Scattering Computations of Snow Aggregates from Simple Geometrical Particle Models

1. Scattering Computations of Snow Aggregates from Simple Geometrical Particle Models Liang Liao (GESTAR/MSU) and Robert Meneghini (Code 612, NASA) (a) (c) Simplified snow particle models are used to approximate and compare radar and radiometer scattering parameters of the aggregates over GPM DPR and GMI frequencies. (b) Earth Sciences Division - Atmospheres

2. Name: Liang Liao, NASA/GSFC, Code 612 and GESTAR/MSU, Robert Meneghini, NASA/GSFC, Code 612 E-mail: Liang.Liao-1@nasa.gov Phone: 301-614-5718 Reference: Liao, L., R. Meneghini, H. K. Nowell and G. Liu, 2013: Scattering computations of snow aggregates from simple geometrical particle models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,6, 1409-1417. doi: 10.1109/JSTARS.2013.2255262. Contributors: Holly Kreutzer Nowell and Guosheng Liu (FSU). Technical Description of Figures: Figure : (a) Schematic diagrams show procedures for examining two simple geometric models by comparing their scattering parameters (top and bottom panels), which include backscattering (b) and extinction (e) coefficients and the asymmetry factor (g), with direct numerical computations from aggregates (middle panel). One model uses variable snow density (ρ=f(Dmax)), and mass density therein (ρ) is defined by the ratio of the aggregate mass to the volume of a circumscribing sphere with a diameter equal to the maximum dimension (Dmax) of the aggregate. The other model uses fixed snow density (ρ=const.), and the diameter (D) determined by the mass of the aggregate. (b) A future study will include use of stratified-sphere and multi-layer spheroids wherein the density within particle can be described as a function of radius for better representation of aggregates. (c) Comparisons of backscattering (left column) and extinction (right column) coefficients of three snow aggregates with the results from the sphere, oblate and prolate spheroids with a fixed snow density of 0.3 g/cm3. The oblate and prolate spheroids are randomly oriented with aspect ratios of 0.5 and 2, respectively. Scientific significance:.An understanding of the electromagnetic scattering characteristics of snow aggregates is needed for the proper interpretation of radar and radiometer measurements from cloud and precipitation. Because of the complex and irregular shapes of snowflakes, numerical techniques are often used to compute their scattering properties. The numerical results, though accurate, may come at the expense of an excessive amount of computational time so that computations are often confined to small-to-moderate particle sizes and to a select set of frequencies. Although particle models using simple geometric shapes with an effective dielectric constant for the ice–air mixture have been used frequently in an attempt to provide approximate results for aggregates, their accuracy in reproducing scattering parameters used for radars and radiometers has not been consistently checked. The primary purpose of this study is to evaluate the accuracy of using simple particle shapes (spherical/spheroidal) with two different mass density assumptions, namely the variable and fixed snow density models, to reproduce the scattering properties of snow aggregates over the range of GPM frequencies from 10 to 183 GHz. Our results indicate that the scattering properties of aggregates can be fairly well reproduced by using a sphere/spheroidal particle model with a fixed mass densityand setting the mass of the sphere/spheroid to be the same as that of the aggregate. A mass density of 0.2 g/cm3 serves the purpose well up to frequencies of about 35 GHz and equivalent ice diameters up to 2.5 mm. At frequencies of 89 GHz and above, however, a higher snow density of 0.3 g/cm3 provides better overall agreement. Randomly oriented spheroids perform better than spheres by suppressing the strong resonance/anti-resonance oscillations that are manifest in the results from the sphere. Relevance for future science and relationship to Decadal Survey: Accurately characterizing electromagnetic scattering from snow aggregates is one of the essential components in the development of radar and radiometer algorithms for estimates of precipitation rate, and is therefore closely relevant to ongoing GPM mission. Earth Sciences Division - Atmospheres

3. MCRS : A novel tool for simulating radiometer observations from model dataG. Wind, A. da Silva, P. Norris, S. Platnick, Code 613, 610, 610.1, NASA GSFC, SSAI, Inc, USRA MCRS with GEOS-5 and Terra MODIS 05.31.2013 at 11:15 UTC Figure 2: Operational cloud optical thickness retrieval from real and simulated data Figure 1:SWIR composite of real and simulated granule The MCRS simulator allows user to create a synthetic scene as if a spacecraft actually passed over. MCRS output can be directly fed into operational remote sensing retrieval processing chain such as MODAPS or PEATE. Figure 3:Joint histogram of cloud optical thickness vs cloud top pressure Earth Sciences Division - Atmospheres

4. Name: Gala Wind, NASA/GSFC, Code 613, SSAI, Inc. E-mail:Gala.Wind@nasa.gov Phone: 301-614-6221 References: 1. Wind, G., A.M. Da Silva, P.M. Norris, and S.E. Platnick (2013). Multi-sensor cloud retrieval simulator and remote sensing from model parameters - Part 1: Synthetic sensor radiance formulation Geoscientific Model Development, 6, 2049-2062 doi:10.5194/gmd-6-2049-2013 2. Norris, P. M. and A. M. da Silva, 2013: Monte Carlo Bayesian inference on a statistical model of sub-gridcolumn moisture variability using high-resolution cloud observations. Part I: Method. Submitted to Quarterly Journal of Royal Meteorological Society 3. da Silva, A. M. and P. M. Norris, 2013: Monte Carlo Bayesian inference on a statistical model of sub-gridcolumn moisture variability using high-resolution cloud observations. Part II: Sensitivity tests and results. Submitted to Quarterly Journal of Royal Meteorological Society Data Sources: MODAPS MODIS Operational geolocation data product MOD03 and Level 1B radiance MOD021KM, GMAO GEOS-5 v.5.7.2 operational model output. Technical Description of Figures: Figure 1:This figure is an example of an MCRS simulation result from GEOS-5. MODIS geolocation was combined with model fields of pressure, temperature, water vapor, ozone, surface parameters and cloud layer information in order to produce equivalent MODIS radiances in 24 MODIS channels. This figure shows SWIR false color (channels 2, 6 and 7 in order) for real and simulated granule In SWIR composite, ice clouds appear red and liquid water clouds yellow and white. Figure 2: This figure shows direct granule output of operational MODAPS MODIS PGE06 product executed on real and simulated input. Figure 3: This figure shows some statistics of operational result of figure 2. Scientific significance:The MCRS system allows anyone to do detailed and low cost analysis of retrieval algorithms either operational or under development. It also allows for prototyping of new instruments and comparison to existing ones. It may never replace the need for direct in-situ measurements for algorithm validation, but it provides ability to do algorithm closure studies, basically answer the question “do you get back what you’ve put in?”, on a large scale rather than only in places where a ground station exists for example. It can also be used in reverse to validate model performance and aid in model development. Currently GEOS-5 does not assimilate any MODIS pixel that contains cloud. We hope that the MCRS system may aid in developing GEOS-5 and other models further to allow them to make use of cloud-containing data. After all, it is a shame to throw away 70% of perfectly good input. Relevance for Future Missions: Other research groups with instruments under development have already expressed desire to conduct experiments with the MCRS system in order to estimate future instrument’s ability to perform retrievals of various parameters and receive error estimates. Forecast models provide an invaluable source of ancillary data for most satellite data products. If models can be improved by using the MCRS, then operational data products may improve right along with them. This is a cross-discipline project truly bringing together the remote sensing and modeling communities. The MCRS system is continuously evolving and more papers about its new features and its usage are currently in the pipeline. The modular design of MCRS allows rapid part replacement so that simulator core, instrument or model sections can be replaced by user as desired. The code is open source and the data is freely available upon request. Earth Sciences Division - Atmospheres

