there are more observations of rain and mixed precipitation at lower elevations and fewer at higher elevations. Likewise, there are more observations of snow at higher elevations.

For fun: When did you send us your observations?

graph showing when people sent in observations

For fun, we took a look at the timing of your observations by hour (on 0-24 hour military time) and by day of the week. It seems like a lot of people like to observe over their morning coffee and during the day, but rarely at night.  Sunday was a popular day to submit, and it also looks like you all like to take the day off on Friday!

How does Tahoe Rain or Snow work?

Screenshots of the Tahoe Rain or Snow app

Tahoe Rain or Snow is a community of weather observers who use the Citizen Science Tahoe app to share data with scientists. During a winter storm, observers let us know “What is falling from the sky right now?” (rain, snow, or mixed precipitation) along with a geo-tag of their location.

We use this information to better understand how much water falls from the sky during the winter in the Sierra Nevada. Counter to common logic, precipitation often falls as snow at air temperatures 1°C–3°C (33.8°F–37.4°F), particularly at higher elevations and on the drier, eastern side of the Sierra Nevada. However, we have very few recorded visual observations of precipitation phase despite millions of visits to the Sierras every year.

Why does this matter? Rain and snow have different effects on the land surface. Of greatest concern to downstream water users in Nevada and California are the differences in water storage and flooding. Snowpacks act as ideal natural reservoirs—storing water throughout the winter and early spring, then releasing it as temperatures warm, and both human and natural demand increase. Rainfall does not produce this same accumulation-melt effect, instead running off during precipitation. Importantly, large rainfall events from atmospheric rivers produce such rapid runoff that flooding, landslides, and infrastructure damage can result. These storms often come in near the freezing point, making it essential we can accurately predict rain versus snow.

We are using the crowdsourced data to improve satellite and computer model predictions of rain and snow in the Sierra Nevada.

CONTACT

Monica Arienzo
(Science-related inquiries)
Monica.Arienzo@dri.edu

Meghan Collins
(Observer-related inquiries)
Meghan.Collins@dri.edu

Keith Jennings
Keith.Jennings@dri.edu 

LOCATION

Desert Research Institute
2215 Raggio Parkway
Reno, NV 89512

North American Monsoon

North American Monsoon

Dec 12, 2019

North American Monsoon

Project Description

The North American Monsoon (NAM) is a large-scale feature having a strong impact on summer rainfall patterns and amounts over North America. For example, it supplies about 60%-80%, 45% and 35% of the annual precipitation for northwestern Mexico, New Mexico (NM) and Arizona (AZ), respectively. Also, anomalously wet NAMs in Arizona are strongly anti-correlated with anomalously dry summers in the mid-west. Although regional climate models have succeeded in reproducing some features of the NAM, its onset, strength and regional extent are not well predicted, and a physical understanding of key processes governing its life-cycle remain elusive.

Here we propose a partial mechanistic understanding of the NAM incorporating local- and planetary-scale processes that quantitatively relates the Gulf of California (GC) sea surface temperatures (SSTs) to the timing, amount and extent of NAM rainfall. The proposed hypothesis is supported with satellite observations of SST, sea surface height (SSH) and rainfall amount; temperature and humidity profiles from ship soundings launched over the GC; climatologies of SST, outgoing longwave radiation (OLR) and 500 hPa geopotential height reanalysis.

A physical understanding of the NAM is needed to guide improvements in regional and global scale modeling of the NAM and its remote impacts on the summer circulation and precipitation patterns over North America. This understanding is also needed to predict the NAM’s response to global warming.

Summer precipitation bias (% difference; model-observation) for regional models indicates that summer rainfall is generally most under-predicted in the NAM region. (shown by several studies).

Many studies implicate the GC as the dominant moisture source for the monsoon. Our empirical studies of SSTs and NAM rainfall (Erfani and Mitchell, 2014; Mitchell et al. 2002) show that:

1) Monsoon rainfall did not occur prior to the onset of GC SSTs exceeding 26°C, and the incremental advance of SSTs 26°C up the mainland coast of Mexico appears necessary for the northward advance of the monsoon.

