A radiation closure study of Arctic stratus cloud microphysical properties using the collocated satellite-surface data and Fu-Liou radiative transfer model

Cold-air outbreaks over high-latitude oceans typically include mixed-phase clouds and precipitation, in particular supercooled liquid clouds that support snow and graupel through ice growth processes. The partitioning of the total water into the liquid and ice phases impacts both weather and climate prediction, but accurate measurements on the phase partitioning remain difficult to acquire, especially near–real time. Here, we present a machine learning approach to retrieve liquid water path (LWP) using passive microwave measurements combined with vertically integrated radar reflectivities. The approach is an extension of Cadeddu et al. (2009), with the novel addition of radar reflectivity. The machine learning models are trained using the Passive and Active Microwave Radiative Transfer (PAMTRA) code applied to output from numerical simulations of three independent cold-air outbreaks sampled during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. Brightness temperatures corresponding to the four sidebands of an upward-looking G-band (183 GHz) vapor radiometer, along with the vertically integrated reflectivity from a zenith-pointing 95-GHz Wyoming Cloud Radar, are simulated from the perspective of a near-surface aircraft track. The radar reflectivity helps discriminate the snow contribution to the brightness temperatures. The machine learning models are thereafter tested on a simulation of an independent cold-air outbreak during COMBLE and against measurements from the U.S. Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. This machine learning approach is shown to provide robust, computationally efficient, near-real-time measurements of LWP and water vapor path during the Cold-Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) campaign in February–April 2024. Significance Statement Precipitation from mixed-phase clouds over the high-latitude open waters impacts shipping, fishing, and coastal communities. Mixed-phase clouds are also a modeling challenge, inhibiting weather and climate prediction skill. More comprehensive measurements of the cloud liquid and ice water path occupying the same vertical column improve our understanding and ability to represent this challenging cloud type. Here, we propose a new retrieval to determine the liquid water path within air columns that also contain ice.

The accuracy and precision are determined of cloud liquid water path (LWP) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat-8 using 1 yr of LWP retrievals from microwave radiometer (MWR) measurements of two CloudNET stations in northern Europe. The MWR retrievals of LWP have a precision that is superior to current satellite remote sensing techniques, which justifies their use as validation data. The Cloud Physical Properties (CPP) algorithm of the Satellite Application Facility on Climate Monitoring (CM-SAF) is used to retrieve LWP from SEVIRI reflectances at 0.6 and 1.6 μm. The results show large differences in the accuracy and precision of LWP retrievals from SEVIRI between summer and winter. During summer, the instantaneous LWP retrievals from SEVIRI agree well with those from the MWRs. The accuracy is better than 5 g m−2 and the precision is better than 30 g m−2, which is similar to the precision of LWP retrievals from MWR. The added value of the 15-min sampling frequency of Meteosat-8 becomes evident in the validation of the daily median and diurnal variations in LWP retrievals from SEVIRI. The daily median LWP values from SEVIRI and MWR are highly correlated (correlation > 0.95) and have a precision better than 15 g m−2. In addition, SEVIRI and MWR reveal similar diurnal variations in retrieved LWP values. The peak LWP values occur around noon. During winter, SEVIRI generally overestimates the instantaneous LWP values from MWR, the accuracy drops to about 10 g m2, and the precision to about 30 g m−2. The most likely reason for these lower accuracies is the shortcoming of CPP, and similar one-dimensional retrieval algorithms, to model inhomogeneous clouds. It is suggested that neglecting cloud inhomogeneities leads to a significant overestimation of LWP retrievals from SEVIRI over northern Europe during winter.

