scholarly journals On the Vertical Structure of Modeled and Observed Deep Convective Storms: Insights for Precipitation Retrieval and Microphysical Parameterization

2005 ◽  
Vol 44 (12) ◽  
pp. 1866-1884 ◽  
Author(s):  
Jamie L. Smedsmo ◽  
Efi Foufoula-Georgiou ◽  
Venugopal Vuruputur ◽  
Fanyou Kong ◽  
Kelvin Droegemeier
Keyword(s):  
High Resolution ◽  
Vertical Structure ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Future Research ◽  
Surface Precipitation ◽  
Convective Storms ◽  
Precipitation Forecasting ◽  
Radar Echoes ◽  
Microphysical Parameterization

Abstract An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

scholarly journals A simple insect removal algorithm for 35-GHz cloud radar measurements

2017 ◽  
Author(s):  
Madhu Chandra R. Kalapureddy ◽  
Sukanya Patra ◽  
Subrata K. Das ◽  
Sachin M. Deshpande ◽  
Kaustav Chakravarty ◽  
...  
Keyword(s):  
High Resolution ◽  
Vertical Structure ◽  
Cloud Droplet ◽  
Radar Reflectivity ◽  
Noise Floor ◽  
Cloud Radar ◽  
Cloud Height ◽  
Radar Echoes ◽  
Sensitivity Curves ◽  
Radar Measurements

Abstract. One of the key parameters that must be included in the analysis of atmospheric constituents (gases and particles) and clouds is the vertical structure of the atmosphere. Therefore high-resolution vertical profile observations of the atmospheric targets are required for both theoretical and practical evaluation and as inputs to increase accuracy of atmospheric models. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure in a resourceful way. However, extracting intended meteorological cloud content from the overall measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work a technique is proposed to identify and separate cloud and non-hydrometeor returns from a cloud radar measurements. Firstly the observed cloud reflectivity profile must be evaluated against the theoretical radar sensitivity curves. This step helps to determine the range of receiver noise floor above which it can be identified as signal or an atmospheric echo. However it should be noted that the signal above the noise floor may be contaminated by the air-borne non-meteorological targets such as insects, birds, or airplanes. The second step in this analysis statistically reviews the continual radar echoes to determine the signal de-correlation period. Cloud echoes are observed to be temporally more coherent, homogenous and have a longer de-correlation period than insects and noise. This step critically helps in separating the clouds from insects and noise which show shorter de-correlation periods. The above two steps ensure the identification and removal of non-hydrometeor contributions from the cloud radar reflectivity profile which can then be used for inferring unbiased vertical cloud structure. However these two steps are insufficient for recovering the weakly echoing cloud boundaries associated with the sharp reduction in cloud droplet size and concentrations. In the final step in order to obtain intact cloud height information, identified cloud echo peak(s) needs to be backtracked along the either sides on the reflectivity profile till its value falls close to the mean noise floor. The proposed algorithm potentially identify cloud height solely through the characterization of high resolution cloud radar reflectivity measurements with the theoretical echo sensitivity curves and observed echo statistics for the cloud tracking (TEST). This technique is found to be more robust in identifying and filtering out the contributions due to insects and noise which may contaminate a cloud reflectivity profile. With this algorithm it is possible to improve monsoon tropical cloud characterization using cloud radar.

Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

2021 ◽  
Vol 149 (4) ◽  
pp. 1153-1172
Author(s):  
David S. Henderson ◽  
Jason A. Otkin ◽  
John R. Mecikalski
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Cloud Water ◽  
Convective Initiation ◽  
Model Simulations ◽  
Brightness Temperatures ◽  
Cloud Characteristics ◽  
Verification Methods

AbstractThe evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

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 Initial Development and Testing of a Convection-Allowing Model Scorecard

2019 ◽  
Vol 100 (12) ◽  
pp. ES367-ES384 ◽  
Author(s):  
Burkely T. Gallo ◽  
Christina P. Kalb ◽  
John Halley Gotway ◽  
Henry H. Fisher ◽  
Brett Roberts ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Model Performance ◽  
Skill Score ◽  
Initial Development ◽  
Convective Storms ◽  
Critical Success ◽  
Critical Success Index ◽  
Evidence Based Decision Making ◽  
Success Index

