Parameterization of the Vertical Velocity Equation for Shallow Cumulus Clouds

2012 ◽  
Vol 140 (8) ◽  
pp. 2424-2436 ◽  
Author(s):  
Stephan R. de Roode ◽  
A. Pier Siebesma ◽  
Harm J. J. Jonker ◽  
Yoerik de Voogd
Keyword(s):  
Pressure Gradient ◽  
Vertical Velocity ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Thermodynamic Quantities ◽  
Small Contribution ◽  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Sink Term ◽  
Velocity Equation

Abstract The application of a steady-state vertical velocity equation for parameterized moist convective updrafts in climate and weather prediction models is currently common practice. This equation usually contains an advection, a buoyancy, and a lateral entrainment term, whereas the effects of pressure gradient and subplume contributions are typically incorporated as proportionality constants a and b for the buoyancy and the entrainment terms, respectively. A summary of proposed values of these proportionality constants a and b in the literature demonstrates that there is a large uncertainty in their most appropriate values. To shed new light on this situation an analysis is presented of the full vertical budget equation for shallow cumulus clouds obtained from large eddy simulations of three different Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) intercomparison cases. It is found that the pressure gradient term is the dominant sink term in the vertical velocity budget, whereas the entrainment term only gives a small contribution. This result is at odds with the parameterized vertical velocity equation in the literature as it employs the entrainment term as the major sink term. As a practical solution the damping effect of the pressure term may be parameterized in terms of the lateral entrainment rates as used for thermodynamic quantities like the total specific humidity. By using a least squares method, case-dependent optimal values are obtained for the proportionality constants a and b, which are linearly related with each other. This relation can be explained from a linear relationship between the lateral entrainment rate and the buoyancy.

scholarly journals Size dependence in chord characteristics from simulated and observed continental shallow cumulus

2020 ◽  
Vol 20 (17) ◽  
pp. 10211-10230
Author(s):  
Philipp J. Griewank ◽  
Thijs Heus ◽  
Neil P. Lareau ◽  
Roel A. J. Neggers
Keyword(s):  
Vertical Velocity ◽  
3D Model ◽  
Model Output ◽  
Cumulus Clouds ◽  
Mean Values ◽  
Shallow Cumulus ◽  
1D Model ◽  
The Difference ◽  
The Individual ◽  
Lidar Observations

Abstract. In this study we compare long-term Doppler and Raman lidar observations against a full month of large eddy simulations of continental shallow cumulus clouds. The goal is to evaluate if the simulations can reproduce the mean observed vertical velocity and moisture structure of cumulus clouds and their associated subcloud circulations, as well as to establish if these properties depend on the size of the cloud. We propose methods to compare continuous chords of cloud detected from Doppler and Raman lidars with equivalent chords derived from 1D and 3D model output. While the individual chords are highly variable, composites of thousands of observed and millions of simulated chords contain a clear signal. We find that the simulations underestimate cloud size and fraction but successfully reproduce the observed structure of vertical velocity and moisture perturbations. There is a clear scaling of vertical velocity and moisture anomalies below the chords with chord size, but the moisture anomalies are only 1 %–2 % higher than the horizontal mean values. The differences between the observations and simulations are smaller than the difference in sampling the modeled chords in time or space. The shape of the vertical velocity and moisture anomalies from cloud chords sampled spatially from 3D model snapshots is almost perfectly symmetric. In contrast, the chords sampled temporally from the lidar observations and 1D model output have a marked asymmetry, with stronger updrafts and higher moisture anomalies occurring earlier on.

scholarly journals Size-dependence in chord characteristics from simulated and observed continental shallow cumulus

2020 ◽  
Author(s):  
Philipp J. Griewank ◽  
Thijs Heus ◽  
Neil P. Lareau ◽  
Roel A. J. Neggers
Keyword(s):  
Vertical Velocity ◽  
3D Model ◽  
Model Output ◽  
Cumulus Clouds ◽  
Mean Values ◽  
Shallow Cumulus ◽  
1D Model ◽  
The Difference ◽  
The Individual ◽  
Lidar Observations

