scholarly journals Characteristic Atmospheric Radiative Heating Rate Profiles in Arctic Clouds as Observed at Barrow, Alaska

2018 ◽  
Vol 57 (4) ◽  
pp. 953-968 ◽  
Author(s):  
D. D. Turner ◽  
M. D. Shupe ◽  
A. B. Zwink
Keyword(s):  
Heating Rate ◽  
Liquid Water ◽  
Single Layer ◽  
Radiative Heating ◽  
Radiative Transfer Model ◽  
Radiative Cooling ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Water Path ◽  
Order Of Magnitude

AbstractA 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

Using deep learning to emulate and accelerate a radiative-transfer model

2021 ◽  
Author(s):  
Ryan Lagerquist ◽  
David Turner ◽  
Imme Ebert-Uphoff ◽  
Jebb Stewart ◽  
Venita Hagerty
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Heating Rate ◽  
Weather Prediction ◽  
Computing Time ◽  
Single Layer ◽  
Radiative Heating ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Heating Rates

AbstractThis paper describes the development of U-net++ models, a type of neural network that performs deep learning, to emulate the shortwave Rapid Radiative-transfer Model (RRTM). The goal is to emulate the RRTM accurately in a small fraction of the computing time, creating a U-net++ that could be used as a parameterization in numerical weather prediction (NWP). Target variables are surface downwelling flux, top-of-atmosphere upwelling flux (), net flux, and a profile of radiative-heating rates. We have devised several ways to make the U-net++ models knowledge-guided, recently identified as a key priority in machine learning (ML) applications to the geosciences. We conduct two experiments to find the best U-net++ configurations. In Experiment 1, we train on non-tropical sites and test on tropical sites, to assess extreme spatial generalization. In Experiment 2, we train on sites from all regions and test on different sites from all regions, with the goal of creating the best possible model for use in NWP. The selected model from Experiment 1 shows impressive skill on the tropical testing sites, except four notable deficiencies: large bias and error for heating rate in the upper stratosphere, unreliable for profiles with single-layer liquid cloud, large heating-rate bias in the mid-troposphere for profiles with multi-layer liquid cloud, and negative bias at lowzenith angles for all flux components and tropospheric heating rates. The selected model from Experiment 2 corrects all but the first deficiency, and both models run ~104 times faster than the RRTM. Our code is available publicly.

scholarly journals A quality control procedure for Fengyun-3A microwave temperature sounder with emphasis on a new cloud detection algorithm

2016 ◽  
Vol 66 (1) ◽  
pp. 19
Author(s):  
Xiang Wang ◽  
Yifang Ren ◽  
Gang Li
Keyword(s):  
Quality Control ◽  
Liquid Water ◽  
Detection Algorithm ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Control Procedure ◽  
Cloud Detection ◽  
Liquid Water Path ◽  
Model Simulations ◽  
Water Path

A cloud detection algorithm for satellite radiance from the microwave temperature sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-three series (FY-3A) is proposed based on the measurements at the frequencies of 50.3 and 53.6 GHz. The cloud liquid water path index (LWP index) is calculated using the brightness temperature at these two channels. Analysis of one case carried out in January2010shows the great consistency be-tween this new algorithm result and the available liquid water path product from the Meteorological Operational satellite A (MetOp-A).In general, about 60% of the global MWTS data are considered to be contaminated by cloud by virtue of the new cloud detection algorithm. A quality control (QC) procedure is applied to MWTS measurements with emphasis on the cloud detection. The QC steps are composed of (i) channel 2 over sea ice, land and coastal field of views (FOVs); (ii) channels 2 and 3 over cloudy FOVs; and (iii) outliers with large differences between observations and model simulations. After QC, MWTS measurements of channels 2–4 agree very well with the model simulations using the National Centres for Environmental Prediction (NCEP) forecast data as radiative transfer model input; the scan biases are reduced significantly, especially at the edges of the swath; and the frequency distributions of the differences between observations and model simulations become more Gaussian-like.

scholarly journals Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds

2020 ◽  
Vol 13 (3) ◽  
pp. 1485-1499 ◽  
Author(s):  
Maria P. Cadeddu ◽  
Virendra P. Ghate ◽  
Mario Mech
Keyword(s):  
Liquid Water ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Cloud Base ◽  
Radiative Cooling ◽  
Liquid Water Path ◽  
Cell Systems ◽  
Water Path ◽  
Closed Cell ◽  
Stratocumulus Clouds