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate boundary layer temperature and humidity profiles are crucial for successful forecasting of fog, and accurate retrievals of liquid water path are important for understanding the climatological significance of fog. Passive ground-based remote sensing systems such as microwave radiometers (MWRs) that have multiple channels between 22&ndash;31 GHz and 51&ndash;59 GHz, and infrared spectrometers such as the Atmospheric Emitted Radiance Interferometer (AERI) which measures spectrally resolved infrared radiation (3.3 to 19.2 <em>&mu;m</em>) can retrieve both thermodynamic profiles and liquid water path. Both instruments are capable of long-term unattended operation and have the potential to support operational forecasting. Here we compare physical retrievals of boundary layer thermodynamic profiles and liquid water path during 13 cases of supercooled radiation fog from a MWR and an AERI collocated in central Greenland. We compare both sets of retrievals to in-situ measurements from radiosondes and surface-based temperature and humidity sensors. The retrievals based on AERI observations accurately capture shallow surface-based temperature inversions (0&ndash;10 m a.g.l) with lapse rates of up to -1.2 &deg;C m^&minus;1, whereas the strength of the surface-based temperature inversions retrieved from MWR observations are uncorrelated with in-situ measurements. For all but one case study, the retrievals based on AERI observations detect fog onset (defined by a threshold in liquid water path) earlier than those based on MWR observations by up to 4 hours. We propose that due to the high sensitivity of the AERI instrument to near-surface temperature and small changes in liquid water path, the AERI (or an equivalent infrared spectrometer) could be a useful instrument for improving fog monitoring and nowcasting.

The effects of sampling resolution on the surface albedos of dominant land cover types in the North American boreal region

Dual-field-of-view Raman lidar measurements, detecting Raman-scattered light with two fields of view simultaneously, are used for the first time to retrieve cloud microphysical properties. The measurements are performed with the Multiwavelength Atmospheric Raman Lidar for Temperature, Humidity, and Aerosol Profiling (MARTHA) at the Leibniz Institute for Tropospheric Research in Leipzig, Germany. Light that is scattered in forward direction by cloud droplets and inelastically backscattered by N2 molecules is detected. A forward iterative algorithm uses the measured signals to derive profiles of the effective cloud droplet radius, extinction coefficient, and liquid-water content of the investigated clouds. The setup, algorithm, error analysis, and a measurement example are presented. The obtained liquid-water path is validated by observations with a microwave radiometer. With the capability to retrieve aerosol properties as well as cloud microphysical properties, the Raman lidar MARTHA is an ideal tool for studies of the aerosol indirect effect.

Two methods for retrieving cloud droplet effective radius re from ground‐based near‐infrared spectral measurements of path‐integrated liquid water paths (PLWPs) are described. In one method the PLWP is compared with column measurements of liquid water path (LWP) from a dual channel microwave radiometer (MWR) to estimate the cloud path enhancement, which is then used to derive the cloud droplet effective radius. In the second method, PLWP is combined with absolutely calibrated zenith radiances at 500 nm to retrieve re and LWP simultaneously. Both techniques are used in a case study of marine stratocumulus at the Barrow, Alaska (71.32°N, 156.62°W) Atmospheric Radiation Measurement Program (ARM) site on 17 September 2004. The first method performed best for moderately thick clouds (LWP ≥ 100 g m−2), but the accuracy is limited by uncertainties in the MWR LWP on which it relies. The second method performed well over a wider range of values with 1σ retrieval errors of &lt;4 g m−2 (∼4%) and ∼3 μm (∼7%) for 15 ≤ LWP ≤ 170 g m−2. The LWPs retrieved using the radiance‐PLWP method were highly correlated (r2 = 0.96) with LWPs from the MWR (with a bias subtracted) derived using the ARM statistical method. A limited comparison (LWP &lt; 100 g m−2) to millimeter wave cloud radar showed that values of re retrieved using the radiance‐PLWP method were consistently higher (by ∼3 μm) than the LWC‐weighted mean re from the radar. Additional field studies are needed to resolve this discrepancy, although this first comparison is promising.