Abstract Evaluation of numerical weather prediction (NWP) is critical for both forecasters and researchers. Through such evaluation, forecasters can understand the strengths and weaknesses of NWP guidance, and researchers can work to improve NWP models. However, evaluating high-resolution convection-allowing models (CAMs) requires unique verification metrics tailored to high-resolution output, particularly when considering extreme events. Metrics used and fields evaluated often differ between verification studies, hindering the effort to broadly compare CAMs. The purpose of this article is to summarize the development and initial testing of a CAM-based scorecard, which is intended for broad use across research and operational communities and is similar to scorecards currently available within the enhanced Model Evaluation Tools package (METplus) for evaluating coarser models. Scorecards visualize many verification metrics and attributes simultaneously, providing a broad overview of model performance. A preliminary CAM scorecard was developed and tested during the 2018 Spring Forecasting Experiment using METplus, focused on metrics and attributes relevant to severe convective forecasting. The scorecard compared attributes specific to convection-allowing scales such as reflectivity and surrogate severe fields, using metrics like the critical success index (CSI) and fractions skill score (FSS). While this preliminary scorecard focuses on attributes relevant to severe convective storms, the scorecard framework allows for the inclusion of further metrics relevant to other applications. Development of a CAM scorecard allows for evidence-based decision-making regarding future operational CAM systems as the National Weather Service transitions to a Unified Forecast system as part of the Next-Generation Global Prediction System initiative.

scholarly journals Improving High-Resolution Weather Forecasts Using the Weather Research and Forecasting (WRF) Model with an Updated Kain–Fritsch Scheme

2016 ◽  
Vol 144 (3) ◽  
pp. 833-860 ◽  
Author(s):  
Yue Zheng ◽  
Kiran Alapaty ◽  
Jerold A. Herwehe ◽  
Anthony D. Del Genio ◽  
Dev Niyogi
Keyword(s):  
High Resolution ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Cumulus Parameterization ◽  
Subgrid Scale ◽  
Grid Spacing ◽  
Dynamic Adjustment ◽  
Surface Precipitation

Abstract Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level–based entrainment methodology that includes scale dependency. A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

A Multimoment Bulk Microphysics Parameterization. Part III: Control Simulation of a Hailstorm

2006 ◽  
Vol 63 (12) ◽  
pp. 3114-3136 ◽  
Author(s):  
J. A. Milbrandt ◽  
M. K. Yau
Keyword(s):  
Size Distribution ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Added Value ◽  
Relative Dispersion ◽  
Continuous Increase ◽  
Surface Precipitation ◽  
Additional Information

With continuous increase in the resolution of operational numerical weather prediction models, grid-scale saturation schemes that model cloud microphysics are becoming increasingly important. In Parts I and II of this study, the importance of the relative dispersion of the hydrometeor size distribution in bulk microphysics parameterizations was demonstrated and a closure approach for a three-moment scheme was proposed. In this paper, the full three-moment version of the new multimoment scheme is tested in a 3D simulation of a severe hailstorm. The modeled microphysical fields are examined, with particular attention paid to the simulated hail fields including the maximum hail sizes at the ground. A mesoscale model was initialized using synoptic analyses and successively nested to a resolution of 1 km. When compared to observations of the real storm from a nearby radar, the simulated storm reproduced several of the observed characteristics including the direction and speed of propagation, a bounded weak echo region, hook echo, mesocyclone, and a suspended overhang region. The magnitudes of radar reflectivity and surface precipitation are also well simulated. The mass contents, total number concentrations, equivalent reflectivities, and mean mass diameters of each hydrometeor category in the model were examined. The spatial distributions of the various hydrometeors throughout the storm appeared realistic and their values were consistent with published observations from other storms. Using the three predicted parameters of the gamma size distribution for hail, a method was introduced to determine the maximum hail size simulated from a bulk scheme that is physically observable. The observed storm produced golf ball–sized hail while the simulation produced walnut-sized hail at approximately the same time and location. The results suggest that because of the additional information provided about the size distribution, there is added value in prognosing the relative dispersion parameter of a given hydrometeor category in a bulk scheme.

scholarly journals High-resolution stable isotope signature of a land-falling atmospheric river in southern Norway

2021 ◽  
Vol 2 (3) ◽  
pp. 713-737
Author(s):  
Yongbiao Weng ◽  
Aina Johannessen ◽  
Harald Sodemann
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Water Vapour ◽  
Isotope Composition ◽  
Initial Period ◽  
Cloud Microphysics ◽  
Cloud Base ◽  
Near Surface ◽  
Surface Precipitation ◽  
Moisture Source