Abstract. The partition of cloud and drizzle water path in precipitating clouds plays a key role in determining the cloud lifetime and its evolution. A technique to quantify cloud and drizzle water path by combining measurements from a three-channel microwave radiometer (23.8, 30, and 90 GHz) with those from a vertically pointing Doppler cloud radar and a ceilometer is presented. The technique is showcased using 1 d of observations to derive precipitable water vapor, liquid water path, cloud water path, drizzle water path below the cloud base, and drizzle water path above the cloud base in precipitating stratocumulus clouds. The resulting cloud and drizzle water path within the cloud are in good qualitative agreement with the information extracted from the radar Doppler spectra. The technique is then applied to 10 d each of precipitating closed and open cellular marine stratocumuli. In the closed-cell systems only ∼20 % of the available drizzle in the cloud falls below the cloud base, compared to ∼40 % in the open-cell systems. In closed-cell systems precipitation is associated with radiative cooling at the cloud top <-100Wm-2 and a liquid water path >200 g m−2. However, drizzle in the cloud begins to exist at weak radiative cooling and liquid water path >∼150 g m−2. Our results collectively demonstrate that neglecting scattering effects for frequencies at and above 90 GHz leads to overestimation of the total liquid water path of about 10 %–15 %, while their inclusion paves the path for retrieving drizzle properties within the cloud.

Exploring the Assimilation of ATMS Cloud Retrieval Products and its Benefits for CrIS All-sky Radiance Simulations

2021 ◽  
Author(s):  
Xin Li ◽  
Xiaolei Zou ◽  
Mingjian Zeng ◽  
Ning Wang ◽  
Fei Tang
Keyword(s):  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Water Path ◽  
Sky Radiance ◽  
Cloud Liquid Water Path ◽  
Cross Track ◽  
Ice Water ◽  
Statistical Interpolation

AbstractAimed at improving all-sky Cross-track Infrared Sounder (CrIS) radiance assimilation, this study explores the benefits for CrIS all-sky radiance simulations, focusing on the accuracy of background cloud information, through assimilating cloud liquid water path (LWP), ice water path (IWP), and rain water path (RWP) data retrieved from the Advanced Technology Microwave Sounder (ATMS). The Community Radiative Transfer Model (CRTM), which considers cloud scattering and absorption processes, is applied to simulate CrIS radiances. The Gridpoint Statistical Interpolation ensemble-variational data assimilation (DA) is updated by incorporating ensemble covariances of hydrometeor variables and observation operators of LWP, IWP, and RWP. First, two DA experiments named DActrl and DAcwp are conducted with (DAcwp) and without (DActrl) assimilating ATMS LWP, IWP, and RWP data. Assimilating ATMS cloud retrieval data results in better spatial distributions of hydrometers for both a Meiyu rainfall case and a typhoon case. Analyses of DActrl and DAcwp are then used as input to the CRTM to generate CrIS all-sky radiance simulations SMallsky_DActrl and SMallsky_DAcwp, respectively. Improvements in the DAcwp analyses of hydrometeor variables are found to benefit CrIS radiance simulations, especially in cloudy regions. A long period of statistics reveals that the biases and standard deviations of all-sky observations minus simulations from SMallsky_DAcwp are notably smaller than those from SMallsky_DActrl. This pilot study suggests the potential benefit of combining the use of microwave cloud retrieval products for all-sky infrared DA.

Radiative Relaxation Time Scales Quantified from Sudden Stratospheric Warmings

2021 ◽  
Vol 78 (1) ◽  
pp. 269-286
Author(s):  
Kevin Bloxam ◽  
Yi Huang
Keyword(s):  
Negative Temperature ◽  
Recovery Phase ◽  
Radiative Heating ◽  
Radiative Transfer Model ◽  
Radiative Cooling ◽  
Transfer Model ◽  
Relaxation Rates ◽  
Heating Rates ◽  
Radiative Relaxation ◽  
Stratospheric Temperature