Algorithm for retrieving surface pressure from hyper-spectral measurements in oxygen A-band

Abstract. The mean monthly shortwave (SW) radiation budget at the top of atmosphere (TOA) was computed on 2.5° longitude-latitude resolution for the 14-year period from 1984 to 1997, using a radiative transfer model with long-term climatological data from the International Satellite Cloud Climatology Project (ISCCP-D2) supplemented by data from the National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP-NCAR) Global Reanalysis project, and other global data bases such as TIROS Operational Vertical Sounder (TOVS) and Global Aerosol Data Set (GADS). The model radiative fluxes at TOA were validated against Earth Radiation Budget Experiment (ERBE) S4 scanner satellite data (1985–1989). The model is able to predict the seasonal and geographical variation of SW TOA fluxes. On a mean annual and global basis, the model is in very good agreement with ERBE, overestimating the outgoing SW radiation at TOA (OSR) by 0.93 Wm-2 (or by 0.92%), within the ERBE uncertainties. At pixel level, the OSR differences between model and ERBE are mostly within ±10 Wm-2, with ±5 Wm-2 over extended regions, while there exist some geographic areas with differences of up to 40 Wm-2, associated with uncertainties in cloud properties and surface albedo. The 14-year average model results give a planetary albedo equal to 29.6% and a TOA OSR flux of 101.2 Wm-2. A significant linearly decreasing trend in OSR and planetary albedo was found, equal to 2.3 Wm-2 and 0.6% (in absolute values), respectively, over the 14-year period (from January 1984 to December 1997), indicating an increasing solar planetary warming. This planetary SW radiative heating occurs in the tropical and sub-tropical areas (20° S–20° N), with clouds being the most likely cause. The computed global mean OSR anomaly ranges within ±4 Wm-2, with signals from El Niño and La Niña events or Pinatubo eruption, whereas significant negative OSR anomalies, starting from year 1992, are also detected.

The surface energy budget plays a critical role in determining the mass balance of the Greenland Ice Sheet, which in turn has significant implications for global sea levels. Nearly three years of data (January 2011–October 2013) are used to characterize the annual cycle of surface radiative fluxes and cloud radiative forcing (CRF) from the central Greenland Ice Sheet at Summit Station. The annual average CRF is 33 W m−2, representing a substantial net cloud warming of the central Greenland surface. Unlike at other Arctic sites, clouds warm the surface during the summer. The surface albedo is high at Summit throughout the year, limiting the cooling effect of the shortwave CRF and thus the total CRF is dominated by cloud longwave warming effects in all months. All monthly mean CRF values are positive (warming), as are 98.5% of 3-hourly cases. The annual cycle of CRF is largely driven by the occurrence of liquid-bearing clouds, with a minimum in spring and maximum in late summer. Optically thick liquid-bearing clouds [liquid water path (LWP) &amp;gt; 30 g m−2] produce an average longwave CRF of 85 W m−2. Shortwave CRF is sensitive to solar zenith angle and LWP. When the sun is well above the horizon (solar zenith angle &amp;lt; 65°), a maximum cloud surface warming occurs in the presence of optically thin liquid-bearing clouds. Ice clouds occur frequently above Summit and have mean longwave CRF values ranging from 10 to 60 W m−2, dependent on cloud thickness.

Precipitable water vapor (PWV) and liquid water path (LWP) retrievals using zenith view passive microwave observations near the 22.235 GHz water vapor line are known to be of high accuracy for most mid-latitude profiles. However in very dry atmospheres (PWV<10 mm), such as those experienced in the Arctic winter, the percentage accuracy of these retrievals becomes poor due to the weakness of the 22 GHz feature. The authors describe an adaptable physical retrieval algorithm which uses any number of channels in the range 10-300 GHz to retrieve PWV and LWP. Using a database of model high latitude profiles, the authors investigate the performance of various channel combinations in the range 23 to 183 GHz for retrieving PWV and LWP. The authors demonstrate that the best retrieval performance over the expected range of PWV and LWP in high-latitude climates is made with combinations that include channels near the 22 GHz absorption line and the much stronger 183.31 GHz line. This analysis is applicable to optimizing the measurement strategy for studying the influence of water vapor on the Earth's radiation budget in Arctic areas.

