Impact of Environmental Instability on Convective Precipitation Uncertainty Associated with the Nature of the Rimed Ice Species in a Bulk Microphysics Scheme

2013 ◽  
Vol 141 (8) ◽  
pp. 2841-2849 ◽  
Author(s):  
Kwinten Van Weverberg
Keyword(s):  
Environmental Conditions ◽  
Deep Convection ◽  
Weather Prediction ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Surface Precipitation ◽  
Squall Lines ◽  
Environmental Instability ◽  
Cloud Modeling ◽  
The Impact

Abstract Despite a number of studies dedicated to the sensitivity of deep convection simulations to the properties of the rimed ice species in microphysics schemes, no consensus has been achieved on the nature of the impact. Considering the need for improved quantitative precipitation forecasts, it is crucial that the cloud modeling community better understands the reasons for these differing conclusions and knows the relevance of these sensitivities for the numerical weather prediction. This study examines the role of environmental conditions and storm type on the sensitivity of precipitation simulations to the nature of the rimed ice species (graupel or hail). Idealized 3D simulations of supercells/multicells and squall lines have been performed in varying thermodynamic environments. It has been shown that for simulation periods of sufficient length (>2 h), graupel-containing and hail-containing storms produce domain-averaged surface precipitation that is more similar than many earlier studies suggest. While graupel is lofted to higher altitudes and has a longer residence time aloft than hail, these simulations suggest that most of this graupel eventually reaches the surface and the surface precipitation rates of hail- and graupel-containing storms converge. However, environmental conditions play an important role in the magnitude of this sensitivity. Storms in large-CAPE environments (typical of storms in the U.S. Midwest) are more sensitive than their low-CAPE counterparts (typical of storms in Europe) to the nature of the rimed ice species in terms of domain-average surface precipitation. Supercells/multicells are more sensitive than squall lines to the nature of the rimed ice species in terms of spatial precipitation distribution and peak precipitation, disregarding of the amount of CAPE.

scholarly journals What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 362 ◽  
Author(s):  
Alexander V. Ryzhkov ◽  
Jeffrey Snyder ◽  
Jacob T. Carlin ◽  
Alexander Khain ◽  
Mark Pinsky
Keyword(s):  
Cloud Model ◽  
Weather Prediction ◽  
Primary Source ◽  
Polarimetric Radar ◽  
Microphysical Process ◽  
Weather Radars ◽  
Cloud Models ◽  
Cloud Modeling ◽  
Bin Microphysics ◽  
The Impact

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

scholarly journals Numerical simulations of June 7, 2020 convective precipitation over Slovakia using deterministic, probabilistic, and convection-permitting approaches

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 571-607
Author(s):  
André Simon ◽  
Martin Belluš ◽  
Katarína Čatlošová ◽  
Mária Derková ◽  
Martin Dian ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Weather Prediction ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Small Scale ◽  
Limited Area ◽  
Systematic Effect ◽  
The Impact

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

scholarly journals The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6

2021 ◽  
Author(s):  
Alberto Caldas-Alvarez ◽  
Samiro Khodayar ◽  
Peter Knippertz
Keyword(s):  
High Resolution ◽  
Temporal Resolution ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Convective Precipitation ◽  
Small Scale ◽  
High Temporal Resolution ◽  
The Impact