AbstractSudden stratospheric warmings (SSWs) are impressive events that occur in the winter hemisphere’s polar stratosphere and are capable of producing temperature anomalies upward of +50 K within a matter of days. While much work has been dedicated toward determining how SSWs occur and their ability to interact with the underlying troposphere, one underexplored aspect is the role of radiation, especially during the recovery phase of SSWs. Using a radiative transfer model and a heating rate analysis for distinct layers of the stratosphere averaged over the 60°–90°N polar region, this paper accounts for the radiative contribution to the removal of the anomalous temperatures associated with SSWs. In total 17 events are investigated over the 1979–2016 period. This paper reveals that in the absence of dynamical heating following major SSWs, longwave radiative cooling dominates and often results in a strong negative temperature anomaly. The polar winter stratospheric temperature change driven by the radiative cooling is characterized by an exponential decay of temperature with an increasing e-folding time of 5.7 ± 2.0 to 14.6 ± 4.4 days from the upper to middle stratosphere. The variability of the radiative relaxation rates among the SSWs was determined to be most impacted by the initial temperature of the stratosphere and the combined dynamic and solar heating rates following the onset of the events. We also found that trace-gas anomalies have little impact on the radiative heating rates and the temperature evolution during the SSWs in the mid- to upper stratosphere.

scholarly journals Implications of Limited Liquid Water Path on Static Mixing within Arctic Low-Level Clouds

2014 ◽  
Vol 53 (12) ◽  
pp. 2775-2789 ◽  
Author(s):  
Joseph Sedlar
Keyword(s):  
Mixed Layer ◽  
Liquid Water ◽  
Heat Budget ◽  
Single Layer ◽  
Specific Humidity ◽  
The Arctic ◽  
Liquid Water Path ◽  
Stable Layer ◽  
Water Path ◽  
Low Level

AbstractObservations of cloud properties and thermodynamics from two Arctic locations, Barrow, Alaska, and Surface Heat Budget of the Arctic (SHEBA), are examined. A comparison of in-cloud thermodynamic mixing characteristics for low-level, single-layer clouds from nearly a decade of data at Barrow and one full annual cycle over the sea ice at SHEBA is performed. These cloud types occur relatively frequently, evident in 27%–30% of all cloudy cases. To understand the role of liquid water path (LWP), or lack thereof, on static in-cloud mixing, cloud layers are separated into optically thin and optically thick LWP subclasses. Clouds with larger LWPs tend to have a deeper in-cloud mixed layer relative to optically thinner clouds. However, both cloud LWP subclasses are frequently characterized by an in-cloud stable layer above the mixed layer top. The depth of the stable layer generally correlates with an increased temperature gradient across the layer. This layer often contains a specific humidity inversion, but it is more frequently present when cloud LWP is optically thinner (LWP < 50 g m−2). It is suggested that horizontal thermodynamic advection plays a key role modifying the vertical extent of in-cloud mixing and likewise the depth of in-cloud stable layers. Furthermore, longwave atmospheric opacity above the cloud top is generally enhanced during cases with optically thinner clouds. Thermodynamic advection, cloud condensate distribution within the stable layer, and enhanced atmospheric radiation above the cloud are found to introduce a thermodynamic–radiative feedback that potentially modifies the extent of LWP and subsequent in-cloud mixing.

scholarly journals On the Improvement of COAMPS Weather Forecasts Using an Advanced Radiative Transfer Model

2009 ◽  
Vol 24 (1) ◽  
pp. 286-306 ◽  
Author(s):  
Ming Liu ◽  
Jason E. Nachamkin ◽  
Douglas L. Westphal
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Liquid Water ◽  
Atmospheric Stability ◽  
Sensitivity Study ◽  
Radiative Heating ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
The Impact

Abstract Fu–Liou’s delta-four-stream (with a two-stream option) radiative transfer model has been implemented in the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)1 to calculate solar and thermal infrared fluxes in 6 shortwave and 12 longwave bands. The model performance is evaluated at high resolution for clear-sky and overcast conditions against the observations from the Southern Great Plains of the Atmospheric Radiation Measurement Program. In both cases, use of the Fu–Liou model provides significant improvement over the operational implementation of the standard Harshvardhan radiation parameterization in both shortwave and longwave fluxes. A sensitivity study of radiative flux on clouds reveals that the choices of cloud effective radius schemes for ice and liquid water are critical to the flux calculation due to the effects on cloud optical properties. The sensitivity study guides the selection of optimal cloud optical properties for use in the Fu–Liou parameterization as implemented in COAMPS. The new model is then used to produce 3-day forecasts over the continental United States for a winter and a summer month. The verifications of parallel runs using the standard and new parameterizations show that Fu–Liou dramatically reduces the model’s systematic warm bias in the upper troposphere in both winter and summer. The resultant cooling modifies the atmospheric stability and moisture transport, resulting in a significant reduction in the upper-tropospheric wet bias. Overall ice and liquid water paths are also reduced. At the surface, Fu–Liou reduces the negative temperature and sea level pressure biases by providing more accurate radiative heating rates to the land surface model. The error reductions increase with forecast length as the impact of improved radiative fluxes accumulates over time. A combination of the two- and four-stream options results in major computational efficiency gains with minimal loss in accuracy.

