In this study, a machine learning-based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Measurement Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure the precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflec- tivity observations above the clutter are included in a fi xed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A neural network (NN) model is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN's limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.

The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated in order to establish routine storm-scale polarimetric radar observations and cloud-process modeling at NASA GSFC Wallops Flight Facility (WFF), where various continental and maritime convective systems are being observed. The ultimate goals of BLOSSOM include: * Establish a long-term super site to improve understanding of cloud physical states and processes over the WFF site through bilateral storm-scale observations and modeling. * Provide routine meteorological large-scale forcing input to support cloud-resolving models (CRMs), large-eddy simulation (LES) models, and single-column models (SCMs) for the improvement of cloud microphysics and convection parameterizations. * Provide routine storm-scale cloud-precipitation simulations as well as storm-scale measurements using ground-based polarimetric Doppler radar and in-situ data. * Collect and organize value-added data from the cloud-process simulations, ground-based polarimetric radar, and NASA satellite observations for the community. This presentation will highlights a few case studies to test the concept of BLOSSOM, including the creation of ensemble large-scale forcing, configuring and performing cloud-process simulations with different bulk microphysics using the Goddard Cumulus Ensemble (GCE) model, organizing and streaming NASA S-band dual-POLarimetric radar (NPOL) and other WFF instrument data, and validating the ensemble GCE simulations through formulating statistical composites by comparing observed and simulated polarimetric radar signals using the POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS). Different spatial grid spacing (1km vs 250m) of the GCE simulations will be also evaluated to examine resolution impact on representing time-series as well as time-integrated composites of polarimetric radar signals.

The Goddard Convective-Stratiform Heating (CSH) algorithm has been used to retrieve latent heating (LH) associated with clouds and cloud systems in support of the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission. The CSH algorithm requires the use of a cloud-resolving model (CRM) to simulate LH profiles to build look-up tables. This paper describes the current V6 CSH and its differences/similarities versus the previous V5 CSH. Long-term CRM simulations were conducted to identify the impact of the CRM resolution and convective-stratiform separation method on LH structure/profiles. The TRMM and GPM Combined radar-radiometer algorithm-derived surface rain rates and their associated precipitation properties were the input to the CSH algorithm. CSH V6-retrieved regional LH profiles in the tropics and subtropics display the classic signatures of heating in the convective region and heating over cooling in the stratiform region. Because there is no direct measurement of LH structure, the performance of the CSH V6 algorithm is examined by comparing its vertically integrated heating (or equivalent surface rain rate) against the surface rain rate derived from the TRMM/GPM Combined algorithm. The CSH three-month and zonal mean equivalent surface rain rates are in good agreement with the Combined rain rates over the Inter Tropical Convergence Zone region; the agreement is best over the ocean. CSH three-month and zonal mean equivalent surface rain rates are larger than the Combined rain rates over land in both the tropics and subtropics. CSH three-month mean equivalent surface rain rates also have local differences with the Combined rain rates that can be smoothed by area averaging to larger horizontal resolutions (from the CSH standard grid of 0.25° × 0.25° to 0.5° × 0.5° or 1.0° × 1.0°). CSH equivalent surface rain rates have more light rain rates but less larger rates compared to the GPM Combined surface rain rates.

Fundamental relationships exist between cloud and precipitation development and their dynamic processes. Latent heat released by cloud/precipitation formation affects cloud vertical motions, which in turn affect convective cloud development. Here, a cloud‐resolving model is used to relate cloud properties and latent heating with cloud drafts using 15‐day simulations for an oceanic and continental environment. The results show condensation, deposition, and freezing occur mainly in moderate (3–5 m s −1 ) to strong (>10 m s −1 ) updrafts, evaporation and sublimation mainly in weak (1–2 m s −1 ) to moderate downdrafts, and melting in moderate updrafts and downdrafts. Active updrafts cover only a small percentage of the model domain but contribute significantly to the latent heat release and are associated with large proportions of the hydrometeors. Active updrafts with vertical velocities exceeding 1 and 2 m s −1 account for more than 75% and 50%, respectively, of the condensation, deposition, and freezing in both the oceanic and continental cases. However, active downdrafts with vertical velocity magnitudes exceeding |1 m s −1 | account for less than 40% and 25%, respectively, of the evaporation and sublimation. More evaporation and sublimation than condensation and deposition occur in the inactive cloud regions. Sensitivity tests are also conducted to assess the impact of model grid spacing (1,000 m vs. 250 m) and microphysical schemes (3 ice classes vs. 4 ice classes) on latent heat release and hydrometeor amount. The results show that model resolution had more impact than the microphysics on the simulated cloud properties in both cases.