Abstract. Heavy precipitation is one of the most devastating weather extremes in the western Mediterranean region. Our capacity to prevent negative impacts from such extreme events requires advancements in numerical weather prediction, data assimilation and new observation techniques. In this paper we investigate the impact of two state-of-the-art data sets with very high resolution, Global Positioning System-Zenith Total Delays (GPS-ZTD) with a 10 min temporal resolution and radiosondes with ~700 levels, on the representation of convective precipitation in nudging experiments. Specifically, we investigate whether the high temporal resolution, quality, and coverage of GPS-ZTDs can outweigh their lack of vertical information or if radiosonde profiles are more valuable despite their scarce coverage and low temporal resolution (24 h to 6 h). The study focuses on the Intensive Observation Period 6 (IOP6) of the Hydrological Cycle in the Mediterranean eXperiment (HyMeX; 24 September 2012). This event is selected due to its severity (100 mm/12 h), the availability of observations for nudging and validation, and the large observation impact found in preliminary sensitivity experiments. We systematically compare simulations performed with the COnsortium for Small scale MOdelling (COSMO) model assimilating GPS, high- and low vertical resolution radiosoundings in model resolutions of 7 km, 2.8 km and 500 m. The results show that the additional GPS and radiosonde observations cannot compensate errors in the model dynamics and physics. In this regard the reference COSMO runs have an atmospheric moisture wet bias prior to precipitation onset but a negative bias in rainfall, indicative of deficiencies in the numerics and physics, unable to convert the moisture excess into sufficient precipitation. Nudging GPS and high-resolution soundings corrects atmospheric humidity, but even further reduces total precipitation. This case study also demonstrates the potential impact of individual observations in highly unstable environments. We show that assimilating a low-resolution sounding from Nimes (southern France) while precipitation is taking place induces a 40 % increase in precipitation during the subsequent three hours. This precipitation increase is brought about by the moistening of the 700  hPa level (7.5 g kg−1) upstream of the main precipitating systems, reducing the entrainment of dry air above the boundary layer. The moist layer was missed by GPS observations and high-resolution soundings alike, pointing to the importance of profile information and timing. However, assimilating GPS was beneficial for simulating the temporal evolution of precipitation. Finally, regarding the scale dependency, no resolution is particularly sensitive to a specific observation type, however the 2.8 km run has overall better scores, possibly as this is the optimally tuned operational version of COSMO. In follow-up experiments the Icosahedral Nonhydrostatic Model (ICON) will be investigated for this case study to assert whether its numerical and physics updates, compared to its predecessor COSMO, are able to improve the quality of the simulations.

Secondary Ice Production in Antarctic Clouds: a process neglected in large-scale models

2020 ◽  
Author(s):  
Georgia Sotiropoulou ◽  
Etienne Vignon ◽  
Gillian Young ◽  
Thomas Lachlan-Cope ◽  
Alexis Berne ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weddell Sea ◽  
Microphysics Scheme ◽  
Ice Multiplication ◽  
Ice Production ◽  
Low Sensitivity ◽  
The Impact

<p>In-situ measurements of Antarctic clouds frequently show that ice crystal number concentrations  are much higher than the available ice-nucleating particles, suggesting that Secondary Ice Production (SIP) may be active. Here we investigate the impact of two SIP mechanisms, Hallett-Mossop (H-M)and collisional break-up (BR), on a case from the Microphysics of Antarctic Clouds (MAC) campaign in Weddell Sea using the Weather and Research Forecasting (WRF) model. H-M is already included in the default version of the Morrison microphysics scheme in WRF; for BR we implement different parameterizations and compare their performance. H-M alone is not effective enough to reproduce the observed concentrations. In contrast, BR can result in realistic ice multiplication, independently of whether H-M is active or not. In particular, the Phillips parameterization results in very good agreement with observations, but its performance depends on the prescribed rimed fraction of the colliding ice particles. Finally, our results show low sensitivity to primary ice nucleation, as long as there are enough primary ice crystals to initiate ice-ice collisions. Our findings suggest that BR is a potentially important SIP mechanism in the pristine Antarctic atmosphere that is currently not represented in weather-prediction and climate models.</p>

Improvements in Detection of Light Precipitation with the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)

2016 ◽  
Vol 33 (4) ◽  
pp. 653-667 ◽  
Author(s):  
Atsushi Hamada ◽  
Yukari N. Takayabu
Keyword(s):  
Deep Convection ◽  
Convective Precipitation ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Precipitation Measurement ◽  
Subtropical Oceans ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
The Impact ◽  
Light Precipitation