scholarly journals A MODIS-Derived Value-Added Climatology of Maritime Cloud Liquid Water Path That Conserves Solar Reflectance

2017 ◽  
Vol 56 (6) ◽  
pp. 1767-1781
Author(s):  
Amanda Gumber ◽  
Michael J. Foster
Keyword(s):  
Monte Carlo ◽  
Radiative Transfer ◽  
Liquid Water ◽  
Value Added ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Cloud Liquid Water Path ◽  
Visible Reflectance

AbstractA dataset is generated from a method to retrieve distributions of cloud liquid water path over partially cloudy scenes. The method was introduced in a 2011 paper by Foster and coauthors that described the theory and provided test cases. Here it has been applied to Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and collection-6 cloud products, resulting in a value-added dataset that contains adjusted distributions of cloud liquid water path for more than 10 years for marine liquid cloud for both Aqua and Terra. This method adjusts horizontal distributions of cloud optical properties to be more consistent with observed visible reflectance and is especially useful in areas where cloud optical retrievals fail or are considered to be of low quality. Potential uses of this dataset include validation of climate and radiative transfer models and facilitation of studies that intercompare satellite records. Results show that the fit method is able to reduce bias between observed visible reflectance and that derived from optical retrievals by up to an average improvement of 3%. The level of improvement is dependent on several factors, including seasonality, viewing geometry, cloud fraction, and cloud heterogeneity. Applications of this dataset are explored through a satellite intercomparison with PATMOS-x and Global Change Observation Mission–First Water (GCOM-W1; “SHIZUKU”) AMSR-2 and use of a Monte Carlo radiative transfer model. From the 3D Monte Carlo model simulations, albedo biases are found when the method is applied, with seasonal averages that range over 0.02–0.06.

AMSU-B Observations of Mixed-Phase Clouds over Land

2005 ◽  
Vol 44 (1) ◽  
pp. 72-85 ◽  
Author(s):  
M. N. Deeter ◽  
J. Vivekanandan
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Mixed Phase ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Radiative Effects ◽  
Liquid Water Path ◽  
Water Path ◽  
Cloud Properties ◽  
Mixed Phase Clouds

Abstract Measurements from passive microwave satellite instruments such as the Advanced Microwave Sounding Unit B (AMSU-B) are sensitive to both liquid and ice cloud particles. Radiative transfer modeling is exploited to simulate the response of the AMSU-B instrument to mixed-phase clouds over land. The plane-parallel radiative transfer model employed for the study accounts for scattering and absorption from cloud ice as well as absorption and emission from trace gases and cloud liquid. The radiative effects of mixed-phase clouds on AMSU-B window channels (i.e., 89 and 150 GHz) and water vapor line channels (i.e., 183 ± 1, 3, and 7 GHz) are studied. Sensitivities to noncloud parameters, including surface temperature, surface emissivity, and atmospheric temperature and water vapor profiles, are also quantified. Modeling results indicate that both cloud phases generally have significant radiative effects and that the 150- and 183 ± 7-GHz channels are typically the most sensitive channels to integrated cloud properties (i.e., liquid water path and ice water path). However, results also indicate that AMSU-B measurements alone are probably insufficient for retrieving all mixed-phase cloud properties of interest. These results are supported by comparisons of AMSU-B observations of a mixed-phase cloud over the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site with corresponding calculated clear-sky values.

scholarly journals Cloud<i>_</i>cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

2020 ◽  
Vol 12 (3) ◽  
pp. 2121-2135
Author(s):  
Caroline A. Poulsen ◽  
Gregory R. McGarragh ◽  
Gareth E. Thomas ◽  
Martin Stengel ◽  
Matthew W. Christensen ◽  
...  
Keyword(s):  
Liquid Water ◽  
European Space Agency ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Data Set ◽  
Water Path ◽  
Cloud Properties ◽  
Wide Range ◽  
Good Agreement

Abstract. We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

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