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1. Toward autonomous surface-based infrared remote sensing of polar clouds: cloud-height retrievals

   https://doi.org/10.5194/amt-9-3641-2016  

   Rowe, Penny M. ; Cox, Christopher J. ; Walden, Von P. ( January 2016 , Atmospheric Measurement Techniques)

   Abstract. Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO₂ slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than  ∼ 0.3, cloud-height retrievals from simulated spectra using CO₂ slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5cm⁻¹, mean biases are found to be  ∼ 0.2km for clouds with bases below 2 and −0.2km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO₂ slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors inmore »the atmospheric state as well as both instrument noise and bias of 0.2mW/(m²srcm⁻¹), at a resolution of 4cm⁻¹, average retrieval errors are found to be less than  ∼ 0.5km for cloud bases within 1km of the surface, increasing to  ∼ 1.5km at 4km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.

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2. Microphysical processes producing high ice water contents (HIWCs) in tropical convective clouds during the HAIC-HIWC field campaign: evaluation of simulations using bulk microphysical schemes

   https://doi.org/10.5194/acp-21-6919-2021  

   Huang, Yongjie ; Wu, Wei ; McFarquhar, Greg M. ; Wang, Xuguang ; Morrison, Hugh ; Ryzhkov, Alexander ; Hu, Yachao ; Wolde, Mengistu ; Nguyen, Cuong ; Schwarzenboeck, Alfons ; et al ( January 2021 , Atmospheric Chemistry and Physics)

   Abstract. Regions with high ice water content (HIWC), composed of mainly small ice crystals, frequently occur over convective clouds in the tropics. Such regions can have median mass diameters (MMDs) <300 µm and equivalent radar reflectivities <20 dBZ. To explore formation mechanisms for these HIWCs, high-resolution simulations of tropical convective clouds observed on 26 May 2015 during the High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) international field campaign based out of Cayenne, French Guiana, are conducted using the Weather Research and Forecasting (WRF) model with four different bulk microphysics schemes: the WRF single‐moment 6‐class microphysics scheme (WSM6), the Morrison scheme, and the Predicted Particle Properties (P3) scheme with one- and two-ice options. The simulations are evaluated against data from airborne radar and multiple cloud microphysics probes installed on the French Falcon 20 and Canadian National Research Council (NRC) Convair 580 sampling clouds at different heights. WRF simulations with different microphysics schemes generally reproduce the vertical profiles of temperature, dew-point temperature, and winds during this event compared with radiosonde data, and the coverage and evolution of this tropical convective system compared to satellite retrievals. All of the simulations overestimate the intensity and spatial extent of radar reflectivity by over 30 % above themore »melting layer compared to the airborne X-band radar reflectivity data. They also miss the peak of the observed ice number distribution function for 0.1« less
3. Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

   https://doi.org/10.5194/amt-11-529-2018  

   DeSouza-Machado, Sergio ; Strow, L. Larrabee ; Tangborn, Andrew ; Huang, Xianglei ; Chen, Xiuhong ; Liu, Xu ; Wu, Wan ; Yang, Qiguang ( January 2018 , Atmospheric Measurement Techniques)

   Abstract. One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2–4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation modelmore »allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90%, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model are used in this paper.

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4. A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations

   https://doi.org/10.5194/amt-13-2257-2020  

   Wang, Chenxi ; Platnick, Steven ; Meyer, Kerry ; Zhang, Zhibo ; Zhou, Yaping ( January 2020 , Atmospheric Measurement Techniques)

   Abstract. We trained two Random Forest (RF) machine learning models for cloud mask andcloud thermodynamic-phase detection using spectral observations from Visible InfraredImaging Radiometer Suite (VIIRS)on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidarwith Orthogonal Polarization (CALIOP) were carefully selected toprovide reference labels. The two RF models were trained for all-day anddaytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPPVIIRS training samples cover a broad-viewing zenith angle range, which is agreat benefit to overall model performance. The all-day model uses three VIIRSinfrared (IR) bands (8.6, 11, and 12 µm), and the daytime model uses fiveNear-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 µm) together with the three IR bands to detect clear, liquid water, and icecloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland,grassland, snow and ice, barren desert, and shrubland, were consideredseparately to enhance performance for both models. Detection of cloudypixels and thermodynamic phase with the two RF models was compared againstcollocated CALIOP products from 2017. It is shown that, when using a conservativescreening process that excludes the most challenging cloudy pixels forpassive remote sensing, the two RF modelsmore »have high accuracy rates incomparison to the CALIOP reference for both cloud detection andthermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask andphase products are also evaluated, with results showing that the two RFmodels and the MODIS MYD06 optical property phase product are the top threealgorithms with respect to lidar observations during the daytime. During thenighttime, the RF all-day model works best for both cloud detection andphase, particularly for pixels over snow and ice surfaces. The present RFmodels can be extended to other similar passive instruments if trainingsamples can be collected from CALIOP or other lidars. However, the qualityof reference labels and potential sampling issues that may impact modelperformance would need further attention.« less
5. Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

   https://doi.org/10.1007/s00376-022-2036-z  

   Zhao, Lijun ; Wang, Yuan ; Zhao, Chuanfeng ; Dong, Xiquan ; Yung, Yuk L. ( September 2022 , Advances in Atmospheric Sciences)

   Abstract The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smallermore »bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.« less