Cloud microphysical property retrievals from the active microwave instrument on a satellite require the cloud droplet size distribution obtained from aircraft observations as a priori data in the iteration procedure. The cloud lognormal size distributions derived from 12 flights over Beijing, China, in 2008–09 were characterized to evaluate and improve regional CloudSat cloud water content retrievals. We present the distribution parameters of stratiform cloud droplet (diameter <500 μm and <1500 μm) and discuss the effect of large particles on distribution parameter fitting. Based on three retrieval schemes with different lognormal size distribution parameters, the vertical distribution of cloud liquid and ice water content were derived and then compared with the aircraft observations. The results showed that the liquid water content (LWC) retrievals from large particle size distributions were more consistent with the vertical distribution of cloud water content profiles derived from in situ data on 25 September 2006. We then applied two schemes with different a priori data derived from flight data to CloudSat overpasses in northern China during April-October in 2008 and 2009. The CloudSat cloud water path (CWP) retrievals were compared with Moderate Resolution Imaging Spectroradiometer (MODIS) liquid water path (LWP) data. The results indicated that considering a priori data including large particle size information can significantly improve the consistency between the CloudSat CWP and MODIS CWP. These results strongly suggest that it is necessary to consider particles with diameters greater than 50 μm in CloudSat LWC retrievals.

Errors in top-of-atmosphere (TOA) radiative fluxes from the Clouds and the Earth’s Radiant Energy System (CERES) instrument due to uncertainties in radiance-to-flux conversion from CERES Terra angular distribution models (ADMs) are evaluated through a series of consistency tests. These tests show that the overall bias in regional monthly mean shortwave (SW) TOA flux is less than 0.2 W m−2 and the regional RMS error ranges from 0.70 to 1.4 W m−2. In contrast, SW TOA fluxes inferred using theoretical ADMs that assume clouds are plane parallel are overestimated by 3–4 W m−2 and exhibit a strong latitudinal dependence. In the longwave (LW), the bias error ranges from 0.2 to 0.4 W m−2 and regional RMS errors remain smaller than 0.7 W m−2. Global mean albedos derived from ADMs developed during the Earth Radiation Budget Experiment (ERBE) and applied to CERES measurements show a systematic increase with viewing zenith angle of 4%–8%, while albedos from the CERES Terra ADMs show a smaller increase of 1%–2%. The LW fluxes from the ERBE ADMs show a systematic decrease with viewing zenith angle of 2%–2.4%, whereas fluxes from the CERES Terra ADMs remain within 0.7%–0.8% at all angles. Based on several months of multiangle CERES along-track data, the SW TOA flux consistency between nadir- and oblique-viewing zenith angles is generally 5% (&amp;lt;17 W m−2) over land and ocean and 9% (26 W m−2) in polar regions, and LW TOA flux consistency is approximate 3% (7 W m−2) over all surfaces. Based on these results and a theoretically derived conversion between TOA flux consistency and TOA flux error, the best estimate of the error in CERES TOA flux due to the radiance-to-flux conversion is 3% (10 W m−2) in the SW and 1.8% (3–5 W m−2) in the LW. Monthly mean TOA fluxes based on ERBE ADMs are larger than monthly mean TOA fluxes based on CERES Terra ADMs by 1.8 and 1.3 W m−2 in the SW and LW, respectively.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative sensitivity, i.e., the response of the radiative flux to climate perturbations, is essential to understanding climate variability. The sensitivity kernels computed by radiative transfer models have been broadly used for assessing the climate forcing and feedbacks in global warming. As these assessments are largely focused on the top of atmosphere (TOA) radiation budget, less attention has been paid to the surface radiation budget or the associated surface radiative sensitivity kernels. Based on the fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis, we produce a new set of radiative kernels for both the TOA and surface radiative fluxes, which is made available at <a href="http://doi.org/10.17632/vmg3s67568.1" target="_blank" rel="noopener">http://doi.org/10.17632/vmg3s67568.1</a> (H. Huang, 2022). By comparing with other published radiative kernels, we find that the TOA kernels are in agreement in terms of global mean radiative sensitivity and analyzed overall feedback strength. The unexplained residual in the radiation closure tests is found to be generally within 10 %, no matter which kernel dataset is used. The inter-kernel bias-induced uncertainty, as measured by the standard deviation of the global mean feedback parameter value, is typically no more than 10 % in the longwave and 20 % in the shortwave; this uncertainty is much smaller than the inter-climate model spread of the feedbacks. However, there exist more significant regional biases in kernel values, due to the dependence of radiative sensitivity on the atmospheric states, and this contributes to more significant radiation non-closure at the regional scale, such as in the Arctic and Southern Ocean regions. On the other hand, we find relatively larger discrepancies in the surface kernels. Although several kernels can achieve as good radiation closure compared to the TOA kernels, affirming the validity of kernel method for the surface radiation budget analysis, the non-closure residual in certain kernels may amount to over 100 % of the total radiation change. The intercomparison of the surface kernels reveals important biases, such as in the radiative sensitivity to air temperature in the lowermost atmospheric layers adjacent to the surface, which is of critical importance to the overall surface feedback strength.