Abstract. In this study, we compare long-term lidar observations against a full month of large eddy simulations of continental shallow cumulus clouds. The goal is to evaluate if the simulations can reproduce the mean observed vertical velocity and moisture structure of cumulus clouds, and to establish if these properties depend on the size of the cloud. We propose methods to compare continuous chords of cloud detected from Doppler and Raman lidars with equivalent chords derived from 1D and 3D model output. While the individual chords are highly variable, composites of thousands of observed and millions of simulated chords contain a clear signal. We find that the simulations underestimate cloud size and fraction, but successfully reproduce the observed structure of vertical velocity and moisture perturbations. There is a clear scaling of vertical velocity and moisture anomalies below the chords with chord size, but the moisture anomalies are only 1–2 % higher than the horizontally mean values. The differences between the observations and simulations is smaller than the difference in sampling the modelled chords in time or space. The shape of the vertical velocity and moisture anomalies from cloud chords sampled spatially from 3D model snapshots is almost perfectly symmetric. In contrast, the chords sampled temporally from the lidar observations and 1D model output have a marked asymmetry with stronger updrafts and higher moisture anomalies occurring earlier on.

scholarly journals Sensitivities of Lagrangian modeling of mid-latitude cirrus clouds to trajectory data quality

2015 ◽  
Vol 15 (5) ◽  
pp. 7535-7584 ◽  
Author(s):  
E. Kienast-Sjögren ◽  
A. K. Miltenberger ◽  
B. P. Luo ◽  
T. Peter
Keyword(s):  
Cooling Rate ◽  
Temporal Resolution ◽  
Vertical Velocity ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Number Density ◽  
Wind Fields ◽  
Ice Nuclei ◽  
Velocity Variance ◽  
Nuclei Number

Abstract. Simulations of cirrus are subject to uncertainties in model physics and meteorological input data. Here we model cirrus clouds, whose extinction has been measured with an elastic backscatter Lidar at Jungfraujoch research station in the Swiss Alps, and investigate the sensitivities to input data uncertainties (trajectory resolution, unresolved vertical velocities, ice nuclei number density and upstream specific humidity). Simulations with a microphysical stacked box model have been performed along trajectories derived from the high-resolution numerical weather prediction model COSMO-2 (2.2 km grid spacing). For the calculation of the trajectories we experimented with model wind fields at temporal resolutions between 20 s and 1 h. While the temporal resolution affects the trajectory path only marginally, it has a strong impact on the vertical velocity variance resolved along the trajectories, and therefore on the cooling rate distribution. In the present example, the temporal resolution of the wind fields must be chosen to be better than 5 min in order to resolve vertical velocities and cooling rates required to explain the measured extinction. The simulation improves slightly if the temporal resolution is increased further to 20 s. This means that on the selected day the cooling rate spectra calculated by COSMO-2 suffice to achieve agreement with the cirrus measurements. On that day cooling rate spectra are characterized bysignificantly lower vertical velocity amplitudes than those found previously in some aircraft campaigns (SUCCESS, MACPEX). A climatological analysis of the vertical velocity variance in the Alpine region based on COSMO-2 analyses and balloon sounding data suggests large day-to-day variability in small-scale temperature fluctuations. This demonstrates the necessity to apply numerical weather prediction models with high spatial and temporal resolutions in cirrus modeling, whereas using climatological means for the amplitude of the unresolved air motions does generally not suffice. The box model simulations further suggest that uncertainties in the upstream specific humidity (±10% of the model prediction) and in the ice nuclei number density are more important for the modeled cirrus cloud than the unresolved temperature fluctuations, if temporally highly resolved trajectories are used. For the presented case the simulations are incompatible with ice nuclei number densities larger than 20 L−1 and insensitive to variations below this value.