2) For the period June–August, 75% of the rainfall in the Arizona–New Mexico region (AZNM) occurred after northern GC SSTs exceeded 29°C, with relatively heavy rains typically beginning 0–7 days after this exceedance.

3) For a given year, SSTs in the southern and central GC reached 29.5°C during a similar time frame, but such warming was delayed in the northern GC. This warming delay coincided with a rainfall delay for AZNM relative to regions farther south.

The figures show the relationship between GC SSTs and NAM rainfall.  Further details on our methods and results can be found in our Journal of Geophysical Research (JGR) paper: Erfani and Mitchell (2014), and our Journal of Climate (JC) paper: Mitchell et al. (2002).


AZNM region cumulative normalized rainfall for periods having N. GC SSTs ≤ indicated SST. Time is implicit with increasing SSTs.

Mean rainfall rates for the AZNM region for N. GC SST intervals of 0.5°C, based on five June–August seasons (Mitchell et al. 2002).

The 2012 Arizona Monsoon

Since  Mitchell et al. (2002) was published, we have analyzed 3 other monsoon seasons  at higher temporal resolution regarding SST and AZ rainfall amounts, resulting  in similar findings. Above is the 2012 analysis. These findings indicate relatively heavy rainfall begins after the mean N. GC SST reaches ~ 29.5°C. On  the other hand, relatively heavy convective rainfall occurred in the Great  Basin and Arizona during the first days of July in 2013 when the N. GC SST was well below this threshold. Thus, our local scale  mechanism may describe the main factors governing the Arizona monsoon onset during most years but not all years (Erfani and Mitchell, 2014).

The time evolution of SSTs in 2 different regions of the GC for August 2003. Note that the Las Vegas flood occurs a few days after the warmest northern GC SST.

Since observational, modeling and paleoclimate studies have implicated Gulf of California (GC) sea surface temperatures (SSTs) as critical to the development of the North American monsoon (NAM), it is important to study the ocean processes that affect GC SSTs.  The entrance of the GC is a region where two surface currents meet: southward cool waters of the California Current (CC) and northward warm waters of the Mexican Coastal Current (MCC).  The interaction of these two currents controls the seasonal variation of surface circulation in this region.  Numerical simulations and modern observations found that the northward MCC can be formed locally by the wind stress curl. 

In late May or early June, the sea surface height (SSH) topography changes about the time the surface winds in this region slacken, resulting in geostrophic currents that advect tropical surface water into the GC instead of down the coast. The altered circulation in late May-early June may explain the rapid retraction of the 28.5°C isotherm toward the coast in late June, and the corresponding intrusion into the GC, in sharp contrast to SSTs off Baja California that are sometimes ~ 10°C cooler.

The figures below depict the time evolution of SSHs and SSTs. More details can be found in our Journal of Geophysical Research (JGR) paper: Erfani and Mitchell (2014), our Journal of Climate (JC) paper: Mitchell et al. (2002),  and our Atmospheric System Research (ASR) meeting poster: Erfani et al. (2013).

time-evolution-SSHs-SSTs-51503-lg time-evolution-SSHs-SSTs-52503

Above: Sea surface height (SSH) topography with the 50 cm contour in black.  The blue arrows indicate geostrophic current velocities.  The black contour indicates how the direction of these currents (that follow contours of SSH) is altered between May 15th and May 25th.

june-5day-1 june-5day-6

Above: The evolution of the 26°C and the 28.5°C isotherms, based on SST climatology (1983-2000) from Jet Propulsion Laboratory/ Physical Oceanography. Distributed Active Archive Center (JPL/PO.DAAC) at 18 km resolution.  Images are 5-day means centered on 3 June and 28 June.  Courtesy of Dr. Miguel Lavin at Ensenada Center for Scientific Research and Higher Education (CICESE).