Despite recent improvements in satellite instrument calibration and the algorithms used to determine reflected solar (SW) and emitted thermal (LW) top-of-atmosphere (TOA) radiative fluxes, a sizeable imbalance persists in the average global net radiation at the TOA from satellite observations. This imbalance is problematic in applications that use earth radiation budget (ERB) data for climate model evaluation, estimate the earth’s annual global mean energy budget, and in studies that infer meridional heat transports. This study provides a detailed error analysis of TOA fluxes based on the latest generation of Clouds and the Earth’s Radiant Energy System (CERES) gridded monthly mean data products [the monthly TOA/surface averages geostationary (SRBAVG-GEO)] and uses an objective constrainment algorithm to adjust SW and LW TOA fluxes within their range of uncertainty to remove the inconsistency between average global net TOA flux and heat storage in the earth–atmosphere system. The 5-yr global mean CERES net flux from the standard CERES product is 6.5 W m−2, much larger than the best estimate of 0.85 W m−2 based on observed ocean heat content data and model simulations. The major sources of uncertainty in the CERES estimate are from instrument calibration (4.2 W m−2) and the assumed value for total solar irradiance (1 W m−2). After adjustment, the global mean CERES SW TOA flux is 99.5 W m−2, corresponding to an albedo of 0.293, and the global mean LW TOA flux is 239.6 W m−2. These values differ markedly from previously published adjusted global means based on the ERB Experiment in which the global mean SW TOA flux is 107 W m−2 and the LW TOA flux is 234 W m−2.

Variations in land surface albedo and snow-cover strongly impact the global biosphere, particularly through the snow-albedo feedback on climate. The seasonal freeze-thaw (FT) transition is coupled with snowpack melt dynamics and strongly impacts surface water mobility and the energy budget in the northern (≥45°N) arctic and boreal region (ABR). However, understanding of the regional variation in snowmelt and its effect on the surface energy budget are limited due to sparse in situ measurements of these processes and environmental constraints on effective monitoring within the ABR. In this study, we combined synergistic observations from overlapping satellite optical-infrared and microwave sensor records to quantify the regional patterns and seasonal progression in wet snow conditions during the spring snowmelt and autumn snow accumulation periods across Alaska and western Canada. The integrated satellite record included daily landscape FT status from AMSR microwave brightness temperature retrievals; and snow-cover extent, black sky albedo and net shortwave solar radiation (Rsnet) derived from MODIS and AVHRR observations. The integrated satellite records were analyzed with in situ surface air temperature and humidity observations from regional weather stations over a two-year study period (2015–2016) overlapping with the NASA ABoVE (Arctic Boreal Vulnerability Experiment). Our results show a large (79%) mean decline in land surface albedo between dry snow and snow-free conditions during the spring (March–June) and autumn (August–November) transition periods. Onset of diurnal thawing and refreezing of the surface snow layer and associated wet snow conditions in spring contributed to an approximate 25% decrease in snow cover albedo that extended over a seven to 21 week snowpack depletion period. The lower wet snow albedo enhances Rsnet by approximately 74% (9–10 MJ m−2 d−1) relative to dry snow conditions, reinforcing snowmelt and surface warming, and contributing to growing season onset and activation of biological and hydrological processes in the ABR. These results contribute to better understanding of snow albedo feedbacks to Arctic amplification, and the representation of these processes in global Earth system models.