Evaluation of the physics suite in NOAA’s GFSv16 using field-campaign observations and diagnosis of physics tendencies

2021 ◽  
Author(s):  
Jian-Wen Bao ◽  
Sara Michelson ◽  
Evelyn Grell
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Environment ◽  
Cumulus Clouds ◽  
Western Atlantic ◽  
Field Campaign ◽  
Parameterization Schemes ◽  
Shallow Cumulus ◽  
The Impact

<p>Shallow cumulus clouds play an important role in the weather in the Atlantic Tropical Convergence Zone.  Their interaction with the atmospheric environment and oceanic mixing processes has a significant impact on the convective organization and tropical dynamics.  It is still a scientific challenge for numerical weather prediction models to accurately simulate them due to deficiencies in the model’s representation of physical processes. </p><p>In this study, we investigate how the physics parameterization schemes in NOAA’s most recent operational global forecast system (GFSv16) perform in the simulation of shallow cumulus clouds in the western Atlantic in terms of their interaction with the large-scale atmospheric dynamics.  Previous studies have indicated that the impact of physics parameterization schemes on model’s tendencies during the first few hours can provide critical information on their suitability for short- and medium-range forecasts.<strong> </strong> Therefore, we first evaluate the GFSv16 forecasts against the observations obtained from the European field campaign called the ATOMIC/EUREC4A that occurred between 12 January and 23 February 2020.  We then diagnose the sensitivity of the GFSv16 physics tendencies to changes to the physics parameterization schemes over the first 6 hours of the forecast, which is the timescale before dynamical feedback becomes significant. Using the information from the observational evaluation and physics tendency diagnosis, we further explore possible improvement in the physical process representation that can positively affect the physics tendencies and lead to overall forecast improvement beyond 6 hours.</p>

scholarly journals Quantifying the bias of radiative heating rates in numerical weather prediction models for shallow cumulus clouds

2019 ◽  
Vol 19 (12) ◽  
pp. 8083-8100 ◽  
Author(s):  
Nina Črnivec ◽  
Bernhard Mayer
Keyword(s):  
Monte Carlo ◽  
Numerical Weather Prediction ◽  
Cloud Cover ◽  
Weather Prediction ◽  
Cloud Layer ◽  
Cumulus Cloud ◽  
Grid Spacing ◽  
Cumulus Clouds ◽  
Radiation Model ◽  
Shallow Cumulus

Abstract. The interaction between radiation and clouds represents a source of uncertainty in numerical weather prediction (NWP) due to both intrinsic problems of one-dimensional radiation schemes and poor representation of clouds. The underlying question addressed in this study is how large the NWP radiative bias is for shallow cumulus clouds and how it scales with various input parameters of radiation schemes, such as solar zenith angle, surface albedo, cloud cover and liquid water path. A set of radiative transfer calculations was carried out for a realistically evolving shallow cumulus cloud field stemming from a large-eddy simulation (LES). The benchmark experiments were performed on the highly resolved LES cloud scenes (25 m grid spacing) using a three-dimensional Monte Carlo radiation model. An absence of middle and high clouds is assumed above the shallow cumulus cloud layer. In order to imitate the poor representation of shallow cumulus in NWP models, cloud optical properties were horizontally averaged over the cloudy part of the boxes with dimensions comparable to NWP horizontal grid spacing (several kilometers), and the common δ-Eddington two-stream method with maximum-random overlap assumption for partial cloudiness was applied (denoted as the “1-D” experiment). The bias of the 1-D experiment relative to the benchmark was investigated in the solar and thermal parts of the spectrum, examining the vertical profile of heating rate within the cloud layer and the net surface flux. It is found that, during daytime and nighttime, the destabilization of the cloud layer in the benchmark experiment is artificially enhanced by an overestimation of the cooling at cloud top and an overestimation of the warming at cloud bottom in the 1-D experiment (a bias of about −15 K d−1 is observed locally for stratocumulus scenarios). This destabilization, driven by the thermal radiation, is maximized during nighttime, since during daytime the solar radiation has a stabilizing tendency. The daytime bias at the surface is governed by the solar fluxes, where the 1-D solar net flux overestimates (underestimates) the corresponding benchmark at low (high) Sun. The overestimation at low Sun (bias up to 80 % over land and ocean) is largest at intermediate cloud cover, while the underestimation at high Sun (bias up to −40 % over land and ocean) peaks at larger cloud cover (80 % and beyond). At nighttime, the 1-D experiment overestimates the amount of benchmark surface cooling with the maximal bias of about 50 % peaked at intermediate cloud cover. Moreover, an additional experiment was carried out by running the Monte Carlo radiation model in the independent column mode on cloud scenes preserving their LES structure (denoted as the “ICA” experiment). The ICA is clearly more accurate than the 1-D experiment (with respect to the same benchmark). This highlights the importance of an improved representation of clouds even at the resolution of today's regional (limited-area) numerical models, which needs to be considered if NWP radiative biases are to be efficiently reduced. All in all, this paper provides a systematic documentation of NWP radiative biases, which is a necessary first step towards an improved treatment of radiation–cloud interaction in atmospheric models.