Local-scale measurements and modeling

Local-scale measurements and modeling demonstrate that relatively heavy summer precipitation in Arizona generally begins within several days after northern Gulf of California (GC) sea surface temperatures (SSTs) exceed 29°C.  The mechanism for this relates to the marine boundary layer (MBL) over the northern GC.  For SSTs < 29°C, GC air is capped by a strong inversion ~ 50-200 m above the surface, restricting GC moisture to this MBL.  Note SSTs ≥ 29.5°C (i.e. SSTs ≥ threshold SST) correspond with inversion caps 55% in lowest 2 km (this was generally true for higher levels too).  So, the inversion generally disappears once SSTs exceed 29°C, allowing MBL moisture to mix with free troposphere. This results in a deep, moist layer that can be advected inland to produce thunderstorms. – More details can be found in the below figures and also in our Journal of Geophysical Research (JGR) paper: Erfani and Mitchell (2014),  and in our Atmospheric System Research (ASR) meeting poster: Erfani et al. (2013).

Legend:
GC = Gulf of California | SSTs = Sea surface temperatures | MBL = Marine boundary layer | R/V = research vessel | RH = relative humidity | OD = optical depth | 

Analysis of the 2004 Monsoon

The local-scale analysis is based on soundings lauched from a research vessel (R/V) in the Gulf of California (GC) during June and August, 2004, during the North American Monsoon Experiment (NAME).  The figure below shows the location of R/V soundings (blue circles) and approximate R/V cruise path (blue lines) in GC (a) during June 2004 and (b) during August 2004 (Erfani and Mitchell, 2014).

The figure below represents a vertical profile of (a) temperature and (b) relative humidity (RH) for 10% of R/V rawinsondes having the strongest inversion cap; and (c) temperature and (d) RH for 10% of R/V rawinsondes having the weakest inversion cap. The black solid lines show the mean profiles; grey shaded areas represent the range of 1 standard deviation from the mean profiles; black dashed lines depict the adiabatic lapse rate. (a) And (b) were associated with the lowest SSTs (~25 °C), whereas (c) and (d) corresponded to the highest SSTs (~ 30 °C) (Erfani and Mitchell, 2014). 

The figure below shows idealized soundings summarizing an MM5 modeling study showing the dependence of the MBL inversion on SSTs when the GC SST = 29°C (green) and when the GC SST = 30°C (red dashed)

The figure below shows (a) Dependence of inversion cap on mean SSTs and (b) dependence of low-level RHs on inversion cap based on mean low-level RH for all rawinsondes on board the R/V in the GC north of 24.1°N during June and August 2004. Bars are indicative of standard deviations, and the numbers on data points represent the frequency of data in their bins. Data points with no standard deviation are based on less than three rawinsondes (Erfani and Mitchell, 2014).

Analysis of the 2012 Monsoon

Represented below is 24 hour rainfall amounts for the periods 10 days before (left) and after (right) the SST threshold in the lower 2/3 of the GC was attained.

Below is shown as (a) Relation between SSTs (red) and convective cloud frequency and (b) relation between convective cloud frequency and cloud top height over the NAM core region during 2012 before (red) and after (blue) the SSTs first exceeded 29°C in the lower 2/3 of the GC.  Regarding (a), uncertainties were estimated from the difference in mean SSTs for the central and southern GC regions.  OD means cloud optical depth.

Large-scale climatologies of North American monsoon

On the large scale, climatologies of North American monsoon (NAM) region, sea surface temperature (SST), outgoing longwave radiation (OLR) and NCEP/NCAR 500 hPa geopotential height reanalysis from 1983 to 2010 support the hypothesis that relatively warm GC (Gulf of California) SSTs (≥ 27.5°C) are generally required for widespread deep convection to initiate in the NAM region, and that the poleward evolution of the NAM anticyclone during June-July is driven by the associated descending air north or north east of the convective region. Indeed, the Rossby wave response to the monsoon latent heating forms a region of adiabatic descent and the anticyclone develops east of monsoon system.  As warm Pacific SSTs propagate northwards up the Mexican coastline, deep convection follows this northward advance, advancing the position of the anticyclone.  This evolution brings mid-level tropical moisture into the NAM region.  More details are provided in our Journal of Geophysical Research (JGR) paper: Erfani and Mitchell (2014), and our Atmospheric System Research (ASR) meeting poster: Erfani et al. (2013).