AbstractThis paper demonstrates the impact of the enhancement in detectability by the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core observatory. By setting two minimum detectable reflectivities—12 and 18 dBZ—artificially to 6 months of GPM DPR measurements, the precipitation occurrence and volume increase by ~21.1% and ~1.9%, respectively, between 40°S and 40°N.GPM DPR is found to be able to detect light precipitation, which mainly consists of two distinct types. One type is shallow precipitation, which is most significant for convective precipitation over eastern parts of subtropical oceans, where deep convection is typically suppressed. The other type is probably associated with lower parts of anvil clouds associated with organized precipitation systems.While these echoes have lower reflectivities than the official value of the minimum detectable reflectivity, they are found to mostly consist of true precipitation signals, suggesting that the official value may be too conservative for some sort of meteorological analyses. These results are expected to further the understanding of both global energy and water budgets and the diabatic heating distribution.

scholarly journals Predicting the Snow-to-Liquid Ratio of Surface Precipitation Using a Bulk Microphysics Scheme

2012 ◽  
Vol 140 (8) ◽  
pp. 2461-2476 ◽  
Author(s):  
J. A. Milbrandt ◽  
A. Glazer ◽  
D. Jacob
Keyword(s):  
Subjective Evaluation ◽  
Weather Prediction ◽  
Total Solid ◽  
Liquid Ratio ◽  
Microphysics Scheme ◽  
New Approach ◽  
Surface Precipitation ◽  
Precipitation Forecasting ◽  
Nwp Model ◽  
Crystal Aggregate

Abstract Bulk microphysics parameterizations play an increasingly important role for quantitative precipitation forecasting (QPF) in operational numerical weather prediction (NWP). For wintertime, numerical prediction of snowfall amounts is done by applying an estimated snow-to-liquid ratio to the liquid-equivalent QPF from the NWP model. A method has been developed to use prognostic fields from a detailed bulk scheme to predict the instantaneous snow-to-liquid ratio of precipitating snow. By exploiting aspects of the parameterization of the large crystal/aggregate (snow) category, which allow for a prediction of the mean particle size and a corresponding realistic bulk density, combined with pristine ice and graupel fields, the total volume flux of ice-phase precipitation (excluding hail) is computed, independently from the computation of the total solid mass flux. Ultimately, the accumulated unmelted solid precipitation quantity is thus predicted without having to estimate the average snow-to-liquid ratio for a given event, as is typically done for wintertime QPF. The new technique has been implemented into the two-moment version of the Milbrandt–Yau microphysics scheme, which was used in a high-resolution (2.5 and 1 km) NWP modeling system over the Vancouver–Whistler region of Canada in support of forecasting for the Vancouver 2010 Olympic and Paralympic Games. Experimental fields were produced including the instantaneous snow-to-liquid ratio and the snowfall accumulation predicted directly from the scheme using the new approach. Subjective evaluation indicates that the model can discriminate between low-density and high-density snow for instantaneous precipitation. Comparison of the predicted snow-to-liquid ratio to observed climatologies indicates that the scheme produces a realistic probability distribution.

scholarly journals Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME

2012 ◽  
Vol 51 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Lisa Bengtsson ◽  
Sander Tijm ◽  
Filip Váňa ◽  
Gunilla Svensson
Keyword(s):  
Prediction Models ◽  
Mesoscale Model ◽  
Deep Convection ◽  
Weather Prediction ◽  
Nonlinear Flow ◽  
Dynamical Model ◽  
Convective Precipitation ◽  
Physical Parameters ◽  
Rain Intensity ◽  
Horizontal Diffusion

AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

scholarly journals Cross-validation of GPS tomography models and methodological improvements using CORS network