scholarly journals Quantifying the bias of radiative heating rates in NWP models for shallow cumulus clouds

2019 ◽  
Author(s):  
Nina Črnivec ◽  
Bernhard Mayer
Keyword(s):  
Monte Carlo ◽  
Cloud Cover ◽  
Weather Prediction ◽  
Cloud Layer ◽  
Radiative Heating ◽  
Cumulus Cloud ◽  
Surface Cooling ◽  
Cumulus Clouds ◽  
Radiation Model ◽  
Shallow Cumulus

Abstract. The interaction between radiation and clouds represents a source of uncertainty in numerical weather prediction (NWP) due to both intrinsic problems of one-dimensional radiation schemes and poor representation of clouds. The underlying question addressed in this study is how large is the NWP radiative bias for shallow cumulus clouds and how does it scale with various input parameters of radiation schemes, such as solar zenith angle, surface albedo, cloud cover and liquid water path. A set of radiative transfer calculations was carried out for a realistically evolving shallow cumulus cloud field stemming from a large-eddy simulation (LES). The benchmark experiments were performed on the highly-resolved LES cloud scenes using a three-dimensional Monte Carlo radiation model. An absence of middle and high cloud is assumed above the shallow cumulus cloud layer. In order to imitate poor representation of shallow cumulus in NWP models, cloud optical properties were horizontally averaged over the cloudy part of the boxes with dimensions comparable to NWP horizontal grid spacing (several km) and the common δ-Eddington two-stream method with maximum-random overlap assumption for partial cloudiness was applied (denoted as 1-D experiment). The bias of the 1-D experiment relative to the benchmark was investigated in the solar and thermal part of the spectrum, examining the vertical profile of heating rate within the cloud layer and net surface flux. It is found that during daytime and nighttime, the destabilization of the cloud layer in the benchmark experiment is artifically enhanced by an overestimation of the cooling at cloud top and an overestimation of the warming at cloud bottom in the 1-D experiment (bias of about −15 K day−1 is observed locally for stratocumulus scenarios). This destabilization, driven by the thermal radiation, is maximized during nighttime, since during daytime the solar radiation has a stabilizing tendency. The daytime bias at the surface is governed by the solar fluxes, where the 1-D solar net flux overestimates (underestimates) the corresponding benchmark at low (high) sun. The overestimation at low sun (bias up to 80 % over land and ocean) is largest at intermediate cloud cover, while underestimation at high sun (bias up to −40 % over land and ocean) is peaked at larger cloud cover (80 % and beyond). At nighttime, the 1-D experiment overestimates the amount of benchmark surface cooling with the maximal bias of about 50 % peaked at intermediate cloud cover. Moreover, an additional experiment was carried out by running the Monte Carlo radiation model in the independent column mode on cloud scenes preserving their LES structure (denoted as ICA experiment). The ICA is predominantly more accurate than the 1-D experiment (with respect to the same benchmark). This highlights the importance of an improved representation of clouds even at the resolution of today's regional (limited-area) numerical models, which needs to be considered if NWP radiative biases are to be efficiently reduced. All in all, this paper provides a systematic documentation of NWP radiative biases, which is a necessary first step towards an improved treatment of radiation–cloud interaction in atmospheric models.