Legend:
OLR = outgoing longwave radiation | 

The firgure below shows time evolution of the 27.5°C SST isotherm (red curves), the anticyclone center at 500 hPa geopotential height (blue ellipses), and the 240–250 W m−2 outgoing longwave radiation (OLR) gradient (shades of golden) from 18 May to 13 July for the 1983–2010 climatology.  The OLR approximates the NAM boundary; a transition between regions of rising air (wet) and descending air (dry) (Erfani and Mitchell, 2014).

1893 2010 sst olr climatology

The images below show time evolution of a zonal vertical cross section of the 1983–2010 specific humidity (q) (shaded) and horizontal wind vector climatology at 25°N latitude from 18 May to 8 July every 10 days. Specific humidity shading interval is 0.5 g kg−1 (Erfani and Mitchell, 2014).

1983 2010 time evolution zonal cross section

Below is an animation of SST climatology from 1983 to 2000 provided from NASA/JPL data, courtesy of Miguel Lavin, CICESE. Abrupt poleward advance of warmest water is observed from 10 to 20 June.  Northern GC SST is warmer than 29°C from 20 to 25 July. (click to play and click to stop)

Below is an animation of 500 hPa streamline climatology from 1971 to 2000 provided from NCEP/NCAR reanalysis data. Rapid poleward advance of center of 500 hPa high is observed from 8 to 20 June.  Center of 500 hPa high moves from New Mexico to 4-corners region from 22 to 25 July. (click to play and click to stop)

The three images below represent the July mean surface-600 mb pressure integrated divergence (s-1) and surface-600 mb pressure integrated streamline pattern.  Taken from Gochis et al. 2002, Sensitivity of the Modeled North American Monsoon Regional Climate to Convective Parameterization, Mon. Wea. Rev., Vol. 130, 1282-1298.

MM5 simulations of the anticyclone at 600 mb for July, using 3 convection schemes: (a) Betts- Miller-Janjic, (b) Kain-Fritsch and (c) Grell.

Simulations are based on Reynolds-Smith SST data (optimum interpolation method), which under-estimate the GC SSTs by 2-6°C during July, especially in the northern GC.  The position of the anticyclone below US-Mexico border is consistent with hypothesis that its position depends on the latitude of the warmest coastal SSTs.

 july-mean-surface-a july-mean-surface-b 

july-mean-surface-c

 

Reynolds-Smith climatological SSTs (1974-1993) for July with OLR fields (numbers and solid curves).  Dark orange = 29°C, with 1°C change per color change.  Northern GC is about 24°C.

july

 

Below is a simulation of the NAM for July when Gulf of California SSTs were fixed at 29.5°C.  The position of the 500 hPa anticyclone is now consistent with climatology, possibly due to realistic GC SSTs.  Taken from Stensrud, D. J., R. L. Gall, S. L. Mullen and K. W. Howard, 1995: Model climatology of the Mexican monsoon. J. Climate, 8, 1775-1794.

circulation

 

Modeling of the 1999 N.A. monsoon onset; the Las Vegas flood

The following modeling results are from the Ph.D. dissertation of Dr. Dorothea Ivanova (Ivanova, D., 2004: Cirrus clouds parameterization for global climate models (GCMs) and North American monsoon modeling study. Ph.D. dissertation, University of Nevada, Reno, 181 pp.). The Arizona/Great Basin onset of the 1999 North American monsoon was simulated using the MM5 regional scale model. This onset also produced a major flooding event in Las Vegas, Nevada. Resolution of the boundary layer was maximized, based on the Hong-Pan (or MRF) boundary layer scheme.

The results generally reproduce the observations under “local scale mechanism”, with the removal of the GC marine boundary layer inversion as SSTs in the northern Gulf of California (GC) approach 30°C. This increased the water vapor mixing ratio at lower levels over the GC and over the southwestern United States, as low-level winds over the northern GC were from the southeast. This in turn increased thunderstorm activity and rainfall amounts over the Southwest. The northern GC SST-rainfall relationship shown under “local scale mechanism” was reproduced in this modeling study.

