An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely-structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2–10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.

Space-based quantitative snowfall remote sensing has advanced in the last decade to using single-scattering properties derived from realistically shaped hydrometeors, whose value is amply demonstrated with the drastic improvement in the consistency between active and passive retrievals and has benefited the active-passive combined algorithm of NASA’s Global Precipitation Measurement mission. However, the two principal processes required to obtain these scattering properties, i.e. the numerical generation of synthetic hydrometeors with realistic morphology and the subsequent solution of the electromagnetic scattering problem, are orders of magnitude more demanding in computation and storage resources than those required for hydrometeors with shapes based on simplifying assumptions. Recent evidence from microwave radiometer polarization signals suggests that uniform random orientation of the hydrometeors in solid precipitation is rarely a valid assumption. Axially symmetric scattering properties derived from orientation averages, in turn, rarely apply. The consistent quantitative physical retrieval of snowfall now calls for polarimetric and orientation-dependent scattering properties. Due to the general lack of symmetry for the solid hydrometeors, each orientation has a unique scattering solution. Since hundreds of orientations may be needed for each particle the storage demand grows proportionally. This challenge is not unique to snowfall remote sensing. Space-based quantitative remote sensing of cloud ice and aerosol face similar problems, for the particles of concern in these applications are mostly non-spherical and complexly shaped as well. Furthermore, heterogeneous composition of the pertinent particles, such as melting hydrometeors, hydrometeors with pollutant enclosures, and mixed composition aerosol or dust particles, further exacerbates the problem. For example, as we attempt to deal with the nearly ubiquitous melting layers in precipitation systems, we have discovered that, since the solid hydrometeors in the melting layer may likely be at different stages of melting, we must consider a range of liquid mass fraction for each solid hydrometeor. We thus need tens of melting instances at different liquid mass fractions with their associated scattering properties, ballooning the resource requirement further by ~10 fold! We, as a community in particulate matter remote sensing, can ill afford to repeat such computationally intensive electromagnetic scattering calculations or duplicate the needed storage for storing their results. We must find a strategy to sustainably enhance the long-term availability, accessibility, and usability of these valuable data. For such a purpose, we need first a more suitable and better designed means than “data files” to warehouse the realistic hydrometeor structures along with associated single-scattering properties and second a flexible and extensible means to disseminate the warehoused data. Both of these must also be scalable and performant. In terms of technological choices, we recommend employing a parallel (distributed) database management system for warehousing with web services enabled for access and dissemination. We believe the data centers and services of NASA through its Earth Science Data Systems program provide the best long-term solution to this challenge.

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

To address the acute challenge posed by the melting layer to accurate surface precipitation retrievals from space, we ensure the compositional consistency in ice, liquid, and total masses of synthetic melting hydrometeors with a method of stochastic compensation. The method is applied to simulated melting hydrometeors prior to calculating their scattering properties using the discrete dipole approximation (DDA). We investigate the impact of this stochastic compensation to calculated scattering properties by contrasting it with a naïve approach and report our findings.

Precipitation flag (precipitating or not; stratiform or convective) is a key parameter for us to make betterretrieval of precipitation characteristics as well as to understand the cloud-precipitati...

We highlight recent progress in and discuss future plans for the OpenSSP particle and scattering property database. Ongoing work has focused on expanding the types of particles to include polycrystals and melting snowflakes. Future expansion will include rimed particles, hail, and aligned snowflakes.

Understanding the connection between microphysical properties of falling snow and remote sensing observations requires detailed models of snow particles and the related electromagnetic scattering properties. To this end, we have created a database of synthetically-grown snow particles along with electromagnetic scattering properties calculated using advanced computational methods. We showcase the breadth of the catalog, current work, applications of the catalog, and future plans.

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.

Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

The GPM mission collects essential rain and snow data for scientific studies and societal benefit.

This presentation includes: basic considerations, emerging best common practices for multi-frequency radar and radiometer precipitation retrievals, defining the retrieval parameter set, dimensionality and solver options, examples, and ARTS wish list.

AbstractYanai and coauthors utilized the meteorological data collected from a sounding network to present a pioneering work in 1973 on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2). Latent heating (LH) is one of the most dominant terms in Q1. Yanai’s paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q1 data have been used to generate the look-up tables in Tropical Rainfall Measuring Mission (TRMM) LH algorithms. A set of algorithms developed for retrieving LH profiles from TRMM-based rainfall profiles is described and evaluated, including details concerning their intrinsic space–time resolutions. Included in the paper are results from a variety of validation analyses that define the uncertainty of the LH profile estimates. Also, examples of how TRMM-retrieved LH profiles have been used ...

In this paper, the operational Global Precipitation Measurement (GPM) mission combined radar-radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss-Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals.