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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

Abstract

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

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Mircea Grecu, Lin Tian, Gerald M. Heymsfield, Ali Tokay, William S. Olson, Andrew J. Heymsfield, and Aaron Bansemer

Abstract

In this study, a nonparametric method to estimate precipitating ice from multiple-frequency radar observations is investigated. The method does not require any assumptions regarding the distribution of ice particle sizes and relies on an efficient search procedure to incorporate information from observed particle size distributions (PSDs) in the estimation process. Similar to other approaches rooted in optimal-estimation theory, the nonparametric method is robust in the presence of noise in observations and uncertainties in the forward models. Over 200 000 PSDs derived from in situ observations collected during the Olympic Mountains Experiment (OLYMPEX) and Integrated Precipitation and Hydrology Experiment (IPHEX) field campaigns are used in the development and evaluation of the nonparametric estimation method. These PSDs are used to create a database of ice-related variables and associated computed radar reflectivity factors at the Ku, Ka, and W bands. The computed reflectivity factors are used to derive precipitating ice estimates and investigate the associated errors and uncertainties. The method is applied to triple-frequency radar observations collected during OLYMPEX and IPHEX. Direct comparisons of estimated ice variables with estimates from in situ instruments show results consistent with the error analysis. Global application of the method requires an extension of the supporting PSD database, which can be achieved through the processing of information from additional past and future field campaigns.

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Hannah C. Barnes, Joseph P. Zagrodnik, Lynn A. McMurdie, Angela K. Rowe, and Robert A. Houze Jr.

Abstract

This study examines Kelvin–Helmholtz (KH) waves observed by dual-polarization radar in several precipitating midlatitude cyclones during the Olympic Mountains Experiment (OLYMPEX) field campaign along the windward side of the Olympic Mountains in Washington State and in a strong stationary frontal zone in Iowa during the Iowa Flood Studies (IFloodS) field campaign. While KH waves develop regardless of the presence or absence of mountainous terrain, this study indicates that the large-scale flow can be modified when encountering a mountain range in such a way as to promote development of KH waves on the windward side and to alter their physical structure (i.e., orientation and amplitude). OLYMPEX sampled numerous instances of KH waves in precipitating clouds, and this study examines their effects on microphysical processes above, near, and below the melting layer. The dual-polarization radar data indicate that KH waves above the melting layer promote aggregation. KH waves centered in the melting layer produce the most notable signatures in dual-polarization variables, with the patterns suggesting that the KH waves promote both riming and aggregation. Both above and near the melting layer ice particles show no preferred orientation likely because of tumbling in turbulent air motions. KH waves below the melting layer facilitate the generation of large drops via coalescence and/or vapor deposition, increasing mean drop size and rain rate by only slight amounts in the OLYMPEX storms.

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Bin Pei and Firat Y. Testik

Abstract

In this study a new radar rainfall estimation algorithm—rainfall estimation using simulated raindrop size distributions (RESID)—was developed. This algorithm development was based upon the recent finding that measured and simulated raindrop size distributions (DSDs) with matching triplets of dual-polarization radar observables (i.e., horizontal reflectivity, differential reflectivity, and specific differential phase) produce similar rain rates. The RESID algorithm utilizes a large database of simulated gamma DSDs, theoretical rain rates calculated from the simulated DSDs, the corresponding dual-polarization radar observables, and a set of cost functions. The cost functions were developed using both the measured and simulated dual-polarization radar observables. For a given triplet of measured radar observables, RESID chooses a suitable cost function from the set and then identifies nine of the simulated DSDs from the database that minimize the value of the chosen cost function. The rain rate associated with the given radar observable triplet is estimated by averaging the calculated theoretical rain rates for the identified simulated DSDs. This algorithm is designed to reduce the effects of radar measurement noise on rain-rate retrievals and is not subject to the regression uncertainty introduced in the conventional development of the rain-rate estimators. The rainfall estimation capability of our new algorithm was demonstrated by comparing its performance with two benchmark algorithms through the use of rain gauge measurements from the Midlatitude Continental Convective Clouds Experiment (MC3E) and the Olympic Mountains Experiment (OLYMPEx). This comparison showed favorable performance of the new algorithm for the rainfall events observed during the field campaigns.

Open access
Robert Conrick, Clifford F. Mass, and Qi Zhong

Abstract

Two Kelvin–Helmholtz (KH) wave events over western Washington State were simulated and evaluated using observations from the Olympic Mountains Experiment (OLYMPEX) field campaign. The events, 12 and 17 December 2015, were simulated realistically by the WRF-ARW Model, duplicating the mesoscale environment, location, and structure of embedded KH waves, which had observed wavelengths of approximately 5 km. In simulations of both cases, waves developed from instability within an intense shear layer, caused by low-level easterly flow surmounted by westerly winds aloft. The low-level easterlies resulted from blocking by the Olympic Mountains in the 12 December case, while in the 17 December event, the easterly flow was produced by the synoptic environment. Simulated microphysics were evaluated for both cases using OLYMPEX observations. When the KH waves were within the melting level, simulated microphysical fields, such as hydrometeor mixing ratios, evinced considerable oscillatory behavior. In contrast, when waves were located below the melting level, the microphysical response was attenuated. Turning off the model’s microphysics scheme and latent heating resulted in weakened KH wave activity, while removing the Olympic Mountains eliminated KH waves in the 12 December event but not the 17 December case. Finally, the impact of several microphysics parameterizations on KH activity was evaluated for both events.

