Invariant Imbedded T-Matrix Method for Axial Symmetric Hydrometeors with Extreme Aspect Ratios

The single-scattering properties (SSPs) of hydrometeors are the fundamental quantities for physics-based precipitation retrievals. Thus, efficient computation of their electromagnetic scattering is of great value. Whereas the semi-analytical T-Matrix methods are likely the most efficient for nonspherical hydrometeors with axial symmetry, they are not suitable for arbitrarily shaped hydrometeors absent of any significant symmetry, for which volume integral methods such as those based on Discrete Dipole Approximation (DDA) are required. Currently the two leading T-matrix methods are the Extended Boundary Condition Method (EBCM) and the Invariant Imbedding T-matrix Method incorporating Lorentz-Mie Separation of Variables (IITM+SOV). EBCM is known to outperform IITM+SOV for hydrometeors with modest aspect ratios. However, in cases when aspect ratios become extreme, such as needle-like particles with large height to diameter values, EBCM fails to converge. Such hydrometeors with extreme aspect ratios are known to be present in solid precipitation and their SSPs are required to model the radiative responses accurately. In these cases, IITM+SOV is shown to converge. An efficient, parallelized C++ implementation for both EBCM and IITM+SOV has been developed to conduct a performance comparison between EBCM, IITM+SOV, and DDSCAT (a popular implementation of DDA). We present the comparison results and discuss details. Our intent is to release the combined ECBM IITM+SOV software to the community under an open source license

Active and Passive Radiative Transfer Simulations for GPM-Related Field Campaigns

Using a three-dimensional radiative transfer model combined with cloud-resolving model output, we simulate active and passive sensor observations of clouds and precipitaiton. This combination of tools allows us to diagnose the contributions of various hydrometeor types. Radar multiple scattering is most closely associated with the presence of graupel. At Wband, massive amounts multiple scattering in deep convection can decorrelate the reflectivity profile from the vertical structure, but for less intense events, multiple scattering could be a useful indicator of riming. For passive sensors, polarization differences at 166 GHz indicate the presence of horizontally aligned frozen particles with pronounced aspect ratios, while high concentrations of more isotropic aggregates and graupel dampen the polarization difference while also contributing to the lowest brightness temperature depressions. The insights into remote sensing measurements will facilitate the development of improved algorithms and advanced sensors

What Role Does Hydrological Science Play in the Age of Machine Learning?

ABSTRACT: This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished

Three-Dimensional Sensor Forward Modeling of Clouds and Precipitation in the Multi-Instrument Inverse Solver Testbed (MIIST)

Sensor forward models are an essential tool for interpreting remote sensing observations and performing quantitative estimates of geophysical parameters. Our three-dimensional forward modeling and retrieval framework allows us to perform detailed analyses of NASA field campaign datasets for a deeper understanding of the remote sensing of clouds and precipitation. This presentation details the componenets of this radiative transfer model used to simulate active (radar) and passive (microwave radiometer) observations, and we give some relevant examples based on both model precipitation systems and actual observations

Active and Passive Radiative Transfer Simulations for GPM-Related Field Campaigns

Radiative transfer modeling is an important tool for interpreting remote sensing observations. It allows us to determine how sensor characteristics will impact observations, and it gives us a framework for us to test assumptions about the phenomena we are attempting to observe. In this work, we use cloud simulations for precipitation events observed during various GPM-related field campaigns. The simulations show how various properties of clouds and precipitation affect the measurements

Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment

Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry Gas absorptiono Line-by-line calculationso Fast transmittance tables Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations Requires full Stokes matriceso And absorption vector Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon) Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs Atmospheric Informationo Temperatureo Pressure / heighto Water vapor Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference Corresponds to the presence of aligned icecrystals Look at trends for both simulations andobservations Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input Single frequency radar retrievals Multiple scattering enhancementapparent at W band Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015 DC-8 and ER-2 flightso Focus on APR-3 (DC-8) Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults Retrievals match probeso Good qualitative match Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction Significant amounts ofsupercooled liquid wate

Exogenous proteinases in dairy technology

Glavna primjena proteinaza u mljekarskoj tehnologiji je u proizvodnji sira. Prikazana je prva enzimatska te druga ne-enzimatska faza koagulacije mlijeka sirilom. Ukratko su prodiskutirane mogućnosti zamjene telećeg sirila, a u detalje razvoj imobilizirajućih sirila. Razmatrana je također mogućnost ubrzanja zrenja sira dodavanjem proteinaza. Dat je pregled sporedne upotrebe proteinaza uključujući proizvodnju proteinskih hidrolizata, modifikaciju proteina i proizvodnju dječje hrane.The principal applications of proteinases in dairy technology are in cheese manufacture. The enzymatic primary phase and non-enzymatic secondary phase of rennet coagulation of milk are reviewed. Aspects of veal rennet substitutes are briefly discussed and developments in immobilized rennets considered in detail. The possibility of accelerating cheese ripening via added proteinases is also considered. Minor applications of proteinases including production of protein hidrolyzates, protein modification and baby food manufacture are reviewed