MM5 modeling study 2003

The nested grid and courser domain of the MM5 modeling study, conducted in 2003.

model terrain cross section

The model terrain nested grid with lines denoting cross sections 1 (AB) and 2 (CD). These cross-sections were evaluated regarding the evolution of the atmospheric circulation, water vapor mixing ratio and potential temperature during 72-hour simulations. The green square depicts the northern Gulf of California (GC) as defined for our modeling study.

 

4 ocean regions - SST evolution model

The left panel shows the 4 ocean regions (A-D) focused upon in this study, while the right panel shows the observed mean time evolution of sea surface temperatures (SSTs) in these ocean regions, where pre-GOC is region A and N. GOC is region D. Note that the northern GC SST lags behind the SST of other regions until late July, when all regions exhibit an SST ~ 30°C. This SST evolution is modeled in this study, including the impact this has on model soundings over the GC (e.g. inversions), water vapor mixing ratios, winds, convective available potential energy (CAPE) and regional rainfall amounts.

 

MM5 model soundings A-B

MM5 model soundings C

MM5 model soundings over the GC along cross-section AB at 60 h (5 am LST), for 3 conditions. Red curve = air temperature; blue curve = dew point temperature. Sounding A: northern GC SST = 26°C, 28°C further south (regions C, B, and A). Sounding B: northern GC SST = 29°C, 30°C further south. Sounding C: northern GC SST = 30°C, 30°C further south. As northern GC SSTs increase, the inversion over the northern GC decreases and disappears in Sounding C. This same behavior was observed with actual soundings over the GC (see “local scale mechanism”). Note that winds below 500 hPa are southeasterly, approximately parallel to GC axis.

water vapor graphs A-B

water vapor graph C

Water vapor mixing ratios (g/kg) and wind velocity components along cross-section AB at 60 h (5 am LST) for 3 simulations: A, northern GC SST = 26°C, 28°C further south (regions C, B and A); B, northern GC SST = 29°C, 30°C further south; C, northern GC SST = 30°C, 30°C further south. As SSTs along cross-section AB increase, the water vapor mixing ratio at low levels also increases.

water vapor graphs A-B 2

Water vapor mixing ratios (g/kg) and wind velocity components along cross-section CD at 60 h (5 am LST) for the 29°C/30°C simulation (panel A; northern GC SSTs = 29°C, 30°C further south) and for the 30°C/30°C simulation (panel B).

CAPE graph

Magnitude of convective available potential energy (CAPE) at 60, 66 and 72 h (5 am, 11 am and 5 pm LST; top-to-bottom) for simulations 26°C/28°C, 29°C/30°C and 30°C/30°C (left-to-right). These results are consistent with our observational results under “local scale mechanism”.

CIN graph

Magnitude of convective inhibition energy (CIN) at 60, 66 and 72 h (5 am, 11 am and 5 pm LST; top-to-bottom) for simulations 26°C/28°C, 29°C/30°C and 30°C/30°C (left-to-right). These results are consistent with our observational results under “local scale mechanism”.

 

rainfall amounts graphs

Rainfall amounts (cm) at 72 h (over last 6 hours; 5 pm LST) for the 29°C/30°C simulation (left) and the 30°C/30°C simulation (right). The 26°C/28°C simulation was similar to the 29°C/30°C simulation but with slightly less rainfall.

MM5 model experiments

The table shows all the MM5 model experiments: SST values correspond to the evolution of GC SSTs based on June- August climatology. The plot shows normalized rainfall rates over the Arizona region as a function of the N. GC SST. The observed five year mean values for June-August correspond to the Arizona/New Mexico region defined above. The circles indicate 6-hour means predicted by MM5 at 72 h for AZ, southern Nevada, southern California, and extreme northern Mexico, based on the simulations described in the adjacent table.

These results suggest that the proposed local mechanism applies to most Arizona NAM onsets, but 2013 appears to be an exception, as noted under “Overview”. Nonetheless this mechanism appears to be a major contributor of low-level moisture for the NAM.

This section provides links for tracking the evolution of the North American monsoon (NAM) as described in this website.  The links may be of interest to anyone interested in the onset and evolution of the NAM in the Arizona-Great Basin region.