Abstract. Heavy precipitation at the west coast of Norway is often connected to elongated meridional structures of high integrated water vapour transport known as atmospheric rivers (ARs). Here we present high-resolution measurements of stable isotopes in near-surface water vapour and precipitation during a land-falling AR in southwestern Norway on 7 December 2016. In our analysis, we aim to identify the influences of moisture source conditions, weather system characteristics, and post-condensation processes on the isotope signal in near-surface water vapour and precipitation. A total of 71 precipitation samples were collected during the 24 h sampling period, mostly taken at sampling intervals of 10–20 min. The isotope composition of near-surface vapour was continuously monitored in situ with a cavity ring-down spectrometer. Local meteorological conditions were in addition observed from a vertical pointing rain radar, a laser disdrometer, and automatic weather stations. We observe a stretched, “W”-shaped evolution of isotope composition during the event. Combining paired precipitation and vapour isotopes with meteorological observations, we define four different stages of the event. The two most depleted periods in the isotope δ values are associated with frontal transitions, namely a combination of two warm fronts that follow each other within a few hours and an upper-level cold front. The d-excess shows a single maximum and a step-wise decline in precipitation and a gradual decrease in near-surface vapour. Thereby, the isotopic evolution of the near-surface vapour closely follows that of the precipitation with a time delay of about 30 min, except for the first stage of the event. Analysis using an isotopic below-cloud exchange framework shows that the initial period of low and even negative d-excess in precipitation was caused by evaporation below cloud base. The isotope signal from the cloud level became apparent at ground level after a transition period that lasted up to several hours. Moisture source diagnostics for the periods when the cloud signal dominates show that the moisture source conditions are then partly reflected in surface precipitation and water vapour isotopes. In our study, the isotope signal in surface precipitation during the AR event reflects the combined influence of atmospheric dynamics, moisture sources, and atmospheric distillation, as well as cloud microphysics and below-cloud processes. Based on this finding, we recommend careful interpretation of results obtained from Rayleigh distillation models in such events, in particular for the interpretation of surface vapour and precipitation from stratiform clouds.

scholarly journals High-resolution stable isotope signature of a land-falling Atmospheric River in southern Norway

2020 ◽  
Author(s):  
Yongbiao Weng ◽  
Harald Sodemann ◽  
Aina Johannessen
Keyword(s):  
High Resolution ◽  
Water Vapour ◽  
Isotope Composition ◽  
Cloud Microphysics ◽  
Convective Precipitation ◽  
Cloud Base ◽  
Near Surface ◽  
Surface Precipitation ◽  
Moisture Source ◽  
Isotopic Signal

Abstract. Heavy precipitation at the west coast of Norway is often connected to elongated meridional structures of high integrated water vapour transport known as Atmospheric Rivers (AR). Here we present high-resolution measurements of stable isotopes in water vapour and precipitation during a land-falling AR event in western Norway on 07 December 2016. In our analysis, we aim to identify the influences of moisture source conditions, weather system characteristics, and post-condensation processes on the isotopic signal in near-surface water vapour and surface precipitation. A total of 71 precipitation samples were collected during the 24-h sampling period, mostly taken at sampling intervals of 10–20 min. The isotope composition of near-surface vapour was continuously monitored in-situ with a cavity ring-down spectrometer. Local meteorological conditions were in addition observed from a vertical pointing rain radar, a laser disdrometer, and automatic weather stations. We observe a stretched, W-shaped evolution of isotope composition during the event. Combining isotopic and meteorological observations, we define four different stages of the event. The two most depletion periods in the isotope δ values are associated with frontal transitions, namely a combination of two warm fronts that follow each other within a few hours, and an upper-level cold front. The d-excess shows a single maximum, and a step-wise decline in both precipitation and a gradual decrease in near-surface vapour. Thereby, isotopic evolution of the near-surface vapour closely follows the precipitation with a time delay of about 30 min, except for the first stage of the event. Analysis using an isotopic below-cloud exchange framework shows that the initial period of low and even negative d-excess in precipitation was caused by evaporation below cloud base. At the ground, a near-constant signal representative of the airmass above is only reached after transition periods of several hours. Moisture source diagnostics for the event show that the moisture source conditions for these steady periods are partly reflected in the surface precipitation at these times. Based on our observations, we revisit the interpretation of precipitation isotope measurements during AR events in previous studies. Given that the isotopic signal in surface precipitation reflects a combination of atmospheric dynamics through moisture sources and atmospheric distillation, as well as cloud microphysics and below-cloud processes, we recommend caution regarding how Rayleigh distillation models are used during data interpretation. While the isotope composition in water vapour during convective precipitation events may be more adequately represented by idealized Rayleigh models, additional factors should be taken into account when interpreting a surface precipitation isotope signal from stratiform clouds.

scholarly journals Examination of Mixed-Phase Precipitation Forecasts from the High-Resolution Rapid Refresh Model Using Surface Observations and Sounding Data

2017 ◽  
Vol 32 (3) ◽  
pp. 949-967 ◽  
Author(s):  
Kyoko Ikeda ◽  
Matthias Steiner ◽  
Gregory Thompson
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Temperature Profile ◽  
Weather Prediction ◽  
Mixed Phase ◽  
Phase Precipitation ◽  
Vertical Temperature ◽  
Surface Precipitation ◽  
Temperature Bias ◽  
Precipitation Phase

Abstract Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of <2°C and a vertical temperature profile similar to the sounding observations. However, the surface temperature bias was ~4°C in weather systems involving cold-air damming in the eastern United States, resulting in an incorrect surface precipitation phase or the duration (areal coverage) of freezing rain being much shorter (smaller) than the observation. Cases with predicted snow in regions of observed mixed-phase precipitation present subtle difference in the elevated layer with temperatures near 0°C and the near-surface layer.

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