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

Abstract

Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.

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Annareli Morales, Hugh Morrison, and Derek J. Posselt

Abstract

This study explores the sensitivity of clouds and precipitation to microphysical parameter perturbations using idealized simulations of moist, nearly neutral flow over a bell-shaped mountain. Numerous parameters are perturbed within the Morrison microphysics scheme. The parameters that most affect cloud and precipitation characteristics are the snow fall speed coefficient As, snow particle density ρs, rain accretion (WRA), and ice–cloud water collection efficiency (ECI). Surface precipitation rates are affected by A s and ρ s through changes to the precipitation efficiency caused by direct and indirect impacts on snow fall speed, respectively. WRA and ECI both affect the amount of cloud water removed, but the precipitation sensitivity differs. Large WRA results in increased precipitation efficiency and cloud water removal below the freezing level, indirectly decreasing cloud condensation rates; the net result is little precipitation sensitivity. Large ECI removes cloud water above the freezing level but with little influence on overall condensation rates. Two environmental experiments are performed to test the robustness of the results: 1) reduction of the wind speed profile by 30% (LowU) and 2) decreasing the surface potential temperature to induce a freezing level below the mountain top (LowFL). Parameter perturbations within LowU result in similar mechanisms acting on precipitation, but a much weaker sensitivity compared to the control. The LowFL case shows ρ s is no longer a dominant parameter and A s now induces changes to cloud condensation, since more of the cloud depth is present above the freezing level. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations.

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David J. Purnell and Daniel J. Kirshbaum

Abstract

The synoptic controls on orographic precipitation during the Olympics Mountains Experiment (OLYMPEX) are investigated using observations and numerical simulations. Observational precipitation retrievals for six warm-frontal (WF), six warm-sector (WS), and six postfrontal (PF) periods indicate that heavy precipitation occurred in both WF and WS periods, but the latter saw larger orographic enhancements. Such enhancements extended well upstream of the terrain in WF periods but were focused over the windward slopes in both PF and WS periods. Quasi-idealized simulations, constrained by OLYMPEX data, reproduce the key synoptic sensitivities of the OLYMPEX precipitation distributions and thus facilitate physical interpretation. These sensitivities are largely explained by three upstream parameters: the large-scale precipitation rate , the impinging horizontal moisture flux I, and the low-level static stability. Both WF and WS events exhibit large and I, and thus, heavy orographic precipitation, which is greatly enhanced in amplitude and areal extent by the seeder–feeder process. However, the stronger stability of the WF periods, particularly within the frontal inversion (even when it lies above crest level), causes their precipitation enhancement to weaken and shift upstream. In contrast, the small and I, larger static stability, and absence of stratiform feeder clouds in the nominally unsaturated and convective PF events yield much lighter time- and area-averaged precipitation. Modest enhancements still occur over the windward slopes due to the local development and invigoration of shallow convective showers.

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Joseph P. Zagrodnik, Lynn A. McMurdie, and Robert A. Houze Jr.

Abstract

The Olympic Mountains Experiment (OLYMPEX) documented precipitation and drop size distributions (DSDs) in landfalling midlatitude cyclones with gauges and disdrometers located at various distances from the coast and at different elevations on the windward side of the mountain range. Statistics of the drop size and gauge data for the season and case study analysis of a high-rainfall-producing storm of the atmospheric river type show that DSDs during stratiform raining periods exhibit considerable variability in regions of complex terrain. Seasonal statistics show that different relative proportions of drop sizes are present, depending on synoptic and mesoscale conditions, which vary within a single storm. The most frequent DSD regime contains modest concentrations of both small and large drops with synoptic factors near their climatological norms and moderate precipitation enhancement on the lower windward slopes. The heaviest rains are the most strongly enhanced on the lower slope and have DSDs marked by large concentrations of small to medium drops and varying concentrations of large drops. During the heavy-rain period of the case examined here, the low-level flow was onshore and entirely up terrain, the melting level was ~2.5 km, and stability moist neutral so that large amounts of small raindrops were produced. At the same time, melting ice particles produced at upper levels contributed varying amounts of large drops to the DSD, depending on the subsynoptic variability of the storm structure. When the low-level flow is directed downslope and offshore, small-drop production at low altitudes is reduced or eliminated.

Open access
Andrew Heymsfield, Aaron Bansemer, Norman B. Wood, Guosheng Liu, Simone Tanelli, Ousmane O. Sy, Michael Poellot, and Chuntao Liu

Abstract

Two methods for deriving relationships between the equivalent radar reflectivity factor Z e and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Z eS relationships. In the second method, measurements of Z e at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Z eS relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.

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