scholarly journals A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations

2018 ◽  
Vol 75 (11) ◽  
pp. 4031-4047 ◽  
Author(s):  
Yign Noh ◽  
Donggun Oh ◽  
Fabian Hoffmann ◽  
Siegfried Raasch
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Heaviside Function ◽  
Mixing Ratio ◽  
Cumulus Clouds ◽  
Cloud Droplets ◽  
Shallow Cumulus ◽  
Initial Stage ◽  
Accretion Rates ◽  
The Moment

Abstract Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.

scholarly journals Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Error Variance ◽  
Specific Humidity ◽  
Temperature Error ◽  
Observation Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

scholarly journals Sensitivities of Lagrangian modelling of mid-latitude cirrus clouds to trajectory data quality

2015 ◽  
Vol 15 (13) ◽  
pp. 7429-7447 ◽  
Author(s):  
E. Kienast-Sjögren ◽  
A. K. Miltenberger ◽  
B. P. Luo ◽  
T. Peter
Keyword(s):  
Cooling Rate ◽  
Temporal Resolution ◽  
Vertical Velocity ◽  
Input Data ◽  
Temperature Fluctuations ◽  
Box Model ◽  
Specific Humidity ◽  
Number Density ◽  
Ice Nuclei ◽  
Nuclei Number

Abstract. Simulations of cirrus are subject to uncertainties in model physics and meteorological input data. Here we model cirrus clouds along air mass trajectories, whose extinction has been measured with an elastic backscatter lidar at Jungfraujoch research station in the Swiss Alps, with a microphysical stacked box model. The sensitivities of these simulations to input data uncertainties (trajectory resolution, unresolved vertical velocities, ice nuclei number density and upstream specific humidity) are investigated. Variations in the temporal resolution of the wind field data (COSMO-Model at 2.2 km resolution) between 20 s and 1 h have only a marginal impact on the trajectory path, while the representation of the vertical velocity variability and therefore the cooling rate distribution are significantly affected. A temporal resolution better than 5 min must be chosen in order to resolve cooling rates required to explain the measured extinction. A further increase in the temporal resolution improves the simulation results slightly. The close match between the modelled and observed extinction profile for high-resolution trajectories suggests that the cooling rate spectra calculated by the COSMO-2 model suffice on the selected day. The modelled cooling rate spectra are, however, characterized by significantly lower vertical velocity amplitudes than those found previously in some aircraft campaigns (SUCCESS, MACPEX). A climatological analysis of the vertical velocity amplitude in the Alpine region based on COSMO-2 analyses and balloon sounding data suggests large day-to-day variability in small-scale temperature fluctuations. This demonstrates the necessity to apply numerical weather prediction models with high spatial and temporal resolutions in cirrus modelling, whereas using climatological means for the amplitude of the unresolved air motions does generally not suffice. The box model simulations further suggest that uncertainties in the upstream specific humidity (± 10 % of the model prediction) and in the ice nuclei number density (0–100 L−1) are more important for the modelled cirrus cloud than the unresolved temperature fluctuations if temporally highly resolved trajectories are used. For the presented case the simulations are incompatible with ice nuclei number densities larger than 20 L−1 and insensitive to variations below this value.

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