1. Advection of tropical surface water into the Gulf of California (GC) 

2. Wind and moisture fields showing moisture transport out of the GC 

3. Infrared satellite imagery for deep convection

4. Northern GC SST-rainfall relationship

– Sea surface temperatures (SST)

– Satellite rainfall estimates

    – Other websites

    CONTACT

    David Mitchell, Ph.D.
    Project Director
    David.Mitchell@dri.edu 

    LOCATION

    Desert Research Institute
    2215 Raggio Parkway
    Reno, NV 89512

    DIVISION

    Atmospheric Sciences

    Portable In-Situ Wind ERosion Lab: PI-SWERL

    Portable In-Situ Wind ERosion Lab: PI-SWERL

    Dec 20, 2018

    Portable In-Situ Wind ERosion Lab: PI-SWERL

    The PI-SWERL, which stands for Portable In-Situ Wind ERosion Lab, has been in development at DRI since 2000. The PI-SWERL concept was motivated by the need for a portable device to test and measure the potential for wind erosion and dust emissions from real-world surfaces. Traditional wind tunnels used for this purpose required long setup times and in some cases a crew of several people to operate. The goal in developing the PI-SWERL was to provide a turn-key device that was easy to move, required minimal setup, and could be operated by one person. A prototype developed in 2000 and tested alongside the University of Guelph large, field wind tunnel provided early indication of the feasibility of the PI-SWERL concept. Since then several models have been used in numerous field investigations. The latest miniature version (MPS-2a) has been a workhorse instrument for DRI since 2005 and its design has remained essentially constant. In 2007, DRI licensed the PI-SWERL technology to Dust-Quant LLC to make it commercially available to users worldwide.

    The PI-SWERL is contained in an open-bottomed, cylindrical chamber operated by a direct-current motor that spins a metal, annular ring about 2.5 in. above and parallel to the soil surface. Principles of fluid mechanics allow simulation of high winds that produce dust storms. The spinning ring creates known wind shear, lofting soil and dust particles and passing them through particulate monitors. The PI-SWERL electronically measures the number and size of entrained particles over the duration of a test cycle, typically under 10 minutes. By controlling the speed of the ring to simulate varying wind speeds, the potential for a soil surface to produce PM10 dust emissions can be determined under a range of simulated wind conditions.

    PI-SWERL allows for elucidation of effects of specific road characteristics with respect to dust emissions. It can be used to assess the effect of pavement properties on dust emissions, potential for windblown dust on unpaved roads, effectiveness of surface treatments on reducing emissions, emissions from road shoulders, and potential for aerodynamically driven emissions for vehicles traveling at different speeds.

    • PI-SWERL device and portable buggy

      PI-SWERL device and its portable buggy

    • PI-SWERL device

      PI-SWERL device

    • PI-SWERL_diagram

      PI-SWERL_diagram

    The PI-SWERL (US Patent 7,155,966) measures the amount of dust emitted from a surface when a known amount of wind shear is applied.  A flat annular blade inside the chamber rotates at prescribed speeds to simulate different amounts of surface shear stress.  Although it uses a different principal of operation, it can be thought of as analogous to a miniature wind tunnel.

    PI-SWERL and Wind Tunnel Simulations

    PI-SWERL and Wind Tunnel Simulations

    The PI-SWERL was collocated with the University of Guelph large field wind tunnel at seventeen sites in the Mojave desert, spanning graveled roads to silty playas (Sweeney, et. al., 2008). Agreement between the two methods of estimating dust emissions was good with a correlation coefficient of 0.76 and a nearly 1:1 slope.

    • PI-SWERL data: PM10 dust emissions from a chemically treated test plot (right photo) normalized to dust emissions from a test plot that has not been treated (left) over 1 year exposure (Kavouras, et. al., 2007). X-axis: friction velocity (m/s) – a measure of surface wind shear.

      PI-SWERL data: PM10 dust emissions from a chemically treated test plot (right photo) normalized to dust emissions from a test plot that has not been treated (left) over 1 year exposure (Kavouras, et. al., 2007). X-axis: friction velocity (m/s) – a measure of surface wind shear.