This study contrasts midlatitude continental and tropical maritime deep convective cores using polarimetric radar observables and retrievals from selected deep convection episodes during the MC3E and TWPICE field campaigns. The continental convective cores produce stronger radar reflectivities throughout the profiles, while maritime convective cores produce more positive differential reflectivity Z dr and larger specific differential phase K dp above the melting level. Hydrometeor identification retrievals revealed the presence of large fractions of rimed ice particles (snow aggregates) in the continental (maritime) convective cores, consistent with the Z dr and K dp observations. The regional cloud-resolving model simulations with bulk and size-resolved bin microphysics are conducted for the selected cases, and the simulation outputs are converted into polarimetric radar signals and retrievals identical to the observational composites. Both the bulk and the bin microphysics reproduce realistic land and ocean (L-O) contrasts in reflectivity, polarimetric variables of rain drops, and hydrometeor profiles, but there are still large uncertainties in describing Z dr and K dp of ice crystals associated with the ice particle shapes/orientation assumptions. Sensitivity experiments are conducted by swapping background aerosols between the continental and maritime environments, revealing that background aerosols play a role in shaping the distinct L-O contrasts in radar reflectivity associated with raindrop sizes, in addition to the dominant role of background thermodynamics.

In this study, the impact of different configurations of the Goddard radiation scheme on convection-permitting simulations (CPSs) of the West African monsoon (WAM) is investigated using the NASA-Unified WRF (NU-WRF). These CPSs had 3 km grid spacing to explicitly simulate the evolution of mesoscale convective systems (MCSs) and their interaction with radiative processes across the WAM domain and were able to reproduce realistic precipitation and energy budget fields when compared with satellite data, although low clouds were overestimated. Sensitivity experiments reveal that (1) lowering the radiation update frequency (i.e., longer radiation update time) increases precipitation and cloudiness over the WAM region by enhancing the monsoon circulation, (2) deactivation of precipitation radiative forcing suppresses cloudiness over the WAM region, and (3) aggregating radiation columns reduces low clouds over ocean and tropical West Africa. The changes in radiation configuration immediately modulate the radiative heating and low clouds over ocean. On the 2nd day of the simulations, patterns of latitudinal air temperature profiles were already similar to the patterns of monthly composites for all radiation sensitivity experiments. Low cloud maintenance within the WAM system is tightly connected with radiation processes; thus, proper coupling between microphysics and radiation processes must be established for each modeling framework.

Cloud and precipitation systems are simulated with a multi-scale modeling framework (MMF) and compared over the Tropics and Subtropics against the Tropical Rainfall Measuring Mission (TRMM) Radar-defined Precipitation Features (RPFs) product. A methodology, in close analogy to the TRMM RPFs, is developed to produce simulated precipitation features (PFs) from the output of the embedded two-dimensional (2D) cloud-resolving models (CRMs) within an MMF. Despite the limitations of 2D CRMs, the simulated population distribution, horizontal and vertical structure of PFs, and the geographical location and local rainfall contribution of mesoscale convective systems (MCSs) are in good agreement with the TRMM observations. However, some model discrepancies are found and can be identified and quantified within the PF distributions. Using model biases in relative population and rainfall contributions, PFs can be characterized into four size categories: small, medium to large, very large, and extremely large. Four different major mechanisms might account for the model biases in each different category: (1) the two-dimensionality of the CRMs, (2) a positive convection-wind-evaporation feedback loop, (3) an artificial dynamic constraint in a bounded CRM domain with cyclic boundaries, and (4) the limited CRM domain size. The second and fourth mechanisms tend to contribute to the excessive tropical precipitation biases commonly found in most MMFs, whereas the other mechanisms reduce rainfall contributions from small and very large PFs. MMF sensitivity experiments with various CRM domain sizes and grid spacings showed that larger domains (higher resolutions) tend to shift PF populations toward larger (smaller) sizes.

A series of model simulations were conducted to investigate the effects of cloud condensation nuclei (CCN) loading and convective available potential energy (CAPE) on tropical maritime and midlatitude continental deep convection. Dynamical downscaling from global aerosol reanalysis was used to represent aerosol fields for the two convective regimes. We describe a control run and multiple sensitivity experiments using a limited‐area model, employing spectral‐bin cloud microphysics. The CCN loading is perturbed between the target maritime and continental conditions, roughly 40–2,000 cm−3 at 850 hPa and 1% supersaturation. Surface precipitation rates monotonically increase with increasing CCN loading for both the maritime and continental situations, while these monotonic increases are disrupted in the simulations with reduced CAPE. The increase in precipitation is in the form of convective precipitation, at the expense of stratiform precipitation. CCN increases promote increases in supercooled cloud water, in agreement with previous modeling studies. However, in the simulations investigated herein, the changes in supercooled water have different impacts on the cloud microphysics in the maritime and continental simulations. Increased supercooled water contents lead to more hail and less graupel in the continental simulation. For the maritime simulation, enhanced supercooled cloud water contents promote an increase in graupel since little or no hail is produced. This distinction is due to the difference in relative magnitudes and peak altitudes of supercooled water and snow amounts, which is further attributable to moisture and dynamical differences in the two cases.

