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Thomas A. Jones, Sundar A. Christopher, and Walt Petersen

Abstract

Dual-polarimetric microwave wavelength radar observations of an apartment fire in Huntsville, Alabama, on 3 March 2008 are examined to determine the radar-observable properties of ash and fire debris lofted into the atmosphere. Dual-polarimetric observations are collected at close range (<20 km) by the 5-cm (C band) Advanced Radar for Meteorological and Operational Research (ARMOR) radar operated by the University of Alabama in Huntsville. Precipitation radars, such as ARMOR, are not sensitive to aerosol-sized (D < 10 μm) smoke particles, but they are sensitive to the larger ash and burnt debris embedded within the smoke plume. The authors also assess if turbulent eddies caused by the heat of the fire cause Bragg scattering to occur at the 5-cm wavelength.

In this example, the mean reflectivity within the debris plume from the 1.3° elevation scan was 9.0 dBZ, with a few values exceeding 20 dBZ. The plume is present more than 20 km downstream of the fire, with debris lofted at least 1 km above ground level into the atmosphere. Velocities up to 20 m s−1 are present within the plume, indicating that the travel time for the debris from its source to the maximum range of detection is less than 20 min. Dual-polarization observations show that backscattered radiation is dominated by nonspherical, large, oblate targets as indicated by nonzero differential reflectivity values (mean = 1.7 dB) and low correlation coefficients (0.49). Boundary layer convective rolls are also observed that have very low reflectivity values (−6.0 dBZ); however, differential reflectivity is much larger (3.2 dB). This is likely the result of noise, because ARMOR differential reflectivity is not reliable for reflectivity values <0 dBZ. Also, copolar correlation is even lower compared to the debris plume (0.42). The remainder of the data mainly consists of atmospheric and ground-clutter noise. The large differential phase values coupled with positive differential reflectivity strongly indicate that the source of much of the return from the debris plume is particle scattering. However, given the significant degree of noise present, a substantial contribution from Bragg scattering cannot be entirely ruled out.

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

Abstract

Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m3 m−3 were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.

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Francisco J. Tapiador, Rémy Roca, Anthony Del Genio, Boris Dewitte, Walt Petersen, and Fuqing Zhang

Abstract

Precipitation has often been used to gauge the performances of numerical weather and climate models, sometimes together with other variables such as temperature, humidity, geopotential, and clouds. Precipitation, however, is singular in that it can present a high spatial variability and probably the sharpest gradients among all meteorological fields. Moreover, its quantitative measurement is plagued with difficulties, and there are even notable differences among different reference datasets. Several additional issues sometimes lead to questions about its usefulness in model validation. This essay discusses the use of precipitation for model verification and validation and the crucial role of highly precise and reliable satellite estimates, such as those from NASA’s Global Precipitation Mission Core Observatory.

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Jessica M. Erlingis, Jonathan J. Gourley, Pierre-Emmanuel Kirstetter, Emmanouil N. Anagnostou, John Kalogiros, Marios N. Anagnostou, and Walt Petersen

Abstract

During May and June 2014, NOAA X-Pol (NOXP), the National Severe Storms Laboratory’s dual-polarized X-band mobile radar, was deployed to the Pigeon River basin in the Great Smoky Mountains of North Carolina as part of the NASA Integrated Precipitation and Hydrology Experiment. Rain gauges and disdrometers were positioned within the basin to verify precipitation estimates from various radar and satellite precipitation algorithms. First, the performance of the Self-Consistent Optimal Parameterization–Microphysics Estimation (SCOP-ME) algorithm for NOXP was examined using ground instrumentation as validation and was found to perform similarly to or slightly outperform other precipitation algorithms over the course of the intensive observation period (IOP). Radar data were also used to examine ridge–valley differences in radar and microphysical parameters for a case of stratiform precipitation passing over the mountains. Inferred coalescence microphysical processes were found to dominate within the upslope region, while a combination of processes were present as the system propagated over the valley. This suggests that enhanced updrafts aided by orographic lift sustain convection over the upslope regions, leading to larger median drop diameters.

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Abebe Sine Gebregiorgis, Pierre-Emmanuel Kirstetter, Yang E. Hong, Nicholas J. Carr, Jonathan J. Gourley, Walt Petersen, and Yaoyao Zheng

Abstract

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided the global community a widely used multisatellite (and multisensor type) estimate of quasi-global precipitation. One of the TMPA level-3 products, 3B42RT/TMPA-RT (where RT indicates real time), is a merged product of microwave (MW) and infrared (IR) precipitation estimates, which attempts to exploit the most desirable aspects of both types of sensors, namely, quality rainfall estimation and spatiotemporal resolution. This study extensively and systematically evaluates multisatellite precipitation errors by tracking the sensor-specific error sources and quantifying the biases originating from multiple sensors. High-resolution, ground-based radar precipitation estimates from the Multi-Radar Multi-Sensor (MRMS) system, developed by the National Severe Storms Laboratory (NSSL), are utilized as reference data. The analysis procedure involves segregating the grid precipitation estimate as a function of sensor source, decomposing the bias, and then quantifying the error contribution per grid. The results of this study reveal that while all three aspects of detection (i.e., hit, missed-rain, and false-rain biases) contribute to the total bias associated with IR precipitation estimates, overestimation bias (positive hit bias) and missed precipitation are the dominant error sources for MW precipitation estimates. Considering only MW sensors, the TRMM Microwave Imager (TMI) shows the largest missed-rain and overestimation biases (nearly double that of the other MW estimates) per grid box during the summer and winter seasons. The Special Sensor Microwave Imagers/Sounders (SSMIS on board F17 and F16) also show major error during winter and spring, respectively.

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Tuukka Petäjä, Ewan J. O’Connor, Dmitri Moisseev, Victoria A. Sinclair, Antti J. Manninen, Riikka Väänänen, Annakaisa von Lerber, Joel A. Thornton, Keri Nicoll, Walt Petersen, V. Chandrasekar, James N. Smith, Paul M. Winkler, Olaf Krüger, Hannele Hakola, Hilkka Timonen, David Brus, Tuomas Laurila, Eija Asmi, Marja-Liisa Riekkola, Lucia Mona, Paola Massoli, Ronny Engelmann, Mika Komppula, Jian Wang, Chongai Kuang, Jaana Bäck, Annele Virtanen, Janne Levula, Michael Ritsche, and Nicki Hickmon

Abstract

During Biogenic Aerosols—Effects on Clouds and Climate (BAECC), the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program deployed the Second ARM Mobile Facility (AMF2) to Hyytiälä, Finland, for an 8-month intensive measurement campaign from February to September 2014. The primary research goal is to understand the role of biogenic aerosols in cloud formation. Hyytiälä is host to the Station for Measuring Ecosystem–Atmosphere Relations II (SMEAR II), one of the world’s most comprehensive surface in situ observation sites in a boreal forest environment. The station has been measuring atmospheric aerosols, biogenic emissions, and an extensive suite of parameters relevant to atmosphere–biosphere interactions continuously since 1996. Combining vertical profiles from AMF2 with surface-based in situ SMEAR II observations allows the processes at the surface to be directly related to processes occurring throughout the entire tropospheric column. Together with the inclusion of extensive surface precipitation measurements and intensive observation periods involving aircraft flights and novel radiosonde launches, the complementary observations provide a unique opportunity for investigating aerosol–cloud interactions and cloud-to-precipitation processes in a boreal environment. The BAECC dataset provides opportunities for evaluating and improving models of aerosol sources and transport, cloud microphysical processes, and boundary layer structures. In addition, numerical models are being used to bridge the gap between surface-based and tropospheric observations.

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