    • PI-SWERL data: rapid non-linear increase in PM10 emissions with increase in aerodynamic shear (Pascals, N/m2, proportional to wind speed or tire stress) applied to paved road surface (China, 2008), indicating value of keeping speed limits low.

      PI-SWERL data: rapid non-linear increase in PM10 emissions with increase in aerodynamic shear (Pascals, N/m2, proportional to wind speed or tire stress) applied to paved road surface (China, 2008), indicating value of keeping speed limits low.

    Bacon, S. N., E. V. McDonald, et al. “Total suspended particulate matter emissions at high friction velocities from desert landforms.” Journal of Geophysical Research-Earth Surface 116.

    Buck, B. J., J. King, et al. “Effects of Salt Mineralogy on Dust Emissions, Salton Sea, California.” Soil Science Society of America Journal 75(5): 1971-1985.

    Etyemezian, V., G. Nikolich, et al. (2007). “The Portable In Situ Wind Erosion Laboratory (PI-SWERL): A new method to measure PM(10) potential for windblown dust properties and emissions.” Atmospheric Environment 41(18): 3789-3796.

    Goossens, D. and B. Buck (2009). “Dust dynamics in off-road vehicle trails: Measurements on 16 arid soil types, Nevada, USA.” Journal of Environmental Management 90(11): 3458-3469.

    Kavouras, I. G., V. Etyemezian, et al. (2009). “A New Technique for Characterizing the Efficacy of Fugitive Dust Suppressants.” Journal of the Air & Waste Management Association 59(5): 603-612.

    King, J., V. Etyemezian, et al. “Dust emission variability at the Salton Sea, California, USA.” Aeolian Research 3(1): 67-79.

    Sankey, J. B., J. U. H. Eitel, et al. “Quantifying relationships of burning, roughness, and potential dust emission with laser altimetry of soil surfaces at submeter scales.” Geomorphology 135(1-2): 181-190.

    Sweeney, M., V. Etyemezian, et al. (2008). “Comparison of PI-SWERL with dust emission measurements from a straight-line field wind tunnel.” Journal of Geophysical Research-Earth Surface 113(F1).

    Sweeney, M. R., E. V. McDonald, et al. “Quantifying dust emissions from desert landforms, eastern Mojave Desert, USA.” Geomorphology 135(1-2): 21-34.

    Particulate Emissions Measurement Laboratory Publications from 2003-2008
    2008
    Macpherson, T., W.G. Nickling, J.A. Gillies, and V. Etyemezian (2008). Dust emissions from undisturbed and disturbed supply limited desert surfaces. J. Geophys. Res., Earth Surface (in press).

    Brown, S. W.G. Nickling, and J.A. Gillies (2008). A wind tunnel examination of shear stress partitioning for an assortment of surface roughness distributions. J. Geophys. Res., Earth Surface, 113, doi:10.1029/2007JF000790.

    King, J., W.G. Nickling, and J.A. Gillies (2008). Investigations the law-of-the-wall over sparse roughness elements. J. Geophys. Res., Earth-Surface, 113, F02S07, doi:10.1029/2007JF000804.

    Sweeney, M., V. Etyemezian, T. Macpherson, W. Nickling, J. Gillies, G. Nikolich, and E. McDonald (2008). Comparison of PI-SWERL with dust emission measurements from a straight-line field wind tunnel. J. Geophys. Res., Earth Surface, 113, F01012, doi:10.1029/2007JF000830.

    2007
    Gillies, J.A., V. Etyemezian, H. Kuhns, J. Engelbrecht, S. Uppapalli, and G. Nikolich (2007). Dust emissions caused by backblast from Department of Defense artillery testing. J. Air & Waste Manage. Assoc., 57, doi:10.3155/1047-3289.57.5.551, 551–560.

    Etyemezian, V., G. Nikolich, S. Ahonen, M. Pitchford, M. Sweeney, J. Gillies, and H. Kuhns (2007). The Portable In-Situ Wind Erosion Laboratory (PI-SWERL): a new method to measure PM10 windblown dust properties and potential for emissions. Atmos. Environ., 41, 3789-3796.