The Goddard Convective-Stratiform Heating (CSH) algorithm has been used to retrieve latent heating (LH) associated with clouds and cloud systems in support of the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission. The CSH algorithm required the use of a cloud-resolving model (CRM) to simulate LH profiles to build look-up tables (LUTs). However, the current LUTs in the CSH algorithm are not suitable for retrieving LH profiles at high latitudes or winter conditions that are needed for GPM. The NASA Unified-Weather Research and Forecasting (NU-WRF) model is used to simulate three eastern continental US (CONUS) synoptic winter and three western coastal/offshore events. The relationship between LH structures (or profiles) and other precipitation properties (radar reflectivity, freezing level height, echo-top height, maximum radar reflectivity height and surface precipitation rate) is examined, and a new classification system is adopted with varying ranges for each of these precipitation properties to create LUTs representing high latitude/winter conditions. The performance of the new LUTs is examined using a self-consistency check for one CONUS and one West Coast offshore event by comparing LH profiles retrieved from the LUTs using model-simulated precipitation properties with those originally simulated by the model. The results of the self-consistency check validate the new classification and LUTs. High latitude retrievals from the new LUTs are merged with those from the CSH algorithm to retrieve LH profiles over the GPM domain using precipitation properties retrieved from the GPM combined algorithm.

This paper introduces a synthetic polarimetric radar simulator and retrieval package, POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS), for evaluating cloud‐resolving models (CRMs). POLARRIS is composed of forward (POLARRIS‐f) and inverse (retrieval and diagnostic) components (iPOLARRIS) to generate not only polarimetric radar observables (Zh, Zdr, Kdp, ρhv) but also radar‐consistent geophysical parameters such as hydrometeor identification, vertical velocity, and rainfall rates retrieved from CRM data. To demonstrate its application and uncertainties, POLARRIS is applied to simulations of a mesoscale convective system over the Southern Great Plains on 23 May 2011, using the Weather Research and Forecasting model with both spectral bin microphysics (SBM) and the Goddard single‐moment bulk 4ICE microphysics. Statistical composites reveal a significant dependence of simulated polarimetric observables (Zdr, Kdp) on the assumptions of the particle axis ratio (oblateness) and orientation angle distributions. The simulated polarimetric variables differ considerably between the SBM and 4ICE microphysics in part due to the differences in their ice particle size distributions as revealed by comparisons with aircraft measurements. Regardless of these uncertainties, simulated hydrometeor identification distributions overestimate graupel and hail fractions, especially from the simulation with SBM. To minimize uncertainties in forward model, the particle shape and orientation angle distributions of frozen particles should be predicted in a microphysics scheme in addition to the size distributions and particle densities.

The Goddard convective-stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm's lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dBZ values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from similar to 2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.

The Goddard microphysics scheme was recently improved by adding a 4th ice class (frozen dropshail). This new 4ICE scheme was implemented and tested in the Goddard Cumulus Ensemble model (GCE) for an intense continental squall line and a moderate,less-organized continental case. Simulated peak radar reflectivity profiles were improved both in intensity and shape for both cases as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified - Weather Research and Forecasting model (NU-WRF) and tested on an intense mesoscale convective system that occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). The NU42WRF simulated radar reflectivities, rainfall intensities, and vertical and horizontal structure using the new 4ICE scheme agree as well as or significantly better with observations than when using previous versions of the Goddard 3ICE (graupel or hail) schemes. In the 4ICE scheme, the bin microphysics-based rain evaporation correction produces more erect convective cores, while modification of the unrealistic collection of ice by dry hail produces narrow and intense cores, allowing more slow-falling snow to be transported rearward. Together with a revised snow size mapping, the 4ICE scheme produces a more horizontally stratified trailing stratiform region with a broad, more coherent light rain area. In addition, the NU-WRF 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive open lateral boundaries

This study evaluated the impact of five, single- or double- moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven, intense winter time cyclones impacting the Mid-Atlantic United States. Five-day long WRF simulations were initialized roughly 24 hours prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (5 BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities lead to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatio-temporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF-simulations demonstrate low-to-moderate (0.217-0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude (CFAD) diagrams reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.

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 ...

The Goddard microphysics was recently improved by adding a fourth ice class (frozen drops/hail). This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of ice/snow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries.

NASA-Goddard Space Flight Center, Greenbelt, 20716, United States of America 5 Joint Center for Earth Systems Technology, Baltimore, NASA-Goddard Space Flight Center, Baltimore, 21250, 6 United States of America 7 3Department of Environmental Sciences, Rutgers, The State University of New Jersey, 08850, United States of 8 America 9 Science Systems and Applications, Inc., Lanham, 20706, United States of America 10 Goddard Earth Sciences Technology and Research, Morgan State University, 21251, United States of America 11