5. The effect of dust absorption on its transport and lifecycleP. Colarco1, E. Nowottnick1,2, C. Randles1,3, B. Yi4, P. Yang4, K.-M. Kim3,5, J. Smith6, C. Bardeen7 1Code 614, NASA GSFC, 2NASA Postdoctoral Program, 3GESTAR/Morgan State University, 4Texas A&M University, 5Code 613, NASA GSFC, 6University of Colorado, 7National Center for Atmospheric Research Airborne Saharan dust particles absorb solar and infrared radiation, affecting the weather and climate. We explored these impacts in the NASA GEOS-5 Earth System Model. Speed-up of westward blowing winds caused by dust heating Warming of air caused by absorption of sunlight in dust Outline of dust plume over western Africa A North-South vertical slice through the summertime western Saharan atmosphere, where a dust plume is centered at about 20°N (thick, solid line). Change in temperature caused by dust [°C] Earth Sciences Division - Atmospheres

6. Name: Peter Colarco, NASA GSFC, Code 614 E-mail: peter.r.colarco@nasa.gov Phone: 301-614-6382 References: Colarco, P.R., E.P. Nowottnick, C.A. Randles, B. Yi, P. Yang, K.-M. Kim, J.A. Smith, and C.G. Bardeen, Impact of radiatively interactive dust aerosols in the NASA GEOS-5 climate model: Sensitivity to dust particle shape and refractive index. Journal of Geophysical Research: Atmospheres, doi: 10.1002/2013JD020046 (2014). Data Sources: Simulations were performed with the NASA Goddard Earth Observing System (GEOS-5) Earth system model. The dust source function used in the study was derived from high spatial resolution topographic datasets, and the boundary condition sea surface temperatures were derived from a climatology of ENSO neutral-year observed sea surface temperatures. The optical properties assumed for dust and sea salt particles came from various laboratory and remote-sensing derived estimates. The model performance was evaluated in the context of several NASA remote sensing data sets. These included observations from the Aerosol Robotic Network of ground-based sub photometers, which measure aerosol loading and derive information about the column integrated aerosol particle size distribution and other optical properties. We additionally used measurements of column integrated aerosol loading from the space-based MODIS and MISR sensors, and measurements of aerosol vertical profile from the space-based CALIOP lidar. Technical Description of Figures: Figure 1: Vertical profile of dust, temperature, and zonal wind speed differences for a climatological June-July-August mean between a model simulation with moderate dust absorption and a simulation in which dust is not radiatively active. The average is taken from 15°W to 0° (over the western Sahara). The colored shading indicates differences in atmospheric temperature caused by the radiative heating of dust. The solid (dashed) contours show increases in easterly (westerly) flow associated with the dust heating (in units of m s-1). The thick solid contour is the 100 μg m-3 dust concentration isosurface for the experiment with dust heating. Scientific significance: Dust is emitted from barren land surfaces, where high winds start loose soil particles bouncing along the ground, ultimately breaking them up into tiny fragments that can remain suspended in the atmosphere. The dominant source of dust on Earth is the Sahara Desert in North Africa. Dust is important to Earth’s climate system because it reflects and absorbs solar and thermal radiation, changing the energy balance of the atmosphere, and so literally changing the weather. Dust also interacts with and changes the properties of clouds, and is a contributor to poor air quality when emitted near urban areas. We modeled the emission and transport of dust on Earth using a climate model. Because we were interested in how the radiative properties of dust impact its lifecycle and transport, we tested different assumptions of the dust particle shape and absorption, allowing those effects to feed back on our model. We found for Saharan dust that the more strongly absorbing it is assumed to be, the further north and west and at higher altitudes the dust is transported, in best agreement with observations. Relevance for future science and relationship to Decadal Survey: Better treatment of dust aerosols in climate and weather models will improve climate forcing simulations and weather and air quality forecasting. Since dust particles are non-spherical, and this has important impacts on determining the scattering on incident solar radiation, inclusion of non-spherical dust optics in weather and climate models will improve their utility in observation simulation and interpretation of remote sensing measurements, key to developing and using new satellite observing systems. Earth Sciences Division - Atmospheres