    Gillies, J.A., W.G. Nickling, and J. King (2007). Shear stress partitioning in large patches of roughness in the atmospheric inertial sublayer. Boundary-Layer Meteorology, 122(2), doi: 10.1007/s10546-006-9101-5, 367-396.

    2006
    Gertler, A., H. Kuhns, M. Abu-Allaban, C. Damm, J. Gillies, V. Etyemezian, R. Clayton, and D. Proffitt (2006). A case study of the impact of winter road sand/salt and street sweeping on road dust re-entrainment. Atmos. Environ., 40(31):5976-5985.

    King, J., W.G. Nickling, and J.A. Gillies (2006). Aeolian shear stress ratio measurements within mesquite-dominated landscapes of the Chihuahuan Desert, New Mexico, USA. Geomorphology, 82(3-4):doi:10.1016/j.geomorph.2006.05.004, 229-244.

    Gillies, J.A., W.G. Nickling, and J. King (2006). Aeolian sediment transport through large patches of roughness in the atmospheric inertial sublayer. J. Geophys. Res., Earth Surface, 111, F02006, doi:10.1029/2005JF000434.

    2005
    King, J., W.G. Nickling, and J.A. Gillies (2005). Representation of vegetation and other non-erodible elements in aeolian shear stress partitioning models for predicting transport threshold. J. Geophys. Res., Earth Surface, 110 (F4):F04015 doi:10.1029/2004JF000281.

    Moosmüller, H., R. Varma, W.P. Arnott, H.D. Kuhns, V. Etyemezian, and J.A. Gillies (2005). Scattering cross section emission factors for visibility and radiative transfer applications: military vehicles traveling on unpaved roads. J. Air and Waste Manage. Assoc., 55:1743-1750.

    Gillies, J.A., V. Etyemezian, H. Kuhns, D. Nickolic, and D.A. Gillette (2005). Effect of vehicle characteristics on unpaved road dust emissions. Atmos. Environ., 39:2341-2347.

    Kuhns, H., V. Etyemezian, J.A. Gillies, S. Ahonen, C. Durham, and D. Nikolic (2005). Spatial variability of unpaved road dust emissions factors near El Paso, Texas. J. Air and Waste Manage. Assoc., 55:3-12.

    2004
    Etyemezian, V., J.A. Gillies, H. Kuhns, D. Gillette, S. Ahonen, D. Nikolic, and J. Veranth (2004). Deposition and removal of fugitive dust in the arid southwestern United States : measurements and model results. J. Air and Waste Manage. Assoc., 54:1099-1111.

    2003
    Etyemezian, V., H. Kuhns, J.A. Gillies, M. Green, M. Pitchford, and J. Watson (2003). Vehicle based road dust emissions measurement (I): methods and calibration. Atmos. Environ., 37:4559-4571.

    Etyemezian, V., H. Kuhns, J.A. Gillies, J. Chow, M. Green, K. Hendrickson, M. McGowan, and M. Pitchford (2003). Vehicle based road dust emissions measurement (III): effect of speed, traffic volume, location, and season on PM10 road dust emissions. Atmos. Environ., 37:4583-4593.

    Abu-Allaban, M., J.A. Gillies, and A.W. Gertler (2003). Applications of a multi-lag regression approach to determine on-road PM10 and PM2.5 emission rates. Atmos. Environ., 37:5157-5164.

    Abu-Allaban, M., J.A. Gillies, and A.W. Gertler (2003). Tailpipe, resuspended road dust, and brake-wear emission factors from on-road vehicles. Atmos. Environ., 37:5283-5293.

    CONTACT

    Vic Etyemezian, Ph.D.
    Research Professor
    702.862.5569
    Vic.Etyemezian@dri.edu 

    George Nikolich
    702.862.5413
    George.Nikolich@dri.edu  

    LOCATION

    Desert Research Institute
    755 East Flamingo Road
    Las Vegas, NV 89119

    DIVISION

    Atmospheric Sciences