2018 ◽  
Author(s):  
Hugues Brenot ◽  
Witold Rohm ◽  
Michal Kačmařík ◽  
Gregor Möller ◽  
André Sá ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Deep Convection ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Severe Weather ◽  
Vapour Density ◽  
Independent Observations ◽  
Using Data ◽  
The Impact

Abstract. Using data from the Continuously Operating Reference Stations (CORS), recorded in March 2010 during severe weather in the Victoria State, in southern Australia, sensitivity and statistical results of GPS tomography retrievals (water vapour density and wet refractivity) from 5 models have been tested and verified – considering independent observations from radiosonde and radio occultation profiles. The impact of initial conditions, associated with different time-convergence of tomography inversion, can reduce the normalised RMS of the tomography solution with respect to radiosonde estimates by a multiple (up to more than 3). Thereby it is illustrated that the quality of the apriori data in combination with iterative processing is critical, independently of the choice of the tomography model. However, the use of data stacking and pseudo-slant observations can significantly improve the quality of the retrievals, due to a better geometrical distribution and a better coverage of mid- and low-tropospheric parts. Besides, the impact of the uncertainty of GPS observations has been investigated, showing the interest of using several sets of data input to evaluate tomography retrievals in comparison to independent external measurements, and to estimate simultaneously the quality of NWP outputs. Finally, a comparison of our multi-model tomography with numerical weather prediction from ACCESS-A model shows the relevant use of tomography retrieval to improve the understanding of such severe weather conditions, especially about the initiation of the deep convection.

Reply to “Comments on ‘Do Ultrafine Cloud Condensation Nuclei Invigorate Deep Convection?’”

2021 ◽  
Vol 78 (1) ◽  
pp. 341-350
Author(s):  
Wojciech W. Grabowski ◽  
Hugh Morrison
Keyword(s):  
Negative Impact ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Diagnostic Technique ◽  
Cloud Water ◽  
Latent Heating ◽  
Condensation Nuclei ◽  
Modeling Framework ◽  
Microphysics Scheme ◽  
The Impact

AbstractThis is a rebuttal of Fan and Khain’s comments (hereafter FK21) on a 2020 paper by Grabowski and Morrison (hereafter GM20) that questions the impact of ultrafine cloud condensation nuclei (CCN) on deep convection. GM20 argues that “cold invigoration,” an increase of the updraft speed from lofting and freezing of additional cloud water in polluted environments, is unlikely because the latent heating from freezing of this cloud water approximately recovers the negative impact on the buoyancy from the weight of this water. FK21 suggest a variety of processes that could invalidate our claim. We maintain that our argument is valid and invite the authors to compare their microphysics scheme with ours in the same simplified modeling framework. However, pollution does affect the partitioning of latent heating within the column and likely leads to convection changes beyond a single diurnal cycle through larger-scale circulation changes. This argument explains impacts seen in our idealized mesoscale simulations and in convective–radiative equilibrium simulations by others. We agree with FK21 on the existence of a “warm invigoration” mechanism but question its interpretation. Consistent with the simulations in GM20, we argue that changes in the buoyancy can be explained by the response of the supersaturation to droplet microphysical changes induced by pollution. The buoyancy change is determined by supersaturation differences between pristine and polluted conditions, while condensation rate responds to these supersaturation changes. Finally, we agree with FK21 that the piggybacking modeling technique cannot prove or disprove invigoration; rather, it is a diagnostic technique that can be used to understand mechanisms driving simulation differences.

scholarly journals Comparison of Cloud Microphysics Schemes in a Warn-on-Forecast System Using Synthetic Satellite Objects

2018 ◽  
Vol 33 (6) ◽  
pp. 1681-1708 ◽  
Author(s):  
Thomas A. Jones ◽  
Patrick Skinner ◽  
Kent Knopfmeier ◽  
Edward Mansell ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Prediction Models ◽  
Negative Impact ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Variable Density ◽  
Cloud Microphysics ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Double Moment

